HistoryDataScience.bib

@article{1259Million1980,
  title = {\$12.59 {{Million Federal Contract Awarded}} to {{IP Network Solutions}}},
  year = {1980},
  month = apr,
  journal = {Wall Street Journal},
  pages = {19}
}

@misc{17PDFField,
  title = {(17) ({{PDF}}) {{The Field Guide}} to {{Data Science}}},
  journal = {ResearchGate},
  abstract = {ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free.},
  howpublished = {https://www.researchgate.net/publication/258698880\_The\_Field\_Guide\_to\_Data\_Science},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HAQP43RR/258698880_The_Field_Guide_to_Data_Science.html}
}

@incollection{abbotNewPathScience2009,
  title = {A {{New Path}} for {{Science}}?},
  booktitle = {The {{Fourth Paradigm}}: {{Data-Intensive Scientific Discovery}}},
  author = {Abbot, Mark R.},
  year = {2009},
  pages = {111--116},
  publisher = {{Microsoft Research}},
  address = {{Redmond, WA}}
}

@misc{AccordingMicrosoftFourth,
  title = {According to {{Microsoft}}, the Fourth Paradigm of Science Is Data},
  journal = {Revolutions},
  abstract = {In scientific discovery, the first three paradigms were experimental, theoretical and (more recently) computational science. A new book of essays published by Microsoft (and available for free download -- kudos, MS!) argues that a fourth paradigm of scientific discovery is at hand: the analysis of massive data sets. The book is dedicated to the late Microsoft researcher Dr Jim Gray, who pioneered the idea with the catchphrase: "It's the data, stupid". The basic idea is that our capacity for collecting scientific data has far outstripped our present capacity to analyze it, and so our focus should be on developing technologies...},
  howpublished = {https://blog.revolutionanalytics.com/2009/12/fourth-paradigm.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/84IUKUTL/fourth-paradigm.html}
}

@book{ActaSymbolica1970,
  title = {Acta {{Symbolica}}},
  year = {1970},
  publisher = {{Department of Audiology and Speech Pathology, Memphis State University}},
  abstract = {Vols. for , 1975- includes Proceedings of the Auditory Processing and Learning Disabilities Symposium.},
  googlebooks = {YzYoAQAAIAAJ},
  langid = {english}
}

@book{administrationNASAEP1961,
  title = {{{NASA EP}}.},
  author = {Administration, United States National Aeronautics {and} Space},
  year = {1961},
  publisher = {{National Aeronautics and Space Administration.}},
  googlebooks = {3s0fAAAAIAAJ},
  langid = {english}
}

@book{AdvancesComputers1962,
  title = {Advances in {{Computers}}},
  year = {1962},
  month = jan,
  publisher = {{Academic Press}},
  abstract = {Advances in Computers},
  googlebooks = {leYlwtgF0xsC},
  isbn = {978-0-08-056635-1},
  langid = {english},
  keywords = {Computers / Software Development \& Engineering / General}
}

@article{afcrlReportResearchAFCRL1959,
  title = {Report on {{Research}} at {{AFCRL}}},
  author = {{AFCRL}},
  year = {1959},
  address = {{Bedford, Mass.}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EL8FTNZ4/002138479.html}
}

@techreport{afcrlReportResearchAFCRL1963,
  title = {Report on {{Research}} at {{AFCRL}}: {{July}} 1962 - {{July}} 1963},
  author = {{AFCRL}},
  year = {1963},
  address = {{Bedford, Mass.}},
  institution = {{Air Force Cambridge Research Laboratories}},
  abstract = {For the Period July 1962 -- July 1963},
  langid = {english},
  annotation = {OCLC: 16107187},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9A3YCA3E/data-sciences-lab.png;/Users/rca2t1/Dropbox/Zotero/storage/GU3GLTGE/AFCRL - 1963 - Report on Research at AFCRL.pdf;/Users/rca2t1/Dropbox/Zotero/storage/Y8KL8MSH/dsl-image.png}
}

@techreport{afcrlReportResearchAFCRL1967,
  title = {Report on {{Research}} at {{AFCRL}}: {{July}} 1965 - {{June}} 1967},
  author = {{AFCRL}},
  year = {1967},
  month = nov,
  number = {AFCRL-68-0039},
  address = {{Bedford, Massachusetts}},
  institution = {{Air Foce Cambridge Research Laboratories}},
  abstract = {For the Period July 1965 -- June 1967.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PWAI4I5T/AFCRL - 1967 - Report on Research at AFCRL.pdf}
}

@techreport{afcrlReportResearchAFCRL1970,
  title = {Report on {{Research}} at {{AFCRL}}: {{July}} 1967 \textendash{} {{June}} 1970},
  author = {{AFCRL}},
  year = {1970},
  month = dec,
  number = {AFCRL-71-0022},
  address = {{Bedford, Massachusetts}},
  institution = {{Air Foce Cambridge Research Laboratories}},
  abstract = {For the Period July 1967 \textendash{} June 1970.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DBTZ85XV/AFCRL - 1970 - Report on Research at AFCRL.pdf}
}

@techreport{afcrlReportResearchAFCRL1973,
  title = {Report on {{Research}} at {{AFCRL}}: {{July}} 1970 - {{June}} 1972},
  author = {{AFCRL}},
  year = {1973},
  month = feb,
  googlebooks = {MF2dExiimrsC},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/8IDDRAEQ/AFCRL - 1973 - Report on Research at AFCRL July 1970 - June 1972.pdf}
}

@techreport{afcrlReportResearchAFCRL1975,
  title = {Report on {{Research}} at {{AFCRL}}: {{July}} 1972 - {{June}} 1974},
  author = {{AFCRL}},
  year = {1975},
  month = may,
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UWMZ9JGN/AFCRL - 1975 - Report on Research at AFCRL July 1972 - June 1974.pdf;/Users/rca2t1/Dropbox/Zotero/storage/9EK3ZP7Y/46omSJZFGBoC.html}
}

@book{affairsHearings1969,
  title = {Hearings},
  author = {Affairs, United States Congress House Committee on Foreign},
  year = {1969},
  googlebooks = {tRIaAQAAMAAJ},
  langid = {english}
}

@misc{AFOSRHistory,
  title = {{{AFOSR}} - {{History}}},
  journal = {Air Force Research Laboratory},
  abstract = {A Brief Organizational HistoryDownload the AFOSR 60th Anniversary Monograph for a comprehensive history of AFOSR and its contributions to scientific advancement.The wide-ranging and highly successful},
  howpublished = {http://www.afrl.af.mil/About-Us/Fact-Sheets/Fact-Sheet-Display/Article/2282094/afosr-history/},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FC48ZXVK/afosr-history.html}
}

@article{AgentOrangeLegislation1988,
  title = {"{{Agent Orange Legislation}} and {{Oversight}}," {{Hearing}} before the {{Veterans}}' {{Affairs Committee}}, {{May}} 12, 1988. {{S}}. {{Hrg}}. 100-1025 {{Senate Hearings}}: 100th {{Congress}}: {{Document No}}. 25},
  shorttitle = {"{{Agent Orange Legislation}} and {{Oversight}}," {{Hearing}} before the {{Veterans}}' {{Affairs Committee}}, {{May}} 12, 1988. {{S}}. {{Hrg}}. 100-1025 {{Senate Hearings}}},
  year = {1988},
  journal = {Legislative History of the Veterans' Judicial Review Act; Veteran's Benefits Improvement Act of 1988: P.L. 100-687, 102 Stat. 4105, November 18, 1988},
  volume = {5},
  pages = {I-712},
  langid = {english}
}

@article{AgentOrangeUpdate1980,
  title = {Agent Orange Update and Appendix, Agent Orange Activities (Part {{II}}) : Hearing before the {{Committee}} on {{Veterans}}' {{Affairs}}, {{United States Senate}}, {{Ninety-sixth Congress}}, Second Session, {{September}} 10, 1980.   {{Note}}},
  shorttitle = {Agent Orange Update and Appendix, Agent Orange Activities (Part {{II}})},
  year = {1980},
  journal = {Agent orange update and appendix, agent orange activities (part II) : hearing before the Committee on Veterans' Affairs, United States Senate, Ninety-sixth Congress, second session, September 10, 1980.},
  pages = {I-1368},
  langid = {english}
}

@article{agrawalExplorationDataScience2014,
  title = {Exploration of {{Data Science Techniques}} to {{Predict Fatigue Strength}} of {{Steel}} from {{Composition}} and {{Processing Parameters}}},
  author = {Agrawal, Ankit and Deshpande, Parijat D. and Cecen, Ahmet and Basavarsu, Gautham P. and Choudhary, Alok N. and Kalidindi, Surya R.},
  year = {2014},
  month = dec,
  journal = {Integrating Materials and Manufacturing Innovation},
  volume = {3},
  number = {1},
  pages = {90--108},
  issn = {2193-9772},
  doi = {10.1186/2193-9772-3-8},
  abstract = {This paper describes the use of data analytics tools for predicting the fatigue strength of steels. Several physics-based as well as data-driven approaches have been used to arrive at correlations between various properties of alloys and their compositions and manufacturing process parameters. Data-driven approaches are of significant interest to materials engineers especially in arriving at extreme value properties such as cyclic fatigue, where the current state-of-the-art physics based models have severe limitations. Unfortunately, there is limited amount of documented success in these efforts. In this paper, we explore the application of different data science techniques, including feature selection and predictive modeling, to the fatigue properties of steels, utilizing the data from the National Institute for Material Science (NIMS) public domain database, and present a systematic end-to-end framework for exploring materials informatics. Results demonstrate that several advanced data analytics techniques such as neural networks, decision trees, and multivariate polynomial regression can achieve significant improvement in the prediction accuracy over previous efforts, with R2 values over 0.97. The results have successfully demonstrated the utility of such data mining tools for ranking the composition and process parameters in the order of their potential for predicting fatigue strength of steels, and actually develop predictive models for the same.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/M4CCUCK2/Agrawal et al. - 2014 - Exploration of Data Science Techniques to Predict .pdf}
}

@inproceedings{agrawalFastAlgorithmsMining1994,
  title = {Fast Algorithms for Mining Association Rules},
  booktitle = {Proc. 20th Int. Conf. Very Large Data Bases, {{VLDB}}},
  author = {Agrawal, Rakesh and Srikant, Ramakrishnan},
  year = {1994},
  volume = {1215},
  pages = {487--499},
  publisher = {{Citeseer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/7Q5IF2WK/Agrawal and Srikant - 1994 - Fast algorithms for mining association rules.pdf}
}

@article{agrawalPerspectiveMaterialsInformatics2016,
  title = {Perspective: {{Materials Informatics}} and {{Big Data}}: {{Realization}} of the ``{{Fourth Paradigm}}'' of {{Science}} in {{Materials Science}}},
  shorttitle = {Perspective},
  author = {Agrawal, Ankit and Choudhary, Alok},
  year = {2016},
  month = may,
  journal = {APL Materials},
  volume = {4},
  number = {5},
  pages = {053208},
  publisher = {{American Institute of Physics}},
  doi = {10.1063/1.4946894},
  abstract = {Our ability to collect ``big data'' has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery. The need for data informatics is also emphasized by the Materials Genome Initiative (MGI), further boosting the emerging field of materials informatics. In this article, we look at how data-driven techniques are playing a big role in deciphering processing-structure-property-performance relationships in materials, with illustrative examples of both forward models (property prediction) and inverse models (materials discovery). Such analytics can significantly reduce time-to-insight and accelerate cost-effective materials discovery, which is the goal of MGI.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NH7SZ6CS/Agrawal and Choudhary - 2016 - Perspective Materials Informatics and Big Data R.pdf}
}

@misc{AirForceCambridge2020,
  title = {The {{Air Force Cambridge Research Laboratories}} and the {{New Manhattan Project}}},
  year = {2020},
  month = aug,
  journal = {Activist Post},
  abstract = {If one searches for the term `Project Cloverleaf,' one will find very little credible documentation. But the author has uncovered something...},
  howpublished = {https://www.activistpost.com/2020/08/the-air-force-cambridge-research-laboratories-and-the-new-manhattan-project.html},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TNXIU4FV/the-air-force-cambridge-research-laboratories-and-the-new-manhattan-project.html}
}

@book{airforcecambridgeresearchlaboratoriesu.s.HistoryProgressAFCRL1962,
  title = {History and Progress of {{AFCRL}}, {{Jan}}. 1961-{{June}} 1962; a Detailed Survey of Research at the {{Air Force Cambridge Research Laboratories}}, {{Office}} of {{Aerospace Research}}, {{Bedford}}, {{Mass}}.},
  author = {{Air Force Cambridge Research Laboratories (U.S.)}},
  year = {1962},
  series = {{{AFCRL}} 62-714},
  publisher = {{Air Force Cambridge Research Laboratories, Office of Aerospace Research, United States Air Force}},
  address = {{Bedford, Massachusetts}},
  keywords = {(OCoLC)fst00819166,(OCoLC)fst01411628,Associations; institutions; etc,fast,History},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EPCBTWZ3/Air Force Cambridge Research Laboratories (U.S.) - 1962 - History and progress of AFCRL, Jan. 1961-June 1962.pdf}
}

@misc{AllenRobertBob,
  title = {Allen, {{Robert}} ({{Bob}}) {{E}}. {{Died July}} 1st},
  howpublished = {https://advance.lexis.com/document/teaserdocument/?pdmfid=1516831\&crid=5f352f35-ed0e-4896-9dcc-cbb42fb4169e\&pddocfullpath=\%2Fshared\%2Fdocument\%2Fnews\%2Furn\%3AcontentItem\%3A5CKR-KK01-DY37-30GF-00000-00\&pddocid=urn\%3AcontentItem\%3A5CKR-KK01-DY37-30GF-00000-00\&pdcontentcomponentid=11810\&pdteaserkey=h1\&pditab=allpods\&ecomp=kb63k\&earg=sr8\&prid=3e0f152c-ec6a-4c84-870b-a9fdb34097c1},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/KSG5GXJH/teaserdocument.html}
}

@article{AllenRobertBob2014,
  title = {Allen, {{Robert}} ({{Bob}}) {{E}}. {{Died July}} 1st.},
  year = {2014},
  month = jul,
  journal = {St. Louis Post-Dispatch},
  address = {{Missouri}}
}

@book{alphaYearbook1952,
  title = {Yearbook},
  author = {Alpha, Kappa Tau},
  year = {1952},
  googlebooks = {7BI2AAAAIAAJ},
  langid = {english}
}

@book{altshulerRiseFallAir2013,
  title = {The {{Rise}} and {{Fall}} of {{Air Force Cambridge Research Laboratories}}},
  author = {Altshuler, Edward E.},
  year = {2013},
  month = jan,
  publisher = {{CreateSpace Independent Publishing Platform}},
  abstract = {This monograph provides a chronological account of how a fledgling research laboratory, which evolved from the MIT Radiation Laboratory and the Harvard Radio Research Laboratory after World War II, rose to become one of the premier research laboratories in the world as evidenced by its major accomplishments throughout its 66 year history. After many years of outstanding productivity the laboratory began to slowly decline. Even though the downsizing began in1974, the Hanscom Field Site continued to be very productive until its final days. In 2005 it was placed on the Base Realignment And Closure (BRAC) list and in August 2011 it was closed. Many of the major events that led to this decline were politically motivated. I had the privilege of collaborating with outstanding scientists from May 1960 to May 2011 and was blessed with a very rewarding career. One of the most ironic outcomes of the AFCRL history was the fact that when the laboratory was first established, the original plan was to move the new laboratory to Wright Field in Dayton, Ohio in 1946; this move actually occurred 65 years later. Also, after the Geophysics Research Directorate (GRD), was moved from New Jersey to Cambridge, MA in July 1948, there were numerous attempts to move GRD to Kirtland AFB. This also occurred in 2011.},
  isbn = {978-1-4818-3251-9},
  langid = {english}
}

@book{altshulerRiseFallAir2013a,
  title = {The {{Rise}} and {{Fall}} of {{Air Force Cambridge Research Laboratories}}},
  author = {Altshuler, Edward E.},
  year = {2013},
  month = jan,
  publisher = {{CreateSpace Independent Publishing Platform}},
  abstract = {This monograph provides a chronological account of how a fledgling research laboratory, which evolved from the MIT Radiation Laboratory and the Harvard Radio Research Laboratory after World War II, rose to become one of the premier research laboratories in the world as evidenced by its major accomplishments throughout its 66 year history. After many years of outstanding productivity the laboratory began to slowly decline. Even though the downsizing began in1974, the Hanscom Field Site continued to be very productive until its final days. In 2005 it was placed on the Base Realignment And Closure (BRAC) list and in August 2011 it was closed. Many of the major events that led to this decline were politically motivated. I had the privilege of collaborating with outstanding scientists from May 1960 to May 2011 and was blessed with a very rewarding career. One of the most ironic outcomes of the AFCRL history was the fact that when the laboratory was first established, the original plan was to move the new laboratory to Wright Field in Dayton, Ohio in 1946; this move actually occurred 65 years later. Also, after the Geophysics Research Directorate (GRD), was moved from New Jersey to Cambridge, MA in July 1948, there were numerous attempts to move GRD to Kirtland AFB. This also occurred in 2011.},
  isbn = {978-1-4818-3251-9},
  langid = {english}
}

@book{AlumniQuarterlyFortnightly1916,
  title = {The {{Alumni Quarterly}} and {{Fortnightly Notes}}},
  year = {1916},
  publisher = {{Alumni Association of the University of Illinois}},
  googlebooks = {hqrOAAAAMAAJ},
  langid = {english}
}

@article{alvaradoBigDataThick2017,
  title = {Big {{Data}}, {{Thick Mediation}}, and {{Representational Opacity}}},
  author = {Alvarado, Rafael and Humphreys, Paul},
  year = {2017},
  journal = {New Literary History},
  volume = {48},
  number = {4},
  pages = {729--749},
  issn = {1080-661X},
  doi = {10.1353/nlh.2017.0037},
  abstract = {{$<$}p{$>$}In 2008, the phrase "big data" shifted in meaning. It turned from referring to a problem and an opportunity for organizations with very large data sets to being the talisman for an emerging economic and cultural order that is both celebrated and feared for its deep and pervasive effects on the human condition. Economically, the phrase now denotes a data-mediated form of commerce exemplified by Google. Culturally, the phrase stands for a new form of knowledge and knowledge production. In this essay, we explore the connection between these two implicit meanings, considered as dimensions of a real social and scientific transformation with observable properties. We develop three central concepts: the datasphere, thick mediation, and representational opacity. These concepts provide a theoretical framework for making sense of how the economic and cultural dimensions interact to produce a set of effects, problems, and opportunities, not all of which have been addressed by big data's critics and advocates.{$<$}/p{$>$}},
  langid = {english}
}

@book{AmericanScientist1942,
  title = {American {{Scientist}}},
  year = {1942},
  publisher = {{Sigma Xi}},
  googlebooks = {jgBVAAAAMAAJ},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/L3Z498NJ/data-deluge-1942.png}
}

@book{AmericanScientist1942a,
  title = {American {{Scientist}}},
  year = {1942},
  publisher = {{Sigma Xi}},
  googlebooks = {jgBVAAAAMAAJ},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/8YR3FDWI/data-deluge-1947.png}
}

@article{andersonEndTheoryData2008,
  title = {The {{End}} of {{Theory}}: {{The Data Deluge Makes}} the {{Scientific Method Obsolete}}},
  shorttitle = {The {{End}} of {{Theory}}},
  author = {Anderson, Chris},
  year = {2008},
  month = jun,
  journal = {Wired},
  issn = {1059-1028},
  abstract = {Illustration: Marian Bantjes ``All models are wrong, but some are useful.'' So proclaimed statistician George Box 30 years ago, and he was right. But what choice did we have? Only models, from cosmological equations to theories of human behavior, seemed to be able to consistently, if imperfectly, explain the world around us. Until now. Today companies \textbackslash [\ldots\textbackslash ]},
  keywords = {Discoveries,magazine-16.07},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RAEKLBPE/pb-theory.html}
}

@article{andoEvaluationAdHoc1966,
  title = {Evaluation of an {{Ad Hoc Procedure}} for {{Estimating Parameters}} of {{Some Linear Models}}},
  author = {Ando, A. and Kaufman, G. M.},
  year = {1966},
  journal = {The Review of Economics and Statistics},
  volume = {48},
  number = {3},
  pages = {334--340},
  publisher = {{The MIT Press}},
  issn = {0034-6535},
  doi = {10.2307/1927089},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DZDXQTP4/Ando and Kaufman - 1966 - Evaluation of an Ad Hoc Procedure for Estimating P.pdf}
}

@inproceedings{aparicioDataScienceAI2019,
  title = {Data {{Science}} and {{AI}}: {{Trends Analysis}}},
  shorttitle = {Data {{Science}} and {{AI}}},
  booktitle = {2019 14th {{Iberian Conference}} on {{Information Systems}} and {{Technologies}} ({{CISTI}})},
  author = {Aparicio, S. and Aparicio, J. T. and Costa, C. J.},
  year = {2019},
  month = jun,
  pages = {1--6},
  issn = {2166-0727},
  doi = {10.23919/CISTI.2019.8760820},
  abstract = {This study has the primary goal to analyze the growth of data science through the main search trends. This study was conducted by defining in high level the concept of data science as well as its main components. Supported in those elements, we identified the main trends. We used mainly data from google trends to determine the evolution of search by topics., research area, or simple expressions. It allowed us to reckon that artificial intelligence (AI)suffered a lack of interest until 2012. Then it became an increasingly popular field since 2014. This is due to the progression of machine learning and data science. Results show a cumulative search of data science since 2012.},
  keywords = {AI,artificial intelligence,Computer science,data science,Data science,google trends,learning (artificial intelligence),machine learning,Machine learning,Market research,Mathematics,programming languages,search,search engines,search problems,search trends,Software,trends},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/H6C6S5UM/Aparicio et al. - 2019 - Data Science and AI Trends Analysis.pdf;/Users/rca2t1/Dropbox/Zotero/storage/GZU7KQFC/8760820.html}
}

@book{appropriationsDepartmentDefenseAppropriations1959,
  title = {Department of {{Defense Appropriations}}},
  author = {on Appropriations, United States Congress Senate Committee},
  year = {1959},
  publisher = {{U.S. Government Printing Office}},
  googlebooks = {TGwhAAAAMAAJ},
  langid = {english}
}

@book{appropriationsDepartmentDefenseAppropriations1970,
  title = {Department of {{Defense Appropriations}} for 1971: {{Hearings}} ... {{Ninety-first Congress}}, {{Second Session}}},
  shorttitle = {Department of {{Defense Appropriations}} for 1971},
  author = {on Appropriations, United States Congress House Committee},
  year = {1970},
  publisher = {{U.S. Government Printing Office}},
  googlebooks = {CYC2AAAAIAAJ},
  langid = {english}
}

@book{appropriationsIndependentOfficesAppropiations1965,
  title = {Independent {{Offices Appropiations}} for 1966: {{Hearings}} ... 89th {{Congress}}, 1st {{Session}}, {{Part}} 3},
  shorttitle = {Independent {{Offices Appropiations}} for 1966},
  author = {Appropriations, United States Congress House},
  year = {1965},
  googlebooks = {vkaereBadBMC},
  langid = {english}
}

@book{appropriationsSecondSupplementalAppropriation1958,
  title = {Second {{Supplemental Appropriation Bill}}: 1958, {{Hearings}} ... 85th {{Congress}}, 2d {{Session}}},
  shorttitle = {Second {{Supplemental Appropriation Bill}}},
  author = {Appropriations, United States Congress House},
  year = {1958},
  langid = {english}
}

@article{argamonMythMythicalUnicorn2014,
  title = {The {{Myth}} of the {{Mythical Unicorn}}},
  author = {Argamon, Shlomo},
  year = {2014},
  month = may,
  journal = {Information Management},
  volume = {Vol.1, No.1},
  publisher = {{SourceMedia, Inc.}},
  abstract = {Many have claimed recently that multifaceted data scientists are mythical beings, as impossible to find as unicorns. This itself is a myth, and a dangerous one at that. Hype is cyclic. A new idea excites people, exaggerated claims are made (and often believed), and the idea takes on bigger-than-life proportions. Eventually, however, reality sets in, and a critical backlash begins. There is a strong tendency to take the criticism seriously, much more than the initial hype. However, just as the goodness of the original idea ballooned wildly during the boom, the critique gets overstated significantly during the bust. The clear-eyed observer will avoid getting taken in by either of these sides of the hype cycle and move quickly to the ``plateau of productivity,'' but it is hard to see through the smoke and mirrors.},
  chapter = {News},
  copyright = {\textcopyright{} 2014 Information Management and SourceMedia, Inc. All rights reserved.},
  langid = {english}
}

@article{argamonWellRoundedDataScientist2014,
  title = {The {{Well-Rounded Data Scientist}}},
  author = {Argamon, Shlomo},
  year = {2014},
  month = apr,
  journal = {Information Management},
  volume = {Vol.1, No.1},
  publisher = {{SourceMedia, Inc.}},
  abstract = {The work of interpreting data to help decision-makers goes back some 5,000 years to the bureaucrats and businessmen of ancient Sumer. But dealing with the astronomical size and complexity of modern data sets requires a new, multifaceted set of computational, statistical, and communication and people skills, called ``data science.'' Data scientists are among the most sought-after professionals today, and the need for them will only increase as our world grows ever more complex and interconnected.},
  chapter = {News},
  copyright = {\textcopyright{} 2014 Information Management and SourceMedia, Inc. All rights reserved.},
  langid = {english}
}

@misc{ASACommunity,
  title = {{{ASA Community}}},
  abstract = {The ASA Community is an online gateway for member collaboration and connection.},
  howpublished = {https://community.amstat.org},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/MKBM2J88/the-role-of-statistics-in-data-science-an-asa-statement.html}
}

@misc{asastatisticalcomputing&graphicssectionsPrim92013,
  title = {Prim9},
  author = {{ASA Statistical Computing \& Graphics Sections}},
  year = {2013},
  month = nov
}

@article{atkinsonAnalysisTwopersonGame1958,
  title = {An Analysis of Two-Person Game Situations in Terms of Statistical Learning Theory},
  author = {Atkinson, Richard C. and Suppes, Patrick},
  year = {1958},
  journal = {Journal of Experimental Psychology},
  volume = {55},
  number = {4},
  pages = {369--378},
  publisher = {{American Psychological Association}},
  address = {{US}},
  issn = {0022-1015(Print)},
  doi = {10.1037/h0044476},
  abstract = {"The study deals with an analysis of a zero-sum, two-person game situation in terms of statistical learning theory and game theory\ldots{} . Analysis of the data was in terms of two different but related stochastic models for learning and game theory. Specifically the following detailed comparisons of data and theory were made: (a) mean asymptotic response probabilities, (b) one- and two-stage transition probabilities, and (c) variances associated with asymptotic response probabilities." (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
  keywords = {Games,Learning Theory},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DJQSSKY5/Atkinson and Suppes - 1958 - An analysis of two-person game situations in terms.pdf;/Users/rca2t1/Dropbox/Zotero/storage/NPK5CJFS/1959-07689-001.html}
}

@article{azevedoKDDSEMMACRISPDM2008,
  title = {{{KDD}}, {{SEMMA}} and {{CRISP-DM}}: A Parallel Overview},
  shorttitle = {{{KDD}}, {{SEMMA}} and {{CRISP-DM}}},
  author = {Azevedo, Ana Isabel Roj{\~a}o Louren{\c c}o and Santos, Manuel Filipe},
  year = {2008},
  journal = {IADS-DM},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/S3RTYB4Y/Azevedo and Santos - 2008 - KDD, SEMMA and CRISP-DM a parallel overview.pdf}
}

@book{azevedoKDDSEMMACRISPDM2019,
  title = {{{KDD}}, {{SEMMA}} and {{CRISP-DM}}: A Parallel Overview. {{IADS}}\textemdash{{DM}} (2008)},
  shorttitle = {{{KDD}}, {{SEMMA}} and {{CRISP-DM}}},
  author = {Azevedo, AIRL and Santos, M. F.},
  year = {2019}
}

@article{BackMatter1943,
  title = {Back {{Matter}}},
  year = {1943},
  journal = {The Journal of Symbolic Logic},
  volume = {8},
  number = {4},
  publisher = {{Association for Symbolic Logic}},
  issn = {0022-4812}
}

@article{BackMatter1957,
  title = {Back {{Matter}}},
  year = {1957},
  journal = {Science},
  volume = {126},
  number = {3270},
  pages = {417--422},
  publisher = {{American Association for the Advancement of Science}},
  issn = {0036-8075},
  keywords = {data processing scientistst},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/I58MCTW4/1957 - Back Matter.pdf}
}

@article{BackMatter1963,
  title = {Back {{Matter}}},
  year = {1963},
  journal = {Journal of the Society for Industrial and Applied Mathematics},
  volume = {11},
  number = {2},
  publisher = {{Society for Industrial and Applied Mathematics}},
  issn = {0368-4245},
  keywords = {data processing scientistst},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/U4TBXCF5/1963 - Back Matter.pdf}
}

@article{backusReportAlgorithmicLanguage1960,
  title = {Report on the Algorithmic Language {{ALGOL}} 60},
  author = {Backus, J. W. and Bauer, F. L. and Green, J. and Katz, C. and McCarthy, J. and Perlis, A. J. and Rutishauser, H. and Samelson, K. and Vauquois, B. and Wegstein, J. H. and {van Wijngaarden}, A. and Woodger, M. and Naur, Peter},
  year = {1960},
  month = may,
  journal = {Communications of the ACM},
  volume = {3},
  number = {5},
  pages = {299--314},
  issn = {0001-0782},
  doi = {10.1145/367236.367262},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NQM2HRIC/Backus et al. - 1960 - Report on the algorithmic language ALGOL 60.pdf}
}

@article{backusRevisedReportAlgorithmic1963,
  title = {Revised {{Report}} on the {{Algorithmic Language ALGOL}} 60},
  author = {Backus, J. W. and Bauer, F. L. and Green, J. and Katz, C. and McCarthy, J. and Naur, P. and Perlis, A. J. and Rutishauser, H. and Samelson, K. and Vauquois, B. and Wegstein, J. H. and {van Wijngaarden}, A. and Woodger, M.},
  year = {1963},
  month = jan,
  journal = {The Computer Journal},
  volume = {5},
  number = {4},
  pages = {349--367},
  issn = {0010-4620},
  doi = {10.1093/comjnl/5.4.349},
  abstract = {The report gives a complete defining description of the international algorithmic language ALGOL 60. This is a language suitable for expressing a large class of numerical processes in a form sufficiently concise for direct automatic translation into the language of programmed automatic computers.The introduction contains an account of the preparatory work leading up to the final conference, where the language was defined. In addition the notions reference language, publication language, and hardware representations are explained.In the first chapter a survey of the basic constituents and features of the language is given, and the formal notation, by which the syntactic structure is defined, is explained.The second chapter lists all the basic symbols, and the syntactic units know as identifiers, numbers, and strings are defined. Further, some important notions such as quantity and value are defined.The third chapter explains the rules for forming expressions, and the means of these expressions. Three different types of expressions exist: arithmetic, Boolean (logical), and designational.The fourth chapter describes the operational units of the language, known and statements. The basic statements are: assignment statements (evaluation of a formula), go to statements (explicit break of the sequence of execution of statements), dummy statements, and procedure statements (call for execution of a closed process, defined by a procedure declaration). The formation of more complex structures, having statement character, is explained. Thes include: conditional statements, for statements, compound statements, and blocks.In the fifth chapter the units known as declarations, serving for defining permanent properties of the units entering into a process described in the language, are defined.The report ends with two detailed examples of the use of the language, and an alphabetic index of definitions.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/J7FPL2FU/Backus et al. - 1963 - Revised Report on the Algorithmic Language ALGOL 6.pdf;/Users/rca2t1/Dropbox/Zotero/storage/RAXXS9HJ/316410.html}
}

@inproceedings{baileyFastAlgorithmsMining2002,
  title = {Fast {{Algorithms}} for {{Mining Emerging Patterns}}},
  booktitle = {European {{Conference}} on {{Principles}} of {{Data Mining}} and {{Knowledge Discovery}}},
  author = {Bailey, James and Manoukian, Thomas and Ramamohanarao, Kotagiri},
  year = {2002},
  pages = {39--50},
  publisher = {{Springer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/P5JA8YW4/Bailey et al. - 2002 - Fast algorithms for mining emerging patterns.pdf;/Users/rca2t1/Dropbox/Zotero/storage/PIGKCJFG/3-540-45681-3_4.html}
}

@misc{bandyopadhyayDataScienceProcess2016,
  title = {The {{Data Science Process}}},
  year = {2016},
  month = mar,
  journal = {KDnuggets},
  abstract = {What does a day in the data science life look like? Here is a very helpful framework that is both a way to understand what data scientists do, and a cheat sheet to break down any data science problem.},
  collaborator = {Bandyopadhyay, Raj},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DW6SKR92/data-science-process.html}
}

@techreport{bastianPhraseStructureLanguageTranslator1962,
  title = {A {{Phrase-Structure Language Translator}}},
  author = {Bastian, Jr},
  year = {1962},
  month = aug,
  institution = {{AIR FORCE CAMBRIDGE RESEARCH LABS HANSCOM AFB MA}},
  abstract = {A syntax-directed translator is described with several new features which result in much faster running time and the ability to handle longer input strings. The computer pro ram for the translator is describe in detail, and an example of its operation i provided.},
  chapter = {Technical Reports},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9SMDASVI/Bastian - 1962 - A PHRASE-STRUCTURE LANGUAGE TRANSLATOR.pdf;/Users/rca2t1/Dropbox/Zotero/storage/TCWWR24R/AD0293843.html}
}

@article{batesonCyberneticsSelfTheory1971,
  title = {The {{Cybernetics Of}}'self': {{A Theory}} of {{Alcoholism}}},
  shorttitle = {The Cybernetics of'self'},
  author = {Bateson, Gregory},
  year = {1971},
  journal = {Psychiatry},
  volume = {34},
  number = {1},
  pages = {1--18},
  keywords = {Transduction},
  annotation = {00387},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3BG74UGE/Bateson - 1971 - The cybernetics of'self' A theory of alcoholism.pdf}
}

@article{baumerDataScienceCourse2015,
  title = {A {{Data Science Course}} for {{Undergraduates}}: {{Thinking}} with {{Data}}},
  shorttitle = {A {{Data Science Course}} for {{Undergraduates}}},
  author = {Baumer, Ben},
  year = {2015},
  month = mar,
  journal = {arXiv:1503.05570 [cs, stat]},
  eprint = {1503.05570},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.},
  archiveprefix = {arXiv},
  keywords = {62-01,Computer Science - Computers and Society,Statistics - Computation,Statistics - Other Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/BAB9N8WC/Baumer - 2015 - A Data Science Course for Undergraduates Thinking.pdf;/Users/rca2t1/Dropbox/Zotero/storage/JRA9HK5R/1503.html}
}

@incollection{beasleyBootstrappingMonteCarlo2012,
  title = {Bootstrapping and {{Monte Carlo}} Methods},
  author = {Beasley, William and Rodgers, Joe},
  year = {2012},
  month = jan,
  pages = {407--425},
  doi = {10.1037/13620-022},
  abstract = {Frequently a researcher is interested in a theoretical distribution or characteristics of that distribution, such as its mean, standard deviation, or 2.5 and 97.5 percentiles. One hundred or even 50 years ago, we were restricted practically by computing limitations to theoretical distributions that are described by an explicit equation, such as the binomial or multivariate normal distribution. Using mathematical models of distributions often requires considerable mathematical ability, and also imposes rather severe and often intractable assumptions on the applied researchers (e.g., normality, independence, variance assumptions, and so on). But computer simulations now provide more flexibility specifying distributions, which in turn provide more flexibility specifying models. One contemporary simulation technique is Markov chain Monte Carlo (MCMC) simulation, which can specify arbitrarily complex and nested multivariate distributions. It can even combine different theoretical families of variates. Another contemporary technique is the bootstrap, which can construct sampling distributions of conventional statistics that are free from most (but not all) assumptions. It can even create sampling distributions for new or exotic test statistics that the researcher created for a specific experiment},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DE4Q67GT/Beasley and Rodgers - 2012 - Bootstrapping and Monte Carlo methods.pdf}
}

@article{beatonHowRespondData2016,
  title = {How to {{Respond}} to {{Data Science}}: {{Early Data Criticism}} by {{Lionel Trilling}}},
  shorttitle = {How to {{Respond}} to {{Data Science}}},
  author = {Beaton, Brian},
  year = {2016},
  month = jul,
  journal = {Information \& Culture: A Journal of History},
  volume = {51},
  number = {3},
  pages = {352--372},
  issn = {2166-3033},
  doi = {10.1353/lac.2016.0014},
  abstract = {{$<$}p{$>$}This article was originally drafted just four weeks after the publishing of \emph{Dataclysm}, a 2014 book by Christian Rudder that sought to popularize data and data science by, in part, dismissing the social sciences and humanities as obsolete approaches to knowledge production. In looking for a potential way of responding to data scientists like Rudder, this article examines a 1948\textendash 50 essay about data that was written by Lionel Trilling (1905\textendash 75). Trilling frames data as part of our broader cultural history, which includes literature, drama, epic poetry, and the arts. This article argues that what Trilling models in the essay is a line of writing and thinking about data\textemdash a type of \emph{data criticism}\textemdash that today offers tremendous promise for responding to data science and to evangelists like Christian Rudder.{$<$}/p{$>$}},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/S8FYL6X8/Beaton - 2016 - How to Respond to Data Science Early Data Critici.pdf}
}

@article{beckermanInternationalIncomeComparisons1970,
  title = {International {{Income Comparisons}}: {{A Reply}} to {{Morawetz}}},
  shorttitle = {International {{Income Comparisons}}},
  author = {Beckerman, Wilfred and Bacon, Robert},
  year = {1970},
  journal = {The Economic Journal},
  volume = {80},
  number = {320},
  pages = {981--982},
  publisher = {{[Royal Economic Society, Wiley]}},
  issn = {0013-0133},
  doi = {10.2307/2229932},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/WJZD4C85/Beckerman and Bacon - 1970 - International Income Comparisons A Reply to Moraw.pdf}
}

@article{bellPetascaleComputationalSystems2007,
  title = {Petascale {{Computational Systems}}},
  author = {Bell, Gordon and Gray, Jim and Szalay, Alex},
  year = {2007},
  month = jan,
  journal = {arXiv:cs/0701165},
  eprint = {cs/0701165},
  eprinttype = {arxiv},
  abstract = {Computational science is changing to be data intensive. Super-Computers must be balanced systems; not just CPU farms but also petascale IO and networking arrays. Anyone building CyberInfrastructure should allocate resources to support a balanced Tier-1 through Tier-3 design.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Databases,Computer Science - Hardware Architecture},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/GK2EE4EB/Bell et al. - 2007 - Petascale Computational Systems.pdf;/Users/rca2t1/Dropbox/Zotero/storage/RLKE85G4/0701165.html}
}

@article{belzerConciseSurveyComputer1976,
  title = {Concise {{Survey}} of {{Computer Methods}}. {{Peter Naur}}. {{New York}}: {{Petrocelli Books}}, 397 {{P}}. (1975)},
  shorttitle = {Concise {{Survey}} of {{Computer Methods}}. {{Peter Naur}}. {{New York}}},
  author = {Belzer, Jack},
  year = {1976},
  journal = {Journal of the American Society for Information Science},
  volume = {27},
  number = {2},
  pages = {125--126},
  issn = {1097-4571},
  doi = {10.1002/asi.4630270213},
  copyright = {Copyright \textcopyright{} 1976 Wiley Periodicals, Inc., A Wiley Company},
  langid = {english},
  annotation = {\_eprint: https://asistdl.onlinelibrary.wiley.com/doi/pdf/10.1002/asi.4630270213},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/WD4WTCEX/asi.html}
}

@book{belzerEncyclopediaComputerScience1979,
  title = {Encyclopedia of {{Computer Science}} and {{Technology}}: {{Volume}} 12 - {{Pattern Recognition}}: {{Structural Description Languages}} to {{Reliability}} of {{Computer Systems}}},
  shorttitle = {Encyclopedia of {{Computer Science}} and {{Technology}}},
  author = {Belzer, Jack and Holzman, Albert G. and Kent, Allen},
  year = {1979},
  month = may,
  publisher = {{CRC Press}},
  abstract = {"This comprehensive reference work provides immediate, fingertip access to state-of-the-art technology in nearly 700 self-contained articles written by over 900 international authorities. Each article in the Encyclopedia features current developments and trends in computers, software, vendors, and applications...extensive bibliographies of leading figures in the field, such as Samuel Alexander, John von Neumann, and Norbert Wiener...and in-depth analysis of future directions."},
  isbn = {978-0-8247-2262-3},
  langid = {english},
  keywords = {Computers / Computer Science,Computers / General}
}

@article{bergstraInformaticologyCombiningComputer2012,
  title = {Informaticology: Combining {{Computer Science}}, {{Data Science}}, and {{Fiction Science}}},
  shorttitle = {Informaticology},
  author = {Bergstra, Jan A.},
  year = {2012},
  month = oct,
  journal = {arXiv:1210.6636 [cs]},
  eprint = {1210.6636},
  eprinttype = {arxiv},
  primaryclass = {cs},
  abstract = {Motivated by an intention to remedy current complications with Dutch terminology concerning informatics, the term informaticology is positioned to denote an academic counterpart of informatics where informatics is conceived of as a container for a coherent family of practical disciplines ranging from computer engineering and software engineering to network technology, data center management, information technology, and information management in a broad sense. Informaticology escapes from the limitations of instrumental objectives and the perspective of usage that both restrict the scope of informatics. That is achieved by including fiction science in informaticology and by ranking fiction science on equal terms with computer science and data science, and framing (the study of) game design, evelopment, assessment and distribution, ranging from serious gaming to entertainment gaming, as a chapter of fiction science. A suggestion for the scope of fiction science is specified in some detail. In order to illustrate the coherence of informaticology thus conceived, a potential application of fiction to the ontology of instruction sequences and to software quality assessment is sketched, thereby highlighting a possible role of fiction (science) within informaticology but outside gaming.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Software Engineering},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/7DSGDEEQ/Bergstra - 2012 - Informaticology combining Computer Science, Data .pdf;/Users/rca2t1/Dropbox/Zotero/storage/WC8YJBGB/1210.html}
}

@misc{berkeleyschoolofinformationWhatDataScience2021,
  type = {Academic {{Program}}},
  title = {What Is {{Data Science}}?},
  shorttitle = {What Is {{Data Science}}?},
  author = {{Berkeley School of Information}},
  year = {2021},
  month = mar,
  journal = {UCB-UMT},
  abstract = {Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Learn what data science is and how to become a data scientist.},
  howpublished = {https://ischoolonline.berkeley.edu/data-science/what-is-data-science/},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ZBQP5HMR/what-is-data-science.html}
}

@book{berners-leeWeavingWebOriginal2008,
  title = {Weaving the {{Web}}: {{The Original Design}} and {{Ultimate Destiny}} of the {{World Wide Web}} by {{Its Inventor}}},
  shorttitle = {Weaving the {{Web}}},
  author = {{Berners-Lee}, Tim and Fischetti, Mark},
  year = {2008},
  month = jun,
  publisher = {{Paw Prints}},
  abstract = {Named one of the greatest minds of the 20th century by Time, Tim Berners-Lee is responsible for one of that century's most important advancements: the world wide web.~ Now, this low-profile genius-who never personally profitted from his invention -offers a compelling protrait of his invention.~ He reveals the Web's origins and the creation of the now ubiquitous http and www acronyms and shares his views on such critical issues as censorship, privacy, the increasing power of softeware companies , and the need to find the ideal balance between commercial and social forces.~ He offers insights into the true nature of the Web, showing readers how to use it to its fullest advantage.~ And he presents his own plan for the Web's future, calling for the active support and participation of programmers, computer manufacturers, and social organizations to manage and maintain this valuable resource so that it can remain a powerful force for social change and an outlet for individual creativity.},
  googlebooks = {Unp4PwAACAAJ},
  isbn = {978-1-4395-0036-1},
  langid = {english},
  keywords = {Computers / Web / General}
}

@book{bessingerLiteraryDataProcessing1964,
  title = {Literary {{Data Processing Conference}} Proceedings, {{September}} 9, 10, 11, 1964},
  author = {Bessinger, Jess B. and Parrish, Stephen Maxfield},
  year = {1964},
  publisher = {{IBM, Data Processing Division}},
  googlebooks = {gUQYAQAAMAAJ},
  langid = {english},
  keywords = {Computational linguistics,data processing,Language Arts \& Disciplines / Linguistics,Literature}
}

@article{bhageshpurDataNewOil2019,
  title = {Data {{Is The New Oil}} -- {{And That}}'s {{A Good Thing}}},
  shorttitle = {Council {{Post}}},
  author = {Bhageshpur, Kiran},
  year = {2019},
  month = nov,
  journal = {Forbes},
  abstract = {We must manage the dark side of data, but the advances in data fuels are worth the effort.},
  chapter = {Innovation},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/6RZS6K2V/data-is-the-new-oil-and-thats-a-good-thing.html}
}

@patent{bharatGeneratingUserInformation2016,
  title = {Generating User Information for Use in Targeted Advertising},
  author = {Bharat, Krishna and Lawrence, Stephen and Sahami, Mehran},
  year = {2016},
  month = jan,
  publisher = {{Google Patents}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HKAEGNAZ/Bharat et al. - 2016 - Generating user information for use in targeted ad.pdf;/Users/rca2t1/Dropbox/Zotero/storage/RDMMSKK2/en.html}
}

@incollection{bhowmikDataReductionStrategies2021,
  title = {Data {{Reduction Strategies}}},
  booktitle = {Structural {{Health Monitoring Damage Detection Systems}} for {{Aerospace}}},
  author = {Bhowmik, Basuraj and Quqa, Said and Sause, Markus G. R. and Pakrashi, Vikram and Droubi, Mohamad Ghazi},
  editor = {Sause, Markus G. R. and Jasi{\=u}nien{\.e}, Elena},
  year = {2021},
  series = {Springer {{Aerospace Technology}}},
  pages = {243--272},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-72192-3_9},
  abstract = {Based on the variety of methods available for gathering data for the aircraft health status, the challenge is to reduce the overall amount of data in a trackable and safe manner to ensure that the remaining data are characteristic of the current aircraft status. This chapter will cover available data reduction strategies for this task and discuss the data intensity of the SHM methods of Chaps. 5 to 8 and established approaches to deal with the acquired data. This includes aspects of algorithms and legal issues arising in this context.},
  isbn = {978-3-030-72192-3},
  langid = {english},
  keywords = {Compressed sensing,Data management,Data rate,Data reduction,Model order reduction,Real-time,Sampling rate,Wavelet transform,Wireless sensing},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4RZPJ8IX/Bhowmik et al. - 2021 - Data Reduction Strategies.pdf}
}

@article{BigDataCareer2013,
  title = {Big {{Data Career Switch}}: 4 {{Key Points}}},
  shorttitle = {Big {{Data Career Switch}}},
  year = {2013},
  month = jul,
  journal = {CMP TechWeb},
  publisher = {{CMP Media LLC}},
  abstract = {The U.S. has an employment problem. There are too many jobs and not enough workers \ldots{} in data science. You've probably seen the headlines: Data Scientist: The Sexiest Job of the 21st Century, In a Data Deluge, Companies Seek to Fill a New Role, Geeks Wanted -- Big Data Firms Push Data Scientist Development.},
  copyright = {Copyright \textcopyright 2013 United Business Media LLC. All rights reserved.},
  langid = {english}
}

@incollection{birdThomasKuhn2018,
  title = {Thomas {{Kuhn}}},
  booktitle = {The {{Stanford Encyclopedia}} of {{Philosophy}}},
  author = {Bird, Alexander},
  editor = {Zalta, Edward N.},
  year = {2018},
  edition = {Winter 2018},
  publisher = {{Metaphysics Research Lab, Stanford University}},
  abstract = {Thomas Samuel Kuhn (1922\textendash 1996) is one of the most influentialphilosophers of science of the twentieth century, perhaps the mostinfluential. His 1962 book The Structure of ScientificRevolutions is one of the most cited academic books of alltime. Kuhn's contribution to the philosophy of science marked not onlya break with several key positivist doctrines, but also inaugurated anew style of philosophy of science that brought it closer to thehistory of science. His account of the development of science heldthat science enjoys periods of stable growth punctuated by revisionaryrevolutions. To this thesis, Kuhn added the controversial`incommensurability thesis', that theories from differingperiods suffer from certain deep kinds of failure ofcomparability.}
}

@techreport{bishopJimmyDoolittleCommander2015,
  title = {Jimmy {{Doolittle}}: {{The Commander}} behind the {{Legend}}:},
  shorttitle = {Jimmy {{Doolittle}}},
  author = {Bishop, Benjamin W.},
  year = {2015},
  month = feb,
  address = {{Fort Belvoir, VA}},
  institution = {{Defense Technical Information Center}},
  doi = {10.21236/ADA618925},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RGV36MC9/Bishop - 2015 - Jimmy Doolittle The Commander behind the Legend.pdf}
}

@article{bixHartOpenTexture1991,
  title = {H. {{L}}. {{A}}. {{Hart}} and the "{{Open Texture}}" of {{Language}}},
  author = {Bix, Brian},
  year = {1991},
  journal = {Law and Philosophy},
  volume = {10},
  number = {1},
  pages = {51--72},
  publisher = {{Springer}},
  issn = {0167-5249},
  doi = {10.2307/3504835},
  abstract = {H. L. A. Hart and the "Open Texture" of Language tries to clarify the writings of both Hart and Friedrich Waismann on "open texture". In Waismann's work, "open texture" referred to the potential vagueness of words under extreme (hypothetical) circumstances. Hart's use of the term was quite different, and his work has been misunderstood because those differences were underestimated. Hart should not be read as basing his argument for judicial discretion on the nature of language; primarily, he was putting forward a policy argument for why rules should be applied in a way which would require that discretion.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/BCHML88U/Bix - 1991 - H. L. A. Hart and the Open Texture of Language.pdf}
}

@incollection{bjornerConceptualThreadsDatalogy1988,
  title = {Conceptual {{Threads}} of {{Datalogy}}, {{Informatics}} and {{Information Technology}}: {{Proposal}} for {{Their Foundation}} in {{Secondary}} and {{High School Education}}},
  shorttitle = {Conceptual {{Threads}} of {{Datalogy}}, {{Informatics}} and {{Information Technology}}},
  booktitle = {Children in the {{Information Age}}},
  author = {BJ{\o}RNER, DINES},
  year = {1988},
  pages = {19--36},
  publisher = {{Elsevier}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3J23J2K5/B9780080364643500077.html}
}

@inproceedings{blackwellDealingNewCognitive2000,
  title = {Dealing with {{New Cognitive Dimensions}}},
  booktitle = {Workshop on {{Cognitive Dimensions}}: {{Strengthening}} the {{Cognitive Dimensions Research Community}}., {{University}} of {{Hertfordshire}}},
  author = {Blackwell, Alan F.},
  year = {2000},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LWECWJYX/Blackwell - 2000 - Dealing with New Cognitive Dimensions.pdf}
}

@article{bleiScienceDataScience2017,
  title = {Science and Data Science},
  author = {Blei, David M. and Smyth, Padhraic},
  year = {2017},
  journal = {Proceedings of the National Academy of Sciences of the United States of America},
  volume = {114},
  number = {33},
  pages = {8689--8692},
  publisher = {{National Academy of Sciences}},
  issn = {0027-8424},
  doi = {10.2307/26487026},
  abstract = {Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. In this article, we ask why scientists should care about data science. To answer, we discuss data science from three perspectives: statistical, computational, and human. Although each of the three is a critical component of data science, we argue that the effective combination of all three components is the essence of what data science is about.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/89BRM7Q7/Blei and Smyth - 2017 - Science and data science.pdf}
}

@misc{bloorDataScienceRant2013,
  title = {A {{Data Science Rant}}},
  author = {Bloor, Robin},
  year = {2013},
  month = aug,
  journal = {Inside Analysis},
  abstract = {What is data science? From the hype in the IT press right now, you might think that it is something excitingly new, destined to determine the future prosperity of a whole swathe of companies big an\ldots},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LEVMCHNA/a-data-science-rant.html}
}

@book{blumFoundationsDataScience2020,
  title = {Foundations of {{Data Science}}},
  author = {Blum, Avrim and Hopcroft, John and Kannan, Ravindran},
  year = {2020},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge}},
  doi = {10.1017/9781108755528},
  abstract = {This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.},
  isbn = {978-1-108-48506-7},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2IPTVQIJ/Blum et al. - 2019 - Foundations of Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/HEM3BHV6/Blum et al. - 2020 - Foundations of Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/Z85WB8W7/6A43CE830DE83BED6CC5171E62B0AA9E.html}
}

@misc{bockHistoryInternationalFederation2001,
  title = {A History of the {{International Federation}} of {{Classification Societies}}},
  author = {Bock, Hans-Hermann},
  year = {2001},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RVZFF3ML/Bock - 2001 - A history of the International Federation of Class.pdf}
}

@article{boffeyMansfieldAmendmentNot1970,
  title = {Mansfield {{Amendment Not Yet Dead}}},
  author = {Boffey, Philip M.},
  year = {1970},
  month = nov,
  journal = {Science},
  publisher = {{American Association for the Advancement of Science}},
  doi = {10.1126/science.170.3958.613},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JEI6GWIN/science.170.3958.html}
}

@book{bohme100YearsData1991,
  title = {100 {{Years}} of {{Data Processing}}: {{The Punchcard Century}}},
  shorttitle = {100 {{Years}} of {{Data Processing}}},
  author = {Bohme, Frederick G.},
  year = {1991},
  publisher = {{U.S. Department of Commerce, Bureau of the Census, Data User Services Division}},
  googlebooks = {uCeu4sHRLfgC},
  langid = {english}
}

@article{BoozAllenHamilton2013,
  title = {Booz {{Allen Hamilton Maps}} the {{DNA}} of {{Data Science}} with {{Release}} of {{The Field Guide}} to {{Data Science}}},
  year = {2013},
  month = nov,
  journal = {ENP Newswire},
  publisher = {{Electronic News Publishing Ltd.}},
  abstract = {Release date - 06112013 McLean, Virginia - A commercial airline facing increased market competition leveraged `big data' analytical tools and machine learning on terabytes of data to better serve its most frequent fliers and priority passengers and to improve financial performance.},
  copyright = {(c) 2013, Electronic News Publishing. All Rights Reserved.},
  langid = {english},
  keywords = {Accounting/Consulting,Booz Allen Hamilton Holding Corp.,Business Consultancy,Business/Consumer Services,Management Consulting,The Carlyle Group}
}

@article{BoozAllenHamilton2014,
  title = {Booz {{Allen Hamilton Launches Explore Data Science}} to {{Address Rapidly Growing Shortage}} of {{Data Scientists}}},
  year = {2014},
  month = oct,
  journal = {ENP Newswire},
  publisher = {{Electronic News Publishing Ltd.}},
  abstract = {Release date - 15102014 McLean, VA - Booz Allen Hamilton today announced the release of its Explore Data Science online training program, a self-paced, hands-on course geared toward all levels of data science proficiency - from introductory to professional.},
  copyright = {(c) 2014, Electronic News Publishing. All Rights Reserved.},
  langid = {english},
  keywords = {Accounting/Consulting,Booz Allen Hamilton Holding Corp.,Business Consultancy,Business/Consumer Services,Management Consulting,The Carlyle Group}
}

@article{BoozAllenHamilton2014a,
  title = {Booz {{Allen Hamilton}} and {{Kaggle Launch Inaugural National Data Science Bowl}}},
  year = {2014},
  month = dec,
  journal = {Business Wire},
  publisher = {{Business Wire, Inc.}},
  abstract = {Competition will challenge participants to use data science to affect change on a global scale MCLEAN, Va.--(BUSINESS WIRE)--December 15, 2014--},
  copyright = {(c) 2014 Business Wire. All Rights Reserved.},
  langid = {english},
  keywords = {Accounting/Consulting,Booz Allen Hamilton Holding Corp.,Business Consultancy,Business/Consumer Services,Management Consulting,The Carlyle Group}
}

@article{BoozAllenMaps2013,
  title = {Booz {{Allen Maps}} the {{DNA}} of {{Data Science}} with {{Release}} of {{The Field Guide}} to {{Data Science}}},
  year = {2013},
  month = nov,
  journal = {Business Wire},
  publisher = {{Business Wire, Inc.}},
  abstract = {MCLEAN, Virginia--(BUSINESS WIRE)--November 06, 2013-- A commercial airline facing increased market competition leveraged "big data" analytical tools and machine learning on terabytes of data to better serve its most frequent fliers and priority passengers and to improve financial performance. This is one example in today's increasingly complex world, where data -- no matter its form, shape or size -- is omnipresent and can be used for competitive edge. Leaders at organizations of all shapes and sizes are doing their best to figure out how to turn that data into a resource. They are looking for answers in a new, growing field - data science.},
  copyright = {(c) 2013 Business Wire. All Rights Reserved.},
  langid = {english},
  keywords = {Accounting/Consulting,Booz Allen Hamilton Holding Corp.,Business Consultancy,Business/Consumer Services,Management Consulting,The Carlyle Group}
}

@article{borneRevolutionAstronomyEducation2009,
  title = {The {{Revolution}} in {{Astronomy Education}}: {{Data Science}} for the {{Masses}}},
  shorttitle = {The {{Revolution}} in {{Astronomy Education}}},
  author = {Borne, Kirk D. and Jacoby, Suzanne and Carney, K. and Connolly, A. and Eastman, T. and Raddick, M. J. and Tyson, J. A. and Wallin, J.},
  year = {2009},
  month = sep,
  journal = {arXiv:0909.3895 [astro-ph, physics:physics]},
  eprint = {0909.3895},
  eprinttype = {arxiv},
  primaryclass = {astro-ph, physics:physics},
  abstract = {As our capacity to study ever-expanding domains of our science has increased (including the time domain, non-electromagnetic phenomena, magnetized plasmas, and numerous sky surveys in multiple wavebands with broad spatial coverage and unprecedented depths), so have the horizons of our understanding of the Universe been similarly expanding. This expansion is coupled to the exponential data deluge from multiple sky surveys, which have grown from gigabytes into terabytes during the past decade, and will grow from terabytes into Petabytes (even hundreds of Petabytes) in the next decade. With this increased vastness of information, there is a growing gap between our awareness of that information and our understanding of it. Training the next generation in the fine art of deriving intelligent understanding from data is needed for the success of sciences, communities, projects, agencies, businesses, and economies. This is true for both specialists (scientists) and non-specialists (everyone else: the public, educators and students, workforce). Specialists must learn and apply new data science research techniques in order to advance our understanding of the Universe. Non-specialists require information literacy skills as productive members of the 21st century workforce, integrating foundational skills for lifelong learning in a world increasingly dominated by data. We address the impact of the emerging discipline of data science on astronomy education within two contexts: formal education and lifelong learners.},
  archiveprefix = {arXiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Databases,Computer Science - Digital Libraries,Computer Science - Information Retrieval,Physics - Physics Education},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/44C8QS3Y/Borne et al. - 2009 - The Revolution in Astronomy Education Data Scienc.pdf;/Users/rca2t1/Dropbox/Zotero/storage/D26YNV7E/0909.html}
}

@inproceedings{bosakInformationAlgebra1961,
  title = {An Information {{Algebra}}},
  booktitle = {Proceedings of the 1961 16th {{ACM}} National Meeting},
  author = {Bosak, R.},
  year = {1961},
  month = jan,
  series = {{{ACM}} '61},
  pages = {62.101--62.104},
  publisher = {{Association for Computing Machinery}},
  address = {{New York, NY, USA}},
  doi = {10.1145/800029.808523},
  abstract = {The Algebra is concerned with three undefined concepts; entity, property, and value. Although these concepts are formally undefined, certain intuitive statements can be made. Data processing is the activity of maintaining and processing data to accomplish certain objectives. The data are collections of values of certain selected properties of certain selected entities. e.g. A payroll manager has many objectives of which the primary one is the payment of his employees (the entities). He selects certain properties such as employee number, name, sex, hourly payrate, and he maintains a file in which he records values of these properties for each entity.},
  isbn = {978-1-4503-7388-3},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VW4ENRHH/Bosak - 1961 - An information Algebra.pdf}
}

@article{botelhoMedicineYear20001965,
  title = {Medicine in the {{Year}} 2000},
  author = {Botelho, Stella Y.},
  year = {1965},
  journal = {Science},
  volume = {147},
  number = {3662},
  pages = {1164--1168},
  publisher = {{American Association for the Advancement of Science}},
  issn = {0036-8075},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3PYC42H8/Botelho - 1965 - Medicine in the Year 2000.pdf}
}

@article{bourgoinBigDataBetter1995,
  title = {Big {{Data}}\textendash{{Better Returns}}, {{Leveraging Your Hidden Data Assets}} to {{Improve ROI}}},
  author = {Bourgoin, MO and Smith, SJ},
  year = {1995},
  journal = {Artificial Intelligence in the Capital Markets, Probus Publishing Company}
}

@misc{bourneBioinformaticsDead2021,
  title = {Is `{{Bioinformatics}}' {{Dead}}?},
  author = {Bourne, Philip E.},
  year = {2021},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2A9K6F8X/Bourne - 2021 - Is ‘Bioinformatics’ Dead.pdf}
}

@article{bourneReamingLifeBased2013,
  title = {The Reaming of Life: Based on the 2010 {{Jim Gray eScience Award Lecture}}},
  shorttitle = {The Reaming of Life},
  author = {Bourne, Philip E.},
  year = {2013},
  journal = {Concurrency and Computation: Practice and Experience},
  volume = {25},
  number = {4},
  pages = {445--453},
  publisher = {{Wiley Online Library}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/C9ARB4T4/cpe.html}
}

@incollection{bousquetIntroductionStatisticalLearning2004,
  title = {Introduction to {{Statistical Learning Theory}}},
  booktitle = {Advanced {{Lectures}} on {{Machine Learning}}: {{ML Summer Schools}} 2003, {{Canberra}}, {{Australia}}, {{February}} 2 - 14, 2003, {{T\"ubingen}}, {{Germany}}, {{August}} 4 - 16, 2003, {{Revised Lectures}}},
  author = {Bousquet, Olivier and Boucheron, St{\'e}phane and Lugosi, G{\'a}bor},
  editor = {Bousquet, Olivier and {von Luxburg}, Ulrike and R{\"a}tsch, Gunnar},
  year = {2004},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  pages = {169--207},
  publisher = {{Springer}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-540-28650-9_8},
  abstract = {The goal of statistical learning theory is to study, in a statistical framework, the properties of learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results.},
  isbn = {978-3-540-28650-9},
  langid = {english},
  keywords = {Empirical Process,Empirical Risk,Growth Function,Statistical Learn Theory,Structural Risk Minimization},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/5DNXUWH5/Bousquet et al. - 2004 - Introduction to Statistical Learning Theory.pdf}
}

@inproceedings{brachmanProcessKnowledgeDiscovery1994,
  title = {The {{Process}} of {{Knowledge Discovery}} in {{Databases}}: {{A First Sketch}}.},
  shorttitle = {The {{Process}} of {{Knowledge Discovery}} in {{Databases}}},
  booktitle = {{{KDD}} Workshop},
  author = {Brachman, Ronald J. and Anand, Tej},
  year = {1994},
  volume = {3},
  pages = {1--12},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/BS2Y93JI/Brachman and Anand - 1994 - The Process of Knowledge Discovery in Databases A.pdf;/Users/rca2t1/Dropbox/Zotero/storage/7MZ65QIU/ws94-03-001.html}
}

@article{bradyChallengeBigData2019,
  title = {The {{Challenge}} of {{Big Data}} and {{Data Science}}},
  author = {Brady, Henry E.},
  year = {2019},
  month = may,
  journal = {Annual Review of Political Science},
  volume = {22},
  number = {1},
  pages = {297--323},
  publisher = {{Annual Reviews}},
  issn = {1094-2939},
  doi = {10.1146/annurev-polisci-090216-023229},
  abstract = {Big data and data science are transforming the world in ways that spawn new concerns for social scientists, such as the impacts of the internet on citizens and the media, the repercussions of smart cities, the possibilities of cyber-warfare and cyber-terrorism, the implications of precision medicine, and the consequences of artificial intelligence and automation. Along with these changes in society, powerful new data science methods support research using administrative, internet, textual, and sensor-audio-video data. Burgeoning data and innovative methods facilitate answering previously hard-to-tackle questions about society by offering new ways to form concepts from data, to do descriptive inference, to make causal inferences, and to generate predictions. They also pose challenges as social scientists must grasp the meaning of concepts and predictions generated by convoluted algorithms, weigh the relative value of prediction versus causal inference, and cope with ethical challenges as their methods, such as algorithms for mobilizing voters or determining bail, are adopted by policy makers.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/68BXS3XR/Brady - 2019 - The Challenge of Big Data and Data Science.pdf}
}

@book{bramerPrinciplesDataMining2016,
  title = {Principles of {{Data Mining}}},
  author = {Bramer, Max},
  year = {2016},
  series = {Undergraduate {{Topics}} in {{Computer Science}}},
  publisher = {{Springer}},
  address = {{London}},
  doi = {10.1007/978-1-4471-7307-6_1},
  isbn = {978-1-4471-7307-6},
  langid = {english},
  keywords = {Association Rule,Data Mining Algorithm,Data Warehouse,Fraud Detection,Unlabelled Data},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9MELGEHA/Bramer - 2016 - Introduction to Data Mining.pdf}
}

@article{breimanGeneralEstimatesIntrinsic1976,
  title = {General {{Estimates}} of the {{Intrinsic Variability}} of {{Data}} in {{Nonlinear Regression Models}}},
  author = {Breiman, L. and Meisel, W. S.},
  year = {1976},
  journal = {Journal of the American Statistical Association},
  volume = {71},
  number = {354},
  pages = {301--307},
  publisher = {{[American Statistical Association, Taylor \& Francis, Ltd.]}},
  issn = {0162-1459},
  doi = {10.2307/2285301},
  abstract = {A dependent variable is some unknown function of independent variables plus an error component. If the magnitude of the error could be estimated with minimal assumptions about the underlying functional dependence, then this could be used to judge goodness-of-fit and as a means of selecting a subset of the independent variables which best determine the dependent variable. We propose a procedure for this purpose which is based on a data-directed partitioning of the space into subregions and a fitting of the function in each subregion. The behavior of the procedure is heuristically discussed and illustrated by some simulation examples.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UERDZM3N/Breiman and Meisel - 1976 - General Estimates of the Intrinsic Variability of .pdf}
}

@article{breimanStatisticalModelingTwo2001,
  title = {Statistical {{Modeling}}: {{The Two Cultures}}},
  shorttitle = {Statistical {{Modeling}}},
  author = {Breiman, Leo},
  year = {2001},
  month = aug,
  journal = {Statistical Science},
  volume = {16},
  number = {3},
  pages = {199--231},
  issn = {0883-4237, 2168-8745},
  doi = {10.1214/ss/1009213726},
  abstract = {There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools. Includes comments and a rejoinder by the author.},
  langid = {english},
  mrnumber = {MR1874152},
  zmnumber = {1059.62505},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FDJ6DMNE/Breiman - 2001 - Statistical Modeling The Two Cultures.pdf;/Users/rca2t1/Dropbox/Zotero/storage/45QH645A/1009213726.html}
}

@article{brockhoffDeterminantsResearchDevelopment1970,
  title = {Determinants of {{Research}} and {{Development Expenditure}} in {{Some Chemical Corporations}} in {{Germany}}},
  author = {Brockhoff, Klaus},
  year = {1970},
  journal = {Management International Review},
  volume = {10},
  number = {4/5},
  pages = {71--84},
  publisher = {{Springer}},
  issn = {0025-181X},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FH8LYNTW/Brockhoff - 1970 - Determinants of Research and Development Expenditu.pdf}
}

@misc{bromanAmDataScientist2016,
  title = {I Am a Data Scientist},
  author = {Broman, Karl},
  year = {2016},
  month = apr,
  journal = {The stupidest thing...},
  abstract = {Three years ago this week, I wrote a blog post, ``Data science is statistics''. I was fiercely against the term at that time, as I felt that we already had a data science, and it was called Statistic\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/6A8SS3X4/i-am-a-data-scientist.html}
}

@misc{bromanAmDataScientist2016a,
  title = {I Am a Data Scientist},
  author = {Broman, Karl},
  year = {2016},
  month = apr,
  journal = {The stupidest thing...},
  abstract = {Three years ago this week, I wrote a blog post, ``Data science is statistics''. I was fiercely against the term at that time, as I felt that we already had a data science, and it was called Statistic\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TT9M6IEC/i-am-a-data-scientist.html}
}

@misc{bromanDataScienceStatistics2013,
  title = {Data Science Is Statistics},
  author = {Broman, Karl},
  year = {2013},
  month = apr,
  journal = {The stupidest thing...},
  abstract = {When physicists do mathematics, they don't say they're doing ``number science''. They're doing math. If you're analyzing data, you're doing statistics. You c\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4HH8QXME/data-science-is-statistics.html}
}

@misc{bromanDataScienceStatistics2013a,
  title = {Data Science Is Statistics},
  author = {Broman, Karl},
  year = {2013},
  month = apr,
  journal = {The stupidest thing...},
  abstract = {When physicists do mathematics, they don't say they're doing ``number science''. They're doing math. If you're analyzing data, you're doing statistics. You c\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/QJVE32W3/data-science-is-statistics.html}
}

@misc{bromanReformAcademicStatistics2014,
  title = {Reform Academic Statistics},
  author = {Broman, Karl},
  year = {2014},
  month = may,
  journal = {The stupidest thing...},
  abstract = {Terry Speed recently gave a talk on the role of statisticians in ``Big Data'' initiatives (see the video or just look at the slides). He points to the history of statisticians' discussions of m\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/WY4ZYGYM/reform-academic-statistics.html}
}

@article{brownWhatITNeeds2015,
  title = {What {{IT Needs To Know About The Data Mining Process}}},
  author = {Brown, Meta S.},
  year = {2015},
  month = jul,
  journal = {Forbes},
  abstract = {No business can be data-driven if the only people interested in data analysis are the analysts. Just as the guidance of accountants and attorneys shapes everyday business, analytics must be integrated throughout the organization to provide value. But when it comes to getting everyone on board, accountants and attorneys have a [...]},
  chapter = {Tech},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/X4JG4RZE/what-it-needs-to-know-about-the-data-mining-process.html}
}

@article{bryanDataScienceThree2017,
  title = {Data {{Science}}: {{A Three Ring Circus}} or a {{Big Tent}}?},
  shorttitle = {Data {{Science}}},
  author = {Bryan, Jennifer and Wickham, Hadley},
  year = {2017},
  month = dec,
  abstract = {This is part of a collection of discussion pieces on David Donoho's paper 50 Years of Data Science, appearing in Volume 26, Issue 4 of the Journal of Computational and Graphical Statistics (2017).},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/5S8WVXNF/Bryan and Wickham - 2017 - Data Science A Three Ring Circus or a Big Tent.pdf;/Users/rca2t1/Dropbox/Zotero/storage/E5AP65AM/1712.html}
}

@article{bryceCurriculumGuidelinesBachelor2001,
  title = {Curriculum {{Guidelines}} for {{Bachelor}} of {{Science Degrees}} in {{Statistical Science}}},
  author = {Bryce, G. Rex and Gould, Robert and Notz, William I. and Peck, Roxy L.},
  year = {2001},
  month = feb,
  journal = {American Statistician},
  volume = {55},
  number = {1},
  pages = {7--13},
  abstract = {Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/6VST26X5/39.html}
}

@article{bryceCurriculumGuidelinesBachelor2001a,
  title = {Curriculum {{Guidelines}} for {{Bachelor}} of {{Science Degrees}} in {{Statistical Science}}},
  author = {Bryce, G. Rex and Gould, Robert and Notz, William I. and Peck, Roxy L.},
  year = {2001},
  journal = {The American Statistician},
  volume = {55},
  number = {1},
  pages = {7--13},
  publisher = {{[American Statistical Association, Taylor \& Francis, Ltd.]}},
  issn = {0003-1305},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/C4GC5S3P/Bryce et al. - 2001 - Curriculum Guidelines for Bachelor of Science Degr.pdf}
}

@book{brynjolfssonSecondMachineAge2014,
  title = {The Second Machine Age: {{Work}}, Progress, and Prosperity in a Time of Brilliant Technologies},
  shorttitle = {The Second Machine Age},
  author = {Brynjolfsson, Erik and McAfee, Andrew},
  year = {2014},
  series = {The Second Machine Age: {{Work}}, Progress, and Prosperity in a Time of Brilliant Technologies},
  pages = {306},
  publisher = {{W W Norton \& Co}},
  address = {{New York, NY, US}},
  abstract = {In recent years, Google's autonomous cars have logged thousands of miles on American highways and IBM's Watson trounced the best human Jeopardy! players. Digital technologies\textemdash with hardware, software, and networks at their core\textemdash will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human. In The Second Machine Age MIT's Erik Brynjolfsson and Andrew McAfee\textemdash two thinkers at the forefront of their field\textemdash reveal the forces driving the reinvention of our lives and our economy. As the full impact of digital technologies is felt, we will realize immense bounty in the form of dazzling personal technology, advanced infrastructure, and near-boundless access to the cultural Items that enrich our lives. Amid this bounty will also be wrenching change. Professions of aii kinds\textemdash from lawyers to truck drivers\textemdash will be forever upended. Companies will be forced to transform or die. Recent economic indicators reflect this shift: fewer people are working, and wages are failing even as productivity and profits soar. Drawing on years of research and up-to-the-minute trends, Brynjolfsson and McAfee identify the best strategies for survival and offer a new path to prosperity. These include revamping education so that it prepares people for the next economy instead of the last one, designing new collaborations that pair brute processing power with human ingenuity, and embracing policies that make sense in a radically transformed landscape. A fundamentally optimistic book, The Second Machine Age will alter how we think about Issues of technological, societal, and economic progress. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
  isbn = {978-0-393-23935-5},
  keywords = {Economy,Human Computer Interaction,Human Factors Engineering,Human Machine Systems,Technology},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CS8DIJB7/2014-07087-000.html}
}

@misc{BuildingCommunityData,
  title = {Building a {{Community}} of {{Data Scientists}}: {{An Explorative Analysis}}},
  howpublished = {https://www.jstage.jst.go.jp/article/dsj/8/0/8\_008-004/\_article/-char/ja/},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FREXMN2Y/ja.html}
}

@article{bujaConversationPeterHuber2008,
  title = {A {{Conversation}} with {{Peter Huber}}},
  author = {Buja, Andreas and K{\"u}nsch, Hans R.},
  year = {2008},
  month = feb,
  journal = {Statistical Science},
  volume = {23},
  number = {1},
  eprint = {0808.0777},
  eprinttype = {arxiv},
  pages = {120--135},
  issn = {0883-4237},
  doi = {10.1214/07-STS251},
  abstract = {Peter J. Huber was born on March 25, 1934, in Wohlen, a small town in the Swiss countryside. He obtained a diploma in mathematics in 1958 and a Ph.D. in mathematics in 1961, both from ETH Zurich. His thesis was in pure mathematics, but he then decided to go into statistics. He spent 1961--1963 as a postdoc at the statistics department in Berkeley where he wrote his first and most famous paper on robust statistics, ``Robust Estimation of a Location Parameter.'' After a position as a visiting professor at Cornell University, he became a full professor at ETH Zurich. He worked at ETH until 1978, interspersed by visiting positions at Cornell, Yale, Princeton and Harvard. After leaving ETH, he held professor positions at Harvard University 1978--1988, at MIT 1988--1992, and finally at the University of Bayreuth from 1992 until his retirement in 1999. He now lives in Klosters, a village in the Grisons in the Swiss Alps. Peter Huber has published four books and over 70 papers on statistics and data analysis. In addition, he has written more than a dozen papers and two books on Babylonian mathematics, astronomy and history. In 1972, he delivered the Wald lectures. He is a fellow of the IMS, of the American Association for the Advancement of Science, and of the American Academy of Arts and Sciences. In 1988 he received a Humboldt Award and in 1994 an honorary doctorate from the University of Neuch\textbackslash\^\{a\}tel. In addition to his fundamental results in robust statistics, Peter Huber made important contributions to computational statistics, strategies in data analysis, and applications of statistics in fields such as crystallography, EEGs, and human growth curves.},
  archiveprefix = {arXiv},
  keywords = {Statistics - Methodology},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/GK6DMPQ8/Buja and Künsch - 2008 - A Conversation with Peter Huber.pdf;/Users/rca2t1/Dropbox/Zotero/storage/XUSBBZDJ/0808.html}
}

@article{buluswarHowCompaniesAre2016,
  title = {How Companies Are Using Big Data and Analytics},
  author = {Buluswar, Murli},
  year = {2016},
  month = apr,
  journal = {McKinsey Analytics},
  abstract = {Just how do major organizations use data and analytics to inform strategic and operational decisions? Senior leaders provide insight into the challenges and opportunities.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FTYEJGLV/Buluswar - 1016 - How companies are using big data and analytics.pdf;/Users/rca2t1/Dropbox/Zotero/storage/KHDL4DWA/how-companies-are-using-big-data-and-analytics.html}
}

@article{burkettBurkettAnnouncesHis2003,
  title = {Burkett {{Announces His Retirement}} as {{Director}} of {{Museum}}},
  author = {Burkett, Ron},
  year = {2003},
  journal = {A Newsletter for the White Sands Missile Range Historical Foundation},
  volume = {3},
  number = {3},
  pages = {5},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/539UUR3N/Burkett - 2003 - Burkett Announces His Retirement as Director of Mu.pdf}
}

@book{c.w.bessererandhazelc.bessererGuideSpaceAge1959,
  title = {Guide to the {{Space Age}}},
  author = {{C. W. BESSERER AND HAZEL C. BESSERER}},
  year = {1959},
  publisher = {{PRENTICE HALL, INC.}},
  collaborator = {{Internet Archive}},
  langid = {english}
}

@book{caffoExecutiveDataScience2015,
  title = {Executive {{Data Science}}},
  author = {Caffo, Brian and Peng, Roger D. and Leek, Jeffrey},
  year = {2015},
  month = jun,
  publisher = {{Leanpub}},
  abstract = {This book teaches you how to assemble and lead a data science enterprise so that your organization can move towards extracting information from big data. This book is based on the acclaimed Johns Hopkins Executive Data Science Specialization. Printed copies of this book are available through Lulu.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/WQMFE35R/Caffo et al. - 2015 - Executive Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/A2YXM79I/eds.html}
}

@article{caiStatisticalTheoryPowering2019,
  title = {Statistical {{Theory Powering Data Science}}},
  author = {Cai, Junhui and Mandelbaum, Avishai and Nagaraja, Chaitra H. and Shen, Haipeng and Zhao, Linda},
  year = {2019},
  month = nov,
  journal = {Statistical Science},
  volume = {34},
  number = {4},
  pages = {669--691},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0883-4237, 2168-8745},
  doi = {10.1214/19-STS754},
  abstract = {Statisticians are finding their place in the emerging field of data science. However, many issues considered ``new'' in data science have long histories in statistics. Examples of using statistical thinking are illustrated, which range from exploratory data analysis to measuring uncertainty to accommodating nonrandom samples. These examples are then applied to service networks, baseball predictions and official statistics.},
  keywords = {decennial census,Empirical Bayes,house price index,nonparametric estimation,Queueing theory,Service networks,sports statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LPJSBPD7/Cai et al. - 2019 - Statistical Theory Powering Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/3YV3MSR2/19-STS754.html}
}

@misc{CanaryThatForgot,
  title = {"{{The}} Canary That Forgot Its Song"},
  abstract = {Hello! I was watching the anime Shangri-la and I heard a Japanese kids song which I'm pretty sure is The Canary that forgot its/his song. I tried Googling for the lyrics or maybe just to listen to the whole song which I didn't have much luck. I also tried\ldots},
  howpublished = {https://japanese.livejournal.com/1910649.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JTERZULM/1910649.html}
}

@article{caoDataScienceComprehensive2017,
  title = {Data {{Science}}: {{A Comprehensive Overview}}},
  shorttitle = {Data {{Science}}},
  author = {Cao, Longbing},
  year = {2017},
  month = jun,
  journal = {ACM Computing Surveys},
  volume = {50},
  number = {3},
  pages = {43:1--43:42},
  issn = {0360-0300},
  doi = {10.1145/3076253},
  abstract = {The 21st century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons, and thinking about data science and analytics.},
  keywords = {advanced analytics,Big data,big data analytics,computing,data analysis,data analytics,data DNA,data economy,data education,data engineering,data industry,data innovation,data profession,data science,data scientist,data service,informatics,statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/N4PLI6MB/Cao - 2017 - Data Science A Comprehensive Overview.pdf}
}

@incollection{caoDataScienceThinking2018,
  title = {Data {{Science Thinking}}},
  booktitle = {Data {{Science Thinking}}: {{The Next Scientific}}, {{Technological}} and {{Economic Revolution}}},
  author = {Cao, Longbing},
  editor = {Cao, Longbing},
  year = {2018},
  series = {Data {{Analytics}}},
  pages = {59--90},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-319-95092-1_3},
  abstract = {What makes data science essential and different from existing developments in data mining, machine learning, statistics, and information science?},
  isbn = {978-3-319-95092-1},
  langid = {english},
  keywords = {Complex Data Problems,Data-driven Discovery,Scientific Thought,Thinking Habits,Unscientific Thinking},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JU9EFIWS/Cao - 2018 - Data Science Thinking.pdf}
}

@book{caoDataScienceThinking2018a,
  title = {Data {{Science Thinking}}: {{The Next Scientific}}, {{Technological}} and {{Economic Revolution}}},
  shorttitle = {Data {{Science Thinking}}},
  author = {Cao, Longbing},
  year = {2018},
  month = aug,
  publisher = {{Springer}},
  abstract = {This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.},
  googlebooks = {tiBqDwAAQBAJ},
  isbn = {978-3-319-95092-1},
  langid = {english},
  keywords = {Business \& Economics / Business Mathematics,Business \& Economics / Industries / Computers \& Information Technology,Computers / Databases / Data Mining,Computers / Information Technology,Computers / Intelligence (AI) \& Semantics}
}

@article{carmichaelDataScienceVs2018,
  title = {Data {{Science}} vs. {{Statistics}}: {{Two Cultures}}?},
  shorttitle = {Data {{Science}} vs. {{Statistics}}},
  author = {Carmichael, Iain and Marron, J. S.},
  year = {2018},
  month = jun,
  journal = {Japanese Journal of Statistics and Data Science},
  volume = {1},
  number = {1},
  eprint = {1801.00371},
  eprinttype = {arxiv},
  pages = {117--138},
  issn = {2520-8756, 2520-8764},
  doi = {10.1007/s42081-018-0009-3},
  abstract = {Data science is the business of learning from data, which is traditionally the business of statistics. Data science, however, is often understood as a broader, task-driven and computationally-oriented version of statistics. Both the term data science and the broader idea it conveys have origins in statistics and are a reaction to a narrower view of data analysis. Expanding upon the views of a number of statisticians, this paper encourages a big-tent view of data analysis. We examine how evolving approaches to modern data analysis relate to the existing discipline of statistics (e.g. exploratory analysis, machine learning, reproducibility, computation, communication and the role of theory). Finally, we discuss what these trends mean for the future of statistics by highlighting promising directions for communication, education and research.},
  archiveprefix = {arXiv},
  keywords = {Statistics - Other Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/YISREFG8/Carmichael and Marron - 2018 - Data Science vs. Statistics Two Cultures.pdf;/Users/rca2t1/Dropbox/Zotero/storage/2U5ERAIQ/1801.html}
}

@book{carrollCareersOpportunitiesComputer1967,
  title = {Careers and {{Opportunities}} in {{Computer Science}}},
  author = {Carroll, John Millar},
  year = {1967},
  publisher = {{Dutton}},
  abstract = {Introduction to computer science; Your career as a general programmer; Careers as specialized programmes; Managerial careers in computer scince; Designing and manufacturing computers.},
  googlebooks = {f44mAAAAMAAJ},
  langid = {english}
}

@article{chambersGreaterLesserStatistics1993,
  title = {Greater or {{Lesser Statistics}}: {{A Choice}} for {{Future Research}}},
  shorttitle = {Greater or {{Lesser Statistics}}},
  author = {Chambers, John M.},
  year = {1993},
  month = dec,
  journal = {Statistics and Computing},
  volume = {3},
  number = {4},
  pages = {182--184},
  issn = {1573-1375},
  doi = {10.1007/BF00141776},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/MR273URL/greater.ps;/Users/rca2t1/Dropbox/Zotero/storage/NE4G3NWG/greater.pdf}
}

@article{chapanisPsychologyInstrumentPanel1953,
  title = {Psychology and the {{Instrument Panel}}},
  author = {Chapanis, Alphonse},
  year = {1953},
  journal = {Scientific American},
  volume = {188},
  number = {4},
  pages = {74--84},
  publisher = {{Scientific American, a division of Nature America, Inc.}},
  issn = {0036-8733},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9Q987EVY/Chapanis - 1953 - Psychology and the Instrument Panel.pdf}
}

@book{chaseExperimentalDisplayProcessor1970,
  title = {An {{Experimental Display Processor}} for the {{DX-1}}},
  author = {Chase, Edward N. and {Air Force Cambridge Research Laboratories (U.S.)}},
  year = {1970},
  series = {Instrumentation {{Papers}} ;{{No}}. 164},
  publisher = {{Air Force Cambridge Research Laboratories, Office of Aerospace Research, United States Air Force}},
  address = {{L.G. Hanscom Field, Bedford, Massachusetts}},
  abstract = {An experimental two-console, core-buffered CRT display unit has been developed to advance display console design and improve digital hardware line- and curve-generating techniques. Static or dynamic graphical information can be displayed by either a black and white or full-color display processor under the control of a list processor designed for interfacing to a time-shared main computer. (Author).},
  keywords = {(OCoLC)fst00849102,(OCoLC)fst00872390,(OCoLC)fst00972541,Cathode ray tubes,Computer programming,fast,Information display systems}
}

@article{chatfieldDataScientistsGame2014,
  title = {Data {{Scientists}} as {{Game Changers}} in {{Big Data Environments}}},
  author = {Chatfield, Akemi Takeoka and Shlemoon, Vivian Najem and Redublado, Wilbur and Rahman, Faizur},
  year = {2014},
  journal = {New Zealand},
  pages = {11},
  abstract = {The potential power of big data to generate insights and create new forms of value in the ways which transform organizations and society has been observed by big data-driven organizations and big data experts. Despite the recent sensational declaration of a data scientist as ``the sexiest job of the 21st century'', however, there is a lack of published rigorous studies of what a data scientist is, and what job skills this hottest job title may require. In order to address this gap, we systematically examine relevant source material to extract key definitions and categorize them towards understanding emergent roles and skills of data scientists. We conclude that the current lack of clear skills specifications and the growing skills shortage are key barriers to realizing the potential benefits of big data in organizations, through hiring data scientists and leveraging their game changer roles, indicating important educational implications for our IS field.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/Q7Q4XPZT/Chatfield et al. - 2014 - Data Scientists as Game Changers in Big Data Envir.pdf}
}

@article{chawlaMechanismDesignData2014,
  title = {Mechanism {{Design}} for {{Data Science}}},
  author = {Chawla, Shuchi and Hartline, Jason and Nekipelov, Denis},
  year = {2014},
  month = jun,
  journal = {arXiv:1404.5971 [cs]},
  eprint = {1404.5971},
  eprinttype = {arxiv},
  primaryclass = {cs},
  abstract = {Good economic mechanisms depend on the preferences of participants in the mechanism. For example, the revenue-optimal auction for selling an item is parameterized by a reserve price, and the appropriate reserve price depends on how much the bidders are willing to pay. A mechanism designer can potentially learn about the participants' preferences by observing historical data from the mechanism; the designer could then update the mechanism in response to learned preferences to improve its performance. The challenge of such an approach is that the data corresponds to the actions of the participants and not their preferences. Preferences can potentially be inferred from actions but the degree of inference possible depends on the mechanism. In the optimal auction example, it is impossible to learn anything about preferences of bidders who are not willing to pay the reserve price. These bidders will not cast bids in the auction and, from historical bid data, the auctioneer could never learn that lowering the reserve price would give a higher revenue (even if it would). To address this impossibility, the auctioneer could sacrifice revenue optimality in the initial auction to obtain better inference properties so that the auction's parameters can be adapted to changing preferences in the future. This paper develops the theory for optimal mechanism design subject to good inferability.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Science and Game Theory,J.4},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/IJSWQS7T/Chawla et al. - 2014 - Mechanism Design for Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/NFYHEFGT/1404.html}
}

@article{chenBigDataSurvey2014,
  title = {Big {{Data}}: {{A Survey}}},
  shorttitle = {Big {{Data}}},
  author = {Chen, Min and Mao, Shiwen and Liu, Yunhao},
  year = {2014},
  month = apr,
  journal = {Mobile Networks and Applications},
  volume = {19},
  number = {2},
  pages = {171--209},
  issn = {1572-8153},
  doi = {10.1007/s11036-013-0489-0},
  abstract = {In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/7E6QDZ5T/Chen et al. - 2014 - Big Data A Survey.pdf}
}

@article{chenDataMiningOverview1996,
  title = {Data {{Mining}}: {{An Overview}} from a {{Database Perspective}}},
  shorttitle = {Data {{Mining}}},
  author = {Chen, Ming-Syan and Han, Jiawei and Yu, Philip S.},
  year = {1996},
  journal = {IEEE Transactions on Knowledge and data Engineering},
  volume = {8},
  number = {6},
  pages = {866--883},
  publisher = {{IEEE}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NIW5W276/Chen et al. - 1996 - Data Mining An Overview from a Database Perspecti.pdf;/Users/rca2t1/Dropbox/Zotero/storage/BHRHAK2I/553155.html}
}

@article{chienDataMiningImprove2008,
  title = {Data Mining to Improve Personnel Selection and Enhance Human Capital: {{A}} Case Study in High-Technology Industry},
  shorttitle = {Data Mining to Improve Personnel Selection and Enhance Human Capital},
  author = {Chien, Chen-Fu and Chen, Li-Fei},
  year = {2008},
  journal = {Expert Systems with applications},
  volume = {34},
  number = {1},
  pages = {280--290},
  publisher = {{Elsevier}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SNL7AXP6/S0957417406002776.html}
}

@article{choudhuryCaseStudyData2008,
  title = {Case {{Study}} in {{Data Curation}} at {{Johns Hopkins University}}},
  author = {Choudhury, G. Sayeed},
  year = {2008},
  journal = {Library Trends},
  volume = {57},
  number = {2},
  pages = {211--220},
  publisher = {{Johns Hopkins University Press}},
  issn = {1559-0682},
  doi = {10.1353/lib.0.0028},
  abstract = {At Johns Hopkins University, the institutional repository (IR) is being developed as a component of an overall digital library architecture that will emphasize long-term preservation. The IR represents a set of services that will be developed to support the identified needs or requirements of faculty and students. Given the research-intensive environment at Johns Hopkins, one particular area of interest relates to data sets from a diversity of disciplines ranging from the humanities to the sciences. Essentially, the IR is being developed as a ``gateway'' to the underlying digital archive that will support data curation as part of an evolving cyberinfrastructure featuring open, modular components. In addition to this technological framework, Johns Hopkins is developing new roles and relationships between the library and the academic community, most notably through the development of ``data scientists'' or ``data humanists.'' These developments reflect the realization that the IR is the first step in a longer journey and that for institutional efforts to be successful, they must be integrated into a larger landscape of repositories that serve a distributed and diverse academic community.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/S9CH6AHQ/Choudhury - 2008 - Case Study in Data Curation at Johns Hopkins Unive.pdf}
}

@article{chynowethCommunicationKeyProgrammers2008,
  title = {Communication Is Key for Programmers},
  author = {Chynoweth, Carly},
  year = {2008},
  month = may,
  journal = {The Times},
  address = {{London, England}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/C4J5H556/document.html}
}

@article{clevelandDataScienceAction2001,
  title = {Data {{Science}}: {{An Action Plan}} for {{Expanding}} the {{Technical Areas}} of the {{Field}} of {{Statistics}}},
  shorttitle = {Data {{Science}}},
  author = {Cleveland, William S.},
  year = {2001},
  journal = {International Statistical Review / Revue Internationale de Statistique},
  volume = {69},
  number = {1},
  pages = {21--26},
  issn = {0306-7734},
  doi = {10.2307/1403527},
  abstract = {An action plan to enlarge the technical areas of statistics focuses on the data analyst. The plan sets out six technical areas of work for a university department, and advocates a specific allocation of resources devoted to research in each area and to courses in each area. The value of technical work is judged by the extent to which it benefits the data analyst, either directly or indirectly. The plan is also applicable to government research labs and corporate research organizations. /// Cet article propose un plan d'action pour \'elargir les domaines techniques de la statistique, centr\'e sur l'analyste de donn\'ees. Le plan pr\'esente six domaines techniques de travail pour un d\'epartement d'universit\'e, et recommandre une r\'epartition sp\'ecifique des ressources consacr\'ees \`a la recherche et \`a l'enseignement dans chaque domaine. La valeur du travail technique est \'evalu\'ee par l'int\'er\^et qu'en retire l'analyste de donn\'ees, soit directement soit indirectement. Le plan est aussi applicable aux laboratoires publics de recherche et aux soci\'et\'es de recherche priv\'ees.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HC2C5D2B/Cleveland - 2001 - Data Science An Action Plan for Expanding the Tec.pdf}
}

@misc{CodeConduct2020,
  title = {Code of {{Conduct}}},
  year = {2020},
  journal = {Data Science Association},
  howpublished = {http://www.datascienceassn.org/code-of-conduct.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HAJRGH7P/datasciencecodeofprofessionalconduct.pdf;/Users/rca2t1/Dropbox/Zotero/storage/SRSEHWYH/code-of-conduct.html}
}

@article{colakDataMiningWind2012,
  title = {Data Mining and Wind Power Prediction: {{A}} Literature Review},
  shorttitle = {Data Mining and Wind Power Prediction},
  author = {Colak, Ilhami and Sagiroglu, Seref and Yesilbudak, Mehmet},
  year = {2012},
  journal = {Renewable Energy},
  volume = {46},
  pages = {241--247},
  publisher = {{Elsevier}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VFHPTSXN/S0960148112001541.html}
}

@misc{CommoditizationMassiveData2009,
  title = {The {{Commoditization}} of {{Massive Data Analysis}} - {{O}}'{{Reilly Radar}}},
  year = {2009},
  month = apr,
  howpublished = {https://web.archive.org/web/20090428190430/http://radar.oreilly.com/2008/11/the-commoditization-of-massive.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9Y57TEN4/the-commoditization-of-massive.html}
}

@article{CommunityClevernessRequired2008,
  title = {Community Cleverness Required},
  year = {2008},
  month = sep,
  journal = {Nature},
  volume = {455},
  number = {7209},
  pages = {1--1},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/455001a},
  abstract = {Researchers need to adapt their institutions and practices in response to torrents of new data \textemdash{} and need to complement smart science with smart searching.},
  copyright = {2008 Nature Publishing Group},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/AJJ6NJEZ/2008 - Community cleverness required.pdf;/Users/rca2t1/Dropbox/Zotero/storage/UFGRI472/455001a.html}
}

@book{ComputerIndustryAnnual1971,
  title = {Computer {{Industry Annual}}},
  year = {1971},
  publisher = {{Computer Design Publishing Corporation}},
  googlebooks = {qVcgAQAAIAAJ},
  langid = {english}
}

@misc{ComputerScience831,
  title = {Computer {{Science}} 831: {{Knowledge Discovery}} in {{Databases}}},
  howpublished = {http://www2.cs.uregina.ca/\textasciitilde dbd/cs831/index.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/34LRZZCM/index.html}
}

@book{considineHandbookAppliedInstrumentation1964,
  title = {Handbook of Applied Instrumentation},
  author = {Considine, Douglas M.},
  year = {1964},
  publisher = {{New York, McGraw-Hill}},
  abstract = {Includes bibliographies},
  collaborator = {{Internet Archive}},
  langid = {english},
  keywords = {Measurement}
}

@book{controlConferencePapers1964,
  title = {Conference {{Papers}}},
  author = {Control, American Society for Quality},
  year = {1964},
  abstract = {1949 "Abstracts of papers, third annual convention in conjunction with the third New England Quality Control Conference."},
  googlebooks = {BzyaAAAAIAAJ},
  langid = {english}
}

@misc{conwayDataScienceVenn2010,
  title = {The {{Data Science Venn Diagram}}},
  author = {Conway, Drew},
  year = {2010},
  month = sep,
  journal = {Drew Conway},
  abstract = {On Monday I\textemdash humbly\textemdash joined a group of NYC's most sophisticated thinkers on all things data for a half-day  unconference  to help  O'Reily  organize their upcoming  Strata conference . The break out sessions were fantastic, and the number of people in each allowed for outstanding, expert driven, discu},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/AZTN2TAS/Data_Science_VD.png;/Users/rca2t1/Dropbox/Zotero/storage/ATHBGBBI/the-data-science-venn-diagram.html}
}

@misc{CoreDataScience,
  title = {The {{Core}} of {{Data Science}}},
  howpublished = {http://www.kdnuggets.com/2016/08/core-data-science.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TE8NQWU6/core-data-science.html}
}

@book{corlissScientificSatellites1967,
  title = {Scientific {{Satellites}}},
  author = {Corliss, William R.},
  year = {1967},
  publisher = {{Scientific and Technical Information Division, National Aeronautics and Space Administration}},
  googlebooks = {LyfUW7D\_VOIC},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/KSFTHUT7/Corliss - 1967 - Scientific Satellites.pdf}
}

@book{councilMassiveDataSets1996,
  title = {Massive {{Data Sets}}: {{Proceedings}} of a {{Workshop}}},
  shorttitle = {Massive {{Data Sets}}},
  author = {Council, National Research},
  year = {1996},
  doi = {10.17226/5505},
  abstract = {Download a PDF of "Massive Data Sets" by the National Research Council for free.},
  isbn = {978-0-309-05694-6},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9Z2687GL/Council - 1996 - Massive Data Sets Proceedings of a Workshop.pdf;/Users/rca2t1/Dropbox/Zotero/storage/ZCFEZZ5B/massive-data-sets-proceedings-of-a-workshop.html}
}

@misc{CPSC437537,
  title = {{{CPSC}} 437/537: {{Database Systems}} ({{Fall}} 2022)},
  howpublished = {https://zoo.cs.yale.edu/classes/cs437/index.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/U49U4ZK6/index.html}
}

@incollection{crawfordjr.ConnectionsDataThings1974,
  title = {On the {{Connections}} between {{Data}} and {{Things}} in the {{Real World}}},
  booktitle = {Management of {{Data Elements}} in {{Information Processing}}, {{Proceedings}}},
  author = {Crawford, Jr., Perry},
  year = {1974},
  pages = {51--57},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/48RSG3HU/Crawford, Jr. - 1974 - On the Connections between Data and Things in the .pdf}
}

@article{cuiAlgorithmsMiningConstrained2000,
  title = {Algorithms for Mining Constrained Association Rules},
  author = {Cui, Lixin and Yuan, Senmiao and Zhao, Chunxi},
  year = {2000},
  journal = {CHINESE JOURNAL OF COMPUTERS-CHINESE EDITION-},
  volume = {23},
  number = {2},
  pages = {216--220},
  publisher = {{SCIENCE PRESS}}
}

@article{cukierRiseBigData2013,
  title = {The {{Rise}} of {{Big Data}}: {{How}} It's {{Changing}} the {{Way We Think}} about the {{World Essay}}},
  shorttitle = {The {{Rise}} of {{Big Data}}},
  author = {Cukier, Kenneth and {Mayer-Schoenberger}, Viktor},
  year = {2013},
  journal = {Foreign Affairs},
  volume = {92},
  number = {3},
  pages = {[i]-40},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NU8CACX5/Cukier and Mayer-Schoenberger - 2013 - The Rise of Big Data How it's Changing the Way We.pdf}
}

@misc{dasDataScienceLife2019,
  title = {Data {{Science Life Cycle}} 101 for {{Dummies}} like {{Me}}},
  author = {Das, Sangeet Moy},
  year = {2019},
  month = sep,
  journal = {Medium},
  abstract = {Data science is quickly evolving to be one of the hottest fields in the technology industry. With rapid advancements in computational\ldots},
  howpublished = {https://web.archive.org/web/20191113225625/https://towardsdatascience.com/data-science-life-cycle-101-for-dummies-like-me-e66b47ad8d8f?gi=261acdd4c903},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9IA53YBR/data-science-life-cycle-101-for-dummies-like-me-e66b47ad8d8f.html}
}

@book{DataControl1966,
  title = {Data and {{Control}}},
  year = {1966},
  publisher = {{Business Publications.}},
  googlebooks = {Dl0hAAAAMAAJ},
  langid = {english}
}

@article{DataDataEverywhere2010,
  title = {Data, Data Everywhere},
  year = {2010},
  month = feb,
  journal = {The Economist},
  issn = {0013-0613},
  abstract = {Information has gone from scarce to superabundant. That brings huge new benefits, says Kenneth Cukier (interviewed here)\textemdash but also big headaches},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TVX93S5F/data-data-everywhere.html}
}

@misc{datamanDataScienceModeling2020,
  title = {Data {{Science Modeling Process}} \& {{Six Consultative Roles}}},
  author = {Dataman, Dr},
  year = {2020},
  month = feb,
  journal = {Medium},
  abstract = {There are many online articles talking about the modeling process. Why do I write this post? Two reasons. The first one is to show\ldots},
  howpublished = {https://towardsdatascience.com/data-science-modeling-process-fa6e8e45bf02},
  langid = {english}
}

@book{DataProcessingMagazine1970,
  title = {Data {{Processing Magazine}}},
  year = {1970},
  publisher = {{American Data Processing, Incorporated}},
  googlebooks = {3JYgAAAAMAAJ},
  langid = {english}
}

@article{DataScience2012,
  title = {{Data Science}},
  year = {2012},
  month = oct,
  journal = {Computerwoche},
  publisher = {{IDG Communications Verlag AG}},
  abstract = {Hadoop und NoSQL sind im Big-Data-Segment neben einer Vielzahl von Algorithmen bekannte Gr\"o\ss en. Data Science gilt es zu entdecken. Big Data ist auf der \"Uberholspur. Hadoop, NoSQL und eine Vielzahl von Algorithmen aus der k\"unstlichen Intelligenz (KI) sind die neuen Stars der IT- und Business-Intelligence-Welt. Die von Gartner postulierten drei Vs - Volume, Velocity und Variety - dienen als wegweisende Aspekte einer mannigfaltigen und vielversprechenden "datenbetriebenen" Zukunft. Neben all diesen eher technischen und strategischen Aspekten des Big Data taucht ein weiterer Begriff immer h\"aufiger im Kontext von Big Data auf: Data Science.},
  copyright = {(c) Computerwoche 2012},
  langid = {german},
  keywords = {Consumer Products,Cosmetics/Toiletries,Hair Care Products,Personal Care Products/Appliances}
}

@misc{DataScienceAdvanced,
  title = {Data {{Science}} and {{Advanced Analytics}}},
  journal = {Data Science and Advanced Analytics},
  abstract = {Data Science and Advanced Analytics},
  howpublished = {https://www.dsaa.co/},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/XX9864LT/dsaa.co.html}
}

@misc{DataScienceCorporation,
  title = {Data {{Science Corporation}}},
  journal = {Manta.com},
  howpublished = {https://www.manta.com/c/mm2cgdg/data-science-corporation},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HVMLZDWA/data-science-corporation.html}
}

@article{DataScienceDivision1970,
  title = {Data {{Science Division Losses Revealed}} by {{URS}}},
  year = {1970},
  month = apr,
  journal = {Computerworld},
  pages = {49},
  langid = {english}
}

@misc{DataScienceJournal,
  title = {Data {{Science Journal}}},
  abstract = {The CODATA\&nbsp;Data Science Journal\&nbsp;is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data.All data is in scope, whether born digital or converted from other sources.},
  howpublished = {http://datascience.codata.org/},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JXFV7FA2/datascience.codata.org.html}
}

@misc{DataScienceMinutes,
  title = {Data {{Science In}} 5 {{Minutes}}},
  shorttitle = {Data {{Science In}} 5 {{Minutes}} | {{Data Science For Beginners}} | {{What Is Data Science}}?},
  abstract = {This Data Science tutorial video will give you an idea on the life of a Data Scientist, steps involved in Data science project, roles \&amp; salary offered to a Data Scientist. Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data Science is basically dealing with unstructured and structured data. Data Science is a field that comprises of everything that is related to data cleansing, preparation, and analysis. In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data. Now, let us get started and understand what is Data Science all about. Below topics are explained in this Data Science tutorial: 1. Life of a Data Scientist 2. Steps in Data Science project  - Understanding the business problem  - Data acquisition  - Data preparation  - Exploratory data analysis  - Data modeling  - Visualization and communication  - Deploy \&amp; maintenance 3. Roles offered to a Data Scientist 4. Salary of a Data Scientist To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplile... Download the Data Science career guide to explore and step into the exciting world of data, and follow the path towards your dream career: https://bit.ly/34ZGYRw Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5i... \#DataScience \#WhatIsDataScience \#DataScienceForBeginners \#DataScientist \#DataScienceTutorial \#DataScienceWithPython \#DataScienceWithR \#DataScienceCourse  \#BusinessAnalytics \#DataScience101 \#MachineLearning This Post Graduate Program in Data Analytics, in partnership with Purdue University and in collaboration with IBM, will make you an expert in data analytics. In this Data Analytics course, you'll learn analytics tools and techniques, the languages of R and Python (with no prior programming experience required), how to create data visualizations with Tableau, and how to apply statistics and analytics in a business environment. Simplilearn's PGP in Data Analytics will provide you with extensive expertise in the booming data analytics and data science fields. Key features: 1. Purdue Post Graduate Program Certification 2. Purdue Alumni Association Membership 3. Enrollment in Simplilearn's JobAssist 4. Industry-recognized IBM certificates 5. 180+ hours of Blended Learning 6. 14+ hands-on projects on integrated labs 7. Capstone Project in 3 Domains 8. Masterclasses from Purdue faculty Skills covered: 1. Data analytics 2. Statistical analysis using Excel 3. Data analysis: Python \&amp; R 4. Data visualization: Tableau \&amp; PowerBI 5. Linear and logistic regression modules 6. Clustering using k-meansSupervised learning Program details: Fast track your career in the data analytics field via a comprehensive curriculum covering the concepts of data analytics and statistics foundation, analyzing data using Python and R programming languages, interacting with databases using SQL, and visualizing the data using Tableau and powerBI. Learn more at: https://www.simplilearn.com/post-grad... For more information about Simplilearn's courses, visit:  - Facebook: https://www.facebook.com/Simplilearn  - Twitter: https://twitter.com/simplilearn  - LinkedIn: https://www.linkedin.com/company/simp...  - Website: https://www.simplilearn.com  Get the Android app: http://bit.ly/1WlVo4u  Get the iOS app: http://apple.co/1HIO5J0}
}

@misc{DataSciences,
  title = {Data {{Sciences}}},
  journal = {DataSciences.org},
  howpublished = {http://203.170.84.89/\textasciitilde idawis33/datasciences/?page\_id=2112\&\&category=Foundations\&category=Foundations\&\&paged=8},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SZ77THWB/datasciences.html}
}

@misc{DataSciencesLtd,
  title = {Data {{Sciences Ltd}} - {{Company Profile}} and {{News}}},
  journal = {Bloomberg.com},
  abstract = {Company profile page for Data Sciences Ltd including stock price, company news, press releases, executives, board members, and contact information},
  howpublished = {https://www.bloomberg.com/profile/company/2632618Z:LN},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/WPNHXMNC/2632618ZLN.html}
}

@article{DataScientistsCreate2013,
  title = {Data {{Scientists Create Code Of Professional Conduct}}},
  year = {2013},
  month = oct,
  journal = {CMP TechWeb},
  publisher = {{CMP Media LLC}},
  abstract = {Concerned about big data's potential for misuse, a new organization of data scientists has created a set of rules for their profession. The Data Science Association is a non-profit organization with more than 500 members, even though it's only about 2 months old. Based in Denver, Colorado, its mission is to define the term "data science" and the duties and ethical responsibilities of the people who call themselves data scientists, according to founder and president Michael Walker.},
  copyright = {Copyright \textcopyright 2013 United Business Media LLC. All rights reserved.},
  langid = {english}
}

@book{DataSystems1972,
  title = {Data {{Systems}}},
  year = {1972},
  googlebooks = {vV\_yAAAAMAAJ},
  langid = {english}
}

@misc{davenportDataScientistSexiest2012,
  title = {Data {{Scientist}}: {{The Sexiest Job}} of the 21st {{Century}}},
  shorttitle = {Data {{Scientist}}},
  author = {Davenport, Thomas H. and Patil, D. J.},
  year = {2012},
  month = oct,
  journal = {Harvard Business Review},
  abstract = {Meet the people who can coax treasure out of messy, unstructured data.},
  howpublished = {https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DI7SWIBB/data-scientist-the-sexiest-job-of-the-21st-century.html}
}

@article{davenportRecessionImpactAnalytics2020,
  title = {The {{Recession}}'s {{Impact}} on {{Analytics}} and {{Data Science}}},
  author = {Davenport, Melissa R. Bowers, {and} Thomas H., Jeffrey D. Camm},
  year = {2020},
  month = jun,
  journal = {MIT Sloan Management Review},
  abstract = {There has been a huge demand for data scientists in the past decade. Is that about to change?},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2JPUH2Z3/the-recessions-impact-on-analytics-and-data-science.html}
}

@article{davidianArenWeData2013,
  title = {Aren't We Data Science?},
  author = {Davidian, Marie},
  year = {2013},
  journal = {AMSTAT news: the membership magazine of the American Statistical Association},
  number = {433},
  pages = {3},
  publisher = {{American Statistical Association}},
  issn = {0163-9617},
  abstract = {Autor\'ia: Marie Davidian. Localizaci\'on: AMSTAT news: the membership magazine of the American Statistical Association. N\textordmasculine. 433, 2013. Art\'iculo de Revista en Dialnet.},
  chapter = {AMSTAT news: the membership magazine of the American Statistical Association},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2AMMMS3K/Davidian - 2013 - Aren't we data science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/RII6JX9U/articulo.html}
}

@misc{davisCanaryThatForgot,
  title = {The {{Canary}} That {{Forgot Its Song}}},
  author = {Davis, Miriam},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NLXMS78W/Davis - The Canary that Forgot Its Song.pdf}
}

@book{defensetechnicalinformationcenterDTICADA331231Searching1997,
  title = {{{DTIC ADA331231}}: {{Searching}} the {{Skies}}: The {{Legacy}} of the {{United States Cold War Defense Radar Program}}},
  shorttitle = {{{DTIC ADA331231}}},
  author = {{Defense Technical Information Center}},
  year = {1997},
  month = jun,
  abstract = {The Department of Defense (DOD) Legacy Resource Management Program was established under the Defense Appropriations Act of 1991 to determine how to better integrate the conservation of irreplaceable biological, cultural, and geophysical resources with the dynamic requirements of military missions. One task area of the Legacy Program focused on property associated with the Cold War. This study examines the impact that construction of radar stations and command facilities had on the American landscape. The author explores patterns, themes, and trends that created, influenced, and formed the backdrop to the Cold War defense radar program. The study provides an in-depth look at the radar systems, a state by state listing of the infrastructure that supported the systems, and an extensive bibliography. This historic content can be used to understand and evaluate properties associated with America's detection and command and control system.},
  langid = {english},
  keywords = {*AIR DEFENSE,*EARLY WARNING SYSTEMS,*SEARCH RADAR,AIRCRAFT INTERCEPT CONTROL SYSTEMS,BALLISTIC MISSILE INTERCEPT SYSTEMS,COLD WAR,COMBAT SURVEILLANCE,COMMAND AND CONTROL SYSTEMS,CONSTRUCTION ENGINEERING RESEARCH LAB (ARMY)  CHAMPAIGN IL,DTIC Archive,MILITARY HISTORY,NATIONAL DEFENSE,RADAR EQUIPMENT,RADAR INTERCEPTION.,RADAR STATIONS,RESOURCE MANAGEMENT,Winkler; David F.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/XVWZLIXU/Defense Technical Information Center - 1997 - DTIC ADA331231 Searching the Skies the Legacy of.pdf}
}

@incollection{demchenkoEmergingRoleData2017,
  title = {The {{Emerging Role}} of the {{Data Scientist}} and the Experience of {{Data Science}} Education at the {{University}} of {{Amsterdam}}},
  author = {Demchenko, Yuri},
  year = {2017},
  month = jan,
  file = {/Users/rca2t1/Dropbox/Zotero/storage/AFHGNMMD/Demchenko - 2017 - The Emerging Role of the Data Scientist and the ex.pdf}
}

@book{demchenkoInstructionalModelBuilding2014,
  title = {Instructional {{Model}} for {{Building Effective Big Data Curricula}} for {{Online}} and {{Campus Education}}},
  author = {Demchenko, Yuri and Gruengard, Emanuel and Klous, Sander},
  year = {2014},
  month = dec,
  doi = {10.1109/CloudCom.2014.162},
  abstract = {This paper presents current results and ongoing work to develop effective educational courses on the Big Data (BD) and Data Intensive Technologies (DIT) that is been done at the University of Amsterdam in cooperation with KPMG and by the Laureate Online Education (online partner of the University of Liverpool). The paper introduces the main Big Data concepts: multicomponent Big Data definition and Big Data Architecture Framework that provide the basis for defining the course structure and Common Body of Knowledge for Data Science and Big Data technology domains. The paper presents details on approach, learning model, and course content for two courses at the Laureate Online Education/University of Liverpool and at the University of Amsterdam. The paper provides also background information about existing initiatives and activities related to information exchange and coordination on developing educational materials and programs on Big Data, Data Science, and Research Data Management.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/G9WP2LZ7/Demchenko et al. - 2014 - Instructional Model for Building Effective Big Dat.pdf}
}

@book{departmentNavyManagementReview1967,
  title = {Navy {{Management Review}}},
  author = {Department, United States Navy},
  year = {1967},
  googlebooks = {ZMtHAQAAIAAJ},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/F4RN59JY/Department - 1967 - Navy Management Review.pdf}
}

@book{departmentNavyManagementReview1967a,
  title = {Navy {{Management Review}}},
  author = {Department, United States Navy},
  year = {1967},
  googlebooks = {ZMtHAQAAIAAJ},
  langid = {english}
}

@article{deroureAnchorsShiftingSand2010,
  title = {Anchors in {{Shifting Sand}}: {{The Primacy}} of {{Method}} in the {{Web}} of {{Data}}},
  shorttitle = {Anchors in {{Shifting Sand}}},
  author = {De Roure, David and Goble, Carole},
  year = {2010},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UST6JB6U/De Roure and Goble - 2010 - Anchors in Shifting Sand The Primacy of Method in.pdf;/Users/rca2t1/Dropbox/Zotero/storage/T3KBIXTP/270817.html}
}

@article{deveauxCurriculumGuidelinesUndergraduate2017,
  title = {Curriculum {{Guidelines}} for {{Undergraduate Programs}} in {{Data Science}}},
  author = {De Veaux, Richard D. and Agarwal, Mahesh and Averett, Maia and Baumer, Benjamin S. and Bray, Andrew and Bressoud, Thomas C. and Bryant, Lance and Cheng, Lei Z. and Francis, Amanda and Gould, Robert and Kim, Albert Y. and Kretchmar, Matt and Lu, Qin and Moskol, Ann and Nolan, Deborah and Pelayo, Roberto and Raleigh, Sean and Sethi, Ricky J. and Sondjaja, Mutiara and Tiruviluamala, Neelesh and Uhlig, Paul X. and Washington, Talitha M. and Wesley, Curtis L. and White, David and Ye, Ping},
  year = {2017},
  month = mar,
  journal = {Annual Review of Statistics and Its Application},
  volume = {4},
  number = {1},
  pages = {15--30},
  publisher = {{Annual Reviews}},
  issn = {2326-8298},
  doi = {10.1146/annurev-statistics-060116-053930},
  abstract = {The Park City Math Institute 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in data science. The group consisted of 25 undergraduate faculty from a variety of institutions in the United States, primarily from the disciplines of mathematics, statistics, and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in data science.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RA3BPZKR/De Veaux et al. - 2017 - Curriculum Guidelines for Undergraduate Programs i.pdf;/Users/rca2t1/Dropbox/Zotero/storage/4E7PWGAX/annurev-statistics-060116-053930.html}
}

@book{DevelopmentImplementationRangewide2009,
  title = {Development and {{Implementation}} of {{Range-wide Mission}} and {{Major Capabilities}} at {{White Sands Missile Range}}, {{New Mexico}}: {{Environmental Impact Statement}}},
  shorttitle = {Development and {{Implementation}} of {{Range-wide Mission}} and {{Major Capabilities}} at {{White Sands Missile Range}}, {{New Mexico}}},
  year = {2009},
  googlebooks = {INU3AQAAMAAJ},
  langid = {english}
}

@article{dewanOccipitalAlphaRhythm1967,
  title = {Occipital {{Alpha Rhythm Eye Position}} and {{Lens Accommodation}}},
  author = {Dewan, Edmond M.},
  year = {1967},
  month = jun,
  journal = {Nature},
  volume = {214},
  number = {5092},
  pages = {975--977},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/214975a0},
  abstract = {People can be taught to control voluntarily their own alpha rhythms. This can be used to send messages in Morse code when an electroencephalogram pattern is used as part of a computer programme. Such procedures may help to explain the mechanisms by which the alpha rhythm is ``blocked'' or ``unblocked''.},
  copyright = {1967 Nature Publishing Group},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/8RA244TF/Dewan - 1967 - Occipital Alpha Rhythm Eye Position and Lens Accom.pdf;/Users/rca2t1/Dropbox/Zotero/storage/EVCR9CNU/214975a0.html}
}

@article{dharDataSciencePrediction2013,
  title = {Data Science and Prediction},
  author = {Dhar, Vasant},
  year = {2013},
  month = dec,
  journal = {Communications of the ACM},
  volume = {56},
  number = {12},
  pages = {64--73},
  issn = {0001-0782},
  doi = {10.1145/2500499},
  abstract = {Big data promises automated actionable knowledge creation and predictive models for use by both humans and computers.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/IULJY4JV/Dhar - 2013 - Data science and prediction.pdf;/Users/rca2t1/Dropbox/Zotero/storage/6XICQ627/fulltext.html}
}

@book{didayNewApproachesClassification2013,
  title = {New {{Approaches}} in {{Classification}} and {{Data Analysis}}},
  author = {Diday, Edwin and Lechevallier, Yves and Schader, Martin and Bertrand, Patrice and Burtschy, Bernard},
  year = {2013},
  month = mar,
  publisher = {{Springer Science \& Business Media}},
  abstract = {The subject of this book is the analysis and processing of structural or quantitative data with emphasis on classification methods, new algorithms as well as applications in various fields related to data analysis and classification. The book presents the state of the art in world-wide research and application of methods from the fields indicated above and consists of survey papers as well as research papers.},
  googlebooks = {CH7yCAAAQBAJ},
  isbn = {978-3-642-51175-2},
  langid = {english},
  keywords = {Business \& Economics / Econometrics,Business \& Economics / Economics / General,Business \& Economics / Economics / Theory,Business \& Economics / Statistics,Mathematics / Applied,Mathematics / Probability \& Statistics / General,Medical / Biostatistics}
}

@misc{dieboldBigDataDynamic2000,
  title = {Big {{Data Dynamic Factor Models}} for {{Macroeconomic Measurement}} and {{Forecasting}}},
  author = {Diebold, Francis X.},
  year = {2000},
  abstract = {Discussion Read to the Eighth World Congress of the Econometric Society, Seattle, August. http://www.ssc.upenn.edu/\textasciitilde fdiebold/papers/paper40/temp-wc.PDF.}
}

@inproceedings{dieboldBigDataDynamic2003,
  title = {'{{Big Data}}'{{Dynamic}} Factor Models for Macroeconomic Measurement and Forecasting},
  booktitle = {Advances in {{Economics}} and {{Econometrics}}: {{Theory}} and {{Applications}}, {{Eighth World Congress}} of the {{Econometric Society}},''(Edited by {{M}}. {{Dewatripont}}, {{LP Hansen}} and {{S}}. {{Turnovsky}})},
  author = {Diebold, Francis X.},
  year = {2003},
  pages = {115--122},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/T8TFK4WG/Diebold - 2003 - ’Big Data’Dynamic factor models for macroeconomic .pdf;/Users/rca2t1/Dropbox/Zotero/storage/GJS87NGQ/books.html}
}

@book{DigitalComputerNewsletter1961,
  title = {Digital {{Computer Newsletter}}},
  year = {1961},
  publisher = {{Office of Naval Research, Mathematical Sciences Division.}},
  googlebooks = {DR8pjs4SVR4C},
  langid = {english}
}

@book{DimensionsMagazineNational1958,
  title = {Dimensions: {{The Magazine}} of the {{National Bureau}} of {{Standards}}, {{U}}.{{S}}. {{Department}} of {{Commerce}}},
  shorttitle = {Dimensions},
  year = {1958},
  publisher = {{U.S. Department of Commerce, National Bureau of Standards}},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/WCAZEE3N/content (2).jpeg}
}

@article{DioxinImpactHuman1984,
  title = {Dioxin - {{The Impact}} on {{Human Health Note}}},
  year = {1984},
  journal = {Dioxin--the impact on human health : hearings before the Subcommittee on Natural Resources, Agriculture Research, and Environment of the Committee on Science and Technology, U.S. House of Representatives, Ninety-eighth Congress, first session, June 30; July 13, 28, 1983.},
  pages = {I-252},
  langid = {english}
}

@article{doctorowBigDataWelcome2008,
  title = {Big Data: {{Welcome}} to the Petacentre},
  shorttitle = {Big Data},
  author = {Doctorow, Cory},
  year = {2008},
  month = sep,
  journal = {Nature News},
  volume = {455},
  number = {7209},
  pages = {16--21},
  issn = {0028-0836},
  doi = {10.1038/455016a},
  abstract = {Nature - the world's best science and medicine on your desktop},
  copyright = {\textcopyright{} 2008 Nature Publishing Group},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/95XBRJNT/455016a.pdf;/Users/rca2t1/Dropbox/Zotero/storage/THC8EC7N/455016a.html}
}

@misc{DocumentAmericaHistoricala,
  title = {Document | {{America}}'s {{Historical Newspapers}} | {{Readex}}},
  howpublished = {https://infoweb.newsbank.com/apps/readex/doc?p=EANX\&sort=YMD\_date\%3AA\&page=1\&fld-base-0=alltext\&val-base-0=\%22data\%20science\%22\&val-database-0=\&fld-database-0=database\&fld-nav-0=YMD\_date\&val-nav-0=\&docref=image/v2\%3A11A73E5827618330\%40EANX-12FCC365104A4B0A\%402440275-12FCB93BB7B1DA54\%4071-12FCB93BB7B1DA54\%40\&firsthit=yes},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LA53DLDF/doc.html}
}

@book{dodgeComputationalStatisticsVolume2012,
  title = {Computational {{Statistics}}: {{Volume}} 2: {{Proceedings}} of the 10th {{Symposium}} on {{Computational Statistics}}, {{COMPSTAT}}, {{Neuch\^atel}}, {{Switzerland}}, {{August}} 1992},
  shorttitle = {Computational {{Statistics}}},
  author = {Dodge, Yadolah and Whittaker, Joe},
  year = {2012},
  month = dec,
  publisher = {{Springer Science \& Business Media}},
  abstract = {The papers assembled in this book were presented at the biannual symposium of Inter national Association for Statistical Computing in Neuchcitel, Switzerland, in August of 1992. This congress marked the tenth such meeting from its inception in 1974 at Vienna and maintained the tradition of providing a forum for the open discussion of progress made in computer oriented statistics and the dissemination of new ideas throughout the statistical community. It was gratifying to see how well the groups of theoretical statisti cians, software developers and applied research workers were represented, whose mixing is an event made uniquely possible by this symposium. While maintaining traditions certain new features have been introduced at this con ference: there were a larger number of invited speakers; there was more commercial sponsorship and exhibition space; and a larger body of proceedings have been published. The structure of the proceedings follows a standard format: the papers have been grouped together according to a rough subject matter classification, and within topic follow an approximate aphabetical order. The papers are published in two volumes ac cording to the emphasis of the topics: volume I gives a slight leaning towards statistics and modelling, while volume II is focussed more on computation; but this is certainly only a crude distinction and the volumes have to be thought of as the result of a single en terprise.},
  googlebooks = {ApjvCAAAQBAJ},
  isbn = {978-3-642-48678-4},
  langid = {english},
  keywords = {Business \& Economics / Economics / General,Business \& Economics / Economics / Theory,Business \& Economics / Statistics,Computers / Information Theory,Computers / Programming / Algorithms,Mathematics / Probability \& Statistics / General}
}

@book{dodgeComputationalStatisticsVolume2013,
  title = {Computational {{Statistics}}: {{Volume}} 1: {{Proceedings}} of the 10th {{Symposium}} on {{Computational Statistics}}},
  shorttitle = {Computational {{Statistics}}},
  author = {Dodge, Yadolah and Whittaker, Joe},
  year = {2013},
  month = nov,
  publisher = {{Springer Science \& Business Media}},
  abstract = {The Role of the Computer in Statistics David Cox Nuffield College, Oxford OXIINF, U.K. A classification of statistical problems via their computational demands hinges on four components (I) the amount and complexity of the data, (il) the specificity of the objectives of the analysis, (iii) the broad aspects of the approach to analysis, (ill) the conceptual, mathematical and numerical analytic complexity of the methods. Computational requi rements may be limiting in (I) and (ill), either through the need for special programming effort, or because of the difficulties of initial data management or because of the load of detailed analysis. The implications of modern computational developments for statistical work can be illustrated in the context of the study of specific probabilistic models, the development of general statistical theory, the design of investigations and the analysis of empirical data. While simulation is usually likely to be the most sensible way of investigating specific complex stochastic models, computerized algebra has an obvious role in the more analyti cal work. It seems likely that statistics and applied probability have made insufficient use of developments in numerical analysis associated more with classical applied mathematics, in particular in the solution of large systems of ordinary and partial differential equations, integral equations and integra-differential equations and for the \textcent raction of "useful" in formation from integral transforms. Increasing emphasis on models incorporating specific subject-matter considerations is one route to bridging the gap between statistical ana.},
  googlebooks = {iVv9CAAAQBAJ},
  isbn = {978-3-662-26811-7},
  langid = {english},
  keywords = {Business \& Economics / Economics / General,Business \& Economics / Economics / Theory,Business \& Economics / Statistics,Computers / Information Theory,Computers / Programming / Algorithms,Mathematics / Probability \& Statistics / General}
}

@article{dodgeFlyingCodeSpace2004,
  title = {Flying {{Through Code}}/{{Space}}: {{The Real Virtuality}} of {{Air Travel}}},
  shorttitle = {Flying {{Through Code}}/{{Space}}},
  author = {Dodge, Martin and Kitchin, Rob},
  year = {2004},
  month = feb,
  journal = {Environment and Planning A},
  volume = {36},
  pages = {195--211},
  doi = {10.1068/a3698},
  abstract = {Commercial air travel is a key global industry facilitating the complex daily movements of planes, people, goods, and services across the world. In this paper we analyse contemporary air travel through the conceptualisation of a culture of real virtuality. We contend that air travel now consists of passage through 'code/space'. Such code/space includes travel websites, check-in, security checkpoints, flight decks, air-traffic control, immigration, and customs checkpoints, which together form assemblages that define the practices and experiences of air travel. Code/space is qualitatively different to coded space, in which software influences the production of space, in that code and space are mutually constituted -- produced through one another. This mutual constitution is dyadic so that if either the code or space 'fail', the production of space 'fails'. Our formulation of code/space is nondeterministic and nonuniversal, and how code/space operates and is experienced is embodied through the performances and interactions of the people within the space (between people, and between people and code). In this sense, code/space is constantly in a state of becoming. We illustrate the nature of code/space, and the discursive regimes that support its production, and demonstrate how the code/spaces of an air travel are simultaneously local and global and induce Castells' notions of 'space of flows' and 'timeless time'.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PR34CNMZ/Dodge and Kitchin - 2004 - Flying Through CodeSpace The Real Virtuality of .pdf}
}

@misc{dominiciBigDataBenefit2012,
  title = {Big {{Data}}: {{Benefit}} to {{Society}}, or {{Drowning}} in a {{Data Deluge}}?},
  shorttitle = {Big {{Data}}},
  author = {Dominici, David Castillo},
  year = {2012},
  month = nov,
  journal = {Social Science Space},
  abstract = {With larger data sets offering researchers the potential to look at more subtle interactions, big data is becoming increasingly valuable to social sciences, yet challenges remain.},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CNXM3X2N/big-databenefit-to-society-or-drowning-in-a-data-deluge.html}
}

@misc{DominoDataScience,
  title = {Domino {{Data Science Lifecycle}} \textendash{} {{Data Science Project Management}}},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/6LWHG54E/domino-data-science-lifecycle.html}
}

@article{donoho50YearsData2017,
  title = {50 {{Years}} of {{Data Science}}},
  author = {Donoho, David},
  year = {2017},
  month = oct,
  journal = {Journal of Computational and Graphical Statistics},
  volume = {26},
  number = {4},
  pages = {745--766},
  issn = {1061-8600},
  doi = {10.1080/10618600.2017.1384734},
  abstract = {More than 50 years ago, John Tukey called for a reformation of academic statistics. In ``The Future of Data Analysis,'' he pointed to the existence of an as-yet unrecognized science, whose subject of interest was learning from data, or ``data analysis.'' Ten to 20 years ago, John Chambers, Jeff Wu, Bill Cleveland, and Leo Breiman independently once again urged academic statistics to expand its boundaries beyond the classical domain of theoretical statistics; Chambers called for more emphasis on data preparation and presentation rather than statistical modeling; and Breiman called for emphasis on prediction rather than inference. Cleveland and Wu even suggested the catchy name ``data science'' for this envisioned field. A recent and growing phenomenon has been the emergence of ``data science'' programs at major universities, including UC Berkeley, NYU, MIT, and most prominently, the University of Michigan, which in September 2015 announced a \$100M ``Data Science Initiative'' that aims to hire 35 new faculty. Teaching in these new programs has significant overlap in curricular subject matter with traditional statistics courses; yet many academic statisticians perceive the new programs as ``cultural appropriation.'' This article reviews some ingredients of the current ``data science moment,'' including recent commentary about data science in the popular media, and about how/whether data science is really different from statistics. The now-contemplated field of data science amounts to a superset of the fields of statistics and machine learning, which adds some technology for ``scaling up'' to ``big data.'' This chosen superset is motivated by commercial rather than intellectual developments. Choosing in this way is likely to miss out on the really important intellectual event of the next 50 years. Because all of science itself will soon become data that can be mined, the imminent revolution in data science is not about mere ``scaling up,'' but instead the emergence of scientific studies of data analysis science-wide. In the future, we will be able to predict how a proposal to change data analysis workflows would impact the validity of data analysis across all of science, even predicting the impacts field-by-field. Drawing on work by Tukey, Cleveland, Chambers, and Breiman, I present a vision of data science based on the activities of people who are ``learning from data,'' and I describe an academic field dedicated to improving that activity in an evidence-based manner. This new field is a better academic enlargement of statistics and machine learning than today's data science initiatives, while being able to accommodate the same short-term goals. Based on a presentation at the Tukey Centennial Workshop, Princeton, NJ, September 18, 2015.},
  keywords = {Cross-study analysis,Data analysis,Data science,Meta analysis,Predictive modeling,Quantitative programming environments,Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HWSGEDEQ/Donoho - 2017 - 50 Years of Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/FRWV6XWK/10618600.2017.html}
}

@misc{donohoHighDimensionalDataAnalysis2000,
  title = {High-{{Dimensional Data Analysis}}: {{The Curses}} and {{Blessings}} of {{Dimensionality}}},
  author = {Donoho, David},
  year = {2000},
  month = aug,
  abstract = {My lecture "High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality" was delivered the morning of August 8, 2000, exactly 100 years to the day after David Hilbert's Math. Problems lecture of August 8 1900. However, my 23 problems of that day had largely to do with my own chromosome pairs and the inherent limitations they impose.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/AVUFVMZN/MathChallengeSlides2_2.pdf;/Users/rca2t1/Dropbox/Zotero/storage/BJQRD4AG/Donoho - 2000 - High-Dimensional Data Analysis The Curses and Ble.pdf}
}

@misc{donthaOriginsBigData2017,
  title = {The {{Origins}} of {{Big Data}}},
  author = {Dontha, Ramesh},
  year = {2017},
  month = feb,
  journal = {KDnuggets},
  abstract = {Big Data has truly come of age in 2013 when OED introduced the term ``Big Data'' for the first time. But when was the term Big Data first used and Why? Here are the results of our investigation.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PSZ8PMNB/origins-big-data.html}
}

@article{douglas-jonesIntroductionAnthropologyData2021,
  title = {Introduction: {{Towards}} an {{Anthropology}} of {{Data}}},
  shorttitle = {Introduction},
  author = {{Douglas-Jones}, Rachel and Walford, Antonia and Seaver, Nick},
  year = {2021},
  journal = {Journal of the Royal Anthropological Institute},
  volume = {27},
  number = {S1},
  pages = {9--25},
  issn = {1467-9655},
  doi = {10.1111/1467-9655.13477},
  abstract = {The world is talking `data'. The early cross-disciplinary, business-orientated hype around the potential of `big' data, with its promises of unprecedented insight into social life, has given way. Data now motivates a sweep of dystopian visions, from rampant commodification to the invasion of privacy, political manipulation, and shadowy data doubles. Yet anthropologists have been cautious in taking data itself as their object, even as the social life of data practices becomes manifest in our ethnographies. In this introduction, we argue for an anthropology of data that is ethnographically specific and theoretically ambitious, putting forward a case for why anthropological engagements with the data moment might be not only politically important but also conceptually generative.},
  langid = {english},
  annotation = {\_eprint: https://rai.onlinelibrary.wiley.com/doi/pdf/10.1111/1467-9655.13477},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VUGZM7QU/Douglas-Jones et al. - 2021 - Introduction Towards an Anthropology of Data.pdf;/Users/rca2t1/Dropbox/Zotero/storage/3SYSKLHC/1467-9655.html}
}

@misc{driscollThreeSexySkills2009,
  title = {The {{Three Sexy Skills}} of {{Data Geeks}}},
  author = {Driscoll, Michael E.},
  year = {2009},
  month = may,
  journal = {Dataspore Blog},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/G262CKFE/sexy-data-geeks.html}
}

@article{DrowningDataNKU2013,
  title = {Drowning in Data: {{NKU}} Creates New Data Science Major},
  shorttitle = {Drowning in Data},
  year = {2013},
  month = sep,
  journal = {U-Wire},
  publisher = {{Normans Media Ltd}},
  abstract = {Northern Kentucky University ; Highland Heights, KY - news By},
  copyright = {\textcopyright{} 2013, M2 Communications. All rights reserved.},
  langid = {english}
}

@misc{dumbillSMAQStackBig2010,
  title = {The {{SMAQ}} Stack for Big Data},
  author = {Dumbill, Edd},
  year = {2010},
  month = sep,
  journal = {O'Reilly Radar},
  abstract = {We're at the beginning of a revolution in data-driven products and services, driven by a software stack that enables big data processing on commodity hardware. Learn about the SMAQ stack, and where today's big data tools fit in.},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/Q5U9XM9K/the-smaq-stack-for-big-data.html}
}

@misc{dungateWhatCanHumanities2020,
  title = {What Can the Humanities Do for Data Science?},
  author = {Dungate, Joanna},
  year = {2020},
  month = aug,
  publisher = {{The Alan Turing Institute}},
  abstract = {A new white paper explores the potential for research at the intersection between~data science and humanities.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/E6IWZ3EA/what-can-humanities-do-data-science.html}
}

@book{edwardsClosedWorldComputers1997,
  title = {The {{Closed World}}: {{Computers}} and the {{Politics}} of {{Discourse}} in {{Cold War America}}},
  shorttitle = {The {{Closed World}}},
  author = {Edwards, Paul N.},
  year = {1997},
  month = aug,
  edition = {Reprint edition},
  publisher = {{The MIT Press}},
  address = {{Cambridge, Mass.}},
  abstract = {The Closed World offers a radically new alternative to the canonical histories of computers and cognitive science. Arguing that we can make sense of computers as tools only when we simultaneously grasp their roles as metaphors and political icons, Paul Edwards shows how Cold War social and cultural contexts shaped emerging computer technology\rule{1em}{1pt}and were transformed, in turn, by information machines.The Closed World explores three apparently disparate histories\rule{1em}{1pt}the history of American global power, the history of computing machines, and the history of subjectivity in science and culture\rule{1em}{1pt}through the lens of the American political imagination. In the process, it reveals intimate links between the military projects of the Cold War, the evolution of digital computers, and the origins of cybernetics, cognitive psychology, and artificial intelligence.Edwards begins by describing the emergence of a "closed-world discourse" of global surveillance and control through high-technology military power. The Cold War political goal of "containment" led to the SAGE continental air defense system, Rand Corporation studies of nuclear strategy, and the advanced technologies of the Vietnam War. These and other centralized, computerized military command and control projects\rule{1em}{1pt}for containing world-scale conflicts\rule{1em}{1pt}helped closed-world discourse dominate Cold War political decisions. Their apotheosis was the Reagan-era plan for a "Star Wars" space-based ballistic missile defense.Edwards then shows how these military projects helped computers become axial metaphors in psychological theory. Analyzing the Macy Conferences on cybernetics, the Harvard Psycho-Acoustic Laboratory, and the early history of artificial intelligence, he describes the formation of a "cyborg discourse." By constructing both human minds and artificial intelligences as information machines, cyborg discourse assisted in integrating people into the hyper-complex technological systems of the closed world.Finally, Edwards explores the cyborg as political identity in science fiction\rule{1em}{1pt}from the disembodied, panoptic AI of 2001: A Space Odyssey, to the mechanical robots of Star Wars and the engineered biological androids of Blade Runner\rule{1em}{1pt}where Information Age culture and subjectivity were both reflected and constructed.Inside Technology series},
  isbn = {978-0-262-55028-4},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ITXA6C9B/Edwards - 1997 - The Closed World Computers and the Politics of Di.pdf}
}

@book{eganMessageRepetitionOperating1957,
  title = {Message {{Repetition}}, {{Operating Characteristics}}, and {{Confusion Matrices}} in {{Speech Communication}}},
  author = {Egan, James P.},
  year = {1957},
  publisher = {{Indiana University Hearing and Communication Laboratory}},
  googlebooks = {iUhPAQAAMAAJ},
  langid = {english}
}

@article{ehrenbergAppraisalMarkovBrandSwitching1965,
  title = {An {{Appraisal}} of {{Markov Brand-Switching Models}}},
  author = {Ehrenberg, A. S. C.},
  year = {1965},
  month = nov,
  journal = {Journal of Marketing Research},
  volume = {2},
  number = {4},
  pages = {347--362},
  publisher = {{SAGE Publications Inc}},
  issn = {0022-2437},
  doi = {10.1177/002224376500200402},
  abstract = {Using no mathematics other than a simple arithmetical example, this article reviews fundamental difficulties in applying Markov theory to brand-switching data. No successful practical applications of the theory appear to be available.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/R9YDEBB5/Ehrenberg - 1965 - An Appraisal of Markov Brand-Switching Models.pdf}
}

@book{ehrenbergPrimerDataReduction1982,
  title = {A {{Primer}} in {{Data Reduction}}: {{An Introductory Statistics Textbook}}},
  shorttitle = {A {{Primer}} in {{Data Reduction}}},
  author = {Ehrenberg, A. S. C.},
  year = {1982},
  publisher = {{Wiley}},
  abstract = {A concise, easy to understand introduction that emphasizes the relevance of statistics to real world problems. Describes traditional statistical methods, their uses and limitations, and explains how to reduce numerical data to statistical summaries, how to interpret the results, and how to present the data clearly. Minimizes mathematics without oversimplification and illustrates theories with practical, varied examples. Statistical tables and answers to the exercises are included.},
  googlebooks = {DchwQgAACAAJ},
  isbn = {978-0-471-10134-5},
  langid = {english}
}

@article{eldridgeComputerToolLegal1963,
  title = {The {{Computer}} as a {{Tool}} for {{Legal Research}}},
  author = {Eldridge, William B. and Dennis, Sally F.},
  year = {1963},
  journal = {Law and Contemporary Problems},
  volume = {28},
  number = {1},
  pages = {78--99},
  publisher = {{Duke University School of Law}},
  issn = {0023-9186},
  doi = {10.2307/1190725},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/U2IEIXA2/Eldridge and Dennis - 1963 - The Computer as a Tool for Legal Research.pdf}
}

@book{ElectronicAgeResearch1961,
  title = {Electronic {{Age}}: {{Research}}, {{Manufacturing}}, {{Communications}}, {{Broadcasting}}, {{Television}}},
  shorttitle = {Electronic {{Age}}},
  year = {1961},
  publisher = {{Radio Corporation of America.}},
  googlebooks = {cHc1AQAAIAAJ},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/Y3E57C7M/data-deluge-1961.jpg}
}

@book{ElectronicAgeResearch1961a,
  title = {Electronic {{Age}}: {{Research}}, {{Manufacturing}}, {{Communications}}, {{Broadcasting}}, {{Television}}},
  shorttitle = {Electronic {{Age}}},
  year = {1961},
  publisher = {{Radio Corporation of America.}},
  googlebooks = {cHc1AQAAIAAJ},
  langid = {english}
}

@article{emmert-streibDefiningDataScience2019,
  title = {Defining {{Data Science}} by a {{Data-Driven Quantification}} of the {{Community}}},
  author = {{Emmert-Streib}, Frank and Dehmer, Matthias},
  year = {2019},
  month = mar,
  journal = {Machine Learning and Knowledge Extraction},
  volume = {1},
  number = {1},
  pages = {235--251},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  issn = {2504-4990},
  doi = {10.3390/make1010015},
  abstract = {Data science is a new academic field that has received much attention in recent years. One reason for this is that our increasingly digitalized society generates more and more data in all areas of our lives and science and we are desperately seeking for solutions to deal with this problem. In this paper, we investigate the academic roots of data science. We are using data of scientists and their citations from Google Scholar, who have an interest in data science, to perform a quantitative analysis of the data science community. Furthermore, for decomposing the data science community into its major defining factors corresponding to the most important research fields, we introduce a statistical regression model that is fully automatic and robust with respect to a subsampling of the data. This statistical model allows us to define the `importance' of a field as its predictive abilities. Overall, our method provides an objective answer to the question `What is data science?'.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  langid = {english},
  keywords = {computational social science,data science,dataology,digital society,scientometrics,statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UET6QVE8/Emmert-Streib and Dehmer - 2019 - Defining Data Science by a Data-Driven Quantificat.pdf;/Users/rca2t1/Dropbox/Zotero/storage/E5YRF28E/15.html}
}

@book{emmert-streibInformationTheoryStatistical2009,
  title = {Information {{Theory}} and {{Statistical Learning}}},
  author = {{Emmert-Streib}, Frank and Dehmer, Matthias},
  year = {2009},
  publisher = {{Springer Science \& Business Media}},
  abstract = {Information Theory and Statistical Learning presents theoretical and practical results about information theoretic methods used in the context of statistical learning.  The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of contexts. Each chapter is written by an expert in the field. The book is intended for an interdisciplinary readership working in machine learning, applied statistics, artificial intelligence, biostatistics, computational biology, bioinformatics, web mining or related disciplines. Advance Praise for Information Theory and Statistical Learning: "A new epoch has arrived for information sciences to integrate various disciplines such as information theory, machine learning, statistical inference, data mining, model selection etc. I am enthusiastic about recommending the present book to researchers and students, because it summarizes most of these new emerging subjects and methods, which are otherwise scattered in many places." -- Shun-ichi Amari, RIKEN Brain Science Institute, Professor-Emeritus at the University of Tokyo},
  googlebooks = {WGTF7daUvD4C},
  isbn = {978-0-387-84815-0},
  langid = {english},
  keywords = {Computers / Computer Science,Computers / Data Processing,Computers / Information Technology,Computers / Information Theory,Computers / Intelligence (AI) \& Semantics,Language Arts \& Disciplines / Library \& Information Science / General,Mathematics / Discrete Mathematics,Mathematics / Probability \& Statistics / General,Medical / Biotechnology,Technology \& Engineering / Automation,Technology \& Engineering / Telecommunications}
}

@article{engelkenApplicationDigitalFilters1982,
  title = {Application of Digital Filters in the Processing of Eye Movement Data},
  author = {Engelken, Edward J. and Stevens, Kennith W. and Wolfe, James W.},
  year = {1982},
  month = may,
  journal = {Behavior Research Methods \& Instrumentation},
  volume = {14},
  number = {3},
  pages = {314--319},
  issn = {1554-3528},
  doi = {10.3758/BF03203222},
  abstract = {Digital filter techniques have been applied to the analysis of eye movement data. Methods were developed to calculate eye velocity and eye acceleration in real-time from an electronystag-mogram (ENG) signal that was recorded using a one-pole RC high-pass filter in the preamplifier. Nonrecursive, finite impulse-response digital filters were designed to remove the effects of the RC high-pass filter and calculate the first and second time derivatives of the ENG signal, as well as remove high-frequency noise. Applying these new techniques to the analysis of vestibular nystagmus enables estimation of the transfer characteristics of the vestibuloocular system.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LVITHY4H/Engelken et al. - 1982 - Application of digital filters in the processing o.pdf}
}

@article{englmeierEditorialWhatCan2017,
  title = {Editorial: {{What Can We Expect}} from {{Data Scientists}}},
  shorttitle = {Editorial},
  author = {Englmeier, Kurt and Murtagh, Fionn},
  year = {2017},
  journal = {Journal of theoretical and applied electronic commerce research},
  volume = {12},
  number = {1},
  pages = {i-v},
  publisher = {{Universidad de Talca}},
  issn = {0718-1876},
  doi = {10.4067/S0718-18762017000100001},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/XDZLMDQD/Englmeier and Murtagh - 2017 - Editorial What Can We Expect from Data Scientists.pdf;/Users/rca2t1/Dropbox/Zotero/storage/KVL8GHDB/scielo.html}
}

@misc{ERICED215889Only,
  title = {{{ERIC ED215889}}: {{Only One Science}}: {{Twelfth Annual Report}} of the {{National Science Board}}. : {{ERIC}} : {{Free Download}}, {{Borrow}}, and {{Streaming}}},
  shorttitle = {{{ERIC ED215889}}},
  journal = {Internet Archive},
  abstract = {Departing markedly from previous reports to Congress by the National Science Board, this document presents in an informal, narrative style six stories...},
  howpublished = {https://archive.org/details/ERIC\_ED215889},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/YZDVKKJ3/2up.html}
}

@book{escoufierDataScienceIts1995,
  title = {Data {{Science}} and {{Its Applications}}},
  author = {Escoufier, Yves and Hayashi, Chikio and Fichet, Bernard},
  year = {1995},
  publisher = {{Academic Press/Harcourt Brace}}
}

@article{estesAnalysisVerbalConditioning1954,
  title = {Analysis of a Verbal Conditioning Situation in Terms of Statistical Learning Theory},
  author = {Estes, W. K. and Straughan, J. H.},
  year = {1954},
  journal = {Journal of Experimental Psychology},
  volume = {47},
  number = {4},
  pages = {225--234},
  publisher = {{American Psychological Association}},
  address = {{US}},
  issn = {0022-1015(Print)},
  doi = {10.1037/h0060989},
  abstract = {Learning rates, asymptotic behavior, and sequential properties of response in a verbal conditioning situation were studied in relation to predictions from statistical learning theory. The experimental situation was one in which S was asked to predict which one of two events would occur when the probability is controlled by E. In general it was found that the rate of learning was related to the difference between initial response probability and probability of reinforcement during a series. It was also noted that Ss did not respond to a series as a whole, but that sensitivity to the effects of reinforcements and nonreinforcements increased as a function of trials. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
  keywords = {Conditioning,Learning Theory,Statistics,Verbal Learning},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RJUEFUSH/1955-00445-001.html}
}

@book{estesFoundationsStatisticalLearning1959,
  title = {Foundations of {{Statistical Learning Theory}}: {{The}} Stimulus Sampling Model},
  shorttitle = {Foundations of {{Statistical Learning Theory}}},
  author = {Estes, William Kaye and Suppes, Patrick},
  year = {1959},
  publisher = {{Behavioral Sciences Division, Applied Mathematics and Statistics Laboratory, Stanford University}},
  langid = {english}
}

@article{estesStatisticalTheoryLearning1950,
  title = {Toward a {{Statistical Theory}} of {{Learning}}},
  author = {Estes, William K.},
  year = {1950},
  journal = {Psychological Review},
  volume = {57},
  number = {2},
  pages = {94--107},
  publisher = {{American Psychological Association}},
  address = {{US}},
  issn = {1939-1471(Electronic),0033-295X(Print)},
  doi = {10.1037/h0058559},
  abstract = {"An attempt has been made to clarify some issues in current learning theory by giving a statistical interpretation to the concepts of stimulus and response and by deriving quantitative laws that govern simple behavior systems. Dependent variables, in this formulation, are classes of behavior samples with common quantitative properties; independent variables are statistical distributions of environmental events. Laws of the theory state probability relations between momentary changes in behavioral and environmental variables. From this point of view it has been possible to derive simple relations between probability of response and several commonly used measures of learning, and to develop mathematical expressions describing learning in both classical conditioning and instrumental learning situations under simplified conditions." (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
  keywords = {Learning,Learning Theory,Statistical Analysis,Statistical Variables},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JKLQRC7Q/Estes - 1950 - Toward a Statistical Theory of Learning.pdf;/Users/rca2t1/Dropbox/Zotero/storage/U2RGMMUL/1950-05093-001.html}
}

@article{fayyadDataMiningKnowledge1996,
  title = {From {{Data Mining}} to {{Knowledge Discovery}} in {{Databases}}},
  author = {Fayyad, Usama and {Piatetsky-Shapiro}, Gregory and Smyth, Padhraic},
  year = {1996},
  journal = {AI magazine},
  volume = {17},
  number = {3},
  pages = {37--37},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ALPUIYBW/Fayyad et al. - 1996 - From data mining to knowledge discovery in databas.pdf;/Users/rca2t1/Dropbox/Zotero/storage/3FD2J94L/1230.html}
}

@incollection{fayyadDataMiningKnowledge1996a,
  title = {From {{Data Mining}} to {{Knowledge Discovery}}: {{An Overview}}},
  shorttitle = {From {{Data Mining}} to {{Knowledge Discovery}}},
  booktitle = {Advances in Knowledge Discovery and Data Mining},
  author = {Fayyad, Usama M. and {Piatetsky-Shapiro}, Gregory and Smyth, Padhraic},
  year = {1996},
  month = feb,
  pages = {1--34},
  publisher = {{American Association for Artificial Intelligence}},
  address = {{USA}},
  isbn = {978-0-262-56097-9},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/X2HAZL4W/Fayyad et al. - 1996 - From Data Mining to Knowledge Discovery An Overvi.pdf}
}

@article{fayyadFoundationsDataScience2020,
  title = {Toward {{Foundations}} for {{Data Science}} and {{Analytics}}: {{A Knowledge Framework}} for {{Professional Standards}}},
  shorttitle = {Toward {{Foundations}} for {{Data Science}} and {{Analytics}}},
  author = {Fayyad, Usama and Hamutcu, Hamit},
  year = {2020},
  month = jun,
  journal = {Harvard Data Science Review},
  volume = {2},
  number = {2},
  publisher = {{PubPub}},
  issn = {,},
  doi = {10.1162/99608f92.1a99e67a},
  abstract = {As the industry is racing to harness the power of data, demand for data science professionals is growing at an increasing rate. However, almost every organization has a unique way of defining roles in data science and associated skills and knowledge. This has resulted in a confusing industry landscape for employers, academic and training institutions, and existing and aspiring data science professionals. This article is the first in a series authored by Initiative for Analytics and Data Science Standards (IADSS). We review the history of data science, which we trace back to 1974, and the emergence of data science as a profession in the industry, followed by a classification of knowledge and skills commonly associated with data science professionals, pointing to a lack of detailed and consistent treatment of the topic. We then present a Data Science Knowledge Framework, that we believe can support industry standardization and building measurement and assessment methodologies for data science professionals.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2ZDG52Y2/Fayyad and Hamutcu - 2020 - Toward Foundations for Data Science and Analytics.pdf;/Users/rca2t1/Dropbox/Zotero/storage/N8CI9X9R/3.html}
}

@inproceedings{fayyadKnowledgeDiscoveryData1996,
  title = {Knowledge {{Discovery}} and {{Data Mining}}: {{Towards}} a {{Unifying Framework}}.},
  booktitle = {{{KDD}}},
  author = {Fayyad, Usama M and {Piatetsky-Shapiro}, Gregory and Smyth, Padhraic and others},
  year = {1996},
  volume = {96},
  pages = {82--88},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NAE7V24J/Fayyad et al. - 1996 - Knowledge Discovery and Data Mining Towards a Uni.pdf}
}

@article{fayyadKnowledgeDiscoveryDatabases2001,
  title = {Knowledge Discovery in Databases: {{An}} Overview},
  shorttitle = {Knowledge Discovery in Databases},
  author = {Fayyad, Usama},
  year = {2001},
  journal = {Relational data mining},
  pages = {28--47},
  publisher = {{Springer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/Q43AES9H/978-3-662-04599-2_2.html}
}

@misc{FindingLongTermSolutions2016,
  title = {Finding {{Long-Term Solutions}} to the {{Data Scientist Shortage}}},
  year = {2016},
  month = mar,
  journal = {Datanami},
  abstract = {As we learned in the first part of this series, the gap between demand for skilled data scientists and supply is driving salaries north of \$200,000 in},
  howpublished = {https://www.datanami.com/2016/03/28/finding-long-term-solutions-data-scientist-shortage/},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/GMXSHNKF/finding-long-term-solutions-data-scientist-shortage.html}
}

@article{forsytheKnowledgeAcquisitionExpert1989,
  title = {Knowledge Acquisition for Expert Systems: Some Pitfalls and Suggestions},
  shorttitle = {Knowledge Acquisition for Expert Systems},
  author = {Forsythe, D.E. and Buchanan, Bruce G.},
  year = {1989},
  journal = {IEEE Transactions on Systems, Man and Cybernetics},
  volume = {19},
  number = {3},
  pages = {435--442},
  issn = {0018-9472},
  doi = {10.1109/21.31050},
  abstract = {The interdisciplinary anthropological study of knowledge elicitation (ASKE) project is introduced. The choice of anthropological methodology for the project is discussed, and the relevance of anthropology to knowledge engineering in general is pointed out. Examples are given of pitfalls observed in the knowledge elicitation process, including problems related to both interviewing technique and conceptual approach. Some preliminary conclusions are drawn based on work to date},
  keywords = {anthropological study of knowledge elicitation,Artificial intelligence,ASKE project,Computer science,expert systems,Humans,Intelligent structures,Intelligent systems,knowledge acquisition,Knowledge based systems,knowledge engineering,Laboratories},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/H2RBW5XK/Forsythe and Buchanan - 1989 - Knowledge acquisition for expert systems some pit.pdf;/Users/rca2t1/Dropbox/Zotero/storage/IAIZBZE9/abs_all.html}
}

@misc{FourthParadigmNNLM,
  title = {Fourth {{Paradigm}} | {{NNLM}}},
  howpublished = {https://nnlm.gov/data/thesaurus/fourth-paradigm},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2M43FQDC/fourth-paradigm.html}
}

@misc{FourthParadigmReally,
  title = {Is the {{Fourth Paradigm Really New}}?},
  journal = {Posts},
  abstract = {Author suggests that the so-called \&quot;Fourth Paradigm\&quot; of Science, in which insights are development through examination of vast troves of already existing data, has been in use in geosciences for ...},
  howpublished = {https://serc.carleton.edu/earthandmind/posts/4thpardigm.html},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JQXNW48F/4thpardigm.html}
}

@misc{FRANCISSMITHOBE,
  title = {{{FRANCIS J SMITH OBE MRIA}}},
  howpublished = {http://www.cs.qub.ac.uk/\textasciitilde FJ.Smith/SmithCV.htm},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9BBNVMKJ/SmithCV.html}
}

@article{frankelBigDataDistilling2008,
  title = {Big Data: {{Distilling}} Meaning from Data},
  shorttitle = {Big Data},
  author = {Frankel, Felice and Reid, Rosalind},
  year = {2008},
  month = sep,
  journal = {Nature},
  volume = {455},
  number = {7209},
  pages = {30--30},
  issn = {0028-0836},
  doi = {10.1038/455030a},
  abstract = {Buried in vast streams of data are clues to new science. But we may need to craft new lenses to see them, explain Felice Frankel and Rosalind Reid.},
  copyright = {\textcopyright{} 2008 Nature Publishing Group},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/8WRKS8BV/455030a.pdf;/Users/rca2t1/Dropbox/Zotero/storage/93KME8AA/455030a.html}
}

@article{frawleyKnowledgeDiscoveryDatabases1992,
  title = {Knowledge {{Discovery}} in {{Databases}}: {{An Overview}}},
  shorttitle = {Knowledge {{Discovery}} in {{Databases}}},
  author = {Frawley, William J. and {Piatetsky-Shapiro}, Gregory and Matheus, Christopher J.},
  year = {1992},
  journal = {AI magazine},
  volume = {13},
  number = {3},
  pages = {57--57},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LBVZ58DS/Frawley et al. - 1992 - Knowledge discovery in databases An overview.pdf;/Users/rca2t1/Dropbox/Zotero/storage/6AGPGY35/1011.html}
}

@inproceedings{freemanNeurobiologicalInterpretationSemiotics1997,
  title = {A Neurobiological Interpretation of Semiotics: {{Meaning}} vs. Representation},
  shorttitle = {A Neurobiological Interpretation of Semiotics},
  booktitle = {Systems, {{Man}}, and {{Cybernetics}}, 1997.'{{Computational Cybernetics}} and {{Simulation}}'., 1997 {{IEEE International Conference}} On},
  author = {Freeman, W.J.},
  year = {1997},
  volume = {2},
  pages = {1481--1486},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/XV5ZC6SU/Freeman - 1997 - A neurobiological interpretation of semiotics Mea.pdf;/Users/rca2t1/Dropbox/Zotero/storage/NINJTGTN/abs_all.html}
}

@article{fremont-smithInterdisciplinaryConference1961,
  title = {The {{Interdisciplinary Conference}}},
  author = {{Fremont-Smith}, Frank},
  year = {1961},
  journal = {AIBS Bulletin},
  volume = {11},
  number = {2},
  pages = {17--32},
  publisher = {{[Oxford University Press, American Institute of Biological Sciences]}},
  issn = {0096-7645},
  doi = {10.2307/1292675},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/N9W4IQMC/Fremont-Smith - 1961 - The Interdisciplinary Conference.pdf}
}

@article{friedmanDataMiningStatistics1998,
  title = {Data {{Mining}} and {{Statistics}}: {{What}}'s the Connection?},
  shorttitle = {Data {{Mining}} and {{Statistics}}},
  author = {Friedman, Jerome H.},
  year = {1998},
  journal = {Computing science and statistics},
  volume = {29},
  number = {1},
  pages = {3--9},
  publisher = {{Citeseer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RMRBPAIE/Friedman - 1998 - Data Mining and Statistics What's the connection.pdf}
}

@phdthesis{fryComputationalInformationDesign2004,
  type = {Thesis},
  title = {Computational {{Information Design}}},
  author = {Fry, Benjamin Jotham},
  year = {2004},
  abstract = {The ability to collect, store, and manage data is increasing quickly, but our ability to understand it remains constant. In an attempt to gain better understanding of data, fields such as information visualization, data mining and graphic design are employed, each solving an isolated part of the specific problem, but failing in a broader sense: there are too many unsolved problems in the visualization of complex data. As a solution, this dissertation proposes that the individual fields be brought together as part of a singular process titled Computational Information Design. This dissertation first examines the individual pedagogies of design, information, and computation with a focus on how they support one another as parts of a combined methodology for the exploration, analysis, and representation of complex data. Next, in order to make the process accessible to a wider audience, a tool is introduced to simplify the computational process for beginners, and can be used as a sketch- ing platform by more advanced users. Finally, a series of examples show how the methodology and tool can be used to address a range of data problems, in particular, the human genome.},
  copyright = {M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.},
  langid = {american},
  school = {Massachusetts Institute of Technology},
  annotation = {Accepted: 2005-09-06T20:47:40Z},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VEYQLWB5/Fry - 2004 - Computational Information Design.pdf;/Users/rca2t1/Dropbox/Zotero/storage/P8WNUM3A/26913.html}
}

@article{gallieEssentiallyContestedConcepts1955,
  title = {Essentially {{Contested Concepts}}},
  author = {Gallie, W. B.},
  year = {1955},
  journal = {Proceedings of the Aristotelian Society},
  volume = {56},
  pages = {167--198},
  publisher = {{[Aristotelian Society, Wiley]}},
  issn = {0066-7374},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/N7998LKI/Gallie - 1955 - Essentially Contested Concepts.pdf}
}

@book{gamaLearningDataStreams2007,
  title = {Learning from {{Data Streams}}: {{Processing Techniques}} in {{Sensor Networks}}},
  shorttitle = {Learning from {{Data Streams}}},
  author = {Gama, Jo{\~a}o and Gaber, Mohamed Medhat},
  year = {2007},
  month = oct,
  publisher = {{Springer Science \& Business Media}},
  abstract = {Sensor networks consist of distributed autonomous devices that cooperatively monitor an environment. Sensors are equipped with capacities to store information in memory, process this information and communicate with their neighbors. Processing data streams generated from wireless sensor networks has raised new research challenges over the last few years due to the huge numbers of data streams to be managed continuously and at a very high rate. The book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education. This research monograph delivers to researchers and graduate students the state of the art in data stream processing in sensor networks. The huge bibliography offers an excellent starting point for further reading and future research.},
  isbn = {978-3-540-73678-3},
  langid = {english},
  keywords = {Computers / Information Technology,Computers / Intelligence (AI) \& Semantics,Computers / Networking / Hardware,Computers / System Administration / Storage \& Retrieval,Technology \& Engineering / Electronics / General,Technology \& Engineering / Imaging Systems,Technology \& Engineering / Telecommunications}
}

@inproceedings{ganeshDataMiningShould2002,
  title = {Data {{Mining}}: {{Should It Be Included}} in the {{Statistics Curriculum}}},
  shorttitle = {Data {{Mining}}},
  booktitle = {The 6th International Conference on Teaching Statistics ({{ICOTS}} 6), {{Cape Town}}, {{South Africa}}},
  author = {Ganesh, Siva},
  year = {2002},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TEA8TFYK/Ganesh - 2002 - Data Mining Should It Be Included in the Statisti.pdf}
}

@misc{gannonHowScientistsTackle2014,
  title = {How {{Scientists Tackle NASA}}'s {{Big Data Deluge}}},
  author = {Gannon, Megan},
  year = {2014},
  month = jan,
  journal = {Space.com},
  abstract = {The deluge of mission data poses some big challenges for NASA. But a team at the Jet Propulsion Laboratory is coming up with new strategies to tackle problems of information storage, processing and access.},
  howpublished = {https://www.space.com/23330-nasa-big-data-jet-propulsion-laboratory.html},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/G2QA4VLD/23330-nasa-big-data-jet-propulsion-laboratory.html}
}

@article{garberDataScienceWhat2019,
  title = {Data {{Science}}: {{What}} the {{Educated Citizen Needs}} to {{Know}}},
  shorttitle = {Data {{Science}}},
  author = {Garber, Alan M.},
  year = {2019},
  month = jun,
  journal = {Harvard Data Science Review},
  volume = {1},
  number = {1},
  publisher = {{PubPub}},
  issn = {,},
  doi = {10.1162/99608f92.88ba42cb},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LVDZNTVZ/Garber - 2019 - Data Science What the Educated Citizen Needs to K.pdf;/Users/rca2t1/Dropbox/Zotero/storage/JMFPS5XP/4.html}
}

@misc{garczarekStartingDebateData2020,
  title = {{Starting a Debate: Data Science \textendash{} Occupation or Profession? A Discussion Paper}},
  shorttitle = {{Starting a Debate}},
  author = {Garczarek, Ursula and Steuer, Detlef},
  year = {2020},
  journal = {Archives of Data Science, Series A},
  volume = {6},
  number = {1},
  pages = {16},
  issn = {2363-9881},
  doi = {10.5445/KSP/1000098011/16},
  abstract = {With this contribution at the ECDA-2019 in Bayreuth we want to start a much needed debate about the nature of the work of a data scientist. Is it a mere occupation or does the societal impact together with ethical issues surrounding the work imply data science should become a real profession in the sense of Airaksinen (Airaksinen, 2009). We explore the elements of data science and the responsibility a data scientist has for society. Some barriers are identified and what can be done about them. In this paper, we describe the line of reasoning which was presented, and some lessons learned from the actual discussions with the audience.},
  howpublished = {https://publikationen.bibliothek.kit.edu/1000120418},
  langid = {ngerman},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/M5U2A78Q/Garczarek and Steuer - 2020 - Starting a Debate Data Science – Occupation or Pr.pdf;/Users/rca2t1/Dropbox/Zotero/storage/S47EE5FV/1000120418.html}
}

@misc{garnerDataScienceLadder2016,
  title = {Data {{Science}} up and down the {{Ladder}} of {{Abstraction}}},
  author = {Garner, Henry},
  year = {2016},
  month = dec,
  journal = {InfoQ},
  abstract = {Although Clojure lacks the extensive toolbox and analytic community of the most popular data science languages, R and Python, it provides a powerful environment for developing statistical thinking and for practicing effective data science.},
  howpublished = {https://www.infoq.com/articles/data-science-abstraction},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PWMJZZFQ/data-science-abstraction.html}
}

@techreport{geigerCareerPathsProspects2018,
  type = {Preprint},
  title = {Career {{Paths}} and {{Prospects}} in {{Academic Data Science}}: {{Report}} of the {{Moore-Sloan Data Science Environments Survey}}},
  shorttitle = {Career {{Paths}} and {{Prospects}} in {{Academic Data Science}}},
  author = {Geiger, R. Stuart and Cabasse, Charlotte and Cullens, Chihoko Y. and Nor{\'e}n, Laura and {Fiore-Gartland}, Brittany and Das, Diya and Brady, Henry},
  year = {2018},
  month = jun,
  institution = {{SocArXiv}},
  doi = {10.31235/osf.io/xe823},
  abstract = {This report is based on a 2016 survey of members and affiliates of three institutes of data science at major U.S. research universities, focusing on career paths for data scientists within academia. After considering how our respondents define data science, we identify various activities, priorities, resources, and concerns around data science in academia, especially with respect to data science careers. We end by providing recommendations about how universities can better support an emerging set of roles and responsibilities around data and computation within and across academic fields.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TIXS79F6/Geiger et al. - 2018 - Career Paths and Prospects in Academic Data Scienc.pdf}
}

@book{geronHandsOnMachineLearning2017,
  title = {Hands-{{On Machine Learning}} with {{Scikit-Learn}} and {{TensorFlow}}: {{Concepts}}, {{Tools}}, and {{Techniques}} to {{Build Intelligent Systems}}},
  shorttitle = {Hands-{{On Machine Learning}} with {{Scikit-Learn}} and {{TensorFlow}}},
  author = {G{\'e}ron, Aur{\'e}lien},
  year = {2017},
  month = apr,
  edition = {1 edition},
  publisher = {{O'Reilly Media}},
  address = {{Beijing ; Boston}},
  abstract = {Graphics in this book are printed in black and white.Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks\textemdash scikit-learn and TensorFlow\textemdash author Aur\'elien G\'eron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details},
  isbn = {978-1-4919-6229-9},
  langid = {english}
}

@inproceedings{gilSemanticTaskCenteredCollaborative2015,
  title = {A {{Semantic}}, {{Task-Centered Collaborative Framework}} for {{Science}}},
  booktitle = {Revised {{Selected Papers}} of the {{ESWC}} 2015 {{Satellite Events}} on {{The Semantic Web}}: {{ESWC}} 2015 {{Satellite Events}} - {{Volume}} 9341},
  author = {Gil, Yolanda and Michel, Felix and Ratnakar, Varun and Hauder, Matheus},
  year = {2015},
  month = may,
  pages = {58--61},
  publisher = {{Springer-Verlag}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-319-25639-9_11},
  abstract = {This paper gives an overview of the Organic Data Science framework, a new approach for scientific collaboration that opens the science process and exposes information about shared tasks, participants, and other relevant entities. The framework enables scientists to formulate new tasks and contribute to tasks posed by others. The framework is currently in use by a science community studying the age of water, and is beginning to be used by others.},
  isbn = {978-3-319-25638-2},
  keywords = {Collaborative web platforms,Organic data science,Semantic wiki},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DB9QR4JT/Gil et al. - 2015 - A Semantic, Task-Centered Collaborative Framework .pdf}
}

@techreport{glennanFirstSemiannualReport1959,
  title = {First {{Semiannual Report}} of the {{National Aeronautics}} and {{Space Administration}}},
  author = {Glennan, T. Keith},
  year = {1959},
  month = jun,
  abstract = {The First Semiannual Report of the National Aeronautics and Space Administration (NASA) is submitted to Congress pursuant to section 206 (a) of the National Aeronautics and Space Act of 1958 (Public Law 85-568) to provide for research into problems of flight within and outside the Earth's atmosphere, which states: The Administration shall submit to the President for transmittal to Congress, semiannually and at such other times as it deems desirable, a report on its activities and accomplishments.},
  keywords = {Administration and Management,Law; Political Science and Space Policy},
  annotation = {NTRS Author Affiliations: NASA Headquarters NTRS Document ID: 20150018535 NTRS Research Center: Headquarters (HQ)},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RDMZD5Z7/Glennan - 1959 - First Semiannual Report of the National Aeronautic.pdf;/Users/rca2t1/Dropbox/Zotero/storage/BYTJAF4U/20150018535.html}
}

@book{glickMethodsBiochemicalAnalysis1954,
  title = {Methods of {{Biochemical Analysis}}},
  author = {Glick, David and Suelter, Clarence H.},
  year = {1954},
  publisher = {{Wiley}},
  googlebooks = {XUeTclJJVC8C},
  isbn = {978-0-471-20200-4},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PVTLQHRL/data-deluge-1954.png}
}

@article{glymourStatisticalInferenceData1996,
  title = {Statistical {{Inference}} and {{Data Mining}}},
  author = {Glymour, Clark and Madigan, David and Pregibon, Daryl and Smyth, Padhraic},
  year = {1996},
  journal = {Communications of the ACM},
  volume = {39},
  number = {11},
  pages = {35--41},
  publisher = {{ACM New York, NY, USA}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/I9NYG8NV/Glymour et al. - 1996 - Statistical Inference and Data Mining.pdf}
}

@article{gobleImpactWorkflowTools,
  title = {The {{Impact}} of {{Workflow Tools}} on {{Data-centric Research}}},
  author = {Goble, Carole and Roure, David De},
  pages = {4},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EFJY3NWF/Goble and Roure - The Impact of Workflow Tools on Data-centric Resea.pdf}
}

@article{gobleImpactWorkflowTools2009,
  title = {The {{Impact}} of {{Workflow Tools}} on {{Data-Centric Research}}},
  author = {Goble, Carole and De Roure, David},
  year = {2009},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/IT3CY3Y2/Goble and De Roure - 2009 - The Impact of Workflow Tools on Data-Centric Resea.pdf}
}

@misc{GoodbyePollingHello2010,
  title = {Goodbye {{Polling}}, {{Hello Big Data}}},
  year = {2010},
  month = nov,
  journal = {Engage},
  abstract = {Whenever a pundit rushes to proclaim the ``death of'' something, that''s the surest sign it''ll probably outlive the person making that bold prediction. Nonetheless, as a general rule, I tend to bet on the future and the old incumbent industries and ways of doing things (eventually) being dislodged, even if progress in that direction is \ldots},
  howpublished = {https://enga.ge/goodbye-polling-hello-big-data/},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TNETM8X8/goodbye-polling-hello-big-data.html}
}

@article{goodhardtDirichletComprehensiveModel1984,
  title = {The {{Dirichlet}}: {{A Comprehensive Model}} of {{Buying Behaviour}}},
  shorttitle = {The {{Dirichlet}}},
  author = {Goodhardt, G. J. and Ehrenberg, A. S. C. and Chatfield, C.},
  year = {1984},
  journal = {Journal of the Royal Statistical Society. Series A (General)},
  volume = {147},
  number = {5},
  pages = {621--655},
  publisher = {{[Royal Statistical Society, Wiley]}},
  issn = {0035-9238},
  doi = {10.2307/2981696},
  abstract = {The Dirichlet is a stochastic model of purchase incidence and brand choice which parsimoniously integrates a wide range of already well-established empirical regularities.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/KEHNGQ3U/Goodhardt et al. - 1984 - The Dirichlet A Comprehensive Model of Buying Beh.pdf}
}

@misc{GoogleBooksNgram,
  title = {Google {{Books Ngram Viewer}}: Data Reduction},
  abstract = {Google Books Ngram Viewer},
  howpublished = {https://books.google.com/ngrams/graph?content=data+reduction\&year\_start=1800\&year\_end=2019\&corpus=en-2019\&smoothing=3},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/S2W4DML3/graph.html}
}

@book{gotoRIMSSymposiumSoftware1983,
  title = {{{RIMS Symposium}} on {{Software Science}} and {{Engineering}}: {{Kyoto}}, 1982. {{Proceedings}}},
  shorttitle = {{{RIMS Symposium}} on {{Software Science}} and {{Engineering}}},
  author = {Goto, E. and Furukawa, K. and Nakajima, R. and Nakata, I. and Yonezawa, A.},
  year = {1983},
  month = feb,
  publisher = {{Springer Science \& Business Media}},
  googlebooks = {L2EG2rkwuQsC},
  isbn = {978-3-540-11980-7},
  langid = {english},
  keywords = {Computers / Computer Science,Computers / Information Technology,Computers / Programming / General,Computers / Software Development \& Engineering / General}
}

@book{gouldIFIPGuideConcepts1971,
  title = {{{IFIP Guide}} to {{Concepts}} and {{Terms}} in {{Data Processing}}},
  editor = {Gould, I. H.},
  year = {1971},
  volume = {2},
  publisher = {{North-Holland}},
  address = {{London}},
  copyright = {Copyright \textcopyright{} 1972 John Wiley \& Sons, Ltd},
  langid = {english},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.4380020318

, I. H. Gould (Ed.), North-Holland, London, 1971. No. of pages: 161. Price: \$7.00},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/8DIER96E/Gould - 1971 - IFIP Guide to Concepts and Terms in Data Processin.pdf;/Users/rca2t1/Dropbox/Zotero/storage/BUQWPUU8/spe.html}
}

@article{graceComputerBasedMethodologySystem1964,
  title = {Computer-{{Based Methodology}} for {{System Development}}: {{Site Production}} and {{Reduction System}}},
  author = {Grace, G. L. and Newlands, E.},
  year = {1964},
  month = jun,
  journal = {Human Factors},
  volume = {6},
  number = {3},
  pages = {227--232},
  doi = {10.1177%2F001872086400600301},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EDR4T3MV/Grace and Newlands - 1964 - Computer-Based Methodology for System Development.pdf;/Users/rca2t1/Dropbox/Zotero/storage/QN3YB7BD/001872086400600301.html}
}

@article{grahamArtDataScience2012,
  title = {The {{Art}} of {{Data Science}}},
  author = {Graham, Matthew J.},
  year = {2012},
  journal = {arXiv:1106.3305 [astro-ph]},
  eprint = {1106.3305},
  eprinttype = {arxiv},
  primaryclass = {astro-ph},
  pages = {47--59},
  doi = {10.1007/978-1-4614-3323-1_4},
  abstract = {To flourish in the new data-intensive environment of 21st century science, we need to evolve new skills. These can be expressed in terms of the systemized framework that formed the basis of mediaeval education - the trivium (logic, grammar, and rhetoric) and quadrivium (arithmetic, geometry, music, and astronomy). However, rather than focusing on number, data is the new keystone. We need to understand what rules it obeys, how it is symbolized and communicated and what its relationship to physical space and time is. In this paper, we will review this understanding in terms of the technologies and processes that it requires. We contend that, at least, an appreciation of all these aspects is crucial to enable us to extract scientific information and knowledge from the data sets which threaten to engulf and overwhelm us.},
  archiveprefix = {arXiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Digital Libraries},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/C36CQRRR/Graham - 2012 - The Art of Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/XINY8YB9/1106.html}
}

@misc{grayEScienceTransformedScientific2007,
  title = {{{eScience}} \textendash{} {{A Transformed Scientific Method}}},
  author = {Gray, Jim},
  year = {2007},
  month = jan,
  address = {{Mountain View, CA}},
  collaborator = {Szalay, Alex},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CLPGWDDL/nrc-cstb_escience.ppt}
}

@book{greenBrightBoysMaking2010,
  title = {Bright {{Boys}}: {{The Making}} of {{Information Technology}}},
  shorttitle = {Bright {{Boys}}},
  author = {Green, Tom},
  year = {2010},
  month = mar,
  publisher = {{CRC Press}},
  abstract = {Everything has a beginning. None was more profound-and quite as unexpected-than Information Technology. Here for the first time is the untold story of how our new age came to be and the bright boys who made it happen. What began on the bare floor of an old laundry building eventually grew to rival in size the Manhattan Project. The unexpected},
  googlebooks = {uEzNBQAAQBAJ},
  isbn = {978-1-4398-6522-4},
  langid = {english},
  keywords = {Mathematics / General,Mathematics / Recreations \& Games}
}

@article{greenCognitiveDimensionsNotations1989,
  title = {Cognitive {{Dimensions}} of {{Notations}}},
  author = {Green, Thomas RG},
  year = {1989},
  journal = {People and computers V},
  pages = {443--460},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/GGW7VVDR/Green - 1989 - Cognitive Dimensions of Notations.pdf;/Users/rca2t1/Dropbox/Zotero/storage/YP46ACB4/BTxOtt4X920C.html}
}

@article{griffithsRelativeEfficienciesContextFree1965,
  title = {On the {{Relative Efficiencies}} of {{Context-Free Grammar}}},
  author = {Griffiths, T. V. and Petrick, S. R.},
  year = {1965},
  month = may,
  journal = {Communications of the ACM},
  volume = {8},
  number = {5},
  pages = {289--300},
  issn = {0001-0782},
  doi = {10.1145/364914.364943},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LG7CFMM6/Griffiths and Petrick - 1965 - On the Relative Efficiencies of Context-Free Gramm.pdf}
}

@misc{grometsteinMITLincolnLaboratory2018,
  title = {{{MIT Lincoln Laboratory}}: {{Technology}} in {{Support}} of {{National Security}}},
  author = {Grometstein, Alan},
  year = {2018},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9WI53UUJ/Grometstein - MIT Lincoln Laboratory Technology in Support of N.pdf}
}

@incollection{grommeDataScientistsNew2018,
  title = {Data {{Scientists}}: {{A New Faction}} of the {{Transnational Field}} of {{Statistics}} [Forthcoming]},
  author = {Gromm{\'e}, Francisca and Ruppert, Evelyn and Cakici, Baki},
  year = {2018},
  month = jan,
  file = {/Users/rca2t1/Dropbox/Zotero/storage/C5FKVH5S/Grommé et al. - 2018 - Data Scientists A New Faction of the Transnationa.pdf}
}

@article{groverLureStatisticsData2008,
  title = {The {{Lure}} of {{Statistics}} in {{Data Mining}}},
  author = {Grover, Lovleen Kumar and Mehra, Rajni},
  year = {2008},
  journal = {Journal of Statistics Education},
  volume = {16},
  number = {1},
  publisher = {{Taylor \& Francis}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ZQN3JIKK/Grover and Mehra - 2008 - The Lure of Statistics in Data Mining.pdf;/Users/rca2t1/Dropbox/Zotero/storage/5GNWDIUY/10691898.2008.html}
}

@misc{GrowingYourOwn2017,
  title = {Growing {{Your Own Data Scientists}}},
  year = {2017},
  month = dec,
  journal = {Early Adopter},
  abstract = {CIOs and CTOs must learn to address a challenge, involving the divide between the people who know about the vast amount of new sources of data emanating from machines and other devices (``big data'') and the questions in the enterprise whose answers can be monetized. CIOs and CTOs must learn to address a challenge, involving [\ldots ]},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CTUMFKGN/growing-your-own-data-scientists.html}
}

@article{guptaComprehensiveSurveyData2020,
  title = {A {{Comprehensive Survey}} of {{Data Mining}}},
  author = {Gupta, Manoj Kumar and Chandra, Pravin},
  year = {2020},
  journal = {International Journal of Information Technology},
  pages = {1--15},
  publisher = {{Springer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LZG3C63C/Gupta and Chandra - 2020 - A Comprehensive Survey of Data Mining.pdf}
}

@article{guptaExactTestMean1985,
  title = {An Exact Test for the Mean of a Normal Distribution with a Known Coefficient of Variation},
  author = {Gupta, Ramesh and Tripathi, Ram and Michalek, Joel and White, Thomas},
  year = {1985},
  month = may,
  journal = {Computational Statistics \& Data Analysis},
  volume = {3},
  pages = {219--226},
  issn = {0167-9473},
  doi = {10.1016/0167-9473(85)90085-4},
  abstract = {Tables are presented for the critical value, power and sample size required to attain a given power at a given alternative for the likelihood ratio statistic for testing H0: \texttheta{} = \texttheta 0 versus H1: \texttheta{} = \texttheta 1 {$>$} \texttheta 0, where \texttheta{} is the mean of a normal distribution having variance a2\texttheta 2 with the coefficient of variation, a, assumed known.},
  langid = {english},
  keywords = {Coefficient of variation,Exact test,Normal distribution},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/J2ZGLM5Q/0167947385900854.html}
}

@article{gureckisBehaviorismRebornStatistical,
  title = {Behaviorism {{Reborn}}? {{Statistical Learning}} as {{Simple Conditioning}}},
  author = {Gureckis, Todd M},
  pages = {6},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4THHSQMB/Gureckis - Behaviorism Reborn Statistical Learning as Simple.pdf}
}

@article{haApplicationDataMining1998,
  title = {Application of Data Mining Tools to Hotel Data Mart on the {{Intranet}} for Database Marketing},
  author = {Ha, Sung Ho and Park, Sang Chan},
  year = {1998},
  month = jul,
  journal = {Expert Systems with Applications},
  volume = {15},
  number = {1},
  pages = {1--31},
  issn = {0957-4174},
  doi = {10.1016/S0957-4174(98)00008-6},
  abstract = {Data mining, which is also referred to as knowledge discovery in databases, is the process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions. In this paper, we present the data mining process from data extraction to knowledge interpretation and data mining tasks, and corresponding algorithms. Before applying data mining techniques to a real-world application, we build a data mart on the enterprise Intranet. RFM (recency, frequency, and monetary) data extracted from the data mart are used extensively for our analysis. We then propose a new marketing strategy that fully utilizes the knowledge resulting from data mining.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JYJLIV7E/Ha and Park - 1998 - Application of data mining tools to hotel data mar.pdf;/Users/rca2t1/Dropbox/Zotero/storage/NWGK6S9W/S0957417498000086.html}
}

@article{habibHowHandle2001966,
  title = {How to {{Handle}} 200 {{Million Data Points}} a {{Day}}},
  author = {Habib, E. J. and Keipert, F. A. and Lee, R. C.},
  year = {1966},
  month = feb,
  abstract = {Satellite Telemetry Automatic Reduction System /STARS/ capable of processing data at capacity of 200 million data points per day},
  keywords = {COMPUTERS},
  annotation = {NTRS Author Affiliations:  NTRS Report/Patent Number: ISA PAPER 7.2-2-65 NTRS Document ID: 19660042614 NTRS Research Center: Legacy CDMS (CDMS)},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VCNXSAPU/19660042614.html}
}

@book{haiderGettingStartedData2015,
  title = {Getting {{Started}} with {{Data Science}}: {{Making Sense}} of {{Data}} with {{Analytics}}},
  shorttitle = {Getting {{Started}} with {{Data Science}}},
  author = {Haider, Murtaza},
  year = {2015},
  month = dec,
  publisher = {{IBM Press}},
  abstract = {Master Data Analytics Hands-On by Solving Fascinating Problems You'll Actually Enjoy!   Harvard Business Review recently called data science ``The Sexiest Job of the 21st Century.'' It's not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it's indispensable. Unfortunately, there's been nothing easy about learning data science\textendash until now.  Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell's Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything's software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you'll always know why you're doing what you're doing. You'll master data science by answering fascinating questions, such as:\textbullet{} Are religious individuals more or less likely to have extramarital affairs?\textbullet{} Do attractive professors get better teaching evaluations?\textbullet{} Does the higher price of cigarettes deter smoking?\textbullet{} What determines housing prices more: lot size or the number of bedrooms?\textbullet{} How do teenagers and older people differ in the way they use social media?\textbullet{} Who is more likely to use online dating services?\textbullet{} Why do some purchase iPhones and others Blackberry devices?\textbullet{} Does the presence of children influence a family's spending on alcohol? For each problem, you'll walk through defining your question and the answers you'll need; exploring howothers have approached similar challenges; selecting your data and methods; generating your statistics;organizing your report; and telling your story. Throughout, the focus is squarely on what matters most:transforming data into insights that are clear, accurate, and can be acted upon.},
  googlebooks = {b4YxCwAAQBAJ},
  isbn = {978-0-13-399123-9},
  langid = {english},
  keywords = {Business \& Economics / Statistics,Computers / Databases / Data Mining}
}

@article{halevyUnreasonableEffectivenessData2009,
  title = {The {{Unreasonable Effectiveness}} of {{Data}}},
  author = {Halevy, Alon and Norvig, Peter and Pereira, Fernando},
  year = {2009},
  journal = {IEEE Intelligent Systems},
  volume = {24},
  number = {2},
  pages = {8--12},
  publisher = {{IEEE}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/J78Z43RA/Halevy et al. - 2009 - The Unreasonable Effectiveness of Data.pdf;/Users/rca2t1/Dropbox/Zotero/storage/CNSIJIRY/4804817.html}
}

@incollection{hammerbacherInformationPlatformsRise2009,
  title = {Information {{Platforms}} and the {{Rise}} of the {{Data Scientist}}},
  booktitle = {Beautiful {{Data}}: {{The Stories Behind Elegant Data Solutions}}},
  author = {Hammerbacher, Jeff},
  year = {2009},
  pages = {73--84},
  publisher = {{O'Reilly Media Sebastopol, CA}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4CR9ZXIR/Hammerbacher - 2009 - Information Platforms and the Rise of the Data Sci.pdf;/Users/rca2t1/Dropbox/Zotero/storage/QGPVI5X8/books.html}
}

@article{HandsHistory2005,
  title = {Hands {{Across History}}},
  year = {2005},
  pages = {4},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4Z6EB46Z/Hands Across History.pdf}
}

@article{hardinDataScienceStatistics2015,
  title = {Data {{Science}} in {{Statistics Curricula}}: {{Preparing Students}} to "{{Think}} with {{Data}}"},
  shorttitle = {Data {{Science}} in {{Statistics Curricula}}},
  author = {Hardin, Johanna and Hoerl, Roger and Horton, Nicholas J. and Nolan, Deborah},
  year = {2015},
  month = aug,
  journal = {arXiv:1410.3127 [stat]},
  eprint = {1410.3127},
  eprinttype = {arxiv},
  primaryclass = {stat},
  abstract = {A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to utilize databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this paper is to motivate the importance of data science proficiency and to provide examples and resources for instructors to implement data science in their own statistics curricula. We provide case studies from seven institutions. These varied approaches to teaching data science demonstrate curricular innovations to address new needs. Also included here are examples of assignments designed for courses that foster engagement of undergraduates with data and data science.},
  archiveprefix = {arXiv},
  keywords = {Statistics - Other Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VVXPZY3N/Hardin et al. - 2015 - Data Science in Statistics Curricula Preparing St.pdf;/Users/rca2t1/Dropbox/Zotero/storage/Z4Z4IFVL/1410.html}
}

@article{harmonSomethingSeedyWhen2007,
  title = {Something Seedy Is up When It Comes to {{BYU}}},
  author = {Harmon, Dick},
  year = {2007},
  month = mar,
  journal = {Deseret Morning News},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/XI2KGZYF/document.html}
}

@book{hartSystemsComputerScience1967,
  title = {Systems and {{Computer Science}}: {{Proceedings}} of a {{Conference}} Held at the {{University}} of {{Western Ontario September}} 10-11, 1965},
  shorttitle = {Systems and {{Computer Science}}},
  author = {Hart, John F. and Takasu, Satoru},
  year = {1967},
  month = dec,
  publisher = {{University of Toronto Press}},
  abstract = {This book presents the papers delivered at the Conference on Systems and Computer Science held at the University of Western Ontario in September 1965. The primary purposes of the Conference were the promotion of research and the development of the teaching of computer science in Canadian universities. The papers focus attention on some of the concepts of Computer Science as a new field of study and at the same time provide a background for scientists looking at the subject for the first time. The chief developments in computer science have been concerned with the "applied" rather than the "pure" areas of the field: numerical analysis, applied statistics and operations research, and data processing. But there is something more to computers than the physical components and this book represents an attempt to correct the imbalance between "applied" and "pure" by drawing attention to certain theoretical aspects of computer and information science. Among the topics discussed are the theory of finite and infinite automata, aspects of formal language theory, heuristic and non-heuristic approaches to theorem proving and the mathematical formulation of the theory of general systems. There are also references to the problems of machine design, to software systems including higher-level languages, to multiple control computer models and to applied systems. This collection of papers will appeal first to graduate students and professors in Computer Science. It will also be of interest to computer scientists in industry and in government and university research groups and to the scientific public interested in discovering some of the principal ingredients and directions of the computer and information sciences.},
  googlebooks = {8jM5DwAAQBAJ},
  isbn = {978-1-4875-8982-0},
  langid = {english},
  keywords = {Education / Computers \& Technology,Education / Higher,Science / History}
}

@article{hassaniDataMiningOfficial2014,
  title = {Data {{Mining}} and {{Official Statistics}}: {{The Past}}, the {{Present}} and the {{Future}}},
  shorttitle = {Data {{Mining}} and {{Official Statistics}}},
  author = {Hassani, Hossein and Saporta, Gilbert and Silva, Emmanuel Sirimal},
  year = {2014},
  journal = {Big Data},
  volume = {2},
  number = {1},
  pages = {34--43},
  publisher = {{Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LC6D4N7L/Hassani et al. - 2014 - Data Mining and Official Statistics The Past, the.pdf;/Users/rca2t1/Dropbox/Zotero/storage/3CPSU9UR/big.2013.html}
}

@book{hastieElementsStatisticalLearning2009,
  title = {The {{Elements}} of {{Statistical Learning}}: {{Data Mining}}, {{Inference}}, and {{Prediction}}},
  shorttitle = {The {{Elements}} of {{Statistical Learning}}},
  author = {Hastie, Trevor and Tibshirani, Robert and Friedman, J. H},
  year = {2009},
  abstract = {"During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates."--Publisher's description.},
  isbn = {978-0-387-84857-0 978-0-387-84884-6},
  langid = {english},
  annotation = {OCLC: 300478243},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TS7NHQ64/Hastie et al. - 2009 - The elements of statistical learning data mining,.pdf}
}

@book{hayashiDataScienceClassification1998,
  title = {Data {{Science}}, {{Classification}}, and {{Related Methods}}: {{Proceedings}} of the {{Fifth Conference}} of the {{International Federation}} of {{Classification Societies}} ({{IFCS-96}}), {{Kobe}}, {{Japan}}, {{March}} 27\textendash 30, 1996},
  shorttitle = {Data {{Science}}, {{Classification}}, and {{Related Methods}}},
  editor = {Hayashi, Chikio and Yajima, Keiji and Bock, Hans H. and Ohsumi, Noboru and Tanaka, Yutaka and Baba, Yasumasa},
  year = {1998},
  series = {Studies in {{Classification}}, {{Data Analysis}}, and {{Knowledge Organization}}},
  publisher = {{Springer Japan}},
  doi = {10.1007/978-4-431-65950-1},
  abstract = {This volume, Data Science, Classification, and Related Methods, contains a selection of papers presented at the Fifth Conference of the International Federation of Oassification Societies (IFCS-96), which was held in Kobe, Japan, from March 27 to 30,1996. The volume covers a wide range of topics and perspectives in the growing field of data science, including theoretical and methodological advances in domains relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge discovery and seeking. It gives a broad view of the state of the art and is intended for those in the scientific community who either develop new data analysis methods or gather data and use search tools for analyzing and interpreting large and complex data sets. Presenting a wide field of applications, this book is of interest not only to data analysts, mathematicians, and statisticians but also to scientists from many areas and disciplines concerned with complex data: medicine, biology, space science, geoscience, environmental science, infonnation science, image and pattern analysis, economics, statistics, social sciences, psychology, cognitive science, behavioral science, marketing and survey research, data mining, and knowledge organization.},
  isbn = {978-4-431-70208-5},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/YAK9L8I4/Hayashi et al. - 1998 - Data Science, Classification, and Related Methods.pdf;/Users/rca2t1/Dropbox/Zotero/storage/NKGDGMPA/9784431702085.html}
}

@book{hayashiDataScienceClassification1998a,
  title = {Data Science, Classification, and Related Methods: Proceedings of the Fifth {{Conference}} of the {{International Federation}} of {{Classification Societies}} ({{IFCS-96}}), {{Kobe}}, {{Japan}}, {{March}} 27-30, 1996},
  shorttitle = {Data Science, Classification, and Related Methods},
  author = {Hayashi, Chikio},
  year = {1998},
  publisher = {{Springer}},
  address = {{Kobe, Japan}},
  abstract = {This is the proceedings of the Fifth Conference of the International Federation of Classification Societies held in Kobe, Japan, on March 27-30, 1996: The astounding increase in computer usage over the past decade and the ever-increasing scope of global communication networks have ushered in the information age; however, human intelligence is required to make sense of this sea of data and to use it effectively. Consequently, the rapidly developing cross-disciplinary field of data science has come of age. This volume contains selected papers from the Fifth Conference of the International Federation of Classification Societies (IFCS-96), held in Kobe, Japan, in March 1996. A wide range of topics is covered, including theoretical and methodological advances relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge seeking and discovery. A broad view of the state of the art is presented, making this an essential work not only for data analysts, mathematicians, and statisticians but also for researchers involved in data processing at all stages from data gathering to decision making.},
  isbn = {978-4-431-70208-5},
  langid = {english},
  keywords = {Business \& Economics / Economics / Theory,Computers / Computer Science,Computers / Data Processing,Computers / Expert Systems,Computers / Intelligence (AI) \& Semantics,MATHEMATICS / Probability \& Statistics / General},
  annotation = {00008}
}

@book{hayashiDataScienceClassification2013,
  title = {Data {{Science}}, {{Classification}}, and {{Related Methods}}: {{Proceedings}} of the {{Fifth Conference}} of the {{International Federation}} of {{Classification Societies}} ({{IFCS-96}}), {{Kobe}}, {{Japan}}, {{March}} 27\textendash 30, 1996},
  shorttitle = {Data {{Science}}, {{Classification}}, and {{Related Methods}}},
  author = {Hayashi, Chikio and Yajima, Keiji and Bock, Hans H. and Ohsumi, Noboru and Tanaka, Yutaka and Baba, Yasumasa},
  year = {2013},
  month = nov,
  publisher = {{Springer Science \& Business Media}},
  abstract = {This volume, Data Science, Classification, and Related Methods, contains a selection of papers presented at the Fifth Conference of the International Federation of Oassification Societies (IFCS-96), which was held in Kobe, Japan, from March 27 to 30,1996. The volume covers a wide range of topics and perspectives in the growing field of data science, including theoretical and methodological advances in domains relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge discovery and seeking. It gives a broad view of the state of the art and is intended for those in the scientific community who either develop new data analysis methods or gather data and use search tools for analyzing and interpreting large and complex data sets. Presenting a wide field of applications, this book is of interest not only to data analysts, mathematicians, and statisticians but also to scientists from many areas and disciplines concerned with complex data: medicine, biology, space science, geoscience, environmental science, infonnation science, image and pattern analysis, economics, statistics, social sciences, psychology, cognitive science, behavioral science, marketing and survey research, data mining, and knowledge organization.},
  googlebooks = {YGryCAAAQBAJ},
  isbn = {978-4-431-65950-1},
  langid = {english},
  keywords = {Business \& Economics / Economics / Theory,Computers / Data Modeling \& Design,Computers / Programming / Algorithms,Mathematics / Probability \& Statistics / General,Mathematics / Probability \& Statistics / Stochastic Processes}
}

@incollection{hayashiEvaluationDataQuality2000,
  title = {Evaluation of {{Data Quality}} and {{Data Analysis}}},
  booktitle = {Data {{Analysis}}, {{Classification}}, and {{Related Methods}}},
  author = {Hayashi, C.},
  editor = {Kiers, H. a. L. and Rasson, J. P. and Gronen, P. J. F. and Schader, M.},
  year = {2000},
  pages = {335--340},
  publisher = {{Springer-Verlag Berlin}},
  address = {{Berlin}},
  issn = {1431-8814},
  abstract = {The practical evaluation of data quality is discussed. Such evaluations are essential if we intend to carry out useful data analyses. Here we treat this problem in the context of cross-societal (comparative social) surveys.},
  isbn = {978-3-540-67521-1},
  langid = {english},
  annotation = {WOS:000166471700053}
}

@book{hayashiQuestionnaireConstructionData2002,
  title = {Questionnaire {{Construction}}, {{Data Collection}} and {{Data Analysis}}: {{An Approach}} by the {{Idea}} of {{Data Science}}},
  shorttitle = {Questionnaire {{Construction}}, {{Data Collection}} and {{Data Analysis}}},
  author = {Hayashi, C.},
  editor = {Nishisato, S. and Babe, Y. and Bozdogan, H. and Kanefuji, K.},
  year = {2002},
  journal = {Measurement and Multivariate Analysis},
  pages = {13--24},
  publisher = {{Springer-Verlag Tokyo}},
  address = {{Tokyo}},
  abstract = {Data design, data collection and data quality evaluation are crucial to data analysis if we are to draw out useful relevant information. Analysis of low-information data never bears fruit; however, data analytic methods can be refined. In spite of the importance of this issue in actual data mining and data analysis, I am forced to ask why these problems cannot be discussed at its most essential level. Perhaps it is a matter of the laborious practical work involved or the otherwise plodding pace of research. Indeed, these problems are rarely addressed because in academic circles it is regarded as unsophisticated. In the present paper, I dare to take up these problems, regarding it as one very important to data science.},
  isbn = {978-4-431-70338-9},
  langid = {english},
  annotation = {WOS:000175725900002}
}

@inproceedings{hayashiSimpsonParadoxOne1998,
  title = {Beyond {{Simpson}}'s {{Paradox}}: {{One Problem}} in {{Data Science}}},
  shorttitle = {Beyond {{Simpson}}'s {{Paradox}}},
  booktitle = {Advances in {{Data Science}} and {{Classification}}},
  author = {Hayashi, Chikio and Yamaoka, Kazue},
  editor = {Rizzi, Alfredo and Vichi, Maurizio and Bock, Hans-Hermann},
  year = {1998},
  series = {Studies in {{Classification}}, {{Data Analysis}}, and {{Knowledge Organization}}},
  pages = {65--72},
  publisher = {{Springer}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-642-72253-0_9},
  abstract = {In the present paper, the conditions under which Simpson's paradox does not occur are discussed for various cases. These conditions are first obtained from the descriptive point of view and then on the assumption of prior probability distributions of parameters. The robustness of the results is discussed with respect to the prior probability distributions. Practically, the result is given as the magnitude of odds ratio (or relative risk), i.e., Simpson's paradox does not occur if the odds ratio is more or less than a certain values, depending on various cases.},
  isbn = {978-3-642-72253-0},
  langid = {english},
  keywords = {conditions of non-paradox,descriptive approach,Monte Carlo solution,prior probability distribution of a parameter,robustness of solution,Simpson’s paradox}
}

@inproceedings{hayashiWhatDataScience1998,
  title = {What Is {{Data Science}}? {{Fundamental Concepts}} and a {{Heuristic Example}}},
  shorttitle = {What Is {{Data Science}} ?},
  booktitle = {Data {{Science}}, {{Classification}}, and {{Related Methods}}},
  author = {Hayashi, Chikio},
  editor = {Hayashi, Chikio and Yajima, Keiji and Bock, Hans-Hermann and Ohsumi, Noboru and Tanaka, Yutaka and Baba, Yasumasa},
  year = {1998},
  series = {Studies in {{Classification}}, {{Data Analysis}}, and {{Knowledge Organization}}},
  pages = {40--51},
  publisher = {{Springer Japan}},
  abstract = {SummaryData Science is not only a synthetic concept to unify statistics, data analysis and their related methods but also comprises its results. It includes three phases, design for data, collection of data, and analysis on data. Fundamental concepts and various methods based on it are discussed with a heuristic example.},
  isbn = {978-4-431-65950-1},
  langid = {english},
  keywords = {Comparative Survey,Cultural Sphere,Exploratory Approach,National Character,Opinion Distribution},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/8DLV8W23/Hayashi - 1998 - What is Data Science Fundamental Concepts and a H.pdf}
}

@article{haylesBoundaryDisputesHomeostasis2007,
  title = {Boundary {{Disputes}} : {{Homeostasis}} , {{Reflexivity}} , and the {{Foundations}} of {{Cybernetics}}},
  author = {Hayles, N Katherine},
  year = {2007},
  journal = {Constellations},
  volume = {3},
  number = {1994},
  pages = {1--16},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RXNW8BJB/Boundary Disputes Homeostasis , Reflexivity , and the Foundations of Cybernetics_Constellations.pdf}
}

@book{helveyAgeInformationInterdisciplinary1971,
  title = {The {{Age}} of {{Information}}: {{An Interdisciplinary Survey}} of {{Cybernetics}}},
  shorttitle = {The {{Age}} of {{Information}}},
  author = {Helvey, T. C.},
  year = {1971},
  publisher = {{Educational Technology}},
  googlebooks = {L8YxYRSZYCkC},
  isbn = {978-0-87778-008-3},
  langid = {english}
}

@book{hendersonCooperationConvertibilityCompatibility1966,
  title = {Cooperation, {{Convertibility}}, and {{Compatibility Among Information Systems}}: {{A Literature Review}}},
  shorttitle = {Cooperation, {{Convertibility}}, and {{Compatibility Among Information Systems}}},
  author = {Henderson, Madeline M.},
  year = {1966},
  publisher = {{U.S. Department of Commerce, National Bureau of Standards}},
  googlebooks = {h62fMnaMx6YC},
  langid = {english}
}

@book{hendryDynamicEconometrics1995,
  title = {Dynamic {{Econometrics}}},
  author = {Hendry, David F.},
  year = {1995},
  series = {Advanced {{Texts}} in {{Econometrics}}},
  publisher = {{Oxford Univ Press}},
  address = {{New York}},
  isbn = {978-0-19-828316-4},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/6VCK4EQZ/XcWVN2-2ZqIC.html}
}

@incollection{hennigHowWrongModels2003,
  title = {How {{Wrong Models Become Useful}} - and {{Correct Models Become Dangerous}}},
  booktitle = {Between {{Data Science}} and {{Applied Data Analysis}}},
  author = {Hennig, C.},
  editor = {Schader, M. and Gaul, W. and Vichi, M.},
  year = {2003},
  pages = {235--243},
  publisher = {{Springer-Verlag Berlin}},
  address = {{Berlin}},
  issn = {1431-8814},
  abstract = {The conceptual background of statistics and data analysis is considered from the viewpoint of constructivist philosophy. The relation of formal models to observable reality is discussed as well as the role of model assumptions and especially probability models in data analysis. I argue that approximate correctness of models is an ill-posed problem. The relation of model assumptions to observer-independent reality can never be assessed objectively. Instead, formal models axe useful to learn about the derived statistical methods and to enable clear understanding between different observers. Some illustrations axe given.},
  isbn = {978-3-540-40354-8},
  langid = {english},
  annotation = {WOS:000185702600027},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FJ8JABSZ/Hennig - 2003 - How Wrong Models Become Useful - and Correct Model.pdf}
}

@article{heracleousConceptualizingOrganizationalDiscourse2004,
  title = {Conceptualizing Organizational Discourse as Situated Symbolic Action},
  author = {Heracleous, Loizos and Marshak, Robert J.},
  year = {2004},
  month = oct,
  journal = {Human Relations},
  volume = {57},
  number = {10},
  pages = {1285--1312},
  publisher = {{SAGE Publications Ltd}},
  issn = {0018-7267},
  doi = {10.1177/0018726704048356},
  abstract = {This article presents a conceptualization of organizational discourse as situated symbolic action, drawing from the fields of speech act theory, rhetoric, ethnography of communication and social constructionism. This conceptualization is illustrated through analysis of an episode of negotiated order accessed through an organization development intervention; a meeting of senior managers of Systech, a major IT organization, to decide on a new business model. This perspective helps to respond to some of the key challenges facing the organizational discourse field in terms of developing more clearly specified conceptualizations of discourse suited to the organizational level of analysis, achieving a more holistic and discourse-sensitive understanding of empirical contexts by organizational researchers, and illustrating that organizational discourse analysis is not simply an intellectual luxury but can have pragmatic, relevant implications.},
  langid = {english},
  keywords = {action research,organizational discourse,situated symbolic action},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4II8V6SS/Heracleous and Marshak - 2004 - Conceptualizing organizational discourse as situat.pdf}
}

@misc{hermanFieldGuideData2013,
  title = {Field {{Guide}} to {{Data Science}}},
  author = {Herman, Mark and Rivera, Stephanie and Stephen, Mills and Sullivan, Josh and Guerra, Peter and Cosmas, Alex and Farris, Drew and Kholey, Ed and Yacci, Paul and Keller, Brian and Kherlopian, Armen and Kim, Michael},
  year = {2013},
  publisher = {{Booz Allen Hamilton Inc.}},
  abstract = {This guide is aimed at helping organizations understand how to better use data as a resource.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RVMBILPF/Herman et al. - 2013 - Field Guide to Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/N24K6B46/field-guide-to-data-science.html}
}

@article{hernanDataScienceScience2018,
  title = {Data {{Science Is Science}}'s {{Second Chance}} to {{Get Causal Inference Right}}: {{A Classification}} of {{Data Science Tasks}}},
  shorttitle = {Data {{Science Is Science}}'s {{Second Chance}} to {{Get Causal Inference Right}}},
  author = {Hern{\'a}n, Miguel A. and Hsu, John and Healy, Brian},
  year = {2018},
  journal = {arXiv preprint arXiv:1804.10846},
  eprint = {1804.10846},
  eprinttype = {arxiv},
  archiveprefix = {arXiv},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2E9EW42T/Hernán et al. - 2018 - Data science is science's second chance to get cau.pdf;/Users/rca2t1/Dropbox/Zotero/storage/WNAFVUTT/1804.html}
}

@article{heumannDataProcessingScientists1956,
  title = {Data {{Processing}} for {{Scientists}}},
  author = {Heumann, Karl F.},
  year = {1956},
  journal = {Science},
  volume = {124},
  number = {3226},
  pages = {773--777},
  publisher = {{American Association for the Advancement of Science}},
  issn = {0036-8075},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/M2B5BAHM/Heumann - 1956 - Data Processing for Scientists.pdf}
}

@article{heyFourthParadigm102020,
  title = {The Fourth Paradigm 10 Years On},
  author = {Hey, Tony and Trefethen, Anne},
  year = {2020},
  journal = {Informatik Spektrum},
  volume = {42},
  number = {6},
  pages = {441--447},
  publisher = {{Springer}}
}

@book{heyFourthParadigmDataIntensive2009,
  title = {The {{Fourth Paradigm}}: {{Data-Intensive Scientific Discovery}}},
  shorttitle = {The {{Fourth Paradigm}}},
  author = {Hey, Tony and Tansley, Stewart and Tolle, Kristin},
  year = {2009},
  month = oct,
  abstract = {Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud computing technologies. [\ldots ]},
  isbn = {978-0-9825442-0-4},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HJS3BZ53/Hey et al. - 2009 - The Fourth Paradigm Data-Intensive Scientific Dis.pdf;/Users/rca2t1/Dropbox/Zotero/storage/S7I5M7BK/fourth-paradigm-data-intensive-scientific-discovery.html}
}

@misc{heyJimGrayEScience2009,
  title = {Jim {{Gray}} on {{eScience}}: {{A Transformed Scientific Method}}},
  shorttitle = {Jim {{Gray}} on {{eScience}}},
  author = {Hey, Tony and Tansley, Stewart and Tolle, Kristin M.},
  year = {2009},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/V5GATCR2/Hey et al. - 2009 - Jim Gray on eScience A Transformed Scientific Met.pdf}
}

@misc{HiddenFiguresHuman,
  title = {Hidden {{Figures}} and {{Human Computers}}},
  abstract = {The breakout movie Hidden Figures tells the story of three African American women who worked as mathematicians at NASA. The story sheds light on the significant contributions of the three women\textemdash Katherine Johnson, Dorothy Vaughan, and Mary Jackson\textemdash but also the broader impact that women had behind the scenes at NASA. Johnson, Vaughan, and Jackson all began their careers at the National Advisory Committee for Aeronautics~(NACA)\textemdash which later became NASA\textemdash working as ``computers.'' Computers were not what we think of them today. They were people, primarily women, who reduced or analyzed data using mechanical calculators\textemdash we've previously explored the role of computers in astronomy.},
  howpublished = {https://airandspace.si.edu/stories/editorial/hidden-figures-and-human-computers},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9TBR5MLH/hidden-figures-and-human-computers.html}
}

@article{higginsNonmathematicalStatisticsNew1999,
  title = {Nonmathematical {{Statistics}}: {{A New Direction}} for the {{Undergraduate Discipline}}},
  shorttitle = {Nonmathematical {{Statistics}}},
  author = {Higgins, James J.},
  year = {1999},
  month = feb,
  journal = {The American Statistician},
  volume = {53},
  number = {1},
  pages = {1--6},
  publisher = {{Taylor \& Francis}},
  issn = {0003-1305},
  doi = {10.1080/00031305.1999.10474418},
  abstract = {The explosion in the amount of data available to society today has not led to a corresponding growth in undergraduate statistics programs to produce statisticians to deal with such data. Instead, the profession is faced with the specter of ``statistics departments under siege.'' It is time to reexamine the undergraduate discipline in light of society's needs. The traditional emphasis on the mathematics of the discipline may have resulted in insufficient attention being paid to its nonmathematical aspects. These things are very much a part of what a practicing statistician does and what customers of statistics need. They include things like designing scientific studies in a team-oriented environment, ensuring protocol compliance, ensuring data quality, managing the storage/transmission/retrieval of data, and providing descriptive and graphical analyses of data. To bring greater purpose and practicality to programs for the undergraduate statistics major, it will be necessary to give greater prominence to nonmathematical statistics. Courses are suggested that would meet important needs of the undergraduate statistics major and set the discipline of statistics apart from mathematics.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NS8J8JH8/Higgins - 1999 - Nonmathematical Statistics A New Direction for th.pdf;/Users/rca2t1/Dropbox/Zotero/storage/W2GXP54S/00031305.1999.html}
}

@incollection{hippDataMiningAssociation2002,
  title = {Data {{Mining}} of {{Association Rules}} and the {{Process}} of {{Knowledge Discovery}} in {{Databases}}},
  booktitle = {Advances in Data Mining},
  author = {Hipp, Jochen and G{\"u}ntzer, Ulrich and Nakhaeizadeh, Gholamreza},
  year = {2002},
  pages = {15--36},
  publisher = {{Springer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LKASCA4W/Hipp et al. - 2002 - Data mining of association rules and the process o.pdf;/Users/rca2t1/Dropbox/Zotero/storage/333PTWST/3-540-46131-0_2.html}
}

@article{hoaglinJohnTukeyData2003,
  title = {John {{W}}. {{Tukey}} and {{Data Analysis}}},
  author = {Hoaglin, David C.},
  year = {2003},
  month = aug,
  journal = {Statistical Science},
  volume = {18},
  number = {3},
  pages = {311--318},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0883-4237, 2168-8745},
  doi = {10.1214/ss/1076102418},
  abstract = {From the time that John W. Tukey started to do serious work in statistics, he was interested in problems and techniques of data analysis. Some people know him best for exploratory data analysis, which he pioneered, but he also made key contributions in analysis of variance, in regression and through a wide range of applications.This paper reviews illustrative contributions in these areas.},
  keywords = {Analysis of variance,exploratory data analysis,regression},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/474RGY6Y/Hoaglin - 2003 - John W. Tukey and Data Analysis.pdf;/Users/rca2t1/Dropbox/Zotero/storage/TTQSGFUL/1076102418.html}
}

@book{hogbenComputerScienceStatistics1978,
  title = {Computer {{Science}} and {{Statistics--Tenth Annual Symposium}} on the {{Interface}}: {{Proceedings}} of the 10th {{Annual Symposium Held}} at the {{National Bureau}} of {{Standards}}, {{Gaithersburg}}, {{Maryland}}, {{April}} 14-15, 1977},
  shorttitle = {Computer {{Science}} and {{Statistics--Tenth Annual Symposium}} on the {{Interface}}},
  author = {Hogben, David and Fife, Dennis W.},
  year = {1978},
  publisher = {{U.S. Department of Commerce, National Bureau of Standards}},
  googlebooks = {XMzqjQDtAFIC},
  langid = {english}
}

@article{hortonSettingStageData2015,
  title = {Setting the {{Stage}} for {{Data Science}}: {{Integration}} of {{Data Management Skills}} in {{Introductory}} and {{Second Courses}} in {{Statistics}}},
  shorttitle = {Setting the {{Stage}} for {{Data Science}}},
  author = {Horton, Nicholas J. and Baumer, Benjamin S. and Wickham, Hadley},
  year = {2015},
  month = apr,
  journal = {CHANCE},
  volume = {28},
  number = {2},
  eprint = {1502.00318},
  eprinttype = {arxiv},
  pages = {40--50},
  issn = {0933-2480, 1867-2280},
  doi = {10.1080/09332480.2015.1042739},
  abstract = {Many have argued that statistics students need additional facility to express statistical computations. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science and applying these to real-world scenarios, we prepare them to think statistically. In an era of increasingly big data, it is imperative that students develop data-related capacities, beginning with the introductory course. We believe that the integration of these precursors to data science into our curricula-early and often-will help statisticians be part of the dialogue regarding "Big Data" and "Big Questions".},
  archiveprefix = {arXiv},
  keywords = {62-01,Computer Science - Computers and Society,Statistics - Computation,Statistics - Other Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/G95SIU29/Horton et al. - 2015 - Setting the stage for data science integration of.pdf;/Users/rca2t1/Dropbox/Zotero/storage/7SXDTP9R/1502.html}
}

@article{hortonSettingStageData2015a,
  title = {Setting the {{Stage}} for {{Data Science}}: {{Integration}} of {{Data Management Skills}} in {{Introductory}} and {{Second Courses}} in {{Statistics}}},
  shorttitle = {Setting the {{Stage}} for {{Data Science}}},
  author = {Horton, Nicholas J. and Baumer, Benjamin S. and Wickham, Hadley},
  year = {2015},
  month = feb,
  journal = {arXiv:1502.00318 [cs, stat]},
  eprint = {1502.00318},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {Many have argued that statistics students need additional facility to express statistical computations. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science and applying these to real-world scenarios, we prepare them to think statistically. In an era of increasingly big data, it is imperative that students develop data-related capacities, beginning with the introductory course. We believe that the integration of these precursors to data science into our curricula-early and often-will help statisticians be part of the dialogue regarding "Big Data" and "Big Questions".},
  archiveprefix = {arXiv},
  keywords = {62-01,Computer Science - Computers and Society,Statistics - Computation,Statistics - Other Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2ZX7N5EJ/Horton et al. - 2015 - Setting the stage for data science integration of.pdf;/Users/rca2t1/Dropbox/Zotero/storage/CEWK73GD/1502.html}
}

@article{hortonTeachingPrecursorsData2014,
  title = {Teaching Precursors to Data Science in Introductory and Second Courses in Statistics},
  author = {Horton, Nicholas J. and Baumer, Benjamin S. and Wickham, Hadley},
  year = {2014},
  month = jan,
  journal = {arXiv:1401.3269 [cs, stat]},
  eprint = {1401.3269},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {Statistics students need to develop the capacity to make sense of the staggering amount of information collected in our increasingly data-centered world. Data science is an important part of modern statistics, but our introductory and second statistics courses often neglect this fact. This paper discusses ways to provide a practical foundation for students to learn to "compute with data" as defined by Nolan and Temple Lang (2010), as well as develop "data habits of mind" (Finzer, 2013). We describe how introductory and second courses can integrate two key precursors to data science: the use of reproducible analysis tools and access to large databases. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science and applying these to real-world scenarios, we prepare them to think statistically in the era of big data.},
  archiveprefix = {arXiv},
  keywords = {62-07,Computer Science - Computers and Society,Statistics - Computation,Statistics - Other Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3IRG76BA/Horton et al. - 2014 - Teaching precursors to data science in introductor.pdf;/Users/rca2t1/Dropbox/Zotero/storage/R8QFBU8C/1401.html}
}

@article{HotCommodityData2014,
  title = {Hot {{Commodity}}: {{The Data Scientist}}},
  shorttitle = {Video},
  year = {2014},
  month = aug,
  abstract = {Competition for data scientists is heating up as their skills become increasingly important to the world's tech companies. Lily Jamali reports.},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EZAD4U2U/hot-commodity-the-data-scientist.html}
}

@book{houseHearings1958,
  title = {Hearings},
  author = {House, United States Congress},
  year = {1958},
  publisher = {{U.S. Government Printing Office}},
  langid = {english}
}

@book{howeCompatibilityEvaluationData1962,
  title = {Compatibility {{Evaluation}} of {{Data Handling Subsystem}}},
  author = {Howe, H. R. and Griffin, J. R. and Weppner, W. G. and Hershberg, P. I. and {Air Force Cambridge Research Laboratories (U.S.)}},
  year = {1962},
  series = {{{AFCRL}} ;62-1108},
  publisher = {{Air Force Cambridge Research Laboratories, Office of Aerospace Research, United States Air Force}},
  address = {{L. G. Hanscom Field, Massachusetts}},
  keywords = {Data processing,Weather}
}

@article{hsuAlgorithmsMiningAssociation2004,
  title = {Algorithms for Mining Association Rules in Bag Databases},
  author = {Hsu, Ping-Yu and Chen, Yen-Liang and Ling, Chun-Ching},
  year = {2004},
  journal = {Information Sciences},
  volume = {166},
  number = {1-4},
  pages = {31--47},
  publisher = {{Elsevier}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/QWFZBS6B/S0020025503004079.html}
}

@book{huberDataAnalysisWhat2012,
  title = {Data {{Analysis}}: {{What Can Be Learned From}} the {{Past}} 50 {{Years}}},
  shorttitle = {Data {{Analysis}}},
  author = {Huber, Peter J.},
  year = {2012},
  month = jan,
  publisher = {{John Wiley \& Sons}},
  abstract = {This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy \textendash{} when to use which technique \textendash{} are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics.},
  googlebooks = {6OSvDBE0rCIC},
  isbn = {978-1-118-01826-2},
  langid = {english},
  keywords = {Mathematics / Probability \& Statistics / General,Mathematics / Probability \& Statistics / Stochastic Processes}
}

@article{iivariDimensionsInformationSystems1986,
  title = {Dimensions of Information Systems Design: {{A}} Framework for a Long-Range Research Program},
  shorttitle = {Dimensions of Information Systems Design},
  author = {Iivari, Juhani},
  year = {1986},
  month = jan,
  journal = {Information Systems},
  volume = {11},
  number = {2},
  pages = {185--197},
  issn = {0306-4379},
  doi = {10.1016/0306-4379(86)90007-4},
  abstract = {Even though the two IFIP WG8.1 working conferences on the comparative review and feature analysis of information systems methodologies (ISDMs) have made an important contribution, there is a need for supplementary longer-term comparative research which might gradually produce empirically-based knowledge about what are to be regarded as sound principles of IS design in various circumstances. Due to its scope, this research should take the form of broad international cooperation based on a reasonably common research framework, one close in spirit to that of Ives, Hamilton and Davis. A research framework of this kind should include as one element a profound conceptual model for IS design as a process, an area which is left relatively unanalyzed in the model of Ives et al. For that purpose the paper outlines a framework based on the sociocybernetic metamodel for IS design as a starting point for discussion and hopefully a more elaborate framework.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UJZVUBDD/0306437986900074.html}
}

@article{iivariPIOCOModelInformation1987,
  title = {The {{PIOCO Model}} for {{Information Systems Design}}},
  author = {Iivari, Juhani and Koskela, Erkki},
  year = {1987},
  journal = {MIS Quarterly},
  volume = {11},
  number = {3},
  pages = {401--419},
  publisher = {{Management Information Systems Research Center, University of Minnesota}},
  issn = {0276-7783},
  doi = {10.2307/248688},
  abstract = {The PIOCO model is a comprehensive methodology for information systems (IS) design consisting of a metamodel for an information system, the corresponding description languages, a process model for information systems design, and a model for choice and quality criteria. The metamodel for an information system consists of three levels of abstraction and forms a profound and articulated conceptual basis for the PIOCO model for the IS design process. The article gives an overview of the PIOCO approach from a management perspective, emphasizing the role of IS design as an inquiry process supporting the decision-making concerning the information system, the quality criteria related to the IS design, and the use of the PIOCO model as a macro-framework which integrates more detailed micro-level methodologies, methods, techniques and tools.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TMU5MQ6J/Iivari and Koskela - 1987 - The PIOCO Model for Information Systems Design.pdf}
}

@article{iivariPIOCOModelInformation1987a,
  title = {The {{PIOCO Model}} for {{Information Systems Design}}},
  author = {Iivari, Juhani and Koskela, Erkki},
  year = {1987},
  journal = {MIS Quarterly},
  volume = {11},
  number = {3},
  pages = {401--419},
  publisher = {{Management Information Systems Research Center, University of Minnesota}},
  issn = {0276-7783},
  doi = {10.2307/248688},
  abstract = {The PIOCO model is a comprehensive methodology for information systems (IS) design consisting of a metamodel for an information system, the corresponding description languages, a process model for information systems design, and a model for choice and quality criteria. The metamodel for an information system consists of three levels of abstraction and forms a profound and articulated conceptual basis for the PIOCO model for the IS design process. The article gives an overview of the PIOCO approach from a management perspective, emphasizing the role of IS design as an inquiry process supporting the decision-making concerning the information system, the quality criteria related to the IS design, and the use of the PIOCO model as a macro-framework which integrates more detailed micro-level methodologies, methods, techniques and tools.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/MTHRWLNS/Iivari and Koskela - 1987 - The PIOCO Model for Information Systems Design.pdf}
}

@book{IllinoisAlumniNews1916,
  title = {Illinois {{Alumni News}}},
  year = {1916},
  publisher = {{University of Illinois at Urbana-Champaign, for members of the Alumni Association.}},
  googlebooks = {rKxGAQAAMAAJ},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SW3732YZ/data-deluge-1914.png}
}

@article{ImpactWithdrawalDisinvestment1985,
  title = {Impact of Withdrawal and Disinvestment from {{South Africa}} on the {{U}}.{{S}}. {{Economy}} : Hearing before the {{Subcommittee}} on {{Domestic Monetary Policy}} of the {{Committee}} on {{Banking}}, {{Finance}}, and {{Urban Affairs}}, {{House}} of {{Representatives}}, {{Ninety-ninth Congress}}, First Session, {{September}} 26, 1985.   {{Note}}},
  shorttitle = {Impact of Withdrawal and Disinvestment from {{South Africa}} on the {{U}}.{{S}}. {{Economy}}},
  year = {1985},
  journal = {Impact of withdrawal and disinvestment from South Africa on the U.S. Economy : hearing before the Subcommittee on Domestic Monetary Policy of the Committee on Banking, Finance, and Urban Affairs, House of Representatives, Ninety-ninth Congress, first session, September 26, 1985.},
  pages = {I-268},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PSA88KIX/1985 - Impact of withdrawal and disinvestment from South .pdf}
}

@misc{ImpactWorkflowTools,
  title = {"{{The Impact}} of {{Workflow Tools}} on {{Data-centric Research}}" - {{Google Search}}},
  howpublished = {https://www.google.com/search?q=\%22The+Impact+of+Workflow+Tools+on+Data-centric+Research\%22\&client=firefox-b-1-d\&sxsrf=ALeKk02k2gS-OOrFfgs674x68JI68AdcMA\%3A1626704305668\&ei=sYn1YLijKN6v5NoPkZyjgAs\&oq=\%22The+Impact+of+Workflow+Tools+on+Data-centric+Research\%22\&gs\_lcp=Cgdnd3Mtd2l6EAMyBQghEKABOgcIIxCwAxAnOgcIABBHELADOgcIABCwAxBDOgUIABCxAzoHCAAQsQMQQzoICAAQsQMQgwE6AggAOgQIIxAnOgoIABCHAhCxAxAUOgcIABCHAhAUOgQIABBDOgcIIxDqAhAnOgcILhDqAhAnOgQILhAnSgQIQRgAULuTJFjjoyRgvqYkaAJwAngAgAGPAYgByQeSAQMzLjaYAQCgAQGgAQKqAQdnd3Mtd2l6sAEKyAEKwAEB\&sclient=gws-wiz\&ved=0ahUKEwj4v6ziqe\_xAhXeF1kFHRHOCLAQ4dUDCA0\&uact=5},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/AJXHXNAA/search.html}
}

@book{IndustrialResearch1962,
  title = {Industrial {{Research}}},
  year = {1962},
  publisher = {{Magazine Division, Dun-Donnelley Publishing Company}},
  googlebooks = {Wyi4lGNMBXUC},
  langid = {english}
}

@book{informationNewScientist1985,
  title = {New {{Scientist}}},
  author = {Information, Reed Business},
  year = {1985},
  month = sep,
  publisher = {{Reed Business Information}},
  abstract = {New Scientist magazine was launched in 1956 "for all those men and women who are interested in scientific discovery, and in its industrial, commercial and social consequences". The brand's mission is no different today - for its consumers, New Scientist reports, explores and interprets the results of human endeavour set in the context of society and culture.},
  googlebooks = {Z3S9oWs189EC},
  langid = {english}
}

@misc{IntroductionFSQ7Combat1965,
  title = {Introduction to {{AN}}/{{FSQ-7 Combat Direction Central}} and {{AN}}/{{FSQ-8 Combat Control Central}}},
  year = {1965},
  annotation = {1 January 1959 - 15 March 1965},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/8HEAQPHN/SageIntro.html}
}

@misc{IntroductionStatisticalLearning,
  title = {Introduction to {{Statistical Learning Theory}} and {{Support Vector Machines}}},
  howpublished = {http://en.cnki.com.cn/Article\_en/CJFDTotal-MOTO200001005.htm},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NFHPFP4L/CJFDTotal-MOTO200001005.html}
}

@misc{IOSPress,
  title = {{{IOS Press}}},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/BN429AUB/data-science.html}
}

@book{ishikawaSocialBigData2015,
  title = {Social {{Big Data Mining}}},
  author = {Ishikawa, Hiroshi},
  year = {2015},
  month = mar,
  publisher = {{CRC Press}},
  abstract = {This book focuses on the basic concepts and the related technologies of data mining for social medial. Topics include: big data and social data, data mining for making a hypothesis, multivariate analysis for verifying the hypothesis, web mining and media mining, natural language processing, social big data applications, and scalability. It explains},
  googlebooks = {UX0ZBwAAQBAJ},
  isbn = {978-1-4987-1094-7},
  langid = {english},
  keywords = {Computers / Databases / Data Mining,Computers / Machine Theory,Computers / Software Development \& Engineering / Systems Analysis \& Design,Science / Life Sciences / General}
}

@book{izenmanModernMultivariateStatistical2008,
  title = {Modern {{Multivariate Statistical Techniques}}: {{Regression}}, {{Classification}}, and {{Manifold Learning}}},
  shorttitle = {Modern {{Multivariate Statistical Techniques}}},
  author = {Izenman, Alan J.},
  year = {2008},
  month = aug,
  edition = {1st ed. 2008, Corr. 2nd printing 2013 edition},
  publisher = {{Springer}},
  address = {{New York ; London}},
  abstract = {This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.},
  isbn = {978-0-387-78188-4},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LKGI4V83/Izenman - 2008 - Modern Multivariate Statistical Techniques Regres.pdf}
}

@misc{jacobsSkillsRoleCareer2008,
  title = {Skills, {{Role}} \& {{Career Structure}} of {{Data Scientists}} \& {{Curators}}: {{Assessment}} of {{Current Practice}} \& {{Future Needs}}},
  shorttitle = {Skills, {{Role}} \& {{Career Structure}} of {{Data Scientists}} \& {{Curators}}},
  author = {Jacobs, Neil},
  year = {2008},
  abstract = {A project to examine and make recommendations on the role and career development of data scientists, the associated supply of specialist data curation skills to the research community, including an assessment of the value and potential of extending data handling, curation and preservation skills within undergraduate and postgraduate curricula.},
  copyright = {Copyright Jisc unless explicitly acknowledged otherwise.},
  howpublished = {http://www.jisc.ac.uk/whatwedo/programmes/digitalrepositories2007/dataskillscareers.aspx},
  langid = {british},
  keywords = {Data \& Text Mining,Data Services \& Collections,Digital Repositories,Network \& Infrastructure,Research \& Innovation,Staff Developmen},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TF8DGJTP/dataskillscareers.html}
}

@book{jamesIntroductionStatisticalLearning2013,
  title = {An {{Introduction}} to {{Statistical Learning}}},
  author = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
  year = {2013},
  series = {Springer {{Texts}} in {{Statistics}}},
  volume = {103},
  publisher = {{Springer New York}},
  address = {{New York, NY}},
  doi = {10.1007/978-1-4614-7138-7},
  isbn = {978-1-4614-7137-0 978-1-4614-7138-7},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JT5R6T4W/James et al. - 2013 - An Introduction to Statistical Learning.pdf}
}

@book{janssensDataScienceCommand2014,
  title = {Data {{Science}} at the {{Command Line}}},
  author = {Janssens, Jeroen},
  year = {2014},
  edition = {First},
  publisher = {{O'Reilly}},
  abstract = {This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/BMZ3NH9X/www.datascienceatthecommandline.com.html}
}

@book{johnsonDataReductionInstrumentation1961,
  title = {Data {{Reduction Instrumentation}} for {{Radio Propagation Research}}},
  author = {Johnson, Walter E.},
  year = {1961},
  publisher = {{U.S. Department of Commerce, Office of Technical Services}},
  googlebooks = {ga4F03lpmv8C},
  langid = {english}
}

@techreport{johnzstoneDataScienceNSF2014,
  title = {Data {{Science}} at {{NSF}}: {{Report}} of {{StatSNSF}} Commi3ee: {{Revisions Since January MPSAC}}},
  author = {Johnzstone, Iain and Roberts, Fred},
  year = {2014},
  month = apr,
  file = {/Users/rca2t1/Dropbox/Zotero/storage/QMQV5XWE/Johnzstone and Roberts - 2014 - Data Science at NSF Report of StatSNSF commi3ee .pdf}
}

@article{jorgensonComparisonAlternativeEconometric1970,
  title = {A {{Comparison}} of {{Alternative Econometric Models}} of {{Quarterly Investment Behavior}}},
  author = {Jorgenson, Dale W. and Hunter, Jerald and Nadiri, M. Ishag},
  year = {1970},
  journal = {Econometrica},
  volume = {38},
  number = {2},
  pages = {187--212},
  publisher = {{[Wiley, Econometric Society]}},
  issn = {0012-9682},
  doi = {10.2307/1913003},
  abstract = {In this paper four alternative quarterly econometric models of investment behavior are fitted to a common set of data for individual manufacturing industries in the United States. Goodness of fit and absence of autocorrelation of errors are used as a basis for comparison of the performance of the alternative models. The econometric models are compared with each other and with alternative explanations of data on investment based on surveys of anticipated investment and on mechanical forecasting schemes. The four econometric models included in our study are those of Anderson [2], Eisner [15], Jorgenson and Stephenson [38], and Meyer and Glauber [46]. On the basis of our comparison, the ranking of the alternative models is as follows: (1) Jorgenson-Stephenson, (2) Eisner, (3) Meyer-Glauber, (4) Anderson. Anticipatory data give a better fit to data on investment expenditures than that provided by any of the econometric models. Mechanical forecasting schemes provide a fit that is superior to the Anderson and Meyer-Glauber models. These schemes are slightly inferior to the Eisner model and clearly inferior to the Jorgenson-Stephenson model. The alternative econometric models included in our comparison differ in specification of the time structure of the investment process and in the role ascribed to specific determinants of investment behavior. Both aspects of an econometric model affect its performance so that it is difficult to discriminate among alternative determinants of investment behavior on the basis of our results.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VCTY74BU/Jorgenson et al. - 1970 - A Comparison of Alternative Econometric Models of .pdf}
}

@article{jorgensonPredictivePerformanceEconometric1970,
  title = {The {{Predictive Performance}} of {{Econometric Models}} of {{Quarterly Investment Behavior}}},
  author = {Jorgenson, Dale W. and Hunter, Jerald and Nadiri, M. Ishag},
  year = {1970},
  journal = {Econometrica},
  volume = {38},
  number = {2},
  pages = {213--224},
  publisher = {{[Wiley, Econometric Society]}},
  issn = {0012-9682},
  doi = {10.2307/1913004},
  abstract = {In this paper four alternative quarterly econometric models of investment behavior are compared with regard to predictive performance. Predictive performance may be assessed in two ways: (i) We compare prediction errors for a period of prediction with errors for a period of fit. (ii) We fit investment functions for both periods and test for structural change. These two procedures may be viewed as alternative tests of the hypothesis of structural change; the second is more powerful from the statistical point of view. Test of predictive performance supplement the comparisons of alternative models given in our preceding paper [17]. Goodness of fit may be exaggerated by consideration of a wide range of alternatives and selection of the one that fits best. If goodness of fit is exaggerated, a predictive test should produce evidence of structural change between the period of fit and the period of prediction. Of course, the better an econometric model fits the data, the more stringent this criterion for predictive performance. The econometric models included in our study are those of Anderson [1], Eisner [7], Jorgenson and Stephenson [19], and Meyer and Glauber [21]. On the basis of predictive performance the ranking of the alternative models is as follows: (1) Eisner, (2) Jorgenson-Stephenson, (3) Meyer-Glauber, and (4) Anderson. This ranking is similar to that resulting from comparisons based on goodness of it presented in our preceding paper [17]. For econometric models of quarterly investment behavior, the models that fit the best also have the best predictive performance.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/XMIBYZ5I/Jorgenson et al. - 1970 - The Predictive Performance of Econometric Models o.pdf}
}

@misc{JournalDataScience,
  title = {Journal of {{Data Science}}: {{Volume}} 1, {{Number}} 1, {{January}} 2003},
  howpublished = {http://www.jds-online.com/v1-1},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/IE6NB6HP/v1-1.html}
}

@article{karlowDataInformationSystem1969,
  title = {Data {{Information System}} at the {{National Space Science Data Center}}},
  author = {Karlow, Nick and Vette, James I.},
  year = {1969},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UF48KUU8/Karlow and Vette - 1969 - Data Information System at the National Space Scie.pdf}
}

@article{kauferCondensationSymbolsTheir1993,
  title = {Condensation {{Symbols}}: {{Their Variety}} and {{Rhetorical Function}} in {{Political Discourse}}},
  shorttitle = {Condensation {{Symbols}}},
  author = {Kaufer, David S. and Carley, Kathleen M.},
  year = {1993},
  journal = {Philosophy \& Rhetoric},
  volume = {26},
  number = {3},
  pages = {201--226},
  publisher = {{Penn State University Press}},
  issn = {0031-8213},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/XS7RIE9G/Kaufer and Carley - 1993 - Condensation Symbols Their Variety and Rhetorical.pdf}
}

@article{kayCyberneticsInformationLife1997,
  title = {Cybernetics, {{Information}}, {{Life}}: {{The Emergence}} of {{Scriptural Representations}} of {{Heredity}}},
  shorttitle = {Cybernetics, {{Information}}, {{Life}}},
  author = {Kay, Lily E.},
  year = {1997},
  journal = {Configurations},
  volume = {5},
  number = {1},
  pages = {23--91},
  issn = {1080-6520},
  annotation = {{$<$}p{$>$}Volume 5, Number 1, Winter 1997{$<$}/p{$>$}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/76DUVAPQ/5.1kay.html}
}

@misc{KDD89Workshop,
  title = {{{KDD-89 Workshop}}},
  howpublished = {https://www.kdnuggets.com/meetings-past/kdd89/index.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/H58V5S97/index.html}
}

@misc{KDDProcessOverview,
  title = {{{KDD Process}}/{{Overview}}},
  howpublished = {http://www2.cs.uregina.ca/\textasciitilde dbd/cs831/notes/kdd/1\_kdd.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SX5AJPQC/1_kdd.html}
}

@article{kearnsProposedSECRules1971,
  title = {Proposed {{SEC Rules}} for {{Private Offerings}}: {{The Impact}} on {{Venture Capital Financing Note}}},
  shorttitle = {Proposed {{SEC Rules}} for {{Private Offerings}}},
  author = {Kearns, Gregory A.},
  year = {1971},
  journal = {University of Michigan Journal of Law Reform},
  volume = {5},
  number = {1},
  pages = {122--144},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/65RN9KDL/Kearns - 1971 - Proposed SEC Rules for Private Offerings The Impa.pdf}
}

@book{kelleherDataScience2018,
  title = {Data {{Science}}},
  author = {Kelleher, John D. and Tierney, Brendan},
  year = {2018},
  series = {The {{MIT Press Essential Knowledge Series}}},
  publisher = {{MIT Press}},
  isbn = {978-0-262-53543-4},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VKJ7FIN5/Kelleher and Tierney - 2018 - Data Science.pdf}
}

@book{kennedyStatisticalComputing1980,
  title = {Statistical {{Computing}}},
  author = {Kennedy, William J. and Gentle, James E.},
  year = {1980},
  month = mar,
  series = {Statistics: {{A Series}} of {{Textbooks}} and {{Monographs}}},
  volume = {33},
  publisher = {{Marcel Dekker, Inc.}},
  address = {{New York}},
  abstract = {In this convenient textbook and reference work, the reader will find an introduction to statistical computing and a critical, balanced presentation of the algorithms and computational methods currently in use. Emphasizing the most accurate and widely used of these methods, the book thoroughly describes the algorithms that have been incorporated into the leading software systems of today, and discusses techniques for implementing algorithms in a computer. Statistical Computing contains the detail that researchers need, in the form of a textbook that gives advanced students a broad understanding of the subject, even in its most sophisticated aspects. Complete with exercises and extensive reference lists, Statistical Computing can be applied to a one-semester course for graduate students in statistics, mathematics, computer science, and any field in which numerical methods and algorithms are used in statistical data analyses. Book jacket.},
  isbn = {0-8247-6898-1},
  langid = {english},
  keywords = {Computers / Mathematical \& Statistical Software,Mathematics / Probability \& Statistics / General}
}

@inproceedings{kerolaComparisonLifecycleModels1981,
  title = {A Comparison of Lifecycle Models},
  booktitle = {Proceedings of the 5th International Conference on {{Software}} Engineering},
  author = {Kerola, Pentti and Freeman, Peter},
  year = {1981},
  month = mar,
  series = {{{ICSE}} '81},
  pages = {90--99},
  publisher = {{IEEE Press}},
  address = {{San Diego, California, USA}},
  abstract = {Preliminary comparison of world views in systems development is given. The purpose of the paper is to present the Finnish PSC systemeering model to the software engineering (SE) community and to compare it with the ideas of D. Ross and practical approaches in SE.},
  isbn = {978-0-89791-146-7},
  keywords = {Development strategies,Lifecycle,Systemeering},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FGVPUJ3T/Kerola and Freeman - 1981 - A comparison of lifecycle models.pdf}
}

@article{kettenringBirthLifeDeath1997,
  title = {The Birth, Life, and Death of Statistical Department. {{President}}'s {{Corner}}},
  author = {Kettenring, Jon R.},
  year = {1997},
  journal = {AMSTAT NEWS},
  volume = {245},
  pages = {9--10},
  publisher = {{Amer. Statist. Assoc.}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LRNVDT5S/10010480745.html}
}

@article{kettenringShapingStatisticsSuccess1997,
  title = {Shaping {{Statistics}} for {{Success}} in the 21st {{Century}}},
  author = {Kettenring, Jon R.},
  year = {1997},
  month = dec,
  journal = {Journal of the American Statistical Association},
  volume = {92},
  number = {440},
  pages = {1229--1234},
  publisher = {{Taylor \& Francis}},
  issn = {0162-1459},
  doi = {10.1080/01621459.1997.10473641},
  annotation = {\_eprint: https://doi.org/10.1080/01621459.1997.10473641},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/434V2YGM/Kettenring - 1997 - Shaping Statistics for Success in the 21st Century.pdf;/Users/rca2t1/Dropbox/Zotero/storage/MLS9RUNN/01621459.1997.html}
}

@article{kilicDataMiningStatistics2019,
  title = {Data {{Mining}} and {{Statistics}} in {{Data Science}}},
  author = {K{\i}l{\i}{\c c}, G{\"u}ner G{\"o}zde and Turanl{\i}, M{\"u}nevver and {\"O}zden, {\"U}nal Halit},
  year = {2019},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VQYC3DGI/Kılıç et al. - 2019 - Data Mining and Statistics in Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/TQILA3Q2/3069.html}
}

@article{kingProjectSAGEHalfCentury2010,
  title = {Project {{SAGE}}, a {{Half-Century On}}},
  author = {King, John Leslie},
  year = {2010},
  month = sep,
  journal = {Interactions},
  volume = {17},
  number = {5},
  pages = {53--55},
  issn = {1072-5520, 1558-3449},
  doi = {10.1145/1836216.1836230},
  abstract = {John Leslie King's interest in history was evident at the first CSCW conference in 1986. His review of 15 years of research with technology to support real-time collocated interaction, then called Group Decision Support Systems, revealed that we sometimes learn more slowly from experience than we could. In this article, he describes the little-known system that pioneered real-time human-computer interaction in the 1950s, created the computing profession, and trained hundreds of its earliest practitioners.---Jonathan Grudin},
  langid = {english}
}

@article{kirbyOperationsResearchWorld2008,
  title = {Operations {{Research}} in {{World War Two}}: {{Its Role}} in {{RAF Fighter Command}}},
  shorttitle = {Operations {{Research}} in {{World War Two}}},
  author = {Kirby, M. W.},
  year = {2008},
  journal = {Military Operations Research},
  volume = {13},
  number = {1},
  pages = {65--72},
  publisher = {{Military Operations Research Society}},
  issn = {1082-5983},
  abstract = {The role of Operations Research (OR) in informing British tactics and strategy in World War Two is well known in relation to the anti-U boat war in the North Atlantic and the area bombing of Germany. Published sources have also identified the formal origins of OR in the radar-based man-machine system that was devised as a critical component of the Air Defence of Great Britain in the late 1930s. The purpose of this paper is to put on record the role and organisation of OR in RAF Fighter Command over the divide of the Battle of Britain in 1940. Based upon hitherto unpublished sources, the paper offers insights into wartime perceptions of the role of OR in terms of its remit and organisation. In this respect, it serves as an essential complement to Patrick Blackett's wartime commentaries on the ideal functions of OR and its status within the military hierarchy.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3FJERJD2/Kirby - 2008 - Operations Research in World War Two Its Role in .pdf}
}

@incollection{kirschenbaumDigitalHumanitiesTactical2011,
  title = {Digital {{Humanities As}}/{{Is}} a {{Tactical Term}}},
  shorttitle = {``{{Chapter}} 23},
  booktitle = {Debates in the {{Digital Humanities}}},
  author = {Kirschenbaum, Mathew G.},
  year = {2011},
  volume = {I},
  publisher = {{Univ of Minnesota Press}},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/YAJH3ZLX/c0b0a8ee-95f0-4a9c-9451-e8ad168e3db5.html}
}

@inproceedings{kitagawaDatalogySemiologyEstablishing1981,
  title = {Datalogy and Semiology in Establishing Knowledgeinformation Processing Systems in Researches, {{The Second Intern}}},
  booktitle = {{{HBDS Seminar}}},
  author = {Kitagawa, T.},
  year = {1981},
  pages = {9--13}
}

@article{klineCyberneticsManagementScience,
  title = {Cybernetics , {{Management Science}} , and {{Technology Policy}}},
  author = {Kline, Ronald R},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/798ZGNT9/Cybernetics , Management Science , and Technology Policy_Unknown.pdf}
}

@article{kroeberStructureFunctionPattern1943,
  title = {Structure, {{Function}} and {{Pattern}} in {{Biology}} and {{Anthropology}}},
  author = {Kroeber, A. L.},
  year = {1943},
  journal = {The Scientific Monthly},
  volume = {56},
  number = {2},
  pages = {105--113},
  publisher = {{American Association for the Advancement of Science}},
  issn = {0096-3771},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JUX4K3JA/Kroeber - 1943 - Structure, Function and Pattern in Biology and Ant.pdf}
}

@article{kuonenDataMiningStatistics2004,
  title = {Data Mining and {{Statistics}}: {{What}} Is the Connection?},
  shorttitle = {Data Mining and {{Statistics}}},
  author = {Kuonen, Diego},
  year = {2004},
  journal = {The Data Administration Newsletter},
  volume = {30},
  pages = {1--6},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NJVNCSR9/Kuonen - 2004 - Data mining and Statistics What is the connection.pdf}
}

@article{kusnetzkyWhatBigData2010,
  title = {What Is "{{Big Data}}?"},
  author = {Kusnetzky, Dan},
  year = {2010},
  month = feb,
  journal = {ZDNet},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/G5NTEDFQ/1708.html}
}

@article{laddNonRealTimeSimulationSAGE1959,
  title = {A {{Non-Real-Time Simulation}} of {{SAGE Tracking}} and {{BOMARC Guidance}}},
  author = {Ladd, D. W. and Wolf, E. W.},
  year = {1959},
  month = mar,
  journal = {IRE Transactions on Electronic Computers},
  volume = {EC-8},
  number = {1},
  pages = {36--41},
  issn = {0367-9950},
  doi = {10.1109/TEC.1959.5222759},
  abstract = {The addition of facilities to the SAGE system for control of a new defensive weapon, such as the BOMARC missile, requires extensive modifications to the SAGE computer program. To obtain a better understanding of BOMARC control problems, a program has been written for the IBM Type 704 computer to simulate the proposed employment of BOMARC in the SAGE system. Such a simulation is flexible enough to optimize and evaluate a large range of parameters. On three separate passes through the 704 (with tape storage of intermediate results) the program simulates radar, target, and missile performance, as well as SAGE tracking and missile guidance. A fourth program presents the desired output data in the form of frequency distributions and detailed results pertaining to selected target or missile tracks.},
  keywords = {Computational modeling,Computer simulation,Employment,Frequency,Missiles,Packaging,Radar tracking,System testing,Target tracking,Weapons},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EC5AR9X4/Ladd and Wolf - 1959 - A Non-Real-Time Simulation of SAGE Tracking and BO.pdf;/Users/rca2t1/Dropbox/Zotero/storage/VQHR6QGD/5222759.html}
}

@techreport{laemmelPatternRecognitionDetection1963,
  title = {Pattern {{Recognition}} and {{Detection}} by {{Machine}}},
  author = {Laemmel, Arthur E.},
  year = {1963},
  month = mar,
  institution = {{POLYTECHNIC INST OF BROOKLYN NY MICROWAVE RESEARCH INST}},
  abstract = {Pattern recognition is considered generally, but emphasis is placed on the following points optimum estimation of statistical parameters so as to minimize the probability of incorrect classification, non-Gaussian and non-stationary situations, pattern detection in a continuing time series and calculation of error probabilities. Some of the work is specifically directed toward the problem of radio station recognition. A design procedure for pattern recognizing machines is suggested which uses results from this report and other referenced sources.},
  chapter = {Technical Reports},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4UB7AAQN/Laemmel - 1963 - Pattern Recognition and Detection by Machine.pdf;/Users/rca2t1/Dropbox/Zotero/storage/CMEAI8KG/AD0418387.html}
}

@article{laney3dDataManagement2001,
  title = {3d {{Data Management}}: {{Controlling Data Volume}}, {{Velocity}} and {{Variety}}},
  shorttitle = {3d {{Data Management}}},
  author = {Laney, Doug},
  year = {2001},
  journal = {META Group Research Note},
  volume = {6},
  pages = {70},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/38RWYKIE/Laney - 2001 - 3D data management Controlling data volume, veloc.pdf}
}

@misc{laneyDefiningDifferentiatingRole2012,
  title = {Defining and {{Differentiating}} the {{Role}} of the {{Data Scientist}}},
  author = {Laney, Doug},
  year = {2012},
  month = mar,
  journal = {Gartner},
  howpublished = {https://web.archive.org/web/20120327163714/http://blogs.gartner.com/doug-laney/defining-and-differentiating-the-role-of-the-data-scientist/},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/GQB7KL88/defining-and-differentiating-the-role-of-the-data-scientist.html}
}

@misc{laneyEmergingRoleData2012,
  title = {Emerging {{Role}} of the {{Data Scientist}} and the {{Art}} of {{Data Science}}},
  author = {Laney, Douglas and Kart, Lisa},
  year = {2012},
  month = mar,
  abstract = {Data scientists can be invaluable in generating insights, especially from "big data;" but their unique combination of technical and business skills, together with their heightened demand, makes them difficult to find or cultivate.},
  howpublished = {https://web.archive.org/web/20130115192221/http://www.gartner.com/DisplayDocument?ref=clientFriendlyUrl\&id=1955615},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3EPMGFRQ/DisplayDocument.html}
}

@article{langenkampHowBecomeData2014,
  title = {How to {{Become}} a {{Data Scientist}}},
  author = {Langenkamp, Julie},
  year = {2014},
  month = oct,
  journal = {Information Management},
  volume = {Vol.1, No.1},
  publisher = {{SourceMedia, Inc.}},
  abstract = {In an interview with Information Management, data scientist Irmak Sirer discusses the realities and necessities of data science. Sirer is instructing a 12-week data science boot camp offered through a partnership between test preparation organization Kaplan and Chicago-based data science and design firm Datascope. Boot camps, he explains, are one of the three main routes people can choose to begin a data science career. In an interview with , data scientist Irmak Sirer discusses the realities and necessities of data science. Sirer is also instructing a 12-week data science boot camp offered through a partnership between test preparation organization Kaplan and Chicago-based data science and design firm Datascope. Boot camps, he explains, are one of the three main routes people can choose to begin a data science career.},
  chapter = {News},
  copyright = {\textcopyright{} 2014 Information Management and SourceMedia, Inc. All rights reserved.},
  langid = {english}
}

@misc{laoBeginnerGuideData2019,
  title = {A {{Beginner}}'s {{Guide}} to the {{Data Science Pipeline}}},
  author = {Lao, Randy},
  year = {2019},
  month = dec,
  journal = {Medium},
  abstract = {``Believe it or not, you are no different than Data. Put yourself into Data's shoes and you'll see why.''},
  howpublished = {https://towardsdatascience.com/a-beginners-guide-to-the-data-science-pipeline-a4904b2d8ad3},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HW55BE9L/a-beginners-guide-to-the-data-science-pipeline-a4904b2d8ad3.html}
}

@misc{leekDataScienceOnly2013,
  title = {Data {{Science Only Poses}} a {{Threat}} to (Bio)Statistics {{If We Don}}'t {{Adapt}}},
  author = {Leek, Jeff},
  year = {2013},
  month = apr,
  journal = {Simply Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DYEZ4A64/data-science-only-poses-a-threat-to-biostatistics-if-we-dont-adapt.html}
}

@article{leePostcardsPlanetGoogle2002,
  title = {Postcards {{From Planet Google}}},
  author = {Lee, Jennifer 8},
  year = {2002},
  month = nov,
  journal = {The New York Times},
  issn = {0362-4331},
  abstract = {Popular search engine Google tracks user queries to create snapshot of trends; finds that, despite geographic and ethnic diversity, people are spending much of their time thinking about same things; photos (M)},
  chapter = {Technology},
  langid = {american},
  keywords = {Computers and the Internet,GOOGLE},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/BEB63UBK/postcards-from-planet-google.html}
}

@article{leong-hongTechnicalProfileSeven1977,
  title = {Technical {{Profile}} of {{Seven Data Element Dictionary}}/{{Directory Systems}}},
  author = {{Leong-hong}, B. and Marron, B.},
  year = {1977},
  month = feb,
  langid = {english},
  annotation = {Last Modified: 2008-10-16T14:10-04:00},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RRETV7BW/Leong-hong and Marron - 1977 - Technical Profile of Seven Data Element Dictionary.pdf;/Users/rca2t1/Dropbox/Zotero/storage/BLEPWGJ3/technical-profile-seven-data-element-dictionarydirectory-systems.html}
}

@book{leskovecMiningMassiveDatasets2020,
  title = {Mining of {{Massive Datasets}}: {{Pattern}} Recognition and Machine Learning},
  author = {Leskovec, Jure and Rajaramand, Anand and Ullman, Jeffrey David},
  year = {2020},
  edition = {3rd},
  publisher = {{Cambridge University Press}},
  address = {{New York}},
  abstract = {Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.},
  isbn = {978-1-108-47634-8},
  langid = {english},
  keywords = {textbook},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/963PPNMF/mining-massive-datasets-3rd-edition.html;/Users/rca2t1/Dropbox/Zotero/storage/KBE256QS/www.mmds.org.html}
}

@misc{lev-ramDataScientistHot2011,
  title = {Data Scientist: {{The}} Hot New Gig in Tech},
  shorttitle = {Data Scientist},
  author = {{Lev-Ram}, Michal},
  year = {2011},
  month = sep,
  journal = {Fortune},
  abstract = {Companies that want to make sense of all their bits and bytes are hiring so-called data scientists \textendash{} if they can find any. FORTUNE \textemdash{} The unemployment rate in the U.S. continues to be abysmal (9.1\% in July), but the tech world has spawned a new kind of highly skilled, nerdy-cool job that companies are [\ldots ]},
  howpublished = {https://fortune.com/2011/09/06/data-scientist-the-hot-new-gig-in-tech/},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/5FAFKPFG/data-scientist-the-hot-new-gig-in-tech.html}
}

@book{levi-straussIntroductionWorkMarcel1987,
  title = {Introduction to the {{Work}} of {{Marcel Mauss}}},
  author = {{Levi-Strauss}, Claude},
  year = {1987},
  month = jan,
  publisher = {{Routledge}},
  address = {{London}},
  abstract = {First published in 1987. Routledge is an imprint of Taylor \& Francis, an informa company.},
  isbn = {978-0-415-15158-0},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ICKWSLU5/Levi-Strauss - 1987 - Introduction to the Work of Marcel Mauss.pdf}
}

@book{levyPlexHowGoogle2012,
  title = {In {{The Plex}}: {{How Google Thinks}}, {{Works}}, and {{Shapes Our Lives}}},
  shorttitle = {In {{The Plex}}},
  author = {Levy, Steven and Ganser, L. J.},
  year = {2012},
  month = apr,
  edition = {Unabridged edition},
  publisher = {{Brilliance Audio}},
  abstract = {Few companies in history have ever been as successful and as admired as Google, the company that has transformed the Internet and become an indispensable part of our lives. How has Google done it? Veteran technology reporter Steven Levy was granted unprecedented access to the company, and in this revelatory book he takes readers inside Google headquarters \textemdash{} the Googleplex \textemdash{} to show how Google works. While they were still students at Stanford, Google cofounders Larry Page and Sergey Brin revolutionized Internet search. They followed this brilliant innovation with another, as two of Google's earliest employees found a way to do what no one else had: make billions of dollars from Internet advertising. With this cash cow (until Google's IPO nobody other than Google management had any idea how lucrative the company's ad business was), Google was able to expand dramatically and take on other transformative projects: more efficient data centers, open-source cell phones, free Internet video (YouTube), cloud computing, digitizing books, and much more. The key to Google's success in all these businesses, Levy reveals, is its engineering mind-set and adoption of such Internet values as speed, openness, experimentation, and risk taking. After its unapologetically elitist approach to hiring, Google pampers its engineers \textemdash{} free food and dry cleaning, on-site doctors and masseuses \textemdash{} and gives them all the resources they need to succeed. Even today, with a workforce of more than 23,000, Larry Page signs off on every hire. But has Google lost its innovative edge? It stumbled badly in China\textemdash Levy discloses what went wrong and how Brin disagreed with his peers on the China strategy\textemdash and now with its newest initiative, social networking, Google is chasing a successful competitor for the first time. Some employees are leaving the company for smaller, nimbler start-ups. Can the company that famously decided not to be evil still compete? No other book has ever turned Google inside out as Levy does with In the Plex.},
  isbn = {978-1-4558-7572-6},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9IIQHICQ/Steven Levy - In The Plex_ How Google Thinks, Works, and Shapes Our Lives-Simon & Schuster (2011).pdf;/Users/rca2t1/Dropbox/Zotero/storage/LNYX8D35/Steven Levy - In The Plex_ How Google Thinks, Works, and Shapes Our Lives-Simon & Schuster Adult Publishing Group (2011).epub}
}

@book{LibraryJournal1977,
  title = {Library {{Journal}}},
  year = {1977},
  publisher = {{Library Journal}},
  googlebooks = {tl88AQAAIAAJ},
  langid = {english}
}

@book{LibraryJournal1978,
  title = {Library {{Journal}}},
  year = {1978},
  volume = {103},
  publisher = {{R.R. Bowker Company}},
  abstract = {Includes, beginning Sept. 15, 1954 (and on the 15th of each month, Sept.-May) a special section: School library journal, ISSN 0000-0035, (called Junior libraries, 1954-May 1961). Also issued separately.},
  googlebooks = {uNjgAAAAMAAJ},
  langid = {english}
}

@book{lideCODATA45Years2012,
  title = {{{CODATA}} @ 45 {{Years}}: {{The Story}} of the {{ICSU Committee}} on {{Data}} for {{Science}} and {{Technology}} ({{CODATA}}) from 1966 to 2010},
  author = {Lide, David R. and Wood, Gordon H.},
  year = {2012},
  publisher = {{CODATA}},
  address = {{Paris, France}},
  isbn = {978-0-9917424-0-0},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/24NGUB73/Lide and Wood - CODATA @ 45 Years The Story of the ICSU Committee.pdf;/Users/rca2t1/Dropbox/Zotero/storage/A6LYIT2H/CODATA_45_Years.html}
}

@misc{lincolnlaboratorySAGESemiAutomaticGround2018,
  title = {{{SAGE}}: {{Semi-Automatic Ground Environment Air Defense System}} | {{MIT Lincoln Laboratory}}},
  author = {{Lincoln Laboratory}},
  year = {2018},
  month = jul,
  journal = {Lincoln Laboratory, MIT},
  howpublished = {https://www.ll.mit.edu/about/history/sage-semi-automatic-ground-environment-air-defense-system},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ZVYGIWVZ/SAGE Semi-Automatic Ground Environment Air Defens.pdf;/Users/rca2t1/Dropbox/Zotero/storage/J8TX9VUC/sage-semi-automatic-ground-environment-air-defense-system.html}
}

@article{liuUseDataScience2017,
  title = {The Use of Data Science for Education: {{The}} Case of Social-Emotional Learning},
  shorttitle = {The Use of Data Science for Education},
  author = {Liu, Ming-Chi and Huang, Yueh-Min},
  year = {2017},
  journal = {Smart Learning Environments},
  volume = {4},
  number = {1},
  pages = {1},
  publisher = {{Springer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/5R77LHM6/s40561-016-0040-4.html}
}

@article{lohrBigDataScientistsJanitor2014,
  title = {For {{Big-Data Scientists}}, `{{Janitor Work}}' {{Is Key Hurdle}} to {{Insights}}},
  author = {Lohr, Steve},
  year = {2014},
  month = aug,
  journal = {The New York Times},
  issn = {0362-4331},
  abstract = {The analysis of giant data sets promises unique business insights, but much manual effort is still required to prepare the information for parsing.},
  chapter = {Technology},
  langid = {american},
  keywords = {ClearStory Data,Data-Mining and Database Marketing,Iodine Inc,Lohr; Steve,Paxata Inc,Start-ups},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/GMP4W3HN/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html}
}

@article{lohrStudyMeasuresChatter2009,
  title = {Study {{Measures}} the {{Chatter}} of the {{News Cycle}}},
  author = {Lohr, Steve},
  year = {2009},
  month = jul,
  journal = {The New York Times},
  issn = {0362-4331},
  abstract = {Findings suggested traditional news sites were typically ahead of blogs by two and a half hours when a story line spread.},
  chapter = {Technology},
  langid = {american},
  keywords = {Blogs and Blogging (Internet),Computers and the Internet,News and News Media,Social Networking (Internet)}
}

@article{lohrSureBigData2012,
  title = {Sure, {{Big Data Is Great}}. {{But So Is Intuition}}.},
  author = {Lohr, Steve},
  year = {2012},
  month = dec,
  journal = {The New York Times},
  issn = {0362-4331},
  abstract = {It is easier than ever to measure and monitor people and machines, but the technology of Big Data is not without its shortcomings.},
  chapter = {Technology},
  langid = {american},
  keywords = {Artificial Intelligence,Computers and the Internet,Data Storage,Data-Mining and Database Marketing,Massachusetts Institute of Technology,Mathematics,Science and Technology},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/AV3J3JAX/big-data-is-great-but-dont-forget-intuition.html}
}

@article{lohrTechBoomsWorkers2015,
  title = {As {{Tech Booms}}, {{Workers Turn}} to {{Coding}} for {{Career Change}}},
  author = {Lohr, Steve},
  year = {2015},
  month = jul,
  journal = {The New York Times},
  issn = {0362-4331},
  abstract = {Schools that offer accelerated training in digital skills are drawing more and more ``career changers,'' and graduates can make six-figure base salaries.},
  chapter = {Technology},
  langid = {american},
  keywords = {Careers and Professions,Colleges and Universities,Computers and the Internet,Hiring and Promotion,Labor and Jobs,Lohr; Steve,Vocational Training,Wages and Salaries}
}

@article{lohrTodayGraduateJust2009,
  title = {For {{Today}}'s {{Graduate}}, {{Just One Word}}: {{Statistics}}},
  shorttitle = {For {{Today}}'s {{Graduate}}, {{Just One Word}}},
  author = {Lohr, Steve},
  year = {2009},
  month = aug,
  journal = {The New York Times},
  issn = {0362-4331},
  abstract = {With the explosion of digital data, statisticians can earn \$125,000 in their first year after getting a doctorate.},
  chapter = {Technology},
  langid = {american},
  keywords = {Computers and the Internet,Google Inc,Mathematics,Research,Statistics}
}

@article{lorenzaUnderstandingEmbodiedSituated2011,
  title = {Understanding as an Embodied, Situated and Sequential Achievement in Interaction},
  author = {Lorenza, Mondada},
  year = {2011},
  journal = {Journal of Pragmatics},
  volume = {43},
  number = {2},
  pages = {542--552},
  issn = {0378-2166},
  doi = {10.1016/j.pragma.2010.08.019},
  abstract = {This paper aims at outlining the sequential, situated and embodied dimensions of understanding in interaction. This perspective on understanding, originating in Ryle and Wittgenstein, further developed by ethnomethodology and conversation analysis, focuses on the orderly unfolding of sequences of actions in time. It also focuses on understanding as a collective achievement, publicly displayed and interactively oriented to within the production and the monitoring of action. Its accountability is built through a plurality of displays, claiming and demonstrating understanding, thanks to the mobilization of linguistic and embodied resources at specific sequential positions. The paper discusses evidences of understanding as they are publicly displayed by the participants in interaction: It both offers a discussion of the ethnomethodological and conversation analytic literature, and an analysis an empirical case, focusing on the interplay of embodied and sequential features in the production and monitoring of understanding.},
  keywords = {Accountability,Conversation analysis,Embodiment,Ethnomethodology,Multimodality,Sequentiality,Social interaction,Time,Understanding},
  annotation = {January 2011},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/MX4Q7NGI/Lorenza - 2011 - Understanding as an embodied, situated and sequent.pdf;/Users/rca2t1/Dropbox/Zotero/storage/3TGTVSUE/Lorenza - 2011 - Understanding as an embodied, situated and sequent.html}
}

@book{loukidesWhatDataScience2011,
  title = {What {{Is Data Science}}?},
  author = {Loukides, Mike},
  year = {2011},
  publisher = {{O'Reilly Media, Inc.}},
  abstract = {We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that...},
  isbn = {978-1-4493-3608-0},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/J8WQS5SP/9781449336080.html}
}

@book{loukidesWhatDataScience2011a,
  title = {What {{Is Data Science}}?},
  author = {Loukides, Mike},
  year = {2011},
  month = apr,
  publisher = {{"O'Reilly Media, Inc."}},
  abstract = {We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products.},
  isbn = {978-1-4493-3609-7},
  langid = {english},
  keywords = {Computers / Data Modeling \& Design}
}

@book{loukidesWhatDataScience2011b,
  title = {What Is Data Science?},
  author = {Loukides, Mike},
  year = {2011},
  publisher = {{" O'Reilly Media, Inc."}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/34M339AB/books.html}
}

@article{lovellDataMining1983,
  title = {Data {{Mining}}},
  author = {Lovell, Michael C.},
  year = {1983},
  journal = {The Review of Economics and Statistics},
  volume = {65},
  number = {1},
  pages = {1--12},
  publisher = {{The MIT Press}},
  issn = {0034-6535},
  doi = {10.2307/1924403},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ME68FX9U/Lovell - 1983 - Data Mining.pdf}
}

@article{loweAutomaticComputationAid1951,
  title = {Automatic {{Computation}} as an {{Aid}} in {{Aeronautical Engineering}}},
  author = {Lowe, John},
  year = {1951},
  journal = {Mathematics Magazine},
  volume = {25},
  number = {1},
  pages = {37--42},
  publisher = {{Mathematical Association of America}},
  issn = {0025-570X},
  doi = {10.2307/3029572},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HXLQ322M/Lowe - 1951 - Automatic Computation as an Aid in Aeronautical En.pdf}
}

@article{loyEmbracingDataScience2016,
  title = {Embracing {{Data Science}}},
  author = {Loy, Adam},
  year = {2016},
  month = jul,
  journal = {arXiv:1607.00858 [cs, stat]},
  eprint = {1607.00858},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {Statistics is running the risk of appearing irrelevant to today's undergraduate students. Today's undergraduate students are familiar with data science projects and they judge statistics against what they have seen. Statistics, especially at the introductory level, should take inspiration from data science so that the discipline is not seen as somehow lesser than data science. This article provides a brief overview of data science, outlines ideas for how introductory courses could take inspiration from data science, and provides a reference to materials for developing stand-alone data science courses.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - General Literature,Statistics - Other Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FNHPTAQA/Loy - 2016 - Embracing Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/QURMZV5T/1607.html}
}

@incollection{luxburgStatisticalLearningTheory2011,
  title = {Statistical {{Learning Theory}}: {{Models}}, {{Concepts}}, and {{Results}}},
  shorttitle = {Statistical {{Learning Theory}}},
  booktitle = {Handbook of the {{History}} of {{Logic}}},
  author = {von Luxburg, Ulrike and Sch{\"o}lkopf, Bernhard},
  editor = {Gabbay, Dov M. and Hartmann, Stephan and Woods, John},
  year = {2011},
  month = jan,
  series = {Inductive {{Logic}}},
  volume = {10},
  pages = {651--706},
  publisher = {{North-Holland}},
  doi = {10.1016/B978-0-444-52936-7.50016-1},
  abstract = {Statistical learning theory is regarded as one of the most beautifully developed branches of artificial intelligence. It provides the theoretical basis for many of today's machine learning algorithms. The theory helps to explore what permits to draw valid conclusions from empirical data. This chapter provides an overview of the key ideas and insights of statistical learning theory. The statistical learning theory begins with a class of hypotheses and uses empirical data to select one hypothesis from the class. If the data generating mechanism is benign, then it is observed that the difference between the training error and test error of a hypothesis from the class is small. The statistical learning theory generally avoids metaphysical statements about aspects of the true underlying dependency, and thus is precise by referring to the difference between training and test error. The chapter also describes some other variants of machine learning.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/WDD5LSQ6/Luxburg and Schölkopf - 2011 - Statistical Learning Theory Models, Concepts, and.pdf;/Users/rca2t1/Dropbox/Zotero/storage/2ZQIP6P5/B9780444529367500161.html;/Users/rca2t1/Dropbox/Zotero/storage/HSFC5WHR/B9780444529367500161.html}
}

@article{lynchBigDataHow2008,
  title = {Big Data: {{How}} Do Your Data Grow?},
  shorttitle = {Big Data},
  author = {Lynch, Clifford},
  year = {2008},
  month = sep,
  journal = {Nature},
  volume = {455},
  number = {7209},
  pages = {28--29},
  issn = {0028-0836},
  doi = {10.1038/455028a},
  abstract = {Scientists need to ensure that their results will be managed for the long haul. Maintaining data takes big organization, says Clifford Lynch.},
  copyright = {\textcopyright{} 2008 Nature Publishing Group},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/68XV9B37/455028a.pdf;/Users/rca2t1/Dropbox/Zotero/storage/JM4MBGHC/455028a.html}
}

@article{lyonDealingDataRoles2007,
  title = {Dealing with {{Data}}: {{Roles}}, {{Rights}}, {{Responsibilities}} and {{Relationships}}. {{Consultancy Report}}.},
  shorttitle = {Dealing with {{Data}}},
  author = {Lyon, L.},
  year = {2007},
  month = jun,
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SX8HDFYD/Lyon - 2007 - Dealing with Data Roles, Rights, Responsibilities.pdf;/Users/rca2t1/Dropbox/Zotero/storage/8RIGLVVT/dealing-with-data-roles-rights-responsibilities-and-relationships.html}
}

@misc{manovichCourseScheduleBig,
  title = {Course {{Schedule}}: {{Big Data}}, {{Visualization}}, and {{Digital Humanities}}. {{Spring}} 2013, {{GC CUNY}}.},
  author = {Manovich, Lev},
  howpublished = {https://docs.google.com/document/d/1Mf6qlFNqwLLvVPlWAOy0eK3HfbgLlJgIJSyEImM4uSg/preview?sle=true\#},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JD8H9JNX/preview.html}
}

@misc{MansfieldAmendment,
  title = {The {{Mansfield Amendment}}},
  journal = {NSF: NSB 50th Anniversary},
  howpublished = {https://www.nsf.gov/nsb/documents/2000/nsb00215/nsb50/1970/mansfield.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/5HN4EBIQ/mansfield.html}
}

@techreport{manyikaBigDataNext2011,
  title = {Big {{Data}}: {{The Next Frontier}} for {{Innovation}}, {{Competition}}, and {{Productivity}}},
  shorttitle = {Big {{Data}}},
  author = {Manyika, James and Chui, Michael and Brown, Brad and Bughin, Jacques and Dobbs, Richard and Roxburgh, Charles and Byers, Angela Hung},
  year = {2011},
  month = may,
  institution = {{Mckinsey \& Company}},
  abstract = {Big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus--as long as the right policies and enablers are in place.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VF79Q3RU/Manyika et al. - 2011 - Big data The next frontier for innovation, compet.pdf;/Users/rca2t1/Dropbox/Zotero/storage/WXUNG3AD/big-data-the-next-frontier-for-innovation.html}
}

@article{mariscalSurveyDataMining2010,
  title = {A {{Survey}} of {{Data Mining}} and {{Knowledge Discovery Process Models}} and {{Methodologies}}},
  author = {Mariscal, Gonzalo and Marb{\'a}n, {\'O}scar and Fern{\'a}ndez, Covadonga},
  year = {2010},
  month = jun,
  journal = {The Knowledge Engineering Review},
  volume = {25},
  number = {2},
  pages = {137--166},
  publisher = {{Cambridge University Press}},
  issn = {1469-8005, 0269-8889},
  doi = {10.1017/S0269888910000032},
  abstract = {Up to now, many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success. In this paper, we describe the most used (in industrial and academic projects) and cited (in scientific literature) data mining and knowledge discovery methodologies and process models, providing an overview of its evolution along data mining and knowledge discovery history and setting down the state of the art in this topic. For every approach, we have provided a brief description of the proposed knowledge discovery in databases (KDD) process, discussing about special features, outstanding advantages and disadvantages of every approach. Apart from that, a global comparative of all presented data mining approaches is provided, focusing on the different steps and tasks in which every approach interprets the whole KDD process. As a result of the comparison, we propose a new data mining and knowledge discovery process named refined data mining process for developing any kind of data mining and knowledge discovery project. The refined data mining process is built on specific steps taken from analyzed approaches.},
  langid = {english},
  keywords = {data mining,history of data science,KDD},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/AXGZHR3D/Mariscal et al. - 2010 - A Survey of Data Mining and Knowledge Discovery Pr.pdf;/Users/rca2t1/Dropbox/Zotero/storage/Y9SDMEXY/C2EC780B41545D44AB7F8F7BCBA8D982.html}
}

@misc{marrBriefHistoryBig2015,
  type = {Organization {{Blog}}},
  title = {A {{Brief History}} of {{Big Data Everyone Should Read}}},
  author = {Marr, Bernard},
  year = {2015},
  month = feb,
  journal = {World Economic Forum},
  abstract = {The World Economic Forum is an independent international organization committed to improving the state of the world by engaging business, political, academic and other leaders of society to shape global, regional and industry agendas. Incorporated as a not-for-profit foundation in 1971, and headquartered in Geneva, Switzerland, the Forum is tied to no political, partisan or national interests.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/M8P2ECPQ/a-brief-history-of-big-data-everyone-should-read.html}
}

@misc{marsmanTukeyLamentAre2017,
  title = {Tukey's {{Lament}}: {{Are Statisticians Living}} a {{Lie}}?},
  shorttitle = {Tukey's {{Lament}}},
  author = {Marsman, Maarten},
  year = {2017},
  month = sep,
  journal = {JASP - Free and User-Friendly Statistical Software},
  abstract = {John Tukey is famous. In his youth he coined the term ``bit'' (as an abbreviation of ``binary information digit''), later he promoted exploratory data analysis, and throughout his entire life he worked on a broad range of statistical techniques, some\ldots{} Continue reading \textrightarrow},
  howpublished = {https://jasp-stats.org/2017/09/14/tukeys-lament-statisticians-living-lie/},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SFZ9EQYB/tukeys-lament-statisticians-living-lie.html}
}

@misc{masheyBigDataNext1998,
  type = {{{PowerPoint}}},
  title = {Big {{Data}} ... and the {{Next Wave}} of {{InfraStress}}},
  author = {Mashey, John},
  year = {1998},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/6AZBBEBW/mashey.pdf}
}

@misc{masonDataDrivenCreating2014,
  title = {Data {{Driven}}: {{Creating}} a {{Data Culture}}},
  shorttitle = {Data {{Driven}}},
  author = {Mason, Hilary, DJ Patil},
  year = {2014},
  month = dec,
  journal = {O'Reilly Media},
  abstract = {To succeed with data, businesses must develop a data culture.},
  howpublished = {https://www.oreilly.com/content/data-driven/},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/G6G2HVTF/data-driven.html}
}

@misc{masonTaxonomyDataScience2010,
  title = {A {{Taxonomy}} of {{Data Science}}},
  author = {Mason, Hilary and Wiggins, Christopher},
  year = {2010},
  month = sep,
  journal = {Dataists},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/C2GWUV6X/Mason and Wiggins - 2010 - A Taxonomy of Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/MKKKL2PM/a-taxonomy-of-data-science.html}
}

@book{massaVisualDataTransmission1964,
  title = {Visual {{Data Transmission}}},
  author = {Massa, Ronald J.},
  year = {1964},
  publisher = {{Air Force Cambridge Research Laboratories, Office of Aerospace Research, United States Air Force}},
  abstract = {Statistical coding and visual fidelity criterion approaches to the television bandwidth reduction problem are described and contrasted. A review of techniques based on both approaches is presented. A brief discussion of television signal properties and several television user applications is included. Emphasis is placed on bandwidth and/or power reduction through consideration and utilization of human visual summation properties. A class of reduced rate systems with random-scanning, noninformation bearing masks, and amplitude weighting are presented and discussed. (Author).},
  googlebooks = {YbV4iF0ugRUC},
  langid = {english}
}

@article{matheusSystemsKnowledgeDiscovery1993,
  title = {Systems for {{Knowledge Discovery}} in {{Databases}}},
  author = {Matheus, Christopher J. and Chan, Philip K. and {Piatetsky-Shapiro}, Gregory},
  year = {1993},
  journal = {IEEE Transactions on knowledge and data engineering},
  volume = {5},
  number = {6},
  pages = {903--913},
  publisher = {{IEEE}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/KHJIW874/250073.html}
}

@book{mayer-schonbergerBigDataRevolution2013,
  title = {Big {{Data}}: {{A Revolution}} That {{Will Transform}} How {{We Live}}, {{Work}}, and {{Think}}},
  shorttitle = {Big {{Data}}},
  author = {{Mayer-Sch{\"o}nberger}, Viktor and Cukier, Kenneth},
  year = {2013},
  publisher = {{Houghton Mifflin Harcourt}},
  abstract = {A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large.  Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak?  The key to answering these questions, and many more, is big data. "Big data" refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena--from the price of airline tickets to the text of millions of books--into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven't even done yet, based on big data's ability to predict our future behavior.  In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing. www.big-data-book.com},
  isbn = {978-0-544-00269-2},
  langid = {english},
  keywords = {Business \& Economics / Information Management,Science / General,Technology \& Engineering / General}
}

@article{mayuramaSecondCybernetics1963,
  title = {The Second Cybernetics},
  author = {Mayurama, Magoroh},
  year = {1963},
  journal = {American Scientist},
  volume = {51},
  pages = {164--79},
  annotation = {00014},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/I5A5MEEC/Mayurama - 1963 - The second cybernetics.pdf}
}

@book{mcewenManagementDataElements1974,
  title = {Management of {{Data Elements}} in {{Information Processing}}},
  author = {McEwen, Hazel E.},
  year = {1974},
  publisher = {{National Bureau of Standards, Department of Commerce}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/5TAQWYTJ/McEwen - 1974 - Management of data elements in information process.pdf}
}

@book{mcewenManagementDataElements1975,
  title = {Management of {{Data Elements}} in {{Information Processing}}},
  author = {McEwen, H.E.},
  year = {1975},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/79A293SP/McEwen - 1975 - Management of Data Elements in Information Process.pdf}
}

@article{mckenzieProbingSlowdownMaster2019,
  title = {Probing the Slowdown in Master's Degree Growth},
  author = {McKenzie, Lindsay},
  year = {2019},
  month = dec,
  journal = {Inside Higher Ed},
  abstract = {Analysis suggests projections of rapid growth in the master's degree market were vastly overstated.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2ACVKZYS/probing-slowdown-masters-degree-growth.html}
}

@article{mckinsey&companyHalVarianHow2009,
  title = {Hal {{Varian}} on {{How}} the {{Web Challenges Managers}}},
  author = {{McKinsey \& Company}},
  year = {2009},
  month = jan,
  journal = {McKinsey \& Company},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/IEKUQ8VB/hal-varian-on-how-the-web-challenges-managers.html}
}

@article{mcnamaraGreaterDataScience2017,
  title = {Greater {{Data Science}} at {{Baccalaureate Institutions}}},
  author = {McNamara, Amelia and Horton, Nicholas J. and Baumer, Benjamin S.},
  year = {2017},
  month = oct,
  journal = {arXiv:1710.08728 [stat]},
  eprint = {1710.08728},
  eprinttype = {arxiv},
  primaryclass = {stat},
  abstract = {Donoho's JCGS (in press) paper is a spirited call to action for statisticians, who he points out are losing ground in the field of data science by refusing to accept that data science is its own domain. (Or, at least, a domain that is becoming distinctly defined.) He calls on writings by John Tukey, Bill Cleveland, and Leo Breiman, among others, to remind us that statisticians have been dealing with data science for years, and encourages acceptance of the direction of the field while also ensuring that statistics is tightly integrated. As faculty at baccalaureate institutions (where the growth of undergraduate statistics programs has been dramatic), we are keen to ensure statistics has a place in data science and data science education. In his paper, Donoho is primarily focused on graduate education. At our undergraduate institutions, we are considering many of the same questions.},
  archiveprefix = {arXiv},
  keywords = {Statistics - Machine Learning,Statistics - Other Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TSFFH7QI/McNamara et al. - 2017 - Greater data science at baccalaureate institutions.pdf;/Users/rca2t1/Dropbox/Zotero/storage/ZZEJPAGB/1710.html}
}

@article{mcnicholasDataScience2019,
  title = {Data Science},
  author = {McNicholas, Paul D.},
  year = {2019},
  month = may,
  journal = {FACETS},
  publisher = {{Canadian Science Publishing 65 Auriga Drive, Suite 203, Ottawa, ON K2E 7W6}},
  doi = {10.1139/facets-2019-0005},
  abstract = {Data science},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FKCC88NT/McNicholas - 2019 - Data science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/AMVCKFQU/facets-2019-0005.html}
}

@misc{MembershipInformation,
  title = {Membership {{Information}}},
  journal = {National Consortium for Data Science},
  abstract = {Membership Information Membership in the National Consortium for Data Science is open to organizations in the business, academic, government and nonprofit sectors. Becoming a member gives your organization opportunities to: Promote for a national strategy focusing on data science and the challenges of data science. Collaborate with other members to address key challenges that span \ldots},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/C7AIP97D/membership-information.html}
}

@incollection{mendelsonFewNotesStatistical2003,
  title = {A {{Few Notes}} on {{Statistical Learning Theory}}},
  booktitle = {Advanced {{Lectures}} on {{Machine Learning}}: {{Machine Learning Summer School}} 2002 {{Canberra}}, {{Australia}}, {{February}} 11\textendash 22, 2002 {{Revised Lectures}}},
  author = {Mendelson, Shahar},
  editor = {Mendelson, Shahar and Smola, Alexander J.},
  year = {2003},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  pages = {1--40},
  publisher = {{Springer}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/3-540-36434-X_1},
  abstract = {In these notes our aim is to survey recent (and not so recent) results regarding the mathematical foundations of learning theory. The focus in this article is on the theoretical side and not on the applicative one; hence, we shall not present examples which may be interesting from the practical point of view but have little theoretical significance. This survey is far from being complete and it focuses on problems the author finds interesting (an opinion which is not necessarily shared by the majority of the learning community). Relevant books which present a more evenly balanced approach are, for example 1, 4, 34, 35},
  isbn = {978-3-540-36434-4},
  langid = {english},
  keywords = {Absolute Constant,Boolean Function,Empirical Measure,Empirical Process,Reproduce Kernel Hilbert Space},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/Y6Z5FJ2W/Mendelson - 2003 - A Few Notes on Statistical Learning Theory.pdf}
}

@inproceedings{menziesDataScienceSoftware2013,
  title = {Data Science for Software Engineering},
  booktitle = {2013 35th {{International Conference}} on {{Software Engineering}} ({{ICSE}})},
  author = {Menzies, Tim and Kocaguneli, Ekrem and Peters, Fayola and Turhan, Burak and Minku, Leandro L.},
  year = {2013},
  month = may,
  pages = {1484--1486},
  issn = {1558-1225},
  doi = {10.1109/ICSE.2013.6606752},
  abstract = {Target audience: Software practitioners and researchers wanting to understand the state of the art in using data science for software engineering (SE). Content: In the age of big data, data science (the knowledge of deriving meaningful outcomes from data) is an essential skill that should be equipped by software engineers. It can be used to predict useful information on new projects based on completed projects. This tutorial offers core insights about the state-of-the-art in this important field. What participants will learn: Before data science: this tutorial discusses the tasks needed to deploy machine-learning algorithms to organizations (Part 1: Organization Issues). During data science: from discretization to clustering to dichotomization and statistical analysis. And the rest: When local data is scarce, we show how to adapt data from other organizations to local problems. When privacy concerns block access, we show how to privatize data while still being able to mine it. When working with data of dubious quality, we show how to prune spurious information. When data or models seem too complex, we show how to simplify data mining results. When data is too scarce to support intricate models, we show methods for generating predictions. When the world changes, and old models need to be updated, we show how to handle those updates. When the effect is too complex for one model, we show how to reason across ensembles of models. Pre-requisites: This tutorial makes minimal use of maths of advanced algorithms and would be understandable by developers and technical managers.},
  keywords = {Data mining,Data models,Educational institutions,Predictive models,Software,Software engineering,Tutorials},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/GWV3H7YX/Menzies et al. - 2013 - Data science for software engineering.pdf;/Users/rca2t1/Dropbox/Zotero/storage/3RMVM2HI/6606752.html}
}

@misc{milesWhatDataScientists2018,
  title = {What Do Data Scientists at {{Google}} Know That Most Other Data Scientists Don't?},
  author = {Miles, Jeremy},
  year = {2018},
  month = oct,
  journal = {Quora},
  howpublished = {https://www.quora.com/What-do-data-scientists-at-Google-know-that-most-other-data-scientists-dont/answer/Jeremy-Miles?ch=10\&share=99816136\&srid=3thZX},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ENK342YB/Jeremy-Miles.html}
}

@article{millerCognitiveRevolutionHistorical2003,
  title = {The Cognitive Revolution: A Historical Perspective},
  shorttitle = {The Cognitive Revolution},
  author = {Miller, George A},
  year = {2003},
  month = mar,
  journal = {Trends in Cognitive Sciences},
  volume = {7},
  number = {3},
  pages = {141--144},
  issn = {1364-6613},
  doi = {10.1016/S1364-6613(03)00029-9},
  abstract = {Cognitive science is a child of the 1950s, the product of a time when psychology, anthropology and linguistics were redefining themselves and computer science and neuroscience as disciplines were coming into existence. Psychology could not participate in the cognitive revolution until it had freed itself from behaviorism, thus restoring cognition to scientific respectability. By then, it was becoming clear in several disciplines that the solution to some of their problems depended crucially on solving problems traditionally allocated to other disciplines. Collaboration was called for: this is a personal account of how it came about.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FEXDSJ7F/Miller - 2003 - The cognitive revolution a historical perspective.pdf;/Users/rca2t1/Dropbox/Zotero/storage/DB3SERPM/S1364661303000299.html}
}

@article{millerCRIWesternNew,
  title = {{{CRI}}: {{A Western New York Computational}} and {{Data Science Grid}}},
  shorttitle = {{{CRI}}},
  author = {Miller, Russ and McCourt, Mary and Farian, Homma and Weeks, Charles and Green, Mark},
  abstract = {CRI: Developing the Lemur Toolkit into a Community Resource Proposal: CNS 0454114 PI: Russ Miller Institution: SUNY at BuffaloThis project builds on the Western New York Bio-informatics and Life Scien...},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9Z3SDWGV/CNS-0454114.html}
}

@article{millerDataScienceNumbers2013a,
  title = {Data {{Science}}: {{The Numbers}} of {{Our Lives}}},
  shorttitle = {Data {{Science}}},
  author = {Miller, Claire Cain},
  year = {2013},
  month = apr,
  journal = {The New York Times},
  issn = {0362-4331},
  abstract = {Big data, big money, big skill set now required. Universities are on it.},
  chapter = {Education},
  langid = {american},
  keywords = {Careers and Professions,College of Charleston,Colleges and Universities,Columbia University,Data Science,Data-Mining and Database Marketing,Miller; Claire Cain,University of San Francisco,University of Washington},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/527W7K62/universities-offer-courses-in-a-hot-new-field-data-science.html}
}

@article{millerMoreStatisticsVs2016,
  title = {More on {{Statistics}} vs {{Data Science}}},
  author = {Miller, Steve},
  year = {2016},
  month = dec,
  journal = {Information Management Blogs},
  abstract = {Statistical models, which emphasize inference driven from underlying or generative probability distributions, are concerned with both explanation and prediction, while ML obsesses on prediction.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/WZMDHNBN/more-on-statistics-vs-data-science-10030457-1.html}
}

@misc{millerSiteDataScientists2011,
  title = {A {{Site}} for {{Data Scientists}} to {{Prove Their Skills}} and {{Make Money}}},
  author = {Miller, Claire Cain},
  year = {2011},
  month = nov,
  journal = {Bits Blog},
  abstract = {Kaggle connects data scientists with organizations that need someone to extract meaning from their data, like insurance companies that want to know the likelihood that a patient will be hospitalized.},
  chapter = {Technology},
  langid = {american},
  keywords = {Big Data,Computers and the Internet,Contests and Prizes,Enterprise Computing,Internet,Start-ups},
  annotation = {Cad: 0},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/B6VQA37L/a-site-for-data-scientists-to-prove-their-skills-and-make-money.html}
}

@article{millsSituatedActionsVocabularies1940,
  title = {Situated {{Actions}} and {{Vocabularies}} of {{Motive}}},
  author = {Mills, C. Wright},
  year = {1940},
  month = dec,
  journal = {American Sociological Review},
  volume = {5},
  number = {6},
  pages = {904--913},
  issn = {0003-1224},
  doi = {10.2307/2084524},
  copyright = {Copyright \textcopyright{} 1940 American Sociological Association},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/R7A68HSB/Mills - 1940 - Situated Actions and Vocabularies of Motive.pdf}
}

@misc{mindellCyberneticsKnowledgeDomains2004,
  title = {Cybernetics: {{Knowledge Domains}} in {{Engineering Systems}}},
  author = {Mindell, David A.},
  year = {2004},
  publisher = {{JHU Press}},
  abstract = {Today, we associate the relationship between feedback, control, and computing with Norbert Wiener's 1948 formulation of cybernetics. But the theoretical and practical foundations for cybernetics, control engineering, and digital computing were laid earlier, between the two world wars. In Between Human and Machine: Feedback, Control, and Computing before Cybernetics, David A. Mindell shows how the modern sciences of systems emerged from disparate engineering cultures and their convergence during World War II. Mindell examines four different arenas of control systems research in the United States between the world wars: naval fire control, the Sperry Gyroscope Company, the Bell Telephone Laboratories, and Vannevar Bush's laboratory at MIT. Each of these institutional sites had unique technical problems, organizational imperatives, and working environments, and each fostered a distinct engineering culture. Each also developed technologies to represent the world in a machine.At the beginning of World War II, President Roosevelt established the National Defense Research Committee, one division of which was devoted to control systems. Mindell shows how the NDRC brought together representatives from the four pre-war engineering cultures, and how its projects synthesized conceptions of control, communications, and computing. By the time Wiener articulated his vision, these ideas were already suffusing through engineering. They would profoundly influence the digital world. As a new way to conceptualize the history of computing, this book will be of great interest to historians of science, technology, and culture, as well as computer scientists and theorists. Between Human and Machine: Feedback, Control, and Computing before Cybernetics},
  langid = {english},
  lccn = {0000},
  keywords = {Cybernetics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/H8FDDTUG/mindell-2000-cybernetics.pdf}
}

@book{mindellHumanMachineFeedback2002,
  title = {Between {{Human}} and {{Machine}}: {{Feedback}}, {{Control}}, and {{Computing Before Cybernetics}}},
  shorttitle = {Between {{Human}} and {{Machine}}},
  author = {Mindell, David A.},
  year = {2002},
  month = aug,
  publisher = {{JHU Press}},
  abstract = {Today, we associate the relationship between feedback, control, and computing with Norbert Wiener's 1948 formulation of cybernetics. But the theoretical and practical foundations for cybernetics, control engineering, and digital computing were laid earlier, between the two world wars. In Between Human and Machine: Feedback, Control, and Computing before Cybernetics, David A. Mindell shows how the modern sciences of systems emerged from disparate engineering cultures and their convergence during World War II. Mindell examines four different arenas of control systems research in the United States between the world wars: naval fire control, the Sperry Gyroscope Company, the Bell Telephone Laboratories, and Vannevar Bush's laboratory at MIT. Each of these institutional sites had unique technical problems, organizational imperatives, and working environments, and each fostered a distinct engineering culture. Each also developed technologies to represent the world in a machine.At the beginning of World War II, President Roosevelt established the National Defense Research Committee, one division of which was devoted to control systems. Mindell shows how the NDRC brought together representatives from the four pre-war engineering cultures, and how its projects synthesized conceptions of control, communications, and computing. By the time Wiener articulated his vision, these ideas were already suffusing through engineering. They would profoundly influence the digital world. As a new way to conceptualize the history of computing, this book will be of great interest to historians of science, technology, and culture, as well as computer scientists and theorists. Between Human and Machine: Feedback, Control, and Computing before Cybernetics},
  isbn = {978-0-8018-6895-5},
  langid = {english},
  keywords = {Computers / Data Processing,Computers / History,Science / History,Technology \& Engineering / Automation,Technology \& Engineering / Engineering (General),Technology \& Engineering / History,Technology \& Engineering / Mechanical},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/R2ASLW3G/Mindell - 2002 - Between Human and Machine Feedback, Control, and .pdf}
}

@article{mindellOceanCommonAmerican2003,
  title = {An {{Ocean}} in {{Common}}: {{American Naval Officers}}, {{Scientists}}, and the {{Ocean Environment}} (Review)},
  shorttitle = {An {{Ocean}} in {{Common}}},
  author = {Mindell, David A.},
  year = {2003},
  journal = {Technology and Culture},
  volume = {44},
  number = {2},
  pages = {437--439},
  issn = {1097-3729},
  doi = {10.1353/tech.2003.0082},
  annotation = {{$<$}p{$>$}Volume 44, Number 2, April 2003{$<$}/p{$>$}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/6GU737PD/Mindell - 2003 - An Ocean in Common American Naval Officers, Scien.pdf;/Users/rca2t1/Dropbox/Zotero/storage/7R8SU9BE/44.2mindell.html}
}

@article{mindellOpeningBlackBox2000,
  title = {Opening {{Black}}'s {{Box}}: {{Rethinking Feedback}}'s {{Myth}} of {{Origin}}},
  shorttitle = {Opening {{Black}}'s {{Box}}},
  author = {Mindell, David A.},
  year = {2000},
  journal = {Technology and Culture},
  volume = {41},
  number = {3},
  pages = {405--434},
  issn = {1097-3729},
  abstract = {In lieu of an abstract, here is a brief excerpt of the content:          Technology and Culture 41.3 (2000) 405-434 Figures The specific triumph of the technical imagination rested on the ability to dissociate lifting power from the arm and create a crane: to dissociate work from the action of men and animals and create the water-mill: to dissociate light from the combustion of wood and oil and create the electric lamp. --Lewis Mumford The engineer who embarks on the design of a feedback amplifier must be a creature of mixed emotions. --Hendrik Bode Like any modern episteme worthy of the name, the theory of feedback has a myth of origin. On a sunny August morning in 1927, Harold Black, a twenty-nine-year-old systems engineer, rode the Lackawanna ferry to work at the Bell Telephone Laboratories. Many Bell engineers lived in New Jersey, and on the early morning ferry rides across the Hudson to the Manhattan laboratories they frequently gathered on the forward deck. This morning Black stood alone, staring at the Statue of Liberty, and had an epiphany: "I suddenly realized that if I fed the amplifier output back to the input, in reverse phase, and kept the device from oscillating (singing, as we called it then), I would have exactly what I wanted: a means of canceling out the distortion in the output." As it happened, the New York Times that day contained a blank page, and Black sketched his idea, "a simple canonical diagram of a negative feedback amplifier plus the equations for the amplification with feedback." He rushed into work, asked a technician to wire up a prototype, and gave birth to a foundational circuit of modern electronics. This story has become enshrined as one of the central "flashes of insight" in electrical engineering in this century, periodically retold as an inspiration for engineers. A common textbook on control engineering reprints the story of Black's vision verbatim in the first chapter. At Bell Laboratories from 1927 to 1940, the legend goes, Black, Harry Nyquist, and Hendrik Bode laid the foundations of feedback control that engineers then applied to all types of closed-loop systems, from servomechanisms to thermostats, fire control systems to automatic computers. More than other contemporary narratives of control systems such as automatic pilots or servomechanisms, this story of feedback earned a place in engineering legend and college textbooks. It produced design methods and graphical techniques that carry their author's names (the Bode plot, the Nyquist diagram) and earned telephone engineering a claim to priority in feedback history. Feedback theory, moreover, formed the basis of cybernetics, systems theory, and a host of other post-World War II information sciences, so Black's invention is hailed as a foundation of the information age. Feedback is indeed a fundamental concept in twentieth-century technology, and the Bell Labs feedback theorists did lay critical foundations for it. But the origin myth effaces its sources. It skips over the inventors themselves and the ways in which their backgrounds and prior experience influenced their work. It reveals little about the concrete problems these men worked on when they produced their solutions. The story also removes feedback theory from its engineering culture, that of the telephone network between the world wars. Black's version also does not account for the relationship of his feedback amplifiers to prior traditions of governors and self-regulating machinery. Thus a reexamination of the sources is in order, retelling Black's legend not as a heroic tale but as the story of an engineer solving the technical problems of a particular place and time and trying to convince others to support his solutions. As it turns out, Black did not understand as much about feedback as he later recalled. To make his idea credible, he needed Nyquist's reformulation of the problem of stability and Bode's analysis outlining the tight constraints that a feedback amplifier had to meet. He also needed the Bell System. Negative-feedback amplifiers emerged from efforts to extend the telephone network across the continent, to increase the network's carrying capacity, and to make it work predictably in the face of changes in season, weather, and landscape -- from the context, that is, of building a large technical...},
  annotation = {{$<$}p{$>$}Volume 41, Number 3, July 2000{$<$}/p{$>$}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CNKJFKXH/Mindell - 2000 - Opening Black's Box Rethinking Feedback's Myth of.pdf;/Users/rca2t1/Dropbox/Zotero/storage/P5J2PGWG/41.3mindell.html}
}

@book{mindellWarTechnologyExperience2000,
  title = {War, Technology, and Experience Aboard the {{USS Monitor}}},
  author = {Mindell, David A.},
  year = {2000},
  publisher = {{JHU Press}},
  abstract = {In a familiar story, the USS Monitor battled the CSS Virginia (the armored and refitted USS Merrimack) at Hampton Roads in March of 1862. In War, Technology, and Experience aboard the USS Monitor, David A. Mindell adds a new perspective to the story as he explores how mariners -- fighting "blindly" below the waterline -- lived and coped with the metal monster they called the "iron coffin." Mindell shows how the iron warship emerged as an idea and became practicable, how building it drew upon and forced changes in contemporary manufacturing technology, and how the vessel captured the nineteenth-century American popular and literary imaginations.Combining technical, personal, administrative, and literary analysis, Mindell examines the experience of the men aboard the Monitor and their reactions to the thrills and dangers that accompanied the new machine. The invention surrounded men with iron and threatened their heroism, their self-image as warriors, even their lives. Mindell also examines responses to this strange new warship by Nathaniel Hawthorne and Herman Melville, who prophetically saw in the Civil War a portent of the mechanized warfare of the future. The story of the Monitor shows how technology changes not only the tools but also the very experience of combat, generating effects that are still felt today in the era of "smart bombs" and pushbutton wars."We find new significance in the otherwise well-known history of the Monitor. It is no longer the story of the heroic inventor and his impenetrable weapon thrusting themselves upon a doubtful and conservative bureaucracy... It is no longer the story of a heroic battle and the machine's epic loss soon after. Rather it is a story of people experiencing new machinery, attempting to make sense of its thrills, constrictions, and politics, and sensing its power and impotence -- both in glory and frustration." -- from War, Technology, and Experience aboard the USS Monitor},
  isbn = {978-0-8018-6250-2},
  langid = {english},
  lccn = {0011},
  keywords = {Civil War,history}
}

@misc{mitreSemiAutomaticGroundEnvironment2001,
  title = {Semi-{{Automatic Ground Environment}} ({{SAGE}})},
  author = {{MITRE}},
  year = {2001},
  month = jan,
  journal = {MITRE History},
  howpublished = {https://web.archive.org/web/20110716081954/http://www.mitre.org/about/sage.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NJTX9F9Y/MITRE - 2001 - Semi-Automatic Ground Environment (SAGE).pdf;/Users/rca2t1/Dropbox/Zotero/storage/VHQVWNAC/sage.html}
}

@misc{MITxMASS69x,
  title = {{{MITx}}: {{MAS}}.{{S69x}}: {{Big Data}} and {{Social Physics}}},
  shorttitle = {{{MITx}}},
  journal = {edX},
  abstract = {Understanding big data, how to use it to improve companies, cities, and government, and best-practice for privacy},
  howpublished = {https://www.edx.org/course/mitx/mitx-mas-s69x-big-data-social-physics-1737},
  keywords = {Course,MITx},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DT4NBJ54/MITx MAS.S69x Big Data and Social Physics.html}
}

@misc{miyazakiBriefHistoryData2015,
  title = {A {{Brief History}} of {{Data Analysis}}},
  author = {Miyazaki, Masashi},
  year = {2015},
  month = mar,
  abstract = {Let's take a short journey together through the history of data analysis.},
  howpublished = {https://flydata.com/blog/a-brief-history-of-data-analysis/},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/BQDL7BVC/a-brief-history-of-data-analysis.html}
}

@article{mnookinBumbling1960sData2020,
  title = {The {{Bumbling}} 1960s {{Data Scientists Who Anticipated Facebook}} and {{Google}}},
  author = {Mnookin, Seth},
  year = {2020},
  month = sep,
  journal = {The New York Times},
  issn = {0362-4331},
  abstract = {In ``If Then,'' the historian Jill Lepore recounts the story of the Simulmatics Corporation, which tried to use primitive computing power to shape Americans' behavior.},
  chapter = {Books},
  langid = {american},
  keywords = {Books and Literature,Computers and the Internet,If Then: How the Simulmatics Corporation Invented the Future (Book),Lepore; Jill,Nineteen Hundred Sixties,Politics and Government,Presidential Election of 1960,Vietnam War},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NB6BN9YN/if-then-jill-lepore.html}
}

@article{mnushkinAdventuresBigData2008,
  title = {Adventures with {{Big Data}}: {{How}} to {{Import}} 16 {{Billion Rows}} into a {{Single Table}}},
  author = {Mnushkin, Dmitry},
  year = {2008},
  month = jun,
  journal = {SQL Server Magazine},
  volume = {10},
  number = {6},
  abstract = {So here I was, writing some SQL Server 2005 Reporting Services (SSRS) reports for a user when a coworker asked if I could process some data for him. I'm the SQL Server guy at work, so I immediately volunteered to help. The data arrived several days later on a 1TB external hard drive that contained 20,000 flat files taking up 400GB of space. The data consisted of latitude and longitude coordinates and three extra informational fields. I had no idea how many records the raw data represented because not all the latitude and longitude coordinates were to be included. For latitude and longitude coordinates to be included, their extra fields had to match certain criteria.}
}

@misc{MohawkDataSciences,
  title = {Mohawk {{Data Sciences Corp}}. - {{New York}} 1970's},
  journal = {Scripophily},
  abstract = {Beautifully engraved stock certificate stamped specimen from the  Mohawk Data Sciences Corp.     printed in the 1970's.  This historic document was printed by the Security Columbian Banknote Company an},
  howpublished = {http://www.scripophily.net/modascconewy.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EZHB2N6P/modascconewy.html}
}

@article{MohawkDataSciences1966,
  title = {Mohawk {{Data Sciences}}},
  year = {1966},
  month = jul,
  journal = {The New York Times},
  issn = {0362-4331},
  chapter = {Archives},
  langid = {american}
}

@misc{MohawkDataSciencesa,
  title = {Mohawk {{Data Sciences Corporation}} ({{MDS}}) | {{Selling}} the {{Computer Revolution}} | {{Computer History Museum}}},
  howpublished = {https://www.computerhistory.org/brochures/m-p/},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ZCBVVHWL/m-p.html}
}

@book{MohawkM10065MDS1101,
  title = {Mohawk :: {{M-100-65 MDS-1101 Brochure}}},
  shorttitle = {Mohawk},
  abstract = {From the bitsavers.org collection, a scanned-in computer-related document. mohawk :: M-100-65 MDS-1101 Brochure},
  langid = {english},
  keywords = {magnetic},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PE75T5YH/mohawk  M-100-65 MDS-1101 Brochure.pdf}
}

@article{moirandCommunicativeCognitiveDimensions2003,
  title = {Communicative and {{Cognitive Dimensions}} of {{Discourse}} on {{Science}} in the {{French Mass Media}}},
  author = {Moirand, Sophie},
  year = {2003},
  journal = {Discourse studies},
  volume = {5},
  number = {2},
  pages = {175--206},
  publisher = {{Sage Publications London}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/T6UQBP5L/Moirand - 2003 - Communicative and Cognitive Dimensions of Discours.pdf;/Users/rca2t1/Dropbox/Zotero/storage/KN2XWDB5/1461445603005002003.html}
}

@book{MonthlyCatalogUnited1965,
  title = {Monthly {{Catalog}} of {{United States Government Publications}}},
  year = {1965},
  publisher = {{U.S. Government Printing Office}},
  googlebooks = {r7wYyuESiGAC},
  langid = {english}
}

@article{morawetzInternationalComparisonsIncome1970,
  title = {International {{Comparisons}} of {{Income Levels}}: {{A Comment}}},
  shorttitle = {International {{Comparisons}} of {{Income Levels}}},
  author = {Morawetz, David},
  year = {1970},
  journal = {The Economic Journal},
  volume = {80},
  number = {320},
  pages = {977--980},
  publisher = {{[Royal Economic Society, Wiley]}},
  issn = {0013-0133},
  doi = {10.2307/2229931},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/QQSKECMM/Morawetz - 1970 - International Comparisons of Income Levels A Comm.pdf}
}

@book{morganrothCardiacSafetyNoncardiac2007,
  title = {Cardiac {{Safety}} of {{Noncardiac Drugs}}: {{Practical Guidelines}} for {{Clinical Research}} and {{Drug Development}}},
  shorttitle = {Cardiac {{Safety}} of {{Noncardiac Drugs}}},
  author = {Morganroth, Joel and Gussak, Ihor},
  year = {2007},
  month = oct,
  publisher = {{Springer Science \& Business Media}},
  abstract = {Although it is relatively easy to determine the efficacy of a new drug, it is difficult to establish its safety when administered to millions of patients with multifaceted diseases, co-morbidities, sensitivities, and multiple drug use. In Cardiac Safety of Noncardiac Drugs: Practical Guidelines for Clinical Research and Drug Development, basic and clinical researchers from industry and academia detail the preclinical, clinical, and regulatory principles currently used to assess the cardiac safety of new drugs from their effects on the electrocardiogram (ECG). The authors explain the parameters of cardiac safety at all stages of clinical research and drug development, including both the preclinical and pharmacogenomic aspects generally and the clinical methodologies and technical aspects for investigational drugs based on cardiac repolarization, as defined by the duration of the QTc interval. Additional chapters comprehensively review the application of electrocardiology in clinical research, demonstrating the fundamentals of ECG interpretation in clinical trials, the statistical analysis plans for ECG data obtained in formal clinical trials, and the practical interpretation of the results. Highlights include practical guidance on how to conduct a thorough ECG Trial in New Drug Development, how to use new ECG and web-based technology in clinical research, and how to follow the new FDA requirements for ECG submissions. Authoritative and up-to-date, Cardiac Safety of Noncardiac Drugs: Practical Guidelines for Clinical Research and Drug Development offers clinical researchers in industry and academia expert practical advice on establishing their product's cardiac safety, predicting regulatory actions, and getting it successfully to market.},
  googlebooks = {NUS862kcS2oC},
  isbn = {978-1-59259-884-7},
  langid = {english},
  keywords = {Medical / Pharmacology,Medical / Pharmacy}
}

@misc{mortcollinsventuresBio,
  title = {Bio},
  author = {{Mort Collins Ventures}},
  journal = {MCollins Ventures},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/R4X3E8L7/bio.html}
}

@article{mottleyApplicationOperationsResearchMethods1954,
  title = {The {{Application}} of {{Operations-Research Methods}} to {{Athletic Games}}},
  author = {Mottley, Charles M.},
  year = {1954},
  journal = {Journal of the Operations Research Society of America},
  volume = {2},
  number = {3},
  pages = {335--338},
  publisher = {{INFORMS}},
  issn = {0096-3984},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/BIK3IR9J/Mottley - 1954 - The Application of Operations-Research Methods to .pdf}
}

@article{munaNewFrameworkModelBased2014,
  title = {A {{New Framework}} for a {{Model-Based Data Science Computational Platform}}},
  author = {Muna, Demitri and Huff, Eric},
  year = {2014},
  month = feb,
  journal = {arXiv:1402.5932 [astro-ph]},
  eprint = {1402.5932},
  eprinttype = {arxiv},
  primaryclass = {astro-ph},
  abstract = {Astronomy produces extremely large data sets from ground-based telescopes, space missions, and simulation. The volume and complexity of these rich data sets require new approaches and advanced tools to understand the information contained therein. No one can load this data on their own computer, most cannot even keep it at their institution, and worse, no platform exists that allows one to evaluate their models across the whole of the data. Simply having an extremely large volume of data available in one place is not sufficient; one must be able to make valid, rigorous, scientific comparisons across very different data sets from very different instrumentation. We propose a framework to directly address this which has the following components: a model-based computational platform, streamlined access to large volumes of data, and an educational and social platform for both researchers and the public.},
  archiveprefix = {arXiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9TAPH2FG/Muna and Huff - 2014 - A New Framework for a Model-Based Data Science Com.pdf;/Users/rca2t1/Dropbox/Zotero/storage/RCXRTE9K/1402.html}
}

@article{murtaghDevelopmentDataScience2018,
  title = {The {{Development}} of {{Data Science}}: {{Implications}} for {{Education}}, {{Employment}}, {{Research}}, and the {{Data Revolution}} for {{Sustainable Development}}},
  shorttitle = {The {{Development}} of {{Data Science}}},
  author = {Murtagh, Fionn and Devlin, Keith},
  year = {2018},
  journal = {Big Data and Cognitive Computing},
  volume = {2},
  number = {2},
  pages = {14},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/GVEFSM74/Murtagh and Devlin - 2018 - The Development of Data Science Implications for .pdf;/Users/rca2t1/Dropbox/Zotero/storage/AXRB9T8Q/14.html}
}

@misc{NARAAADSeries,
  title = {{{NARA}} - {{AAD}} - {{Series Description}} - {{Records}} of {{Prime Contracts Awarded}} by the {{Military Services}} and {{Agencies}}, Created, 7/1/1965 - 6/30/1975, Documenting the Period 7/1/1965 - 6/30/1975},
  howpublished = {https://aad.archives.gov/aad/series-description.jsp?s=492\&cat=GS29\&q=\%22data+science\%22\&popup=Y},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EQBQ84B4/series-description.html}
}

@book{NASASP1962,
  title = {{{NASA SP}}.},
  year = {1962},
  publisher = {{Scientific and Technical Information Office, National Aeronautics and Space Administration [available from National Technical Information Service, Springfield, Va.]}},
  langid = {english}
}

@book{NASASP1962a,
  title = {{{NASA SP}}.},
  year = {1962},
  publisher = {{Scientific and Technical Information Office, National Aeronautics and Space Administration}},
  langid = {english}
}

@book{nationalsciencefoundationScientificInformationActivities1962,
  title = {Scientific {{Information Activities}} of {{Federal Agencies}}},
  author = {{National Science Foundation}},
  year = {1962},
  number = {15},
  googlebooks = {opQbAAAAMAAJ},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NISPZNBL/National Science Foundation - 1962 - Scientific Information Activities of Federal Agenc.pdf}
}

@article{NatureBigData2008,
  title = {Nature: {{Big Data}}: {{Knowledge}} in the {{Petabyte Era}}},
  year = {2008},
  month = sep,
  volume = {455},
  number = {7209},
  issn = {1476-4687},
  abstract = {B},
  copyright = {\textcopyright 2021 Macmillan Publishers Limited. All Rights Reserved.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/YGYAF6RI/7209.html}
}

@book{naurComputingHumanActivity1992,
  title = {Computing, a {{Human Activity}}},
  author = {Naur, Peter},
  year = {1992},
  month = jan,
  publisher = {{ACM Press}},
  abstract = {In this comprehensive anthology Peter Naur, one of the world's foremost computer scientists, presents his selected writings from 1951 to 1990. The book features Naur's original and stimulating reflections on the nature of computing, with perceptive analyses of many issues that remain controversial. Comprising the author's published and unpublished writings on scientific, technical, philosophical, and social aspects of computing, the volume highlights his view of computing as, essentially, a human activity.},
  googlebooks = {d4NQAAAAMAAJ},
  isbn = {978-0-201-58069-3},
  langid = {english},
  keywords = {Computers / General},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/J6NEZS7B/comp.html}
}

@book{naurConciseSurveyComputer1974,
  title = {Concise {{Survey}} of {{Computer Methods}}},
  author = {Naur, Peter},
  year = {1974},
  publisher = {{Petrocelli Books}},
  googlebooks = {0KEpAQAAIAAJ},
  isbn = {978-0-88405-314-9},
  langid = {english},
  keywords = {Computers / Data Processing,Reference / Questions \& Answers}
}

@book{naurConciseSurveyComputer1974a,
  title = {Concise {{Survey}} of {{Computer Methods}}},
  author = {Naur, Peter},
  year = {1974},
  edition = {1st edition},
  publisher = {{Petrocelli Books}},
  address = {{New York}},
  isbn = {978-0-88405-314-9},
  langid = {english}
}

@inproceedings{naurDatalogyScienceData1968,
  title = {'{{Datalogy}}', the {{Science}} of {{Data}} and {{Data Processes}}.},
  booktitle = {{{IFIP Congress}} (2)},
  author = {Naur, Peter},
  year = {1968},
  pages = {1383--1387}
}

@book{naurKnowingMystiqueLogic2013,
  title = {Knowing and the {{Mystique}} of {{Logic}} and {{Rules}}: Including {{True Statements}} in {{Knowing}} and {{Action}} * {{Computer Modelling}} of {{Human Knowing Activity}} * {{Coherent}} ...},
  shorttitle = {Knowing and the {{Mystique}} of {{Logic}} and {{Rules}}},
  author = {Naur, P.},
  year = {2013},
  month = mar,
  edition = {1995 edition},
  publisher = {{Springer}},
  abstract = {Human knowing is examined as it emerges from classical  empirical psychology, with its ramifications into language, computing,  science, and scholarship. While the discussion takes empirical support  from a wide range, claims for the significance of logic and rules are  challenged throughout. Highlights of the discussion:    knowing is a matter of habits or dispositions that guide the  person's stream of consciousness;    rules of language have no  significance in language production and understanding, being  descriptions of linguistic styles;    statements that may be true  or false enter into ordinary linguistic activity, not as elements of  messages, but merely as summaries of situations, with a view to  action;    in computer programming the significance of logic,  proof, and formalized description, is incidental and subject to the  programmer's personality;    analysis of computer modelling of the  mental activity shows that in describing human knowing the computer is  irrelevant;    in accounting for the scholarly/scientific  activity, logic and rules are impotent;    a novel theory:  scholarship and science have coherent descriptions as their core.       The discussion addresses questions that are basic to advanced  applications of computers and to students of language and science.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/J3YQQFZE/Naur - 2013 - Knowing and the Mystique of Logic and Rules inclu.pdf}
}

@misc{naurPeterNaurConcise1992,
  title = {Peter {{Naur}}: {{Concise Survey}} of {{Computer Methods}}, 397 p},
  author = {Naur, Peter},
  year = {1992},
  howpublished = {http://www.naur.com/Conc.Surv.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/QSFSLZRD/Conc.Surv.html}
}

@misc{naurPeterNaurConcisen.d.,
  title = {Peter {{Naur}}: {{Concise Survey}} of {{Computer Methods}}, 397 p},
  author = {Naur, Peter},
  year = {n.d.},
  journal = {Introduction to the works of Peter Naur},
  howpublished = {http://www.naur.com/Conc.Surv.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DPEPEAVK/Conc.Surv.html}
}

@article{naurScienceDatalogy1966,
  title = {The {{Science}} of {{Datalogy}}},
  author = {Naur, Peter},
  year = {1966},
  month = jul,
  journal = {Communications of the ACM},
  volume = {9},
  number = {7},
  pages = {485},
  issn = {0001-0782},
  doi = {10.1145/365719.366510},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3B6GR3T9/naur-1966.pdf;/Users/rca2t1/Dropbox/Zotero/storage/CXRKPTN5/Naur - 1966 - The Science of Datalogy.pdf}
}

@book{NavalAviationNews1954,
  title = {Naval {{Aviation News}}},
  year = {1954},
  publisher = {{Chief of Naval Operations}},
  googlebooks = {xGFGAQAAMAAJ},
  langid = {english}
}

@article{nelsonBigDataHarvard2008,
  title = {Big Data: {{The Harvard}} Computers},
  shorttitle = {Big Data},
  author = {Nelson, Sue},
  year = {2008},
  month = sep,
  journal = {Nature},
  volume = {455},
  number = {7209},
  pages = {36--37},
  issn = {0028-0836},
  doi = {10.1038/455036a},
  abstract = {The first mass data crunchers were people, not machines. Sue Nelson looks at the discoveries and legacy of the remarkable women of Harvard's Observatory.},
  copyright = {\textcopyright{} 2008 Nature Publishing Group},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/R2RU66XW/455036a.pdf;/Users/rca2t1/Dropbox/Zotero/storage/28MFNUW5/455036a.html}
}

@article{NewScientist1992,
  title = {New {{Scientist}}},
  year = {1992},
  googlebooks = {EywVAQAAMAAJ},
  langid = {english}
}

@article{NewScientist1995,
  title = {New {{Scientist}}},
  year = {1995},
  googlebooks = {6fjwAAAAMAAJ},
  langid = {english}
}

@article{NewScientist1996,
  title = {New {{Scientist}}},
  year = {1996},
  googlebooks = {wwUVAQAAMAAJ},
  langid = {english}
}

@article{NewScientist1999,
  title = {New {{Scientist}}},
  year = {1999},
  googlebooks = {bBvxAAAAMAAJ},
  langid = {english}
}

@article{NewScientist2001,
  title = {New {{Scientist}}},
  year = {2001},
  googlebooks = {YjHxAAAAMAAJ},
  langid = {english}
}

@article{NewScientist2001a,
  title = {New {{Scientist}}},
  year = {2001},
  googlebooks = {vAsVAQAAMAAJ},
  langid = {english}
}

@misc{NewsletterInternationalFederation,
  title = {Newsletter [{{International Federation}} of {{Classification Societies}} ]},
  howpublished = {https://ifcs.boku.ac.at/site/doku.php?id=newsletter},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ADSAXSCL/doku.html}
}

@article{newtonConversationEmanuelParzen2002,
  title = {A {{Conversation}} with {{Emanuel Parzen}}},
  author = {Newton, H. Joseph},
  year = {2002},
  journal = {Statistical Science},
  volume = {17},
  number = {3},
  pages = {357--378},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0883-4237},
  abstract = {Emanuel Parzen was born in New York City on April 21, 1929. He attended the Bronx High School of Science, received an A.B. in Mathematics from Harvard University in 1949, an M.A. in Mathematics from the University of California at Berkeley in 1951 and his Ph.D. in Mathematics and Statistics in 1953, also at Berkeley. He was a research scientist at Hudson Labs, Physics Department of Columbia University, from 1953 to 1956 and an Assistant Professor of Mathematical Statistics at Columbia from 1955 to 1956. In 1956, he moved to Stanford University, where he stayed until 1970, at which time he joined the faculty at the State University of New York at Buffalo, where he served first as Leading Professor and Chairman of the Department of Statistics and then as Director of Statistical Science. In 1978 he moved to Texas A\&M University as a Distinguished Professor, a post he currently holds. He has been a Fellow at Imperial College London, at IBM Systems Research Institute and at the Center for Advanced Study in the Behavioral Sciences at Stanford, as well as a Visiting Professor at the Sloan School of MIT, the Department of Statistics at Harvard and the Department of Biostatistics at Harvard. In 1959 he married Carol Tenowitz. They have two children and four grandchildren. Professor Parzen has authored or coauthored over 100 papers and 6 books. He has served on innumerable editorial boards and national committees, and has organized several influential conferences and workshops. He has directed the research of many graduate students and provided advice, encouragement and collaboration to students and colleagues around the world. To honor these contributions, he has been elected a Fellow of the American Statistical Association, of the Institute of Mathematical Statistics and of the American Association for the Advancement of Science. In 1994, he was awarded the prestigious Samuel S. Wilks Memorial Medal by the American Statistical Association.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3UREPHSI/Newton - 2002 - A Conversation with Emanuel Parzen.pdf}
}

@misc{NIHRecruitAssociate2015,
  title = {{{NIH}} to Recruit {{Associate Director}} for {{Data Science}}},
  year = {2015},
  month = jul,
  journal = {National Institutes of Health (NIH)},
  abstract = {National Institutes of Health Director Francis S. Collins, M.D., Ph.D., today announced plans to recruit a new senior scientific position, the Associate Director for Data Science.},
  howpublished = {https://www.nih.gov/news-events/news-releases/nih-recruit-associate-director-data-science},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NM85CWTC/nih-recruit-associate-director-data-science.html}
}

@article{nilssonApproachesArtificialIntelligence1993,
  title = {Approaches to {{Artificial Intelligence}}},
  author = {Nilsson, N. J. and Rumelhart, David},
  year = {1993},
  journal = {Santa Fe Institute, NM},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/YL6P436J/Nilsson and Rumelhart - 1993 - Approaches to Artificial Intelligence.pdf}
}

@article{normanCognitionHeadWorld1993,
  title = {Cognition in the {{Head}} and in the {{World}}: {{An Introduction}} to the {{Special Issue}} on {{Situated Action}}},
  shorttitle = {Cognition in the {{Head}} and in the {{World}}},
  author = {Norman, Donald A},
  year = {1993},
  journal = {Cognitive Science},
  volume = {17},
  number = {1},
  pages = {1--6},
  issn = {1551-6709},
  doi = {10.1207/s15516709cog1701_1},
  copyright = {1993 Lawrence Erlbaum Associates, Inc.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ZXE4HDMI/Norman - 1993 - Cognition in the Head and in the World An Introdu.pdf;/Users/rca2t1/Dropbox/Zotero/storage/XC5WQV8M/Norman - 1993 - Cognition in the Head and in the World An Introdu.html}
}

@article{Note1965,
  title = {Note},
  year = {1965},
  journal = {Independent Offices Appropriations for 1966, Hearings before a Subcommittee of the Committee on Appropriations, House of Representatives, Eighty-Ninth Congress, First Session, Pt. 3},
  pages = {I-1038},
  langid = {english}
}

@misc{NSFAwardSearch,
  title = {{{NSF Award Search}}: {{Award}}\#1626983 - {{Envisioning}} the {{Data Science Discipline}}: {{The Undergraduate Perspective}}},
  howpublished = {https://nsf.gov/awardsearch/showAward?AWD\_ID=1626983\&HistoricalAwards=false},
  annotation = {00000},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TSAGGGFX/showAward.html}
}

@misc{nsfMansfieldAmendment2003,
  title = {The {{Mansfield Amendment}}},
  author = {{NSF}},
  year = {2003},
  month = oct,
  journal = {NSF: NSB 50th Anniversary},
  howpublished = {https://web.archive.org/web/20030321112517/https://www.nsf.gov/nsb/documents/2000/nsb00215/nsb50/1970/mansfield.html},
  annotation = {Date inferred through Wayback Machine.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TAG9S3HZ/mansfield.html}
}

@book{officeCatalogCopyrightEntries1979,
  title = {Catalog of {{Copyright Entries}}. {{Third Series}}: 1977: {{July-December}}: {{Index}}},
  shorttitle = {Catalog of {{Copyright Entries}}. {{Third Series}}},
  author = {Office, Library of Congress Copyright},
  year = {1979},
  publisher = {{Copyright Office, Library of Congress}},
  googlebooks = {VUIhAQAAIAAJ},
  langid = {english}
}

@book{officeForecastSpaceTechnology1976,
  title = {A {{Forecast}} of {{Space Technology}}, 1980-2000},
  author = {Office, United States National Aeronautics {and} Space Administration Scientific {and} Technical Information},
  year = {1976},
  publisher = {{U.S. National Aeronautics and Space Administration, Scientific and Technical Information Office}},
  googlebooks = {cmexTKefXQAC},
  langid = {english}
}

@article{officeofnavalresearchmathematicalsciencesdivisionComputingCenters1959,
  title = {Computing {{Centers}}},
  author = {{Office of Naval Research, Mathematical Sciences Division}},
  year = {1959},
  month = jul,
  journal = {Digital Computer Newsletter},
  volume = {11},
  number = {3},
  pages = {2},
  abstract = {COMPUTER AND MATHEMATICAL SCIENCES LABORATORY - AIR IFORCE CAMBRIDGE RESEARCH CENTER - L. G. HANSCOM FIELD, BEDFORD, MASSACHUSETTS A detailed evaluation of radar detection and position location schemes is being carried out on the AFCRC Magnetic Computer (see Digital Computer Newsletter, January 1959). A library of target blip samples characterized by stochastic signals imbedded in noise has been generated on the computer for this purpose. The simulation technique employed is particularly useful in assessing not only the validity of the end results obtained from the theoretical model, such as detection efficiencies, false alarm rates, and position accuracies, as functions of the data processor and target parameters, but also the validity of intermediate assumptions on the nature of the various probability distributions and how they are transformed. The approach makes use of analog simulation techniques for data generation and digital simulation on a general purpose computer of the data processing schemes.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/XDFZXVS4/AD0694658.pdf}
}

@article{ogazTelemetryDataProcessing1989,
  title = {Telemetry {{Data Processing}} at {{White Sands Missile Range}}},
  author = {Ogaz, Juan A.},
  year = {1989},
  month = nov,
  publisher = {{International Foundation for Telemetering}},
  issn = {0884-5123},
  abstract = {Prior to 1985 the National Range had, for a number of years, serious and recurring mission support problems with the IBM 360 Telemetry Data Processing System due to equipment reliability and obsolescence of the system which was installed in 1968. These problems became particularly acute when higher data rate requirements and the need for reliable telemetry data processing dictated that prompt and unusual action was necessary if WSMR was to continue to provide telemetry data processing support. Realizing that the above cited problems of reliability and obsolescence would continue in detriment to the mission of WSMR, Department of Defense (DOD) and the nation, coupled with the loss of thousands of dollars in reimbursables due to WSMR's inability to support missile test requirements, the Systems Engineering Branch was tasked by the Director of National Range to lead a study, and propose and implement solutions to meet current and future requirements in telemetry data processing support. With the explosion in PCM data rates, it had become obvious that WSMR could not continue to upgrade existing systems and meet the demands of the future. More data parameters at higher data rates were being processed in PCM, FM, and PAM. Telemetry formats were becoming more complicated, such as embedded asynchronous subcomms and dynamic format changes. More real-time decisions had to be made for mission safety, verification of location, and mission success. WSMR needed a more versatile system that would synchronize, process and display higher data rates with more accuracy than it had at this time. This paper describes a historical perspective of steps WSMR has taken to satisfy present and future test vehicle telemetry data processing requirements.},
  langid = {american},
  annotation = {Accepted: 2016-06-27T18:42:50Z},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/L64I6T86/Ogaz - 1989 - Telemetry Data Processing at White Sands Missile R.pdf;/Users/rca2t1/Dropbox/Zotero/storage/Z57AVZ66/614840.html}
}

@article{oharaErrorEstimationClenshawCurtis1968,
  title = {Error {{Estimation}} in the {{Clenshaw-Curtis Quadrature Formula}}},
  author = {O'Hara, H. and Smith, Francis Jack},
  year = {1968},
  journal = {Comput. J.},
  volume = {11},
  number = {2},
  pages = {213--219},
  doi = {10.1093/comjnl/11.2.213},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/686GKYWV/O'Hara and Smith - 1968 - Error Estimation in the Clenshaw-Curtis Quadrature.pdf}
}

@article{oharaEvaluationDefiniteIntegrals1969,
  title = {The Evaluation of Definite Integrals by Interval Subdivision},
  author = {O'Hara, H. and Smith, Francis Jack},
  year = {1969},
  journal = {Comput. J.},
  volume = {12},
  number = {2},
  pages = {179--182},
  doi = {10.1093/comjnl/12.2.179},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/BRAFTXQS/O'Hara and Smith - 1969 - The evaluation of definite integrals by interval s.pdf}
}

@incollection{ohsumiDataAnalysisData2000,
  title = {From {{Data Analysis}} to {{Data Science}}},
  booktitle = {Data Analysis, Classification, and Related Methods},
  author = {Ohsumi, Noboru},
  year = {2000},
  pages = {329--334},
  publisher = {{Springer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3C6WPP3A/Ohsumi - 2000 - From Data Analysis to Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/5H6YYNJX/978-3-642-59789-3_52.html}
}

@inproceedings{ohsumiExperimentalSystemNavigating1992,
  title = {An {{Experimental System}} for {{Navigating Statistical Meta-Information}} \textemdash{} {{The Meta-Stat Navigator}}},
  booktitle = {Computational {{Statistics}}},
  author = {Ohsumi, Noboru},
  editor = {Dodge, Yadolah and Whittaker, Joe},
  year = {1992},
  pages = {375--380},
  publisher = {{Physica-Verlag HD}},
  address = {{Heidelberg}},
  doi = {10.1007/978-3-642-48678-4_48},
  abstract = {In research organizations handling statistical information, the volume of stored information resources, including research results, materials, and software, is increasing to the point that conventional separate databases and information management systems have become insufficient to deal with the amount. Increasing diversification in the media used these days interferes with the rapid retrieval and use of the information needed by users. A new system that realizes a presentation environment based on new concepts is needed to inform potential users of the value and effectiveness of using the vast amount of diverse data.},
  isbn = {978-3-642-48678-4},
  langid = {english},
  keywords = {Human Interface,Information Management System,Navigation Function,Statistical Information,User Environment},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SL3NF85A/Ohsumi - 1992 - An Experimental System for Navigating Statistical .pdf}
}

@inproceedings{ohsumiMemoriesChikioHayashi2004,
  title = {Memories of {{Chikio Hayashi}} and {{His Great Achievement}}},
  booktitle = {{{ResearchGate}}},
  author = {Ohsumi, Noboru},
  year = {2004},
  abstract = {ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2MYRVSK5/2004 - Memories of Chikio Hayashi and His Great Achieveme.pdf;/Users/rca2t1/Dropbox/Zotero/storage/NTAT25TK/268719699_Memories_of_Chikio_Hayashi_and_his_great_achievement_2004.html}
}

@incollection{ohsumiNewDataNew1994,
  title = {New {{Data}} and {{New Tools}}: {{A Hypermedia Environment}} for {{Navigating Statistical Knowledge}} in {{Data Science}}},
  booktitle = {New {{Approaches}} in {{Classification}} and {{Data Analysis}}},
  author = {Ohsumi, Noboru},
  year = {1994},
  pages = {45--54},
  publisher = {{Springer-Verlag}},
  address = {{Berlin}}
}

@incollection{ohsumiRoleComputerGraphics1988,
  title = {Role of {{Computer Graphics}} in {{Interpretation}} of {{Clustering Results}}},
  booktitle = {Recent {{Developments}} in {{Clustering}} and {{Data Analysis}}},
  author = {Ohsumi, Noboru},
  editor = {Hayashi, Chikio and Jambu, Michel and Diday, Edwin and Ohsumi, Noboru},
  year = {1988},
  month = jan,
  pages = {201--222},
  publisher = {{Academic Press}},
  doi = {10.1016/B978-0-12-215485-0.50021-1},
  abstract = {This chapter discusses the role of computer graphics in interpretation of clustering results. The utility of computer graphics has won the recognition of many researchers specializing in data analysis, and a variety of graphical representation methods have been proposed by them. From graphical representation, data analysts expect to obtain a firm grasp of the intrinsic structure of collected data by visualizing their features. The hardware environment bears closely upon free use of graphical representation as a means of data analysis. It deserves attention that the functional improvement of microcomputers and workstations has provided freedom of graphical representation and greater ease of graphics equipment operation. These graphical representation methods have some common features\textemdash (1) representing the features of multivariate data, (2) visual inspection of the relationship among variables, (3) observing data distribution, (4) projections based on data transformation and features selection, and (5) enhancement of intrinsic features in data.},
  isbn = {978-0-12-215485-0},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/N3CX8UH9/B9780122154850500211.html}
}

@book{ojedaPracticalDataScience2014,
  title = {Practical {{Data Science Cookbook}}},
  author = {Ojeda, Tony and Murphy, Sean Patrick and Bengfort, Benjamin and Dasgupta, Abhijit},
  year = {2014},
  publisher = {{Packt Publishing Ltd}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/7WZIZ89G/books.html}
}

@misc{OneDataScience,
  title = {One {{Data Science Job Doesn}}'t {{Fit All}} | {{LinkedIn}}},
  howpublished = {https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal/},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/K5FNPYJY/one-data-science-job-doesnt-fit-all-elena-grewal.html}
}

@article{oneillPolynomialCurveFitting1969,
  title = {Polynomial {{Curve Fitting When Abscissas}} and {{Ordinates}} Are Both {{Subject}} to {{Error}}},
  author = {O'Neill, M. and Sinclair, I. G. and Smith, Francis Jack},
  year = {1969},
  journal = {Comput. J.},
  volume = {12},
  number = {1},
  pages = {52--56},
  doi = {10.1093/comjnl/12.1.52},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2ANVI7QX/O'Neill et al. - 1969 - Polynomial Curve Fitting When Abscissas and Ordina.pdf}
}

@book{operationsInteragencyCoordinationInformation1963,
  title = {Interagency {{Coordination}} of {{Information}}: {{Hearings}} ... 87-2},
  shorttitle = {Interagency {{Coordination}} of {{Information}}},
  author = {Operations, United States Congress Senate Government},
  year = {1963},
  googlebooks = {RcZMjATSyiMC},
  langid = {english}
}

@inproceedings{oreillyWebSquaredWeb2009,
  title = {Web {{Squared}}: {{Web}} 2.0 {{Five Years On}}},
  booktitle = {Web 2.0 {{Summit}} 2009},
  author = {O'Reilly, Tim and Battelle, John},
  year = {2009},
  month = oct,
  publisher = {{O'Reilly Media, Inc.}},
  address = {{San Francisco : Santa Monica, CA}},
  abstract = {Co-produced by UBM TechWeb \& O'Reilly Conferences},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/G4BUL26C/O'Reilly and Battelle - 2009 - Web Squared Web 2.0 Five Years On.pdf;/Users/rca2t1/Dropbox/Zotero/storage/UQTDLMKQ/10194.html}
}

@misc{oreillyWhatWeb2005,
  title = {What {{Is Web}} 2.0},
  author = {O'Reilly, Tim},
  year = {2005},
  month = sep,
  abstract = {Tim O'Reilly attempts to clarify just what is meant by Web 2.0, the term first coined at a conference brainstorming session between O'Reilly Media and MediaLive International, which also spawned the Web 2.0 Conference.},
  howpublished = {https://oreilly.com\{file\}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/E7QUTSVQ/what-is-web-20.html}
}

@article{osmanDataScienceNew2015,
  title = {Data {{Science}} as a {{New Frontier}} for {{Design}}},
  author = {Osman, Akin and Mines, Kazak{\c c}i},
  year = {2015},
  month = mar,
  journal = {arXiv:1503.06201 [cs, stat]},
  eprint = {1503.06201},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {The purpose of this paper is to contribute to the challenge of transferring know-how, theories and methods from design research to the design processes in information science and technologies. More specifically, we shall consider a domain, namely data-science, that is becoming rapidly a globally invested research and development axis with strong imperatives for innovation given the data deluge we are currently facing. We argue that, in order to rise to the data-related challenges that the society is facing, data-science initiatives should ensure a renewal of traditional research methodologies that are still largely based on trial-error processes depending on the talent and insights of a single (or a restricted group of) researchers. It is our claim that design theories and methods can provide, at least to some extent, the much-needed framework. We will use a worldwide data-science challenge organized to study a technical problem in physics, namely the detection of Higgs boson, as a use case to demonstrate some of the ways in which design theory and methods can help in analyzing and shaping the innovation dynamics in such projects.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Statistics - Other Statistics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ZCDLEXBY/Osman and Mines - 2015 - Data Science as a New Frontier for Design.pdf;/Users/rca2t1/Dropbox/Zotero/storage/SV6ZDIN5/1503.html}
}

@article{OurMembers1975,
  title = {About Our Members},
  year = {1975},
  journal = {Bulletin of the American Meteorological Society},
  volume = {56},
  number = {3},
  pages = {384--385},
  publisher = {{American Meteorological Society}},
  issn = {0003-0007}
}

@misc{owenWhat50Years2015,
  title = {What ``50 {{Years}} of {{Data Science}}'' {{Leaves Out}}},
  author = {Owen, Sean},
  year = {2015},
  month = nov,
  journal = {Sean Owen},
  abstract = {``50 Years of Data Science'' takes back the word for Statistics and dismisses big data. The reality is grayer.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/C8Z7BWDR/what-50-years-of-data-science-leaves-out-2366c9b61d3d.html}
}

@misc{palmerDataNewOil2006,
  title = {Data Is the {{New Oil}}},
  author = {Palmer, Michael},
  year = {2006},
  month = nov,
  journal = {ANA Marketing Maestros},
  abstract = {By Michael Palmer "Data is the new oil!" Clive Humby, ANA Senior marketer's summit, Kellogg School. Data is just like crude. It's valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals,...},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/IUELAYV2/data_is_the_new.html}
}

@article{parsonsRevisedFocusScope2019,
  title = {Revised {{Focus}} and {{Scope}}},
  author = {Parsons, Mark A.},
  year = {2019},
  month = nov,
  journal = {The Data Science Journal},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/QNG8NERK/datascience.codata.org.html}
}

@article{parzenLPMixedData2013,
  title = {{{LP Mixed Data Science}} : {{Outline}} of {{Theory}}},
  shorttitle = {{{LP Mixed Data Science}}},
  author = {Parzen, Emanuel and Mukhopadhyay, Subhadeep},
  year = {2013},
  month = nov,
  journal = {arXiv:1311.0562 [math, stat]},
  eprint = {1311.0562},
  eprinttype = {arxiv},
  primaryclass = {math, stat},
  abstract = {This article presents the theoretical foundation of a new frontier of research-`LP Mixed Data Science'-that simultaneously extends and integrates the practice of traditional and novel statistical methods for nonparametric exploratory data modeling, and is applicable to the teaching and training of statistics. Statistics journals have great difficulty accepting papers unlike those previously published. For statisticians with new big ideas a practical strategy is to publish them in many small applied studies which enables one to provide references to work of others. This essay outlines the many concepts, new theory, and important algorithms of our new culture of statistical science called LP MIXED DATA SCIENCE. It provides comprehensive solutions to problems of data analysis and nonparametric modeling of many variables that are continuous or discrete, which does not yet have a large literature. It develops a new modeling approach to nonparametric estimation of the multivariate copula density. We discuss the theory which we believe is very elegant (and can provide a framework for United Statistical Algorithms, for traditional Small Data methods and Big Data methods).},
  archiveprefix = {arXiv},
  keywords = {Mathematics - Statistics Theory,Statistics - Methodology},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/KKBUQBLV/Parzen and Mukhopadhyay - 2013 - LP Mixed Data Science  Outline of Theory.pdf;/Users/rca2t1/Dropbox/Zotero/storage/PGU6DAD3/1311.html}
}

@techreport{parzenNonparametricStatisticalData1977,
  type = {Grant {{Technical Report}}},
  title = {Nonparametric {{Statistical Data Science}}:  {{A Unified Approach Based}} on {{Density Estimation}} and {{Testing}} for '{{White Noise}}'},
  shorttitle = {{{DTIC ADA051090}}},
  author = {Parzen, Emanuel},
  year = {1977},
  month = jan,
  number = {ARO-1},
  pages = {63},
  institution = {{Defense Technical Information Center}},
  abstract = {This paper proposes an approach to non-parametric statistical continuous data science which seems to be consistent with the conventional theories and methods of non-parametric inference but seems to point the way to universally applicable procedures (for continuous data) which are asymptotically as efficient as the best conventional goodness of fit and parameter estimation procedures available for each particular problem. The methods described are programmed and found successful in test cases. However, in the space available here only 'Chapter 1' of this work can be discussed. It outline the ideas:  how the basic general applied problems of statistical inference can be formulated as problems of estimation of distribution functions on the unit interval (or the unit hyper-cube), how such problems are more fruitfully treated as density estimation problems, and how to solve density estimation problems one can use the method which is the essence of the highly successful maximum likelihood method of parameter estimation: using a suitable information-theoretic divergence distance between densities, find the smooth density which is closest to a raw estimator of the density.},
  langid = {english},
  keywords = {*NONPARAMETRIC STATISTICS,*STATISTICAL TESTS,*WHITE NOISE,ASYMPTOTIC NORMALITY,BIVARIATE ANALYSIS,DENSITY,DTIC Archive,ESTIMATES,NORMAL DISTRIBUTION,ORDER STATISTICS,Parzen;Emanuel,RANK ORDER STATISTICS,REGRESSION ANALYSIS,RELIABILITY,STATE UNIV OF NEW YORK AT BUFFALO AMHERST  STATISTICAL SCIENCE DIV,STATISTICAL INFERENCE,TIME SERIES ANALYSIS},
  annotation = {DTIC ADA051090},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UJG3SK35/Parzen - 1977 - Nonparametric Statistical Data Science  A Unified.pdf}
}

@techreport{parzenNonparametricStatisticalData1977a,
  title = {Nonparametric {{Statistical Data Science}}: {{A Unified Approach Based}} on {{Density Estimation}} and {{Testing}} for'{{White Noise}}'.},
  shorttitle = {Nonparametric {{Statistical Data Science}}},
  author = {Parzen, Emanuel},
  year = {1977},
  institution = {{STATE UNIV OF NEW YORK AT BUFFALO AMHERST STATISTICAL SCIENCE DIV}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/A9SVFPPG/Parzen - 1977 - Nonparametric Statistical Data Science A Unified .pdf}
}

@article{parzenNonparametricStatisticalData1979,
  title = {Nonparametric {{Statistical Data Modeling}}},
  author = {Parzen, Emanuel},
  year = {1979},
  journal = {Journal of the American Statistical Association},
  volume = {74},
  number = {365},
  pages = {105--121},
  publisher = {{[American Statistical Association, Taylor \& Francis, Ltd.]}},
  issn = {0162-1459},
  doi = {10.2307/2286734},
  abstract = {This article attempts to describe an approach to statistical data analysis which is simultaneously parametric and nonparametric. Given a random sample X\textsubscript{1}, {$\cdots$}, X\textsubscript{n} of a random variable X, one would like (1) to test the parametric goodness-of-fit hypothesis H\textsubscript{0} that the true distribution function F is of the form F(x) = F\textsubscript{0}[ (x - {$\mu$})/{$\sigma$}], where F\textsubscript{0} is specified, and (2) when H\textsubscript{0} is not accepted, to estimate nonparametrically the true density-quantile function fQ(u) and score function J(u) = - (fQ)'(u). The article also introduces density-quantile functions, autoregressive density estimation, estimation of location and scale parameters by regression analysis of the sample quantile function, and quantile-box plots.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/AWD8REAI/Parzen - 1979 - Nonparametric Statistical Data Modeling.pdf}
}

@article{patilBuildingDataScience2011,
  title = {Building Data Science Teams},
  author = {Patil, D. J.},
  year = {2011},
  month = sep,
  journal = {O'Reilly Radar},
  abstract = {A data science team needs people with the right skills and perspectives, and it also requires strong tools, processes, and interaction between the team and the rest of the company.},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NP4NE4GS/building-data-science-teams.html}
}

@misc{PDFZipfTypeToken,
  title = {(4) ({{PDF}}) {{Zipf}} and {{Type-Token}} Rules for the {{English}}, {{Spanish}}, {{Irish}} and {{Latin}} Languages},
  journal = {ResearchGate},
  abstract = {ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free.},
  howpublished = {https://www.researchgate.net/publication/228794781\_Zipf\_and\_Type-Token\_rules\_for\_the\_English\_Spanish\_Irish\_and\_Latin\_languages},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FKV9B8II/228794781_Zipf_and_Type-Token_rules_for_the_English_Spanish_Irish_and_Latin_languages.html}
}

@book{pearsonNotesSpaceTechnology1958,
  title = {Notes on {{Space Technology}}},
  author = {Pearson, Henry A.},
  year = {1958},
  month = jan,
  abstract = {These notes are part of a space technology course given at the Flight Research Division of the NACA Langley Aeronautical Laboratory during the early part of 1958. The course was conducted as a self-education program within the Flight Research Division and the various Sections of the notes were prepared for the most part by members of the Division; however, four of the seventeen Sections were prepared by personnel from the Pilotless Aircraft Research Division and the Compressibility Research Division who were very helpful in making the program more complete. The notes have been compiled on a brief time schedule and it will be apparent to the reader that the present version is incomplete and to some extent may lack uniformity in length, type of presentation, and technical detail in the various Sections. Nevertheless, there has been a demand for the notes from those who have seen them, and it is thought that they might serve a useful purpose if they were made available on a wider basis. It is believed that for the sake of expediency this goal is best achieved by making the material available now in its present unedited form instead of following the usual NACA editing procedures.},
  keywords = {Aeronautics (General)},
  annotation = {NTRS Author Affiliations: Langley Aeronautical Laboratory NTRS Meeting Information: Space Technology Course; 1958-02-01 to 1958-05-01; undefined NTRS Report/Patent Number: NASA-TM-X-69992 NTRS Document ID: 19740074640 NTRS Research Center: Langley Research Center (LaRC)},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/C42VSYBN/Pearson - 1958 - Notes on Space Technology.pdf;/Users/rca2t1/Dropbox/Zotero/storage/6L4B7HHT/19740074640.html}
}

@book{PerceptualCognitiveDevelopment1968,
  title = {Perceptual {{Cognitive Development}}},
  year = {1968},
  volume = {4},
  publisher = {{The Institute}},
  googlebooks = {V4htAAAAMAAJ},
  langid = {english}
}

@misc{PharmaNetProfessionalsAddressing2006,
  title = {{{PharmaNet Professionals Addressing Challenges}} of {{Biopharmaceutical Research}} \& {{Development During Drug Information Association Meeting}} in {{Philadelphia}}},
  year = {2006},
  month = jun,
  howpublished = {https://advance.lexis.com/document/?pdmfid=1516831\&crid=f2ea6a81-ecff-4db9-b6dc-23299202aa72\&pddocfullpath=\%2Fshared\%2Fdocument\%2Fnews\%2Furn\%3AcontentItem\%3A4K75-TVW0-TW8C-J2J7-00000-00\&pdcontentcomponentid=7924\&pdteaserkey=sr20\&pditab=allpods\&ecomp=gb63k\&earg=sr20\&prid=8420657d-9905-49d3-ac82-3e36a8870263},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/AXHRV8VZ/document.html}
}

@article{philipchenDataIntensiveApplicationsChallenges2014,
  title = {Data-{{Intensive Applications}}, {{Challenges}}, {{Techniques}} and {{Technologies}}: {{A Survey}} on {{Big Data}}},
  shorttitle = {Data-{{Intensive Applications}}, {{Challenges}}, {{Techniques}} and {{Technologies}}},
  author = {Philip Chen, C. L. and Zhang, Chun-Yang},
  year = {2014},
  month = aug,
  journal = {Information Sciences},
  volume = {275},
  pages = {314--347},
  issn = {0020-0255},
  doi = {10.1016/j.ins.2014.01.015},
  abstract = {It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore's Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as data-intensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing.},
  langid = {english},
  keywords = {Big Data,Cloud computing,Data-intensive computing,e-Science,Parallel and distributed computing},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/7CJ388ER/S0020025514000346.html}
}

@book{phillipsDigitalAnalyticsPrimer2013,
  title = {Digital {{Analytics Primer}}},
  author = {Phillips, Judah},
  year = {2013},
  month = oct,
  publisher = {{FT Press}},
  abstract = {Learn the concepts and methods for creating economic and business value with digital analytics, mobile analytics, web analytics, and market research and social media data. In  Digital Analytics Primer , pioneering expert Judah Phillips introduces the concepts, terms, and methods that comprise the science and art of digital analysis for web, site, social, video, and other types of quantitative and qualitative data. Business readers\textemdash from new practitioners to experienced executives\textemdash who want to understand how digital analytics can be used to reduce costs and increase profitable revenue throughout the business should read this book. Phillips delivers a comprehensive review of the core concepts, vocabulary, and frameworks, including analytical methods and tools that can help you successfully integrate analytical processes, technology, and people into all aspects of business operations. This unbiased and product-independent primer draws from the author's extensive experience doing and managing analytics in this field.},
  googlebooks = {ZX1eAQAAQBAJ},
  isbn = {978-0-13-355210-2},
  langid = {english},
  keywords = {Business \& Economics / Production \& Operations Management,Business \& Economics / Statistics,Computers / Databases / Data Mining,Computers / Web / Social Media}
}

@article{piatetsky-shapiroKnowledgeDiscoveryReal1991,
  title = {Knowledge {{Discovery}} in {{Real Databases}}: {{A Report}} on the {{IJCAI-89 Workshop}}},
  author = {{Piatetsky-Shapiro}, Gregory},
  year = {1991},
  month = jan,
  journal = {AI Magazine},
  volume = {11},
  number = {5},
  pages = {68--70},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CNBKB2DF/Piatetsky-Shapiro - 1991 - Knowledge Discovery in Real Databases A Report on.pdf;/Users/rca2t1/Dropbox/Zotero/storage/DN54FPH6/kdd-89-report-aimag.html}
}

@article{piatetsky-shapiroKnowledgeDisoveryReal1991,
  title = {Knowledge {{Disovery}} in {{Real Databases}}: {{A Report}} on the {{IJCAI-89 Workshop}}},
  author = {{Piatetsky-Shapiro}, Gregory},
  year = {1991},
  month = jan,
  journal = {AI Magazine},
  volume = {11},
  number = {5},
  pages = {68--70},
  annotation = {https://doi.org/10.1609/aimag.v11i4.873},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TFEGYHQB/Piatetsky-Shapiro - 1991 - Knowledge Disovery in Real Databases A Report on .pdf;/Users/rca2t1/Dropbox/Zotero/storage/BZMR3W7J/kdd-89-report-aimag.html}
}

@inproceedings{piatetsky-shapiroOverviewKnowledgeDiscovery1994,
  title = {An {{Overview}} of {{Knowledge Discovery}} in {{Databases}}: {{Recent Progress}} and {{Challenges}}},
  shorttitle = {An {{Overview}} of {{Knowledge Discovery}} in {{Databases}}},
  booktitle = {Rough {{Sets}}, {{Fuzzy Sets}} and {{Knowledge Discovery}}},
  author = {{Piatetsky-Shapiro}, Gregory},
  editor = {Ziarko, Wojciech P.},
  year = {1994},
  series = {Workshops in {{Computing}}},
  pages = {1--10},
  publisher = {{Springer}},
  address = {{London}},
  doi = {10.1007/978-1-4471-3238-7_1},
  abstract = {I examine the state of the art in Knowledge Discovery in Databases and review progress in several research areas, including discovery of models, multistrategy discovery systems, and detection of changes and deviations. I describe a number of successful applications and discuss the remaining challenges for further research and application development.},
  isbn = {978-1-4471-3238-7},
  langid = {english},
  keywords = {Discovery System,Fault Isolation,Inductive Logic Programming,Knowledge Discovery,Query Answer},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/7MY73SAT/Piatetsky-Shapiro - 1994 - An Overview of Knowledge Discovery in Databases R}
}

@misc{piatetskyDataScienceEnd2013,
  title = {Is {{Data Science The End}} of {{Statistics}}? {{A Discussion}}},
  shorttitle = {Is {{Data Science The End}} of {{Statistics}}?},
  author = {Piatetsky, Gregory},
  year = {2013},
  month = oct,
  journal = {KDnuggets},
  abstract = {Here is an interesting discussion on LinkedIn, started by a provocative post "Data Science: The End of Statistics?" What is the relationship between Data Science and Statistics and in what sense are "Statistics" ending?},
  chapter = {Apr Publications},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CKPIEB34/data-science-end-statistics-discussion.html}
}

@article{plantinDataCleanersPristine2018,
  title = {Data {{Cleaners}} for {{Pristine Datasets}}: {{Visibility}} and {{Invisibility}} of {{Data Processors}} in {{Social Science}}:},
  shorttitle = {Data {{Cleaners}} for {{Pristine Datasets}}},
  author = {Plantin, Jean-Christophe},
  year = {2018},
  month = jun,
  journal = {Science, Technology, \& Human Values},
  publisher = {{SAGE PublicationsSage CA: Los Angeles, CA}},
  doi = {10.1177/0162243918781268},
  abstract = {This article investigates the work of processors who curate and ``clean'' the data sets that researchers submit to data archives for archiving and further dissemi...},
  copyright = {\textcopyright{} The Author(s) 2018},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/YDFIN9GV/0162243918781268.html}
}

@article{pollockForgetBigData,
  title = {Forget {{Big Data}}, {{Small Data Is}} the {{Real Revolution}}},
  author = {Pollock, Rufus},
  journal = {The Guardian},
  issn = {0261-3077},
  abstract = {Rufus Pollock, Founder and Co-Director of the Open Knowledge Foundation, discusses why current hype around big data misses the point, and why the real revolution is small data, loosely joined\textbullet{} More from the Guardian on big data\textbullet{} More data journalism and data visualisations from the Guardian},
  chapter = {News},
  langid = {british},
  keywords = {big data,Global development,Transparency},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4QI22VIS/forget-big-data-small-data-revolution.html}
}

@misc{porterFrameworkDataScience2020,
  title = {A {{Framework}} for {{Data Science}}},
  author = {Porter, Michael D.},
  year = {2020},
  month = may,
  file = {/Users/rca2t1/Dropbox/Zotero/storage/YKL744PW/Porter - 2020 - A Framework for Data Science.pdf}
}

@misc{prangeUseCosetEquivalence1959,
  title = {The {{Use}} of {{Coset Equivalence}} in the {{Analysis}} and {{Decoding}} of {{Group Codes}}},
  author = {Prange, Eugene},
  year = {1959},
  month = jun,
  publisher = {{Defense Documentation Center for Scientific and Technical Information}},
  annotation = {AD-226 767},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9L25PYP8/Prange - 1959 - The Use of Coset Equivalence in the Analysis and D.pdf}
}

@misc{pressDataScientistsDefinition2012,
  title = {Data {{Scientists}}: {{The Definition}} of {{Sexy}}},
  shorttitle = {Data {{Scientists}}},
  author = {Press, Gil},
  year = {2012},
  month = sep,
  journal = {Forbes},
  abstract = {A pocket protector. (Photo credit: Wikipedia) I put ``sexy'' in the title because I'm told that the words in the title make all the difference in getting noticed on the Web. That has certainly proven true for the Harvard Business Review after it included the word ``sexiest'' in the title of [...]},
  chapter = {Tech},
  howpublished = {https://www.forbes.com/sites/gilpress/2012/09/27/data-scientists-the-definition-of-sexy/},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CMDYSVJ8/data-scientists-the-definition-of-sexy.html}
}

@misc{pressSupplyDemandData2015,
  title = {The {{Supply And Demand Of Data Scientists}}: {{What The Surveys Say}}},
  shorttitle = {The {{Supply And Demand Of Data Scientists}}},
  author = {Press, Gil},
  year = {2015},
  month = apr,
  journal = {Forbes},
  abstract = {The results of two surveys about data scientists were released this week, covering both the supply and demand sides of this hot new profession, ``the sexiest job of the 21st Century.''},
  chapter = {Tech},
  howpublished = {https://www.forbes.com/sites/gilpress/2015/04/30/the-supply-and-demand-of-data-scientists-what-the-surveys-say/},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FVH7X4SY/the-supply-and-demand-of-data-scientists-what-the-surveys-say.html}
}

@misc{pressVeryShortHistory2012,
  title = {A {{Very Short History}} of {{Big~Data}}},
  author = {Press, Gil},
  year = {2012},
  month = jun,
  journal = {What's The Big Data?},
  abstract = {In addition to researching A Very Short History of Data Science, I have also been looking at the history of how data became big. Here I focus on the history of attempts to quantify the growth rate ...},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/XVHUXDB6/a-very-short-history-of-big-data.html}
}

@article{pressVeryShortHistory2013,
  title = {A {{Very Short History Of Data Science}}},
  author = {Press, Gil},
  year = {2013},
  month = may,
  journal = {Forbes},
  abstract = {The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science.~ The term ``Data Science'' has emerged only recently to specifically designate a new profession that is expected to make sense of the vast [...]},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HJ8F5TYL/a-very-short-history-of-data-science.html}
}

@patent{PRIORDATASCIENCES1998,
  title = {{{PRIOR DATA SCIENCES LTD}} / {{KEN MACKAY}}, {{Consultant}}},
  year = {1998},
  month = oct,
  abstract = {Version 1 of 1 (1998-10-16 to 2000-02-03) was submitted prior to the Lobbying Act coming into force on July 2, 2008. Due to different information requirements at that time, the registration is presented in the following format.},
  langid = {english},
  annotation = {Last Modified: Insert the date of last modification (YYYY-MM-DD)},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EK6TQJM7/vwRg.html}
}

@book{Proceedings1983,
  title = {Proceedings},
  year = {1983},
  publisher = {{U. S. Army Research Office}},
  googlebooks = {nm7xAAAAMAAJ},
  langid = {english}
}

@book{Proceedings1stAdHoc1965,
  title = {Proceedings of 1st {{Ad-Hoc Forum}} of {{Scientific}} and {{Technical Information Analysis Center Managers}}, {{Directors}}, and {{Professional Analysts}}, {{Held}} at {{Battelle Memorial Institute}}, {{Columbus}}, {{Ohio}}, {{November}} 9-11, 1965},
  year = {1965},
  publisher = {{U.S. Atomic Energy Commission, Division of Technical Information}},
  googlebooks = {nNG\_8YFInMYC},
  langid = {english}
}

@book{ProceedingsConferenceDesign1980,
  title = {Proceedings of the ... {{Conference}} on the {{Design}} of {{Experiments}}},
  year = {1980},
  publisher = {{U.S. Army Research Office}},
  googlebooks = {0LsrAQAAMAAJ},
  langid = {english}
}

@misc{ProceedingsGoogleBooks,
  title = {Proceedings - {{Google Books}}},
  howpublished = {https://www.google.com/books/edition/Proceedings/nm7xAAAAMAAJ?hl=en\&gbpv=0},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TMH366LX/nm7xAAAAMAAJ.html}
}

@misc{ProtocolProjectRanch1979,
  title = {Protocol: {{Project Ranch Hand II}}, {{Epidemiologic Investigation}} of {{Health Effects}} in {{Air Force Personnel Following Exposure}} to "{{Herbicide Orange}}", {{Matched Cohort Design}}},
  shorttitle = {Protocol},
  year = {1979},
  month = dec,
  abstract = {Corporate Author: Epidemiology Division, Data Sciences Division, Clinical Sciences Division, USAF School of Aerospace Medicine (USAFSAM), Brooks AFB, Texas},
  keywords = {Air Force Health Study},
  annotation = {Context Object: ctx\_ver=Z39.88-2004\&rft\_val\_fmt=info\%3Aofi\%2Ffmt\%3Akev\%3Amtx\%3Adc\&rfr\_id=info\%3Asid\%2Fomeka.org\%3Agenerator\&rft.subject=Air+Force+Health+Study\&rft.date=December+12+1979\&rft.title=Protocol\%3A+Project+Ranch+Hand+II\%2C+Epidemiologic+Investigation+of+Health+Effects+in+Air+Force+Personnel+Following+Exposure+to+\%22Herbicide+Orange\%22\%2C+Matched+Cohort+Design\&rft.description=Corporate+Author\%3A+Epidemiology+Division\%2C+Data+Sciences+Division\%2C+Clinical+Sciences+Division\%2C+USAF+School+of+Aerospace+Medicine+\%28USAFSAM\%29\%2C+Brooks+AFB\%2C+Texas\&rft.type=document\&rft.identifier=https\%3A\%2F\%2Fwww.nal.usda.gov\%2Fexhibits\%2Fspeccoll\%2Fitems\%2Fshow\%2F2566},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/98EEWW2Q/1979 - Protocol Project Ranch Hand II, Epidemiologic Inv.pdf;/Users/rca2t1/Dropbox/Zotero/storage/HX2KGLXK/2566.html}
}

@book{Publication1951,
  title = {Publication},
  year = {1951},
  publisher = {{National Academy of Sciences, National Research Council}},
  langid = {english}
}

@misc{ramanathanDataScienceDelusion2016,
  title = {The {{Data Science Delusion}}},
  author = {Ramanathan, Anand},
  year = {2016},
  month = dec,
  journal = {Medium},
  abstract = {Prologue},
  howpublished = {https://medium.com/@anandr42/the-data-science-delusion-7759f4eaac8e},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/39NFR4BT/the-data-science-delusion-7759f4eaac8e.html}
}

@misc{RandalBryantRandy,
  title = {Randal {{Bryant}}, {{Randy H}}. {{Katz}} and {{Edward D}}. {{Lazowska}}, ``{{Big-Data Computing}}: {{Creating Revolutionary Breakthroughs}} in {{Commerce}}, {{Science}} and {{Society}},'' {{December}} 2008, Pp. 1-15, at {{http://www.cra.org/ccc/docs/init/Big\_Data.pdf.}} - {{Google Search}}},
  howpublished = {https://www.google.com/search?q=Randal+Bryant\%2C+Randy+H.+Katz+and+Edward+D.+Lazowska\%2C+\%E2\%80\%9CBig-Data+Computing\%3A+Creating+Revolutionary+Breakthroughs+in+Commerce\%2C+Science+and+Society\%2C\%E2\%80\%9D+December+2008\%2C+pp.+1-15\%2C+at+http\%3A\%2F\%2Fwww.cra.org\%2Fccc\%2Fdocs\%2Finit\%2FBig\_Data.pdf.\&oq=Randal+Bryant\%2C+Randy+H.+Katz+and+Edward+D.+Lazowska\%2C+\%E2\%80\%9CBig-Data+Computing\%3A+Creating+Revolutionary+Breakthroughs+in+Commerce\%2C+Science+and+Society\%2C\%E2\%80\%9D+December+2008\%2C+pp.+1-15\%2C+at+http\%3A\%2F\%2Fwww.cra.org\%2Fccc\%2Fdocs\%2Finit\%2FBig\_Data.pdf.\&aqs=chrome..69i57.911j0j7\&sourceid=chrome\&ie=UTF-8},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/WJDDBURW/search.html}
}

@article{rappaportRitualSanctityCybernetics1971,
  title = {Ritual, {{Sanctity}}, and {{Cybernetics}}},
  author = {Rappaport, Roy A.},
  year = {1971},
  journal = {American Anthropologist},
  series = {New {{Series}}},
  volume = {73},
  number = {1},
  pages = {59--76},
  issn = {00027294},
  abstract = {The role of the sacred in human communication and in the regulation of social and ecological systems is approached through ritual. After a brief review of salient features of Maring ethnography, formal characteristics of rituals making them suitable for communication and regulation functions are examined. From this discussion a concept of sanctity relating it to a problem inherent in symbolic communication is derived. The non-discussive basis of sanctity is then considered and the role of the sacred in the cybernetics of social and ecological systems is then discussed. Next it is suggested that the relationship between sanctity and authority changes with technological development. Finally, some tentative suggestions concerning the origins of the sacred are advanced.},
  lccn = {0150},
  annotation = {ArticleType: primary\_article / Full publication date: Feb., 1971 / Copyright \textcopyright{} 1971 American Anthropological Association},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/QMN9KFW7/Rappaport - 1971 - Ritual, Sanctity, and Cybernetics.pdf}
}

@book{ratnerStatisticalMachineLearningData2017,
  title = {Statistical and {{Machine-Learning Data Mining}}: {{Techniques}} for {{Better Predictive Modeling}} and {{Analysis}} of {{Big Data}}, {{Third Edition}}},
  shorttitle = {Statistical and {{Machine-Learning Data Mining}}},
  author = {Ratner, Bruce},
  year = {2017},
  month = jul,
  publisher = {{CRC Press}},
  abstract = {Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.  What is new in the Third Edition:   The current chapters have been completely rewritten.    The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops.   Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP).   Includes SAS subroutines which can be easily converted to other languages.  As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.},
  googlebooks = {ulAsDwAAQBAJ},
  isbn = {978-1-351-65238-4},
  langid = {english},
  keywords = {Computers / Databases / Data Mining,Computers / Machine Theory,Mathematics / Probability \& Statistics / General}
}

@misc{reddyWhoActuallyCoined2014,
  title = {Who {{Actually Coined}} the {{Term Big Data}}},
  author = {Reddy, Bharath},
  year = {2014},
  month = feb,
  journal = {MyTechLogy},
  abstract = {Big Data as we speak, may be part of many Business leaders day to day conversations and thousands of professionals researching on extracting value from ...},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SVUVK5P2/who-actually-coined-the-term-big-data.html}
}

@article{reganFourthParadigmTony2012,
  title = {The {{Fourth Paradigm}} by {{Tony Hey}}, {{Stewart Tansley}}, and {{Kristin Tolle}}},
  author = {Regan, Clinton Joseph},
  year = {2012},
  journal = {InterActions: UCLA Journal of Education and Information Studies},
  volume = {8},
  number = {1},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DCNT3SCE/Regan - 2012 - The Fourth Paradigm by Tony Hey, Stewart Tansley, .pdf}
}

@incollection{reidManVsMachine2016,
  title = {Man vs. {{Machine}}: {{The Battle}} for the {{Soul}} of {{Data Science}}},
  shorttitle = {Man vs. {{Machine}}},
  booktitle = {Big {{Data Challenges}}: {{Society}}, {{Security}}, {{Innovation}} and {{Ethics}}},
  author = {Reid, David},
  editor = {Bunnik, Anno and Cawley, Anthony and Mulqueen, Michael and Zwitter, Andrej},
  year = {2016},
  pages = {11--22},
  publisher = {{Palgrave Macmillan UK}},
  address = {{London}},
  doi = {10.1057/978-1-349-94885-7_2},
  abstract = {David Reid asks if data science is just a trendy rebadging of statistics, or whether it is something fundamentally new. The chapter describes how two camps\textemdash data scientists and statisticians\textemdash are battling for the `soul' of data science. The essence of this fight is the argument for and against the concept of `automated reasoning'. Just as the wheel allowed mankind to physically carry far greater loads, could another technology\textemdash automated reasoning\textemdash enable people to share intellectual burdens? Advances in Artificial Intelligence using Big Data could mean people are no longer the sole agents of genuine discovery and may soon share this special attribute with genuinely intelligent and inventive machines.},
  isbn = {978-1-349-94885-7},
  langid = {english},
  keywords = {Algorithms,Artificial intelligence,Automated reasoning,Big Data,Data science,Human intelligence,Machine learning,Statistics}
}

@book{Report1964,
  title = {Report},
  year = {1964},
  googlebooks = {T9SjJtvL1jUC},
  langid = {english}
}

@misc{researchanddevelopmentresearchteamDataScientistHiring2019,
  title = {Data {{Scientist Hiring Guide}}},
  author = {{Research and Development Research Team}},
  year = {2019},
  month = oct,
  journal = {Gartner},
  abstract = {Gartner Research on Data Scientist Hiring Guide},
  howpublished = {https://www.gartner.com/en/documents/3970210/data-scientist-hiring-guide},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4LG2CDZT/3970210.html}
}

@book{resnikFormalizationFoundationsGeometry1961,
  title = {The {{Formalization}} of the {{Foundations}} of {{Geometry Within}} the {{System}} of {{Symbolic Logic}}},
  author = {Resnik, Michael D.},
  year = {1961},
  publisher = {{Electronics Research Directorate, Air Force Cambridge Research Laboratories, Office of Aerospace Research, U. S. Air Force}},
  googlebooks = {IaxFAAAAYAAJ},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2M9JGDUQ/Resnik - 1961 - The Formalization of the Foundations of Geometry W.pdf}
}

@article{reuylStalkingGuidedMissile1951,
  title = {Stalking the {{Guided Missile}}: {{New Instruments Track}} and {{Report}} the {{Performance}} of {{Long-Range Rockets}}},
  shorttitle = {Stalking the {{Guided Missile}}},
  author = {Reuyl, Dirk and {de Bey}, L. G.},
  year = {1951},
  journal = {Ordnance},
  volume = {36},
  number = {188},
  pages = {237--241},
  publisher = {{National Defense Industrial Association}},
  issn = {0030-4557},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UKWB74V9/Reuyl and de Bey - 1951 - Stalking the Guided Missile New Instruments Track.pdf}
}

@book{rizziAdvancesDataScience2013,
  title = {Advances in {{Data Science}} and {{Classification}}: {{Proceedings}} of the 6th {{Conference}} of the {{International Federation}} of {{Classification Societies}} ({{IFCS-98}}) {{Universit\`a}} ``{{La Sapienza}}'', {{Rome}}, 21\textendash 24 {{July}}, 1998},
  shorttitle = {Advances in {{Data Science}} and {{Classification}}},
  author = {Rizzi, Alfredo and Vichi, Maurizio and Bock, Hans-Hermann},
  year = {2013},
  publisher = {{Springer Science \& Business Media}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JZM9YHPM/books.html}
}

@misc{rodriguezASABigData2013,
  title = {The {{ASA}} and {{Big Data}}},
  author = {Rodriguez, Robert and Davidian, Marie and Schenker, Nathaniel},
  year = {2013},
  month = jun,
  journal = {Amstat News},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2DDP9ZWL/the-asa-and-big-data.html}
}

@misc{rodriguezBigDataBetter2012,
  title = {Big {{Data}} and {{Better Data}}},
  author = {Rodriguez, Robert},
  year = {2012},
  month = jun,
  journal = {Amstat News},
  abstract = {Bob Rodriguez talks big data in the June President's Corner column.},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/43GZXTXM/June2012.pdf;/Users/rca2t1/Dropbox/Zotero/storage/4UDK73BS/prescorner.html}
}

@article{rooneyBigDataBig2012,
  title = {Big {{Data}}'s {{Big Problem}}: {{Little Talent}}},
  shorttitle = {Big {{Data}}'s {{Big Problem}}},
  author = {Rooney, Ben},
  year = {2012},
  month = apr,
  journal = {Wall Street Journal},
  issn = {0099-9660},
  abstract = {Big Data is the next big thing in technology, but the people who know how to make the most of it are in short supply.},
  chapter = {Tech},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HLWQAKBR/SB10001424052702304723304577365700368073674.html}
}

@techreport{rosenfeldImageAnalysisComputer1998,
  title = {From {{Image Analysis}} to {{Computer Vision}}: {{Motives}}, {{Methods}}, and {{Milestones}}},
  author = {Rosenfeld, Azriel},
  year = {1998},
  month = jul,
  address = {{College Park, MD}},
  institution = {{Center for Automation Research, University of Maryland}},
  annotation = {CAR-TR-892  N00014-95-1-0521 CS-TR-3920},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/D2NN8XEM/Rosenfeld - 1998 - From Image Analysis to Computer Vision Motives, M.pdf}
}

@article{roureImpactWorkflowTools,
  title = {The {{Impact}} of {{Workflow Tools}} on {{Data-centric Research}}},
  author = {RoURE, DAvID DE},
  journal = {F our T h P aradigm},
  pages = {137},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/T8V6PQKD/RoURE - The Impact of Workflow Tools on Data-centric Resea.pdf}
}

@book{russellArtificialIntelligenceModern1995,
  title = {Artificial {{Intelligence}}: {{A Modern Approach}}},
  shorttitle = {Artificial {{Intelligence}}},
  author = {Russell, Stuart Jonathan and Norvig, Peter},
  year = {1995},
  publisher = {{Prentice Hall}},
  abstract = {Intelligent Agents - Stuart Russell and Peter Norvig show how intelligent agents can be built using AI methods, and explain how different agent designs are appropriate depending on the nature of the task and environment. Artificial Intelligence: A Modern Approach is the first AI text to present a unified, coherent picture of the field. The authors focus on the topics and techniques that are most promising for building and analyzing current and future intelligent systems. The material is comprehensive and authoritative, yet cohesive and readable. State of the Art - This book covers the most effective modern techniques for solving real problems, including simulated annealing, memory-bounded search, global ontologies, dynamic belief networks, neural networks, adaptive probabilistic networks, inductive logic programming, computational learning theory, and reinforcement learning. Leading edge AI techniques are integrated into intelligent agent designs, using examples and exercises to lead students from simple, reactive agents to advanced planning agents with natural language capabilities.},
  googlebooks = {CUVeMwAACAAJ},
  isbn = {978-0-13-103805-9},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DXF2CVUG/Russell and Norvig - 1995 - Artificial Intelligence A Modern Approach.pdf}
}

@article{russellArtificialIntelligenceModern2002,
  title = {Artificial Intelligence: A Modern Approach},
  shorttitle = {Artificial Intelligence},
  author = {Russell, Stuart and Norvig, Peter},
  year = {2002},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/AWEDLRBU/Russell and Norvig - 2002 - Artificial intelligence a modern approach.pdf}
}

@book{russellMiningSocialWeb2011,
  title = {Mining the {{Social Web}}: {{Analyzing Data}} from {{Facebook}}, {{Twitter}}, {{LinkedIn}}, and {{Other Social Media Sites}}},
  shorttitle = {Mining the {{Social Web}}},
  author = {Russell, Matthew},
  year = {2011},
  month = jan,
  publisher = {{"O'Reilly Media, Inc."}},
  abstract = {Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they're talking about, or where they're located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed. Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools. Get a straightforward synopsis of the social web landscape Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn Learn how to employ easy-to-use Python tools to slice and dice the data you collect Explore social connections in microformats with the XHTML Friends Network Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits "Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python."  --Jeff Hammerbacher, Chief Scientist, Cloudera  "A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data."  --Alex Martelli, Senior Staff Engineer, Google},
  googlebooks = {SYM1lrQdrdsC},
  isbn = {978-1-4493-8834-8},
  langid = {english},
  keywords = {Computers / Data Science / Data Analytics,Computers / Internet / Social Media,Computers / Internet / Web Programming,Computers / Programming / General,Computers / Software Development \& Engineering / General}
}

@inproceedings{saltzExploringProcessDoing2015,
  title = {Exploring the Process of Doing Data Science via an Ethnographic Study of a Media Advertising Company},
  booktitle = {2015 {{IEEE International Conference}} on {{Big Data}} ({{Big Data}})},
  author = {Saltz, Jeffrey S. and Shamshurin, Ivan},
  year = {2015},
  month = oct,
  pages = {2098--2105},
  doi = {10.1109/BigData.2015.7363992},
  abstract = {This paper presents the results of an ethnographic study focused on how data science projects were conducted within a global media advertising company. Observations, via embedding a researcher within the team, as well as more structured interviews and surveys, are documented. Recommendations to improve the current data science methodology within the company are also discussed. Overall, there had been little focus on the team's process methodology and the suggested process improvements would result in the company's data science projects having less risk and shorter timelines. Other big data teams might also benefit from reviewing and refining their work processes, but more work needs to be done to validate this assumption.},
  keywords = {advertising,Big data,Big Data,Big Data teams,Companies,company data science projects,Context,Data Science,ethnographic study,global media advertising company,media advertising company,process improvements,Process Methodology,Software,team process methodology},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/IIRAUHNY/Saltz and Shamshurin - 2015 - Exploring the process of doing data science via an.pdf;/Users/rca2t1/Dropbox/Zotero/storage/4UXZP9HW/7363992.html}
}

@inproceedings{saltzFrameworkDescribingBig2017,
  title = {A {{Framework}} for {{Describing Big Data Projects}}},
  booktitle = {Business {{Information Systems Workshops}}},
  author = {Saltz, Jeffrey and Shamshurin, Ivan and Connors, Colin},
  editor = {Abramowicz, Witold and Alt, Rainer and Franczyk, Bogdan},
  year = {2017},
  series = {Lecture {{Notes}} in {{Business Information Processing}}},
  pages = {183--195},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-319-52464-1_17},
  abstract = {With the ability to collect, store and analyze an ever-growing diversity of data generated with ever-increasing frequency, Big Data is a rapidly growing field. While tremendous strides have been made in the algorithms and technologies that are used to perform the analytics, much less has been done to determine how the team should work together to do a Big Data project. Our research reports on a set of case studies, where researchers were embedded within Big Data teams. Since project methodologies will likely depend on the attributes of a Big Data effort, we focus our analysis on defining a framework to describe a Big Data project. We then use this framework to describe the organizations we studied and some of the socio-technical challenges linked to these newly defined project characteristics.},
  isbn = {978-3-319-52464-1},
  langid = {english},
  keywords = {Big data,Data science,Process methodology,Project management}
}

@article{saltzPredictingDataScience2017,
  title = {Predicting {{Data Science Sociotechnical Execution Challenges}} by {{Categorizing Data Science Projects}}},
  author = {Saltz, Jeffrey and Shamshurin, Ivan and Connors, Colin},
  year = {2017},
  month = dec,
  journal = {Journal of the Association for Information Science and Technology},
  volume = {68},
  number = {12},
  pages = {2720--2728},
  publisher = {{Wiley}},
  address = {{Hoboken}},
  issn = {2330-1635},
  doi = {10.1002/asi.23873},
  abstract = {The challenge in executing a data science project is more than just identifying the best algorithm and tool set to use. Additional sociotechnical challenges include items such as how to define the project goals and how to ensure the project is effectively managed. This paper reports on a set of case studies where researchers were embedded within data science teams and where the researcher observations and analysis was focused on the attributes that can help describe data science projects and the challenges faced by the teams executing these projects, as opposed to the algorithms and technologies that were used to perform the analytics. Based on our case studies, we identified 14 characteristics that can help describe a data science project. We then used these characteristics to create a model that defines two key dimensions of the project. Finally, by clustering the projects within these two dimensions, we identified four types of data science projects, and based on the type of project, we identified some of the sociotechnical challenges that project teams should expect to encounter when executing data science projects.},
  langid = {english},
  keywords = {big data},
  annotation = {WOS:000414718000003},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/T4H63JQP/Saltz et al. - 2017 - Predicting Data Science Sociotechnical Execution C.pdf}
}

@techreport{samnOptimalStagingScheduling1981,
  title = {Optimal {{Staging}} and {{Scheduling}} in {{Airlift Operations}}.},
  author = {Samn, Sherwood W.},
  year = {1981},
  month = sep,
  institution = {{SCHOOL OF AEROSPACE MEDICINE BROOKS AFB TX}},
  abstract = {This report shows that to achieve maximum aircraft utilization in airlift operations, optimal scheduling and staging can be formulated as a generalized network problem and solved using mixed-integer programming techniques. Author},
  chapter = {Technical Reports},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/T3MIIXKZ/Samn - 1981 - Optimal Staging and Scheduling in Airlift Operatio.pdf;/Users/rca2t1/Dropbox/Zotero/storage/AU9NEB3D/ADA107518.html}
}

@article{samuelArtificialIntelligenceFrontier1962,
  title = {Artificial {{Intelligence}}: {{A Frontier}} of {{Automation}}},
  shorttitle = {Artificial {{Intelligence}}},
  author = {Samuel, Arthur L.},
  year = {1962},
  journal = {The Annals of the American Academy of Political and Social Science},
  volume = {340},
  pages = {10--20},
  publisher = {{[Sage Publications, Inc., American Academy of Political and Social Science]}},
  issn = {0002-7162},
  abstract = {Artificial intelligence is neither a myth nor a threat to man. It relates to a serious attempt to develop machine methods for dealing with some of the perplexing problems that should, in all justice, be delegated to machines but which now seem to require the exercise of human intelligence. Two fundamentally different approaches to the problem are being explored, the one aimed at a complete understanding of the intellectual processes involved and the other aimed at duplicating the assumed specific behavior of the brain. The first approach concerns itself with such matters as search, pattern recognition, learning, planning, and induction; the second approach involves a study of the behavior of random nets. It is fair to conclude that artificial intelligence promises to reduce rather than to augment technological unemployment.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/84KVFGGE/Samuel - 1962 - Artificial Intelligence A Frontier of Automation.pdf}
}

@inproceedings{saportaDataMiningOfficial2000,
  title = {Data {{Mining}} and {{Official Statistics}}},
  booktitle = {Quinta {{Conferenza Nationale}} Di {{Statistica}}},
  author = {Saporta, Gilbert},
  year = {2000},
  pages = {41--46},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NG6E9BMU/Saporta - 2000 - Data Mining and Official Statistics.pdf;/Users/rca2t1/Dropbox/Zotero/storage/AK7797TW/hal-01124591.html}
}

@article{sarganReviewEssaysIndustrial1971,
  title = {Review of {{Essays}} in {{Industrial Econometrics}}},
  author = {Sargan, J. D.},
  year = {1971},
  journal = {The Economic Journal},
  volume = {81},
  number = {321},
  pages = {163--165},
  publisher = {{[Royal Economic Society, Wiley]}},
  issn = {0013-0133},
  doi = {10.2307/2229776},
  collaborator = {Klein, L. R.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3VQ86IGR/Sargan - 1971 - Review of Essays in Industrial Econometrics.pdf}
}

@article{saundersDataScienceData2013,
  title = {Data {{Science}} and {{Data Scientists}}: {{What}}'s in a {{Name}}?},
  shorttitle = {Data {{Science}} and {{Data Scientists}}},
  author = {Saunders, Todd},
  year = {2013},
  month = nov,
  journal = {Information Management},
  volume = {Vol.1, No.1},
  publisher = {{SourceMedia, Inc.}},
  abstract = {Once again, people in the information management field are confronted with a new term surrounded by lots of excitement and promise, but also lots of hype and ambiguity. The term du jour is data science. I will attempt to give a reasonable definition for data science and describe the capabilities possessed by a data scientist. I'll also attempt to compare and contrast data science with business intelligence and data analysis, which often seem to be confused with (or considered synonymous with) with data science. The Hype},
  chapter = {News},
  copyright = {\textcopyright{} 2013 Information Management and SourceMedia, Inc. All rights reserved.},
  langid = {english},
  keywords = {Applications Software,Business Intelligence Software,Computers/Electronics,Computing,Software,Technology}
}

@book{schaderDataScienceApplied2012,
  title = {Between {{Data Science}} and {{Applied Data Analysis}}: {{Proceedings}} of the 26th {{Annual Conference}} of the {{Gesellschaft F\"ur Klassifikation EV}}, {{University}} of {{Mannheim}}, {{July}} 22\textendash 24, 2002},
  shorttitle = {Between {{Data Science}} and {{Applied Data Analysis}}},
  author = {Schader, Martin and Gaul, Wolfgang A. and Vichi, Maurizio},
  year = {2012},
  publisher = {{Springer Science \& Business Media}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PJLLNQCU/books.html}
}

@book{schaffelEmergingShieldAir1991,
  title = {The {{Emerging Shield}}: {{The Air Force}} and the {{Evolution}} of {{Continental Air Defense}}, 1945-1960},
  shorttitle = {The {{Emerging Shield}}},
  author = {Schaffel, Kenneth and {United States. Air Force. Office of Air Force History}},
  year = {1991},
  publisher = {{Washington, D.C. : Office of Air Force History, United States Air Force}},
  abstract = {Contents Foreword v Preface vii Acknowledgments x i Chapters 1. Genesis of the Air Defense Mission 1 The United States and Air Defense: The Early Years 6 Defining the Mission 8  Development of Air Defense Doctrine and Tactics 9  Gordon P. Saville 13  2. Air Defense in World War II 21  The First Air Defense Command, 1940-1941 24 Air Defense in Wartime 36 3. Planning for Air Defense in the Postwar Era 47 Establishment of the Air Defense Command 50 Early Planning Efforts 59 The Radar Fence Plan 67 Attempts to Come to Terms with the Mission 73 Active Operations Begin 76 4. Saville Takes Charge 83 Revision of the Radar Fence Plan 87 Establishment of the Continental Air Command 95  Taking the Case to Congress 97 Fighter Aircraft for Air Defense 98 Further Organizational Changes 102 5. Broadening Dimensions: Air Defense as a Public Issue 107  The Revolt of the Admirals 108 Impact of the Soviet Atomic Bomb 111 Roles and Missions Dilemmas 115 Air Defense Forces in the Field 122  6. Continental Air Defense in the Korean War Period 129  Reorganization and the Impact of the Chinese Intervention 139  Confronting Realities 152 Status of the Fighter-Interceptor Forces 160  7. An Integrated, Efficient, Highly Potent Air Defense System 169 Next to the Real Thing 169 East River 172 The Summer Study Group 174 The Decision to Proceed 191 8. Defensive Systems Become Operational 197 SAGE: A Command and Control Network for Air Defense 197  Warning Lines 209  Other Warning Systems 217  The Air Defense Weapons Force 224  BOMARC 235  9. Organizing to Meet the Threat 241  Continental Air Defense Command: A Joint Command for Air Defense 241  North American Air Defense Command 246  10. Epilogue: Impact of a New Threat 255  Effects of the ICBM Threat on the Air Defenses 260  Aftermath: Deterioration of the Air Defenses 266  Appendices 1. Milestones in U.S. Air Defense to 1960 277  2. ADC Assigned Personnel Strength and Commanders, to 1960 286  3. ADC Commanders 287  Notes 289  Glossary 317  Bibliography 323  Index 335  Photographs  Capt. Gordon P. Saville 15  General George C. Marshall 24  Maj. Gen. James E. Chaney 25  Plotting board, Watertown maneuvers, August 1940 26  Information center and Air Defense Headquarters, Watertown maneuvers 28 Filter board staffed by civilians, January 1941 31  Moored barrage balloon of the type used by the Antiaircraft Artillery Command 34  Antiaircraft artillery searchlights, circa 1940 35  Maj. Gen. Muir S. Fairchild 42  Brig. Gen. Gordon P. Saville 44  General of the Army, Henry H. Arnold 51  General Carl A. Spaatz 53  Maj. Gen. George E. Stratemeyer 55  P-61 Black Widow 60  Brig. Gen. Earle E. Partridge 66  W. Stuart Symington becomes Secretary of the Air Force 70  P-82 Twin Mustang 78  Tu-4 Bull 85  P-80 Shooting Star and B-29 Superfortress 88  AN/CPS-5 search radar 94  F-89 Scorpion 100  F-86 Sabre 101  F-102 Delta Dagger 102  General Hoyt S. Vandenberg becomes Chief of Staff, U.S.  Air Force 108  Lt. Gen. Ennis C. Whitehead 123  F-86D Sabre 124  F-94Starfire 125  General Benjamin W. Chidlaw 149  Plexiglass plotting board, mid-1950s 151  Early computer patch panel and component parts 153  General Frederic H. Smith 154  Tu-95 Bear and Tu-16 Badger 182  General Nathan F. Twining accepting second tour as Chief of Staff 192  SAGE 64X64 magnetic core memory 198  AN/FSQ-7 radar 205  SAGE direction center, Stewart Air Force Base, New York 208 Pinetree Line 210  DEW Line module 213 DEW Line site 214 DEW Line tower 215 DEW Line post 216 Texas Tower 218 RC-121C 221 F-106A Delta Dart 228 F-101B Voodoo 232 GAR-1 Falcon 233 IM-99 BOM ARC 237 NORAD site and Cheyenne Mountain 262 NORAD COC dining room 263 SAGE post, Syracuse, New York 265 Charts  Fighter-Interceptor Squadrons Assigned, December 1946-June 1954 162 Fighter-Interceptor Aircraft on Hand, December 1950- June 1954 163 ADC Day Conventional Fighters (Jet) Interceptors Possessed, 1951-1954 165 Combat-Ready Aircraft and Aircrews 225 ADC Jet All-Weather Interceptors Possessed (Century Series) 1956-1973 (as of December 31) 231 Maps Air Defense Command Long-Term Plan 64 Areas of Responsibility for Air Defense, July 1948-March 1949 89 Deployment of Air Defense Radar, June 1948 91 Deployment of ADC Lashup Radar Network, December 1950 93 Air Defense Identification Zones, 1952 134  Areas of Responsibility for Air Defense, November 1950 139 Air Force Areas of Responsibility for Air Defense, July 1951 146  Army Antiaircraft Artillery Command Subdivisions and Battalions Assigned, December 31, 1953 147  Ground Observer Corps Program, 1952 158  Fighter Forces Available for Air Defense in an Emergency, December 15, 1952 164 The DEW Line 211 Texas Tower Stations 219 Operational Interceptor Force, December 31, 1959 230 NORAD Operational Missile/Gun Force, July 1, 1959 248 NORAD Operational Interceptor Force, July 1, 1959 249 Region Configuration, End of 1983 266 Tables ADC Day Fighters (Jet) Possessed 166 Interceptor Squadrons Assigned ADC, by Type 166 Air Defense Aircraft 227 Comparison of Defense Department, Air Force, and ADC Budgets 269 Air Defense Matrix 270 Digitized by http://www.afhso.af.mil/; Includes bibliographical references (p. 323-333) and index},
  copyright = {http://creativecommons.org/publicdomain/mark/1.0/},
  isbn = {978-0-912799-60-5 978-0-912799-61-2},
  langid = {english},
  keywords = {United States. -- Air Force -- Air Defense Command},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HK2KCDGF/Schaffel and United States. Air Force. Office of Air Force History - 1991 - The Emerging Shield The Air Force and the Evoluti.pdf}
}

@book{schmidtProcessingSizeFrequency1971,
  title = {Processing {{Size}}, {{Frequency}}, and {{Speed Data}} from {{Snow Particle Counters}}},
  author = {Schmidt, R. A.},
  year = {1971},
  publisher = {{Rocky Mountain Forest and Range Experiment Station}},
  googlebooks = {FcZIMxaIYwAC},
  langid = {english},
  keywords = {data processing}
}

@inproceedings{schuttDoingDataScience2013,
  title = {Doing {{Data Science}}: {{Straight Talk}} from the {{Frontline}}},
  shorttitle = {Doing {{Data Science}}},
  author = {Schutt, Rachel and O'Neil, Cathy},
  year = {2013},
  abstract = {Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field thats so clouded in hype? This insightful book, based on Columbia Universitys Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If youre familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include:Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy ONeil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UVQIQQHE/Schutt and O'Neil - 2013 - Doing Data Science Straight Talk from the Frontli.pdf}
}

@book{schwartzMathematicalAspectsComputer1967,
  title = {Mathematical {{Aspects}} of {{Computer Science}}},
  author = {Schwartz, Jacob T. and Society, American Mathematical},
  year = {1967},
  month = dec,
  publisher = {{American Mathematical Soc.}},
  googlebooks = {ynigSICJflYC},
  isbn = {978-0-8218-6728-0},
  langid = {english}
}

@book{ScientificInformationNotes1959,
  title = {Scientific {{Information Notes}}},
  year = {1959},
  publisher = {{National Science Foundation}},
  googlebooks = {A5tUAAAAMAAJ},
  langid = {english}
}

@book{ScientificInformationNotes1963,
  title = {Scientific {{Information Notes}}},
  year = {1963},
  month = apr,
  publisher = {{National Science Foundation.}},
  googlebooks = {GSprMTzzax8C},
  langid = {english}
}

@book{ScientificTechnicalAerospace1968,
  title = {Scientific and {{Technical Aerospace Reports}}},
  year = {1968},
  publisher = {{Scientific and Technical Information Branch, National Aeronautics and Space Administration}},
  googlebooks = {I4ZrQg86C7kC},
  langid = {english}
}

@book{ScitechNews1963,
  title = {Sci-Tech {{News}}},
  year = {1963},
  publisher = {{Sci-Tech News}},
  googlebooks = {MVfpAAAAMAAJ},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4X3WXLZ4/data-deluge-1963.png}
}

@book{ScitechNews1963a,
  title = {Sci-Tech {{News}}},
  year = {1963},
  publisher = {{Sci-Tech News}},
  googlebooks = {MVfpAAAAMAAJ},
  langid = {english}
}

@book{segaranBeautifulDataStories2009,
  title = {Beautiful {{Data}}: {{The Stories Behind Elegant Data Solutions}}},
  shorttitle = {Beautiful {{Data}}},
  author = {Segaran, Toby and Hammerbacher, Jeff},
  year = {2009},
  month = jul,
  publisher = {{"O'Reilly Media, Inc."}},
  abstract = {In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video.With Beautiful Data, you will:Explore the opportunities and challenges involved in working with the vast number of datasets made available by the WebLearn how to visualize trends in urban crime, using maps and data mashupsDiscover the challenges of designing a data processing system that works within the constraints of space travelLearn how crowdsourcing and transparency have combined to advance the state of drug researchUnderstand how new data can automatically trigger alerts when it matches or overlaps pre-existing dataLearn about the massive infrastructure required to create, capture, and process DNA dataThat's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:Nathan YauJonathan Follett and Matt HolmJ.M. HughesRaghu Ramakrishnan, Brian Cooper, and Utkarsh SrivastavaJeff HammerbacherJason Dykes and Jo WoodJeff Jonas and Lisa SokolJud ValeskiAlon Halevy and Jayant MadhavanAaron Koblin with Valdean KlumpMichal MigurskiJeff HeerCoco KrummePeter NorvigMatt Wood and Ben BlackburneJean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon WillighagenLukas Biewald and Brendan O'ConnorHadley Wickham, Deborah Swayne, and David PooleAndrew Gelman, Jonathan P. Kastellec, and Yair GhitzaToby Segaran},
  googlebooks = {zxNglqU1FKgC},
  isbn = {978-1-4493-7929-2},
  langid = {english},
  keywords = {Business \& Economics / General,Computers / Data Modeling \& Design,Computers / Databases / Data Mining,Computers / Desktop Applications / General,Computers / General},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/IXWDM85K/Segaran and Hammerbacher - 2009 - Beautiful Data The Stories Behind Elegant Data So.pdf}
}

@article{shafiqueComparativeStudyData2014,
  title = {A {{Comparative Study}} of {{Data Mining Process Models}} (Kdd, {{Crisp-Dm}} and {{Semma}})},
  author = {Shafique, Umair and Qaiser, Haseeb},
  year = {2014},
  journal = {International Journal of Innovation and Scientific Research},
  volume = {12},
  number = {1},
  pages = {217--222},
  publisher = {{Citeseer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2X47PUTJ/Shafique and Qaiser - 2014 - A comparative study of data mining process models .pdf}
}

@book{shapiro2020Science2006,
  title = {Towards 2020 {{Science}}},
  author = {Shapiro, Ehud and Rison, Stuart and Phillips, Andrew and Herbert, Andrew and Editors and Emmott, Stephen},
  year = {2006},
  month = mar,
  publisher = {{Microsoft}},
  abstract = {The Centre for Technology Management joined 'Towards 2020 Science', an international expert group of distinguished scientists, to facilitate a workshop that was held to debate and consider the role and future of science over the next 14 years. The expert group met for an intense three day workshop, held in Venice during the summer of 2005, to support the development of a new vision and roadmap outlining the evolution, challenges and potential of computational science in the next decade and beyond. The aim behind the workshop was to stimulate debate across and within disciplines; focus the research agenda for computational science, and encourage collaboration between academia, industry and government. The findings and conclusions from this workshop and a further four months of analysis, discussion and debate within the group has been published in and the form of a roadmap that shows a vision of science towards 2020 and the role that computer science can and will play in achieving this vision.}
}

@article{shapiroVerificationPossibleEvaluation1973,
  title = {Is {{Verification Possible}}? {{The Evaluation}} of {{Large Econometric Models}}},
  shorttitle = {Is {{Verification Possible}}?},
  author = {Shapiro, Harold T.},
  year = {1973},
  journal = {American Journal of Agricultural Economics},
  volume = {55},
  number = {2},
  pages = {250--258},
  publisher = {{[Agricultural \& Applied Economics Association, Oxford University Press]}},
  issn = {0002-9092},
  doi = {10.2307/1238449},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4A7DWZIB/Shapiro - 1973 - Is Verification Possible The Evaluation of Large .pdf}
}

@inproceedings{shcherbakovLeanDataScience2014,
  title = {Lean {{Data Science Research Life Cycle}}: {{A Concept}} for {{Data Analysis Software Development}}},
  shorttitle = {Lean {{Data Science Research Life Cycle}}},
  booktitle = {Knowledge-{{Based Software Engineering}}},
  author = {Shcherbakov, Maxim and Shcherbakova, Nataliya and Brebels, Adriaan and Janovsky, Timur and Kamaev, Valery},
  editor = {Kravets, Alla and Shcherbakov, Maxim and Kultsova, Marina and Iijima, Tadashi},
  year = {2014},
  series = {Communications in {{Computer}} and {{Information Science}}},
  pages = {708--716},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-319-11854-3_61},
  abstract = {Data Science is a new study that combines computer science, data mining, data engineering and software development. Based on the concept of lean software development we propose an idea of lean data science research as a technology for data analysis software development. This concept includes the mandatory stages of the life cycle that meet the lean manufacturing principles. We have defined the business understanding stage with defining the targeted questions, the set of lean data analysis sprints and a decision support stage. Each lean data analysis sprint contents of the task statement step, a step of data integration, a step of data analysis and the interpretation of the results. This approach allows to build data analysis software with iterative improvement quality of the results. Some case study have been suggested as examples of the proposed concept.},
  isbn = {978-3-319-11854-3},
  langid = {english},
  keywords = {data analysis,data science,software development},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2NPHIPIW/Shcherbakov et al. - 2014 - Lean Data Science Research Life Cycle A Concept f.pdf}
}

@techreport{sherrySyntacticAnalysisAutomatic1960,
  title = {Syntactic {{Analysis}} in {{Automatic Translation}}},
  author = {Sherry, Murray E.},
  year = {1960},
  month = aug,
  institution = {{HARVARD COLL CAMBRIDGE MA MUSEUM OF COMPARATIVE ZOOLOGY}},
  abstract = {A method for the syntactic analysis of Russian sentences, applied to automatic translation, is divided into a morphological word-by-word phase and a syntactical sentence-by-sentence phase. An idealized canonical stem dictionary is presented, and its significant lexicographic properties are pointed out. This idealized dictionary then serves AS A BASIS FOR EVALUATING THE ACTUAL Harvard Automatic Dictionary. Aspects of morphological analysis of the Russian language and a series of programs written to carry it out are described. To explain the practical problems encountered in an experimental syntactic analysis program, of which a detailed description is given, a new model of natural language is introduced.},
  chapter = {Technical Reports},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RFXNJRT8/Sherry - 1960 - SYNTACTIC ANALYSIS IN AUTOMATIC TRANSLATION.pdf;/Users/rca2t1/Dropbox/Zotero/storage/ZU2FI4D9/AD0253273.html}
}

@article{simberloffLongLivedDigitalData2005,
  title = {Long-{{Lived Digital Data Collections}}: {{Enabling Research}} and {{Education}} in the 21st {{Century}}},
  shorttitle = {Long-{{Lived Digital Data Collections}}},
  author = {Simberloff, Daniel and Barish, B. C. and Droegemeier, K. K. and Etter, D. and Fedoroff, N. and Ford, K. and Lanzerotti, L. and Leshner, A. and Lubchenco, J. and Rossmann, M.},
  year = {2005},
  journal = {National Science Foundation},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CF8LIBKB/Simberloff et al. - 2005 - Long-lived digital data collections enabling rese.pdf}
}

@article{simonDoesScientificDiscovery1973,
  title = {Does {{Scientific Discovery Have}} a {{Logic}}?},
  author = {Simon, Herbert A.},
  year = {1973},
  journal = {Philosophy of Science},
  volume = {40},
  number = {4},
  pages = {471--480},
  publisher = {{[The University of Chicago Press, Philosophy of Science Association]}},
  issn = {0031-8248},
  abstract = {It is often claimed that there can be no such thing as a logic of scientific discovery, but only a logic of verification. By 'logic of discovery' is usually meant a normative theory of discovery processes. The claim that such a normative theory is impossible is shown to be incorrect; and two examples are provided of domains where formal processes of varying efficacy for discovering lawfulness can be constructed and compared. The analysis shows how one can treat operationally and formally phenomena that have usually been dismissed with fuzzy labels like 'intuition' and 'creativity'.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4LC68CHG/Simon - 1973 - Does Scientific Discovery Have a Logic.pdf}
}

@article{simoudisRealityCheckData1996,
  title = {Reality {{Check}} for {{Data Mining}}},
  author = {Simoudis, Evangelos},
  year = {1996},
  month = sep,
  journal = {IEEE Intelligent Systems},
  volume = {11},
  number = {05},
  pages = {26--33},
  publisher = {{IEEE Computer Society}},
  issn = {1541-1672},
  doi = {10.1109/64.539014},
  abstract = {TO COMPETE EFFECTIVELY IN today's marketplace, business managers must take timely advantage of high-return opportunities. Doing so requires that they be able to exploit the mountains of data their organizations generate and collect during daily operations. Yet, the difficulty of discerning the value in that information--of separating the wheat from the chaff--prevents many companies from fully capitalizing on the wealth of data at their disposal. For example, a bank account manager might want to identify a group of married, two-income, affluent customers and send them information about the bank's growth mutual funds, before a competing discount broker can lure them away. The information surely resides in the bank's computer system--and has probably been there in some form for years. The trick, of course, is to find an efficient way to extract and apply it. Data mining -- the process of extracting valid, previously unknown, comprehensible, and actionable information from large databases and using it to make crucial business decisions--currently performs this task for a growing range of businesses. After presenting an overview of current data-mining techniques, this article explores two particularly noteworthy applications of those techniques: market basket analysis and customer segmentation.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/B7XEYRRJ/Simoudis - 1996 - Reality Check for Data Mining.pdf;/Users/rca2t1/Dropbox/Zotero/storage/8EP4B47R/13rRUx0xPx9.html}
}

@article{situngkirIndonesiaEmbracesData2015,
  title = {Indonesia Embraces the {{Data Science}}},
  author = {Situngkir, Hokky},
  year = {2015},
  month = aug,
  journal = {arXiv:1508.02387 [cs]},
  eprint = {1508.02387},
  eprinttype = {arxiv},
  primaryclass = {cs},
  abstract = {The information era is the time when information is not only largely generated, but also vastly processed in order to extract and generated more information. The complex nature of modern living is represented by the various kind of data. Data can be in the forms of signals, images, texts, or manifolds resembling the horizon of observation. The task of the emerging data sciences are to extract information from the data, for people gain new insights of the complex world. The insights may came from the new way of the data representation, be it a visualizations, mapping, or other. The insights may also come from the implementation of mathematical analysis and or computational processing giving new insights of what the states of the nature represented by the data. Both ways implement the methodologies reducing the dimensionality of the data. The relations between the two functions, representation and analysis are the heart of how information in data is transformed mathematically and computationally into new information. The paper discusses some practices, along with various data coming from the social life in Indonesia to gain new insights about Indonesia in the emerging data sciences. The data sciences in Indonesia has made Indonesian Data Cartograms, Indonesian Celebrity Sentiment Mapping, Ethno-Clustering Maps, social media community detection, and a lot more to come, become possible. All of these are depicted as the exemplifications on how Data Science has become integral part of the technology bringing data closer to people.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computers and Society},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/U4PPQED8/Situngkir - 2015 - Indonesia embraces the Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/H65YZHFY/1508.html}
}

@article{smithDataScienceAcademic2006,
  title = {Data {{Science}} as an {{Academic Discipline}}},
  author = {Smith, F.},
  year = {2006},
  month = nov,
  journal = {Data Science Journal},
  volume = {5},
  number = {0},
  pages = {163--164},
  publisher = {{Ubiquity Press}},
  issn = {1683-1470},
  abstract = {The CODATA\&nbsp;Data Science Journal\&nbsp;is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data.All data is in scope, whether born digital or converted from other sources.},
  copyright = {Authors who publish with this journal agree to the following terms:    Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a  Creative Commons Attribution License  that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.  Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.  Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See  The Effect of Open Access ).  All third-party images reproduced on this journal are shared under Educational Fair Use. For more information on  Educational Fair Use , please see  this useful checklist prepared by Columbia University Libraries .   All copyright  of third-party content posted here for research purposes belongs to its original owners.  Unless otherwise stated all references to characters and comic art presented on this journal are \textcopyright, \textregistered{} or \texttrademark{} of their respective owners. No challenge to any owner's rights is intended or should be inferred.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SEM6YJNI/Smith - 2006 - Data Science as an Academic Discipline.pdf;/Users/rca2t1/Dropbox/Zotero/storage/4RHBNRDQ/482.html}
}

@misc{smithDataScienceWhat2011,
  title = {"{{Data Science}}": What's in a Name?},
  shorttitle = {"{{Data Science}}"},
  author = {Smith, David},
  year = {2011},
  month = may,
  journal = {Revolutions},
  abstract = {The terms "Data Science" and "Data Scientist" have only been in common usage for a little over a year, but they've really taken off since then: many companies are now hiring for "data scientists", and entire conferences are run under the name of "data science". But despite the widespread adoption, some have resisted the change from the more traditional terms like "statistician" or "quant" or "data analyst". Personally, I love the term. As a statistician, I was getting tired of explaining that no, I don't spend my time writing down baseball or cricket scores. I think "Data Science" better describes...},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SK9MWUKT/data-science-whats-in-a-name.html}
}

@article{smithManagementInformationAnalysis1972,
  title = {The {{Management}} of {{Information Analysis Centers}}: {{Proceedings}} of a {{Forum Sponsored}} by the {{COSATI Panel}} on {{Information Analysis Centers}}.},
  shorttitle = {The {{Management}} of {{Information Analysis Centers}}},
  author = {Smith, William A.},
  year = {1972},
  publisher = {{ERIC}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/K4XSB2EK/Smith - 1972 - The Management of Information Analysis Centers Pr.pdf;/Users/rca2t1/Dropbox/Zotero/storage/PWCZKP8J/eric.ed.gov.html}
}

@book{snowTwoCulturesScientific2013,
  title = {The {{Two Cultures}} and the {{Scientific Revolution}}},
  author = {Snow, C. P.},
  year = {2013},
  month = dec,
  publisher = {{Martino Fine Books}},
  abstract = {2013 Reprint of 1959 Edition. Exact facsimile of the original edition, not reproduced with Optical Recognition Software. This is the publication of the influential 1959 Rede Lecture by British scientist and novelist C. P. Snow. Its thesis was that "the intellectual life of the whole of western society" was split into the titular two cultures - namely the sciences and the humanities - and that this was a major hindrance to solving the world's problems. Published in book form, Snow's lecture was widely read and discussed on both sides of the Atlantic, leading him to write a 1963 follow-up, "The Two Cultures: And a Second Look: An Expanded Version of The Two Cultures and the Scientific Revolution."},
  googlebooks = {fMx0ngEACAAJ},
  isbn = {978-1-61427-547-3},
  langid = {english}
}

@book{srikantFastAlgorithmsMining1996,
  title = {Fast Algorithms for Mining Association Rules and Sequential Patterns},
  author = {Srikant, Ramakrishnan},
  year = {1996},
  publisher = {{The University of Wisconsin-Madison}}
}

@article{srivastavaDataMiningGoogle2013,
  title = {Data {{Mining}} @ {{Google Data Mining}}},
  author = {Srivastava, Vivek},
  year = {2013},
  abstract = {Data Mining @ Google Data Mining},
  langid = {english},
  annotation = {Date provided by author in personal communication.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/IPTZJKFP/Data_Mining_at_Google_Data_Mining.html}
}

@article{srivastavaUnderstandingLinkageData2015,
  title = {Understanding {{Linkage Between Data Mining}} and {{Statistics}}},
  author = {Srivastava, Jaya and Srivastava, Abhay Kumar},
  year = {2015},
  journal = {International Journal of Engineering Technology, Management and Applied Sciences},
  volume = {3},
  number = {10},
  pages = {4--12},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/C3CS28JM/Srivastava and Srivastava - 2015 - Understanding Linkage Between Data Mining and Stat.pdf}
}

@book{stateDepartmentStateNews1962,
  title = {Department of {{State News Letter}}},
  author = {of State, United States Department},
  year = {1962},
  publisher = {{Bureau of Administration}},
  googlebooks = {cNJIAQAAMAAJ},
  langid = {english}
}

@phdthesis{stevensLifeOutSequence2010,
  title = {Life out of Sequence: {{An}} Ethnographic Account of Bioinformatics from the {{APPANET}} to Post -Genomics},
  shorttitle = {Life out of Sequence},
  author = {Stevens, Hallam},
  year = {2010},
  address = {{United States -- Massachusetts}},
  abstract = {Between the 1960s and the end of the first decade of the twenty-first century computers have transformed from esoteric devices to ubiquitous tools in biological work. Archival research, ethnographic fieldwork, and interviews have been combined to explain and investigate the consequences of this shift. Early computers were used in the military and soon after for solving problems in the physical sciences. As such they were designed to solve specific kinds of problems: namely, problems of data management, simulations with large numbers of discrete data points, and statistical and stochastic problems. Computers also entailed and required the trappings of Big Science: big funding, multi-disciplinary collaboration, and careful management of laboratory spaces. The introduction of computers into the life sciences has imported both this style of knowledge production and these forms of practice and organization into the discipline of biology. Biology questions have become re-oriented from those that seek to understand individual genes or proteins towards those that seek to use the data management and statistical power of computer to understand large sets of biological objects. Meanwhile, biological practice has been transformed to organize and manage the interdisciplinary intersections between biologists, mathematicians, computer scientists, statisticians, and engineers who are all required in order to perform productive biological work. Moreover, the long strings of letters that represent protein and DNA sequences were especially susceptible to the kinds of data and statistical manipulations which could be performed on computers. The computer enabled the study of life 'out of sequence,' finding patterns in the apparently disordered spaces of genomes.},
  copyright = {Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.},
  isbn = {9781109644470},
  langid = {english},
  school = {Harvard University},
  keywords = {APPANET,Bioinformatics,Biological sciences,Ethnography,Genomics,Sequence,Social sciences}
}

@article{stoneFacebookHiresGoogle2008,
  title = {Facebook {{Hires Google Executive}} as {{No}}. 2},
  author = {Stone, Brad and Helft, Miguel},
  year = {2008},
  month = mar,
  journal = {The New York Times},
  issn = {0362-4331},
  abstract = {A Google executive, Sheryl Sandberg, will join Facebook later this month as chief operating officer and will work closely with Mark Zuckerberg, Facebook's co-founder.},
  chapter = {Technology},
  langid = {american},
  keywords = {Appointments and Executive Changes,Facebook.com,Google Inc,Zuckerberg; Mark E},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UNZJS6Y2/04cnd-facebook.html}
}

@article{stracheySystemAnalysisProgramming1966,
  title = {System {{Analysis}} and {{Programming}}},
  author = {Strachey, Christopher},
  year = {1966},
  journal = {Scientific American},
  volume = {215},
  number = {3},
  pages = {112--127},
  publisher = {{Scientific American, a division of Nature America, Inc.}},
  issn = {0036-8733},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/Z94RTYXB/Strachey - 1966 - System Analysis and Programming.pdf;/Users/rca2t1/Dropbox/Zotero/storage/EMWPJT7U/09-01.html}
}

@misc{StudentTextIntroduction1967,
  title = {Student {{Text Introduction}} to {{IBM System}}/360 {{Architecture}}},
  year = {1967},
  publisher = {{International Business Machines Corporation}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HFPRNST9/GC20-1667-1_intro360arch.pdf}
}

@article{studerCRISPMLMachineLearning2020,
  title = {Towards {{CRISP-ML}}({{Q}}): {{A Machine Learning Process Model}} with {{Quality Assurance Methodology}}},
  shorttitle = {Towards {{CRISP-ML}}({{Q}})},
  author = {Studer, Stefan and Bui, Thanh Binh and Drescher, Christian and Hanuschkin, Alexander and Winkler, Ludwig and Peters, Steven and Mueller, Klaus-Robert},
  year = {2020},
  month = mar,
  journal = {arXiv:2003.05155 [cs, stat]},
  eprint = {2003.05155},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {We propose a process model for the development of machine learning applications. It guides machine learning practitioners and project organizations from industry and academia with a checklist of tasks that spans the complete project life-cycle, ranging from the very first idea to the continuous maintenance of any machine learning application. With each task, we propose quality assurance methodology that is drawn from practical experience and scientific literature and that has proven to be general and stable enough to include them in best practices. We expand on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Machine Learning,Computer Science - Software Engineering,Statistics - Machine Learning},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/G5N9EMSG/Studer et al. - 2020 - Towards CRISP-ML(Q) A Machine Learning Process Mo.pdf;/Users/rca2t1/Dropbox/Zotero/storage/37P62DWE/2003.html}
}

@article{subramanianPipelinedDataFlowDelegated2013,
  title = {Pipelined {{Data-Flow Delegated Orchestration}} for {{Data-Intensive Escience Workflows}}},
  author = {Subramanian, Sattanathan and Sztromwasser, Pawe{\textbackslash}l and Puntervoll, P{\textbackslash}aal and Petersen, Kjell},
  year = {2013},
  journal = {International Journal of Web Information Systems},
  publisher = {{Emerald Group Publishing Limited}}
}

@article{suchmanAffiliativeObjects2005,
  title = {Affiliative {{Objects}}},
  author = {Suchman, Lucy},
  year = {2005},
  month = may,
  journal = {Organization},
  volume = {12},
  number = {3},
  pages = {379--399},
  issn = {1350-5084, 1461-7323},
  doi = {10.1177/1350508405051276},
  abstract = {Through the case of a particular organization devoted to technological research and development, this paper investigates how values of the `new' operate in what Appadurai (1986) has characterized as the social life of objects. Drawing on previous scholarship in anthropology and science and technology studies, I adopt the trope of the `affiliative object' to describe the relational dynamics of association (and disassociation) that characterize the identification of objects and persons. This perspective emphasizes the multiplicity of objects within the unfolding and uncertain trajectories of organizational life, as both problem and resource for organization members. The paper examines how `object-centered sociality' (Knorr-Cetina, 1997) is enacted as a strategic, but also contingent, resource in the alignment of professional identities and organizational positionings.},
  langid = {english},
  keywords = {identity,invention,materiality,multiplicity,object-centered sociality},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HFAS4E74/Suchman - 2005 - Affiliative Objects.pdf;/Users/rca2t1/Dropbox/Zotero/storage/R38D7JIP/379.html}
}

@article{suchmanArtificialIntelligenceCraftwork1993,
  title = {Artificial {{Intelligence}} as {{Craftwork}}},
  author = {Suchman, Lucy A. and Trigg, Randall},
  year = {1993},
  journal = {Understanding practice: Perspectives on activity and context},
  pages = {144--178},
  keywords = {artificial intelligence}
}

@article{suchmanCategoriesHavePolitics1994,
  title = {Do {{Categories Have Politics}}?},
  author = {Suchman, Lucy},
  year = {1994},
  journal = {Computer Supported Cooperative Work (CSCW)},
  volume = {2},
  number = {3},
  pages = {177--190},
  issn = {0925-9724},
  doi = {10.1007/BF00749015},
  abstract = {Drawing on writings within the CSCW community and on recent social theory, this paper proposes that the adoption of speech act theory as a foundation for system design carries with it an agenda of discipline and control over organization members' actions. I begin with a brief review of the language/action perspective introduced by Winograd, Flores and their colleagues, focusing in particular on the categorization of speakers' intent. I then turn to some observations on the politics of categorization and, with that framework as back-ground, consider the attempt, throughthe coordinator, to implement a technological system for intention-accounting within organizations. Finally, I suggest the implications of the analysis presented in the paper for the politics of CSCW systems design.},
  keywords = {Categorization,Computer science},
  file = {/Users/ontoligent/Desktop/Suchman_categories_1994.pdf;/Users/rca2t1/Dropbox/Zotero/storage/RDDN2T7S/Suchman - 1994 - Do Categories Have Politics.pdf}
}

@article{suchmanMakingWorkVisible1995,
  title = {Making Work Visible},
  author = {Suchman, Lucy},
  year = {1995},
  month = sep,
  journal = {Commun. ACM},
  volume = {38},
  number = {9},
  pages = {56--ff.},
  issn = {0001-0782},
  doi = {10.1145/223248.223263},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EWWM34BV/Suchman - 1995 - Making work visible.pdf}
}

@article{suchmanOfficeProcedurePractical1983,
  title = {Office {{Procedure}} as {{Practical Action}}: {{Models}} of {{Work}} and {{System Design}}},
  shorttitle = {Office Procedure as Practical Action},
  author = {Suchman, Lucy A.},
  year = {1983},
  month = oct,
  journal = {ACM Trans. Inf. Syst.},
  volume = {1},
  number = {4},
  pages = {320--328},
  issn = {1046-8188},
  doi = {10.1145/357442.357445},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/85J6PZR3/Suchman - 1983 - Office procedure as practical action models of wo.pdf}
}

@book{suchmanPlansSituatedActions1987,
  title = {Plans and {{Situated Actions}}: {{The Problem}} of {{Human-Machine Communication}}},
  shorttitle = {Plans and {{Situated Actions}}},
  author = {Suchman, Lucy A.},
  year = {1987},
  edition = {Second},
  publisher = {{Cambridge University Press}},
  isbn = {0-521-33739-9},
  keywords = {artificial intelligence,situated action}
}

@article{sveinsdottirDatalogyCopenhagenTradition1988,
  title = {Datalogy - {{The Copenhagen Tradition}} of {{Computer Science}}.},
  author = {Sveinsdottir, Edda and Fr{\o}kj{\ae}r, Erik},
  year = {1988},
  month = sep,
  journal = {BIT},
  volume = {28},
  pages = {450--472},
  doi = {10.1007/BF01941128},
  abstract = {Since the middle of the 1960s, computer science has been practised in Denmark under Peter Naur's term datalogy, the science of data and data processes. Starting at Regnecentralen and the University of Copenhagen, the Copenhagen Tradition of computer science has developed its own special characteristics by means of a close connection with applications and other fields of knowledge. The tradition is not least visible in the area of education. Comprehensive project activity is an integral part of the curriculum, thus presenting theory as an aspect of realistic solutions known primarily through actual experience. Peter Naur early recognized the particular educational challenges presented by computer science. His innovations have shown their quality and vitality also at other universities. There is a close connection between computer science training as it has been formed at Copenhagen University, and the view of computer science which has characterized Peter Naur's research. We illustrate how the study of programming and system development conceived as a human activity has been an all-pervasive theme in Naur's work. This approach has set the scene for central research issues in software development which today seem more topical than ever.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/8ZLMR8DY/Sveinsdottir and Frøkjær - 1988 - Datalogy - The Copenhagen Tradition of Computer Sc.pdf}
}

@article{sveinsdottirDatalogyCopenhagenTradition1988a,
  title = {Datalogy \textemdash{} {{The}} Copenhagen Tradition of Computer Science},
  author = {Sveinsdottir, Edda and Fr{\o}kj{\ae}r, Erik},
  year = {1988},
  month = sep,
  journal = {BIT Numerical Mathematics},
  volume = {28},
  number = {3},
  pages = {450--472},
  issn = {1572-9125},
  doi = {10.1007/BF01941128},
  abstract = {Since the middle of the 1960s, computer science has been practised in Denmark under Peter Naur's termdatalogy, the science of data processes. Starting at Regenecentralen and the University of Copenhagen, the Copenhagen Tradition of Computer Science has developed its own special characteristics by means of a close connection with applications and other fields of knowledge. The tradition is not least visible in the area of education. Comprehensive project activity is an integral part of the curriculum, thus presenting theory as an aspect of realistic solutions known primarily through actual experience. Peter Naur early recognized the particular educational challenges presented by computer science. His innovations have shown their quality and vitality also at other universities. There is a close connection between computer science training as it has been formed at Copenhagen University, and the view of computer science which has characterized Peter Naur's research. We illustrate how the study of programming and system development conceived as a human activity has been an all-pervasive theme in Naur's work. This approach has set the scene for central research issues in software development which today seem more topical than ever.},
  langid = {english}
}

@article{sveinsdottirDatalogyCopenhagenTradition1988b,
  title = {Datalogy\textemdash the {{Copenhagen}} Tradition of Computer Science},
  author = {Sveinsdottir, Edda and Fr{\o}kj{\ae}r, Erik},
  year = {1988},
  journal = {BIT Numerical Mathematics},
  volume = {28},
  number = {3},
  pages = {450--472},
  publisher = {{Springer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/BFJX5LQ6/Sveinsdottir and Frøkjær - 1988 - Datalogy—the Copenhagen tradition of computer scie.pdf;/Users/rca2t1/Dropbox/Zotero/storage/QFYIQ8TD/BF01941128.html}
}

@article{sveinsdottirDatalogyCopenhagenTradition1988c,
  title = {Datalogy \textemdash{} {{The}} Copenhagen Tradition of Computer Science},
  author = {Sveinsdottir, Edda and Fr{\o}kj{\ae}r, Erik},
  year = {1988},
  month = sep,
  journal = {BIT Numerical Mathematics},
  volume = {28},
  number = {3},
  pages = {450--472},
  issn = {1572-9125},
  doi = {10.1007/BF01941128},
  abstract = {Since the middle of the 1960s, computer science has been practised in Denmark under Peter Naur's termdatalogy, the science of data processes. Starting at Regenecentralen and the University of Copenhagen, the Copenhagen Tradition of Computer Science has developed its own special characteristics by means of a close connection with applications and other fields of knowledge. The tradition is not least visible in the area of education. Comprehensive project activity is an integral part of the curriculum, thus presenting theory as an aspect of realistic solutions known primarily through actual experience. Peter Naur early recognized the particular educational challenges presented by computer science. His innovations have shown their quality and vitality also at other universities. There is a close connection between computer science training as it has been formed at Copenhagen University, and the view of computer science which has characterized Peter Naur's research. We illustrate how the study of programming and system development conceived as a human activity has been an all-pervasive theme in Naur's work. This approach has set the scene for central research issues in software development which today seem more topical than ever.},
  langid = {english}
}

@misc{swanSkillsRoleCareer2008,
  type = {Programme/{{Project}} Deposit},
  title = {The {{Skills}}, {{Role}} and {{Career Structure}} of {{Data Scientists}} and {{Curators}}: {{An Assessment}} of {{Current Practice}} and {{Future Needs}}},
  shorttitle = {The {{Skills}}, {{Role}} and {{Career Structure}} of {{Data Scientists}} and {{Curators}}},
  author = {Swan, Alma and Brown, Sheridan},
  year = {2008},
  month = sep,
  howpublished = {http://repository.jisc.ac.uk/245/},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/42R6ZYIG/Swan and Brown - 2008 - The skills, role and career structure of data scie.pdf;/Users/rca2t1/Dropbox/Zotero/storage/5FEGM2ND/245.html}
}

@article{targetednewsserviceDataScientistJoins2008,
  title = {Data {{Scientist Joins Rensselaer Tetherless World Research Constellation}}},
  author = {{Targeted News Service}},
  year = {2008},
  month = nov,
  journal = {Targeted News Service},
  address = {{Troy, New York}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/6V6G7QVA/document.html}
}

@book{TechnicalNewsBulletin1959,
  title = {Technical {{News Bulletin}}},
  year = {1959},
  publisher = {{The Bureau}},
  langid = {english}
}

@article{TechnologyServiceCorporation2022,
  title = {Technology {{Service Corporation}}},
  year = {2022},
  month = jan,
  journal = {Wikipedia},
  abstract = {Technology Service Corporation (TSC) is an American engineering company headquartered in Arlington, Virginia, providing services and specialized products to the U.S. government agencies and private industry.},
  copyright = {Creative Commons Attribution-ShareAlike License},
  langid = {english},
  annotation = {Page Version ID: 1066387237},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/DPCC2XIH/Technology_Service_Corporation.html}
}

@inproceedings{terrizzanoDataWranglingChallengingJourney2015,
  title = {{{DataWrangling}}: {{The Challenging Journey}} from the {{Wild}} to the {{Lake}}},
  booktitle = {7th {{Biennial Conference}} on {{Innovative Data Systems Research}}},
  author = {Terrizzano, Ignacio and Schwarz, Peter and Roth, Mary and Colino, John E.},
  year = {2015},
  month = jan,
  address = {{Asilomar, California, USA}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2JN7B572/Terrizzano et al. - 2015 - DataWrangling The Challenging Journey from the Wi.pdf}
}

@misc{theamericanstatisticalassociationASAStatementRole2015,
  title = {{{ASA Statement}} on {{The Role}} of {{Statistics}} in {{Data Science}}},
  author = {{The American Statistical Association}},
  year = {2015},
  month = aug,
  publisher = {{The American Statistical Association}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CAWE2BFX/The American Statistical Association - 2015 - ASA Statement on The Role of Statistics in Data Sc.pdf}
}

@article{ThisJournal,
  title = {About This {{Journal}}},
  journal = {Data Science Journal},
  abstract = {The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data.All data is in scope, whether born digital or converted from other sources.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/887P66MB/datascience.codata.org.html}
}

@misc{TimeTideWait2005,
  title = {Time and {{Tide Wait}} for {{No One}}; the {{Boxing Day Tsunami That Claimed Hundreds}} of {{Thousand}} of {{Lives Was}}},
  year = {2005},
  month = jul,
  howpublished = {https://advance.lexis.com/document/?pdmfid=1516831\&crid=737d0e5d-cd4d-47ac-9a3b-09c28d9ac5aa\&pddocfullpath=\%2Fshared\%2Fdocument\%2Fnews\%2Furn\%3AcontentItem\%3A4GJT-XF20-0159-W3NS-00000-00\&pdcontentcomponentid=245286\&pdteaserkey=sr18\&pditab=allpods\&ecomp=gb63k\&earg=sr18\&prid=72d6b779-5fc4-48ba-a776-47023a9b4224},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/YXGIS57V/document.html}
}

@article{toondersDataNewOil2014,
  title = {Data {{Is}} the {{New Oil}} of the {{Digital Economy}}},
  author = {Toonders, Joris},
  year = {2014},
  month = jul,
  journal = {WIRED},
  annotation = {JORIS TOONDERS, YONEGO},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/U6YVFVLL/data-new-oil-digital-economy.html}
}

@inproceedings{tsaiNewFastAlgorithms2002,
  title = {A New Fast Algorithms for Mining Association Rules in Large Databases},
  booktitle = {{{IEEE International Conference}} on {{Systems}}, {{Man}} and {{Cybernetics}}},
  author = {Tsai, Cheng-Fa and Lin, Yi-Chau and Chen, Chi-Pin},
  year = {2002},
  volume = {7},
  pages = {6--pp},
  publisher = {{IEEE}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2WXDZ8Z7/1175703.html}
}

@article{tukeyDataAnalysisComputation1972,
  title = {Data Analysis, Computation and Mathematics},
  author = {Tukey, John W.},
  year = {1972},
  journal = {Quarterly of Applied Mathematics},
  volume = {30},
  number = {1},
  pages = {51--65},
  issn = {0033-569X, 1552-4485},
  doi = {10.1090/qam/99740},
  abstract = {"Data analysis'' instead of ``statistics'' is a name that allows us to use probability where it is needed and avoid it when we should. Data analysis has to analyze real data. Most real data calls for data investigation, while almost all statistical theory is concerned with data processing. This can be borne, in part because large segments of data investigation are, by themselves, data processing. Summarizing a batch of 20 numbers is a convenient paradigm for more complex aims in data analysis. A particular summary, highly competitive among those known and known about in August 1971, is a hybrid between two moderately complex summaries. Data investigation comes in three stages: exploratory data analysis (no probability), rough confirmatory data analysis (sign test procedures and the like), mustering and borrowing strength (the best of modern robust techniques, and an art of knowing when to stop). Exploratory data analysis can be improved by being made more resistant, either with medians or with fancier summaries. Rough confirmatory data analysis can be improved by facing up to the issues surrounding the choice of what is to be confirmed or disaffirmed. Borrowing strength is imbedded in our classical procedures, though we often forget this. Mustering strength calls for the best in robust summaries we can supply. The sampling behavior of such a summary as the hybrid mentioned above is not going to be learned through the mathematics of certainty, at least as we know it today, especially if we are realistic about the diversity of non-Gaussian situations that are studied. The mathematics of simulation, inevitably involving the mathematically sound ``swindles'' of Monte Carlo, will be our trust and reliance. I illustrate results for a few summaries, including the hybrid mentioned above. Bayesian techniques are still a problem to the author, mainly because there seems to be no agreement on what their essence is. From my own point of view, some statements of their essence are wholly acceptable and others are equally unacceptable. The use of exogeneous information in analyzing a given body of data is a very different thing (a) depending on sample size and (b) depending on just how the exogeneous information is used. It would be a very fine thing if the questions that practical data analysis has to have answered could be answered by the mathematics of certainty. For my own part, I see no escape, for the next decade or so at least, from a dependence on the mathematics of simulation, in which we should heed von Neumann's aphorism as much as we can.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/MVIQM5ZP/Tukey - 1972 - Data analysis, computation and mathematics.pdf;/Users/rca2t1/Dropbox/Zotero/storage/IXKR7AQT/S0033-569X-1972-99740-X.html}
}

@article{tukeyExploratoryDataAnalysis1977,
  title = {Exploratory {{Data Analysis}}},
  author = {Tukey, J. W.},
  year = {1977},
  journal = {Reading, MA},
  volume = {231},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4NTMMC3I/Tukey - 1977 - Exploratory data analysis.pdf}
}

@article{tukeyFutureDataAnalysis1962,
  title = {The {{Future}} of {{Data Analysis}}},
  author = {Tukey, John W.},
  year = {1962},
  journal = {The Annals of Mathematical Statistics},
  volume = {33},
  number = {1},
  pages = {1--67},
  issn = {0003-4851},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/A4MRBYRC/Tukey - 1962 - The Future of Data Analysis.pdf}
}

@article{tukeyNonparametricStatisticalData1979,
  title = {Nonparametric {{Statistical Data Modeling}}: {{Comment}}},
  shorttitle = {Nonparametric {{Statistical Data Modeling}}},
  author = {Tukey, John W.},
  year = {1979},
  journal = {Journal of the American Statistical Association},
  volume = {74},
  number = {365},
  pages = {121--122},
  publisher = {{[American Statistical Association, Taylor \& Francis, Ltd.]}},
  issn = {0162-1459},
  doi = {10.2307/2286735},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3P2YS6ZV/Tukey - 1979 - Nonparametric Statistical Data Modeling Comment.pdf}
}

@article{tukeyPhilosophyMultipleComparisons1991,
  title = {The {{Philosophy}} of {{Multiple Comparisons}}},
  author = {Tukey, John W.},
  year = {1991},
  journal = {Statistical Science},
  volume = {6},
  number = {1},
  pages = {100--116},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0883-4237},
  abstract = {This paper is based on the 1989 Miller Memorial Lecture at Stanford University. The topic was chosen because of Rupert Miller's long involvement and significant contributions to multiple comparison procedures and theory. Our emphasis will be on the major questions that have received relatively little attention--on what one wants multiple comparisons to do, on why one wants to do that, and on how one can communicate the results. Very little attention will be given to how the results can be calculated--after all, there are books about that (e.g., Miller, 1966, 1981; Hochberg and Tamhane, 1987).},
  keywords = {causal inference},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JRZDGDR8/Tukey - 1991 - The Philosophy of Multiple Comparisons.pdf}
}

@book{turnerCognitiveDimensionsSocial2001,
  title = {Cognitive {{Dimensions}} of {{Social Science}}},
  author = {Turner, Mark},
  year = {2001},
  publisher = {{Oxford University Press on Demand}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/QJMEJZE7/8trnCwAAQBAJ.html}
}

@book{UnderstandingDataScience2014,
  title = {Understanding the Data Science Pipeline - {{Practical Data Science Cookbook}}},
  year = {2014},
  month = sep,
  abstract = {Before we start installing any software, we need to understand the repeatable set of steps that we will use for data analysis throughout the book.},
  isbn = {978-1-78398-024-6},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2XGFN3LB/understanding-the-data-science-pipeline.html}
}

@article{UNIVAC2020,
  title = {{{UNIVAC}}},
  year = {2020},
  month = jun,
  journal = {Wikipedia},
  abstract = {UNIVAC (Universal Automatic Computer) is a line of electronic digital stored-program computers starting with the products of the Eckert\textendash Mauchly Computer Corporation. Later the name was applied to a division of the Remington Rand company and successor organizations. The BINAC, built by the Eckert\textendash Mauchly Computer Corporation, was the first general-purpose computer for commercial use.  The descendants of the later UNIVAC 1107 continue today as products of the Unisys company.},
  copyright = {Creative Commons Attribution-ShareAlike License},
  langid = {english},
  annotation = {Page Version ID: 964044972},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ZZ8Z7CY9/index.html}
}

@article{usdepartmentofcommerceInternationalAssociationStatistical1978,
  title = {International {{Association}} for {{Statistical Computing}}},
  author = {{US Department of Commerce}},
  year = {1978},
  journal = {Statistical Reporter},
  volume = {1},
  pages = {332},
  googlebooks = {foXQOEJ8gbcC},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/R7H9G92E/1978 - Statistical Reporter.pdf}
}

@article{UseAnimalsMedical1982,
  title = {The Use of Animals in Medical Research and Testing : Hearings before the {{Subcommittee}} on {{Science}}, {{Research}}, and {{Technology}} of the {{Committee}} on {{Science}} and {{Technology}}, {{U}}.{{S}}. {{House}} of {{Representatives}}, {{Ninety-seventh Congress}}, First Session, {{October}} 13, 14, 1981.   {{Note}}},
  shorttitle = {The Use of Animals in Medical Research and Testing},
  year = {1982},
  journal = {The use of animals in medical research and testing : hearings before the Subcommittee on Science, Research, and Technology of the Committee on Science and Technology, U.S. House of Representatives, Ninety-seventh Congress, first session, October 13, 14, 1981.},
  pages = {I-748},
  langid = {english}
}

@article{usharaniFastAlgorithmsMining2013,
  title = {Fast Algorithms for Mining Association Rules in Datamining},
  author = {Usharani, P.},
  year = {2013},
  journal = {Int J Sci Technol Res},
  volume = {23},
  pages = {21--30},
  publisher = {{Citeseer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/57IRGXBN/Usharani - 2013 - Fast algorithms for mining association rules in da.pdf}
}

@book{vapnikNatureStatisticalLearning2000,
  title = {The {{Nature}} of {{Statistical Learning Theory}}},
  author = {Vapnik, Vladimir},
  year = {2000},
  series = {Statistics for {{Engineering}} and {{Information Science}}},
  edition = {Second},
  publisher = {{Springer}}
}

@article{vapnikOverviewStatisticalLearning1999a,
  title = {An Overview of Statistical Learning Theory},
  author = {Vapnik, V. N.},
  year = {1999},
  month = sep,
  journal = {IEEE Transactions on Neural Networks},
  volume = {10},
  number = {5},
  pages = {988--999},
  issn = {1941-0093},
  doi = {10.1109/72.788640},
  abstract = {Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990's new types of learning algorithms (called support vector machines) based on the developed theory were proposed. This made statistical learning theory not only a tool for the theoretical analysis but also a tool for creating practical algorithms for estimating multidimensional functions. This article presents a very general overview of statistical learning theory including both theoretical and algorithmic aspects of the theory. The goal of this overview is to demonstrate how the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems.},
  keywords = {Algorithm design and analysis,estimation theory,function estimation,generalisation (artificial intelligence),generalization conditions,learning (artificial intelligence),Loss measurement,Machine learning,multidimensional function estimation,Multidimensional systems,Pattern recognition,Probability distribution,Risk management,statistical analysis,Statistical learning,statistical learning theory,support vector machines,Support vector machines},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/U6L46GGN/Vapnik - 1999 - An overview of statistical learning theory.pdf}
}

@book{vapnikStatisticalLearningTheory1998,
  title = {Statistical {{Learning Theory}}},
  author = {Vapnik, Vladimir N. and VAPNIK, VLADIMIR AUTOR},
  year = {1998},
  month = sep,
  publisher = {{Wiley}},
  abstract = {Introduction: The Problem of Induction and Statistical Inference. Two Approaches to the Learning Problem. Appendix to Chapter1: Methods for Solving III-Posed Problems. Estimation of the Probability Measure and Problem of Learning. Conditions for Consistency of Empirical Risk Minimization Principle. Bounds on the Risk for Indicator Loss Functions. Appendix to Chapter 4: Lower Bounds on the Risk of the ERM Principle. Bounds on the Risk for Real-Valued Loss Functions. The Structural Risk Minimization Principle. Appendix to Chapter 6: Estimating Functions on the Basis of Indirect Measurements. Stochastic III-Posed Problems. Estimating the Values of Function at Given Points. Perceptrons and Their Generalizations. The Support Vector Method for Estimating Indicator Functions. The Support Vector Method for Estimating Real-Valued Functions. SV Machines for Pattern Recognition. SV Machines for Function Approximations, Regression Estimation, and Signal Processing. Necessary and Sufficient Conditions for Uniform Convergence of Frequencies to Their Probabilities. Necessary and Sufficient Conditions for Uniform Convergence of Means to Their Expectations. Necessary and Sufficient Conditions for Uniform One-Sided Convergence of Means to Their Expectations.},
  googlebooks = {GowoAQAAMAAJ},
  isbn = {978-0-471-03003-4},
  langid = {english},
  keywords = {Computers / Intelligence (AI) \& Semantics,Mathematics / Mathematical Analysis,Mathematics / Probability \& Statistics / General,Mathematics / Probability \& Statistics / Stochastic Processes}
}

@article{varianBigDataNew2014,
  title = {Big {{Data}}: {{New Tricks}} for {{Econometrics}}},
  author = {Varian, Hal R},
  year = 2014,
  journal = {Journal of Economic Perspectives},
  volume = {28},
  number = {2},
  pages = {3--28},
  abstract = {Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PBBGL5X4/jep.28.2.pdf}
}

@misc{varianHalVarianHow2009,
  title = {Hal {{Varian}} on {{How}} the {{Web Challenges Managers}}},
  author = {Varian, Hal R},
  year = {2009},
  month = jan,
  journal = {McKinsey \& Company},
  collaborator = {Manyika, James},
  howpublished = {https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/hal-varian-on-how-the-web-challenges-managers},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/SXSUT2UI/hal-varian-on-how-the-web-challenges-managers.html}
}

@article{venkateswaranNewsNotesReorganization1963,
  title = {News and {{Notes}}: {{Reorganization}} of {{AFCRL}}},
  author = {Venkateswaran, S. V.},
  year = {1963},
  journal = {Bulletin of the American Meteorological Society},
  volume = {44},
  number = {9},
  pages = {548--629},
  publisher = {{American Meteorological Society}},
  issn = {0003-0007},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/4S7B4VNW/Venkateswaran - 1963 - News and Notes Reorganization of AFCRL.pdf}
}

@article{veraSituatedActionSymbolic1993,
  title = {Situated {{Action}}: {{A Symbolic Interpretation}}},
  shorttitle = {Situated {{Action}}},
  author = {Vera, A.H. and Simon, H.A.},
  year = {1993},
  journal = {Cognitive science},
  volume = {17},
  number = {1},
  pages = {7--48},
  keywords = {situated action},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/XBVJAAWC/Vera and Simon - 1993 - Situated Action A Symbolic Interpretation.pdf}
}

@article{vetteDataManagementNational1970,
  title = {Data Management at the {{National Space Science Data Center}}},
  author = {VETTE, J. I. and Karlow, N.},
  year = {1970},
  month = oct,
  journal = {Journal of Spacecraft and Rockets; Reston},
  volume = {7},
  number = {10},
  pages = {1234--1240},
  publisher = {{American Institute of Aeronautics and Astronautics}},
  address = {{Reston, United States, Reston}},
  issn = {00224650},
  doi = {http://dx.doi.org/10.2514/3.30140},
  copyright = {Copyright American Institute of Aeronautics and Astronautics Oct 1970},
  langid = {english},
  keywords = {Aeronautics And Space Flight,Data centers,Data management},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/9VB2RL25/VETTE and Karlow - 1970 - Data management at the National Space Science Data.pdf}
}

@article{VirgilJohnsonPersonality1969,
  title = {Virgil {{Johnson}} Personality Sketch.},
  year = {1969, April 20},
  journal = {New York Times},
  pages = {3},
  abstract = {Role as Mohawk Data Sciences Corp pres discussed.}
}

@article{Volume455Issue,
  title = {Volume 455 {{Issue}} 7209, 4 {{September}} 2008},
  issn = {1476-4687},
  abstract = {Volume 455 Issue 7209, 4 September 2008},
  copyright = {\textcopyright 2020 Macmillan Publishers Limited. All Rights Reserved.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NIGJFR8Y/7209.html}
}

@misc{vorhiesCRISPDMStandardMethodology2016,
  title = {{{CRISP-DM}} \textendash{} a {{Standard Methodology}} to {{Ensure}} a {{Good Outcome}}},
  author = {Vorhies, William},
  year = {2016},
  month = jul,
  journal = {Data Science Central},
  abstract = {Summary: To ensure quality in your data science group, make sure you're enforcing a standard methodology. This includes not only traditional data analytic pr\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UDF5IMWD/crisp-dm-a-standard-methodology-to-ensure-a-good-outcome.html}
}

@article{waismannManyLevelStructureLanguage1946,
  title = {The {{Many-Level-Structure}} of {{Language}}},
  author = {Waismann, Friedrich},
  year = {1946},
  journal = {Synthese},
  volume = {5},
  number = {5/6},
  pages = {221--229},
  publisher = {{Springer}},
  issn = {0039-7857},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CWKNHBEA/Waismann - 1946 - The Many-Level-Structure of Language.pdf}
}

@article{walkerProfessionalisationDataScience2015,
  title = {The Professionalisation of Data Science},
  author = {Walker, Michael A.},
  year = {2015},
  month = jan,
  journal = {International Journal of Data Science},
  volume = {1},
  number = {1},
  pages = {7--16},
  publisher = {{Inderscience Publishers}},
  issn = {2053-0811},
  doi = {10.1504/IJDS.2015.069048},
  abstract = {Data science is establishing basic foundations to become a profession. Like the professionalisation of law and medicine in the past 100 years, the data science field is at the very beginning of becoming a profession - with competency standards, a code of professional conduct, specialised graduate-level curriculums, certification and licensure and self-regulation. All professions require highly specialised education and training, an ethical code, self-regulation by a professional association and certification and licensing. Data science should become a profession for the same reasons medicine and law became professions: each requires practitioners to have a specialised body of knowledge, a code of conduct and self-regulation by knowledgeable professionals to assure competency and protect the public. The data science community can follow a roadmap for how data science can be professionalised by reviewing the history of the medical and legal professions. Suggested is a seven-step process for the professionalisation of data science.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/MCKRXJNP/Walker - 2015 - The professionalisation of data science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/9PZLRTNW/IJDS.2015.html;/Users/rca2t1/Dropbox/Zotero/storage/Z74T3DFU/IJDS.2015.html}
}

@book{waltonDataReductionLaboratory1970,
  title = {The {{Data Reduction Laboratory}}: {{An Aid}} to the {{Space Scientist}}},
  shorttitle = {The {{Data Reduction Laboratory}}},
  author = {Walton, Barbara A. and Keipert, Frank A. and Quann, John J.},
  year = {1970},
  publisher = {{National Aeronautics and Space Administration}},
  abstract = {Data reduction facility for rapid analysis of space flight telemetry data.},
  googlebooks = {QG8JEbkt49UC},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/23DN9T5X/Walton et al. - 1970 - The Data Reduction Laboratory An Aid to the Space.pdf}
}

@misc{wassermanDataScienceEnd2013,
  title = {Data {{Science}}: {{The End}} of {{Statistics}}?},
  shorttitle = {Data {{Science}}},
  author = {Wasserman, Larry},
  year = {2013},
  month = apr,
  journal = {Normal Deviate},
  abstract = {Data Science: The End of Statistics? As I see newspapers and blogs filled with talk of ``Data Science'' and ``Big Data'' I find myself filled with a mixture of optimism and drea\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PDAKW8K7/data-science-the-end-of-statistics.html}
}

@misc{wassermanDataScienceEnd2013a,
  title = {Data {{Science}}: {{The End}} of {{Statistics}}?},
  shorttitle = {Data {{Science}}},
  author = {Wasserman, Larry},
  year = {2013},
  month = apr,
  journal = {Normal Deviate},
  abstract = {As I see newspapers and blogs filled with talk of ``Data Science'' and ``Big Data'' I find myself filled with a mixture of optimism and drea\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2XEFCPMF/data-science-the-end-of-statistics.html}
}

@book{wathen-dunnModelsPerceptionSpeech1967,
  title = {Models for the {{Perception}} of {{Speech}} and {{Visual Form}}},
  author = {{Wathen-Dunn}, Weiant and {Air Force Cambridge Research Laboratories (U.S.). Data Sciences Laboratory}},
  year = {1967},
  publisher = {{Cambridge, Mass., M.I.T. Press}},
  abstract = {"Sponsored by the Data Sciences Laboratory, Air Force Cambridge Research Laboratories [and held in] Boston, Massachusetts, November 11-14, 1964."; commitment to retain},
  collaborator = {{MIT Press}},
  isbn = {978-0-262-23026-1},
  langid = {english},
  keywords = {PSYCHOLOGY / Cognitive Psychology},
  annotation = {Proceedings of a Symposium}
}

@misc{wattersDataSciencePipeline2011,
  title = {Data Science Is a Pipeline between Academic Disciplines},
  author = {Watters, Audrey},
  year = {2011},
  month = aug,
  journal = {O'Reilly Radar},
  abstract = {Strata speaker and PhD candidate Drew Conway discusses how data science is influencing the processes and outcomes of academic research in the social sciences.},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/M2GSMUWQ/data-science-social-science-academic.html}
}

@misc{WeatherNowBeing1995,
  title = {Weather {{Is Now Being Predicted}} a {{Year}} in {{Advance}}},
  year = {1995},
  month = feb,
  howpublished = {https://advance.lexis.com/document/?pdmfid=1516831\&crid=45f13826-d7e8-4bd1-9495-2b18b206483a\&pddocfullpath=\%2Fshared\%2Fdocument\%2Fnews\%2Furn\%3AcontentItem\%3A3SHH-XB80-009C-B08R-00000-00\&pdcontentcomponentid=166768\&pdteaserkey=sr7\&pditab=allpods\&ecomp=gb63k\&earg=sr7\&prid=74571388-cc9e-4c14-b9ef-114d51287542},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/39R2PJA6/document.html}
}

@book{weissPredictiveDataMining1998,
  title = {Predictive Data Mining: A Practical Guide},
  author = {Weiss, Sholom M and Indurkhya, Nitin},
  year = {1998},
  publisher = {{Morgan Kaufmann}}
}

@misc{WelcomeInternationalFederation,
  title = {Welcome to the {{International Federation}} of {{Classification Societies}} [{{International Federation}} of {{Classification Societies}} ]},
  howpublished = {https://ifcs.boku.ac.at/site/doku.php?id=start},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ZPMYR87B/doku.html}
}

@misc{wengExploratoryDataAnalysis2021,
  title = {Exploratory {{Data Analysis}} ({{EDA}}): {{A Practical Guide}} and {{Template}} for {{Structured Data}}},
  shorttitle = {Exploratory {{Data Analysis}} ({{EDA}})},
  author = {Weng, Jiahao},
  year = {2021},
  month = apr,
  journal = {Medium},
  abstract = {Using a code template, EDA is never easier to get started},
  howpublished = {https://towardsdatascience.com/exploratory-data-analysis-eda-a-practical-guide-and-template-for-structured-data-abfbf3ee3bd9},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/Q4PNAV6V/exploratory-data-analysis-eda-a-practical-guide-and-template-for-structured-data-abfbf3ee3bd9.html}
}

@misc{WhatDataScience,
  title = {What {{Is Data Science}}? {{The Ultimate Guide}}},
  shorttitle = {What {{Is Data Science}}?},
  journal = {SearchEnterpriseAI},
  abstract = {Data science is the process of using advanced analytics to extract valuable information from data for business decision-making, strategic planning and other uses. Learn about how it works, its business benefits, the challenges that data scientists face and data science tools and use cases.},
  howpublished = {https://www.techtarget.com/searchenterpriseai/definition/data-science},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/LVH8GVY9/data-science.html}
}

@misc{WhatItMeans,
  title = {What It Means to Be a Data Scientist. | {{LinkedIn}}},
  howpublished = {https://www.linkedin.com/pulse/what-means-data-scientist-troy-sadkowsky/},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/NYEA8JC5/what-means-data-scientist-troy-sadkowsky.html}
}

@article{whiteReviewCorporateFinance1965,
  title = {Review of {{Corporate Finance}} and {{Fixed Investment}}: {{An Econometric Study}}},
  shorttitle = {Review of {{Corporate Finance}} and {{Fixed Investment}}},
  author = {White, William H.},
  year = {1965},
  journal = {The American Economic Review},
  volume = {55},
  number = {3},
  pages = {615--617},
  publisher = {{American Economic Association}},
  issn = {0002-8282},
  collaborator = {Anderson, W. H. Locke},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/EP2L9NLI/White - 1965 - Review of Corporate Finance and Fixed Investment .pdf}
}

@incollection{WhiteSandsAdministrative,
  title = {White {{Sands Administrative History}}},
  booktitle = {Chapter {{Four}}: {{Global War}} at {{White Sands}} 1940\textendash 1945},
  publisher = {{National Park Service}},
  annotation = {Context Object: ctx\_ver=Z39.88-2004\&rft\_val\_fmt=info\%3Aofi\%2Ffmt\%3Akev\%3Amtx\%3Abook\&rft.genre=bookitem\&rft.btitle=Chapter+Four\%3A+Global+War+at+White+Sands+1940\%E2\%80\%931945\&rft.atitle=White+Sands+Administrative+History\&rft.pub=National+Park+Service\&rft\_id=http\%3A\%2F\%2Fwww.nps.gov\%2Farchive\%2Fwhsa\%2Fadhi\%2Fadhi4d.htm\&rfr\_id=info:sid/en.wikipedia.org:White\_Sands\_Missile\_Range},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/S747LD6G/White_Sands_Missile_Range.html}
}

@misc{WhiteSandsMissile,
  title = {White {{Sands Missile Range-}} 60 {{Years}} of {{Making History Every Day}}, 2005 | {{ArchivesSpace Public Interface}}},
  howpublished = {https://usu.libraryhost.com/repositories/2/archival\_objects/69907},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/QBF4X4BY/69907.html}
}

@misc{WhiteSandsMissile2011,
  title = {White {{Sands Missile Range Factbook}}},
  year = {2011},
  abstract = {Where We Are Introduction White Sands Missile Range (WSMR ) is a unique tri-service installation, dedicated to Test and Evaluation (T\&E) to assess military systems and commercial products. As the largest},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/GY58IC65/89767060-Land-airspace-and-terrain.html}
}

@misc{whitesandsmissilerangemuseumWhiteSandsMissile2021,
  title = {White {{Sands Missile Range Hall}} of {{Fame}}},
  author = {{White Sands Missile Range Museum}},
  year = {2021},
  month = jan,
  journal = {White Sands Missile Range Museum},
  abstract = {The White Sands Missile Range Hall of Fame was founded in 1980 by Major General Duard D. Ball to recognize Missile Range civilian, military, and contractor personnel for outstanding contributions m\ldots},
  howpublished = {https://wsmrmuseum.com/wsmr-historical-foundation-hall-of-fame/},
  langid = {american},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/35TE3EY9/30.html}
}

@article{wickhamSplitapplycombineStrategyData2011,
  title = {The Split-Apply-Combine Strategy for Data Analysis},
  author = {Wickham, Hadley},
  year = {2011},
  journal = {Journal of statistical software},
  volume = {40},
  number = {1},
  pages = {1--29},
  publisher = {{Citeseer}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VZT7WABE/Wickham - 2011 - The split-apply-combine strategy for data analysis.pdf}
}

@article{wickhamTidyData2014,
  title = {Tidy {{Data}}},
  author = {Wickham, Hadley},
  year = {2014},
  month = sep,
  journal = {Journal of Statistical Software},
  volume = {59},
  number = {1},
  pages = {1--23},
  issn = {1548-7660},
  doi = {10.18637/jss.v059.i10},
  copyright = {Copyright (c) 2013 Hadley  Wickham},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/A6GHW3M4/Wickham - 2014 - Tidy Data.pdf}
}

@article{wiemerDataMiningMethodology2019,
  title = {Data {{Mining Methodology}} for {{Engineering Applications}} ({{DMME}})\textemdash{{A Holistic Extension}} to the {{CRISP-DM Model}}},
  author = {Wiemer, Hajo and Drowatzky, Lucas and Ihlenfeldt, Steffen},
  year = {2019},
  journal = {Applied Sciences},
  volume = {9},
  number = {12},
  pages = {2407},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/HV3KUVNV/Wiemer et al. - 2019 - Data Mining Methodology for Engineering Applicatio.pdf;/Users/rca2t1/Dropbox/Zotero/storage/YH8U25TQ/2407.html}
}

@book{wienerCyberneticsControlCommunication1948,
  title = {Cybernetics: {{Or Control}} and {{Communication}} in the {{Animal}} and the {{Machine}}},
  author = {Wiener, Norbert},
  year = {1948},
  publisher = {{Librairie Hermann \& Cie}},
  address = {{Paris, France}},
  lccn = {3436}
}

@book{wienerHumanUseHuman1950,
  title = {Human {{Use}} of {{Human Beings}}: {{Cybernetics}} and {{Society}}},
  shorttitle = {Human Use of Human Beings},
  author = {Wiener, Norbert},
  year = {1950},
  publisher = {{Da Capo Press}},
  annotation = {02963},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/KJCKJ3NC/Wiener - 1988 - Human use of human beings Cybernetics and society.pdf;/Users/rca2t1/Dropbox/Zotero/storage/J6IQ6SPP/books.html}
}

@book{wienerHumanUseHuman1950a,
  title = {The Human Use of Human Beings; Cybernetics and Society.},
  author = {Wiener, Norbert},
  year = {1950},
  publisher = {{Houghton Mifflin,}},
  address = {{Boston,}}
}

@book{wienerHumanUseHuman1967,
  title = {The Human Use of Human Beings: {{Cybernetics}} and Society},
  shorttitle = {The Human Use of Human Beings},
  author = {Wiener, Norbert},
  year = {1967},
  publisher = {{Avon Books}},
  lccn = {1012}
}

@incollection{wienerOrganizationMessage1950,
  title = {Organization of the {{Message}}},
  booktitle = {The {{Human Use}} of {{Human Beings}}},
  author = {Wiener, Norbet},
  year = {1950},
  annotation = {00000},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/IISKKBSM/Wiener - 1950 - Organization of the Message.pdf}
}

@article{wienerProblemsSensoryProsthesis1951,
  title = {Problems of Sensory Prosthesis},
  author = {Wiener, Norbert},
  year = {1951},
  journal = {Bulletin of the American Mathematical Society},
  volume = {57},
  number = {1},
  pages = {27--35},
  issn = {0002-9904, 1936-881X},
  doi = {10.1090/S0002-9904-1951-09433-1},
  abstract = {al Information DOI: http://dx.doi.org/10.1090/S0002-9904-1951-09433-1 PII: S 0002-9904(1951)09433-1},
  keywords = {Cybernetics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RQT3P4FB/Wiener - 1951 - Problems of sensory prosthesis.pdf;/Users/rca2t1/Dropbox/Zotero/storage/KFKZHCWX/S0002-9904-1951-09433-1.html}
}

@article{wienerSoundCommunicationDeaf1949,
  title = {Sound {{Communication}} with the {{Deaf}}},
  author = {Wiener, Norbert},
  year = {1949},
  month = jul,
  journal = {Philosophy of Science},
  volume = {16},
  number = {3},
  pages = {260--262},
  issn = {0031-8248},
  doi = {10.2307/185520},
  copyright = {Copyright \textcopyright{} 1949 Philosophy of Science Association},
  keywords = {Cybernetics},
  annotation = {ArticleType: research-article / Full publication date: Jul., 1949 / Copyright \textcopyright{} 1949 Philosophy of Science Association},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/2Q6Q5K8W/Wiener - 1949 - Sound Communication with the Deaf.pdf}
}

@article{wienerSpeechLanguageLearning1950,
  title = {Speech, {{Language}}, and {{Learning}}},
  author = {Wiener, Norbert},
  year = {1950},
  journal = {The Journal of the Acoustical Society of America},
  volume = {22},
  number = {6},
  pages = {696--697},
  doi = {10.1121/1.1906672},
  keywords = {Cybernetics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/D797ZGUI/Wiener - 1950 - Speech, Language, and Learning.pdf;/Users/rca2t1/Dropbox/Zotero/storage/TKUR8K3A/p696_s1.html}
}

@article{wienerTimeCommunicationNervous1948,
  title = {Time, {{Communication}}, and the {{Nervous System}}},
  author = {Wiener, Norbert},
  year = {1948},
  journal = {Annals of the New York Academy of Sciences},
  volume = {50},
  number = {4},
  pages = {197--220},
  issn = {1749-6632},
  doi = {10.1111/j.1749-6632.1948.tb39853.x},
  langid = {english},
  keywords = {Cybernetics},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/Z5AQSPQF/Wiener - 1948 - Time, Communication, and the Nervous System.pdf;/Users/rca2t1/Dropbox/Zotero/storage/8FZ3HGGU/abstract.html}
}

@article{wienerWhatInformationTheory1956,
  title = {What Is Information Theory},
  author = {Wiener, Norbert},
  year = {1956},
  journal = {IRE Transactions on Information Theory},
  volume = {48},
  keywords = {information,information theory},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/PAH4JUNB/Wiener - 1956 - What is information theory.pdf}
}

@misc{WikiVisuallyCom,
  title = {{{WikiVisually}}.Com},
  abstract = {The entire wiki with photo and video galleries for each article},
  howpublished = {http://wikivisually.com},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/84VLEFZ4/Mohawk_Data_Sciences_Corporation.html}
}

@article{wildsInformationRetrieval1961,
  title = {Information {{Retrieval}}},
  author = {Wilds, Thomas},
  year = {1961},
  journal = {The American Archivist},
  volume = {24},
  number = {3},
  pages = {269--282},
  publisher = {{Society of American Archivists}},
  issn = {0360-9081},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/T244F3JT/Wilds - 1961 - Information Retrieval.pdf}
}

@article{wingDataLifeCycle2019,
  title = {The {{Data Life Cycle}}},
  author = {Wing, Jeannette M.},
  year = {2019},
  month = jul,
  journal = {Harvard Data Science Review},
  volume = {1},
  number = {1},
  publisher = {{PubPub}},
  issn = {,},
  doi = {10.1162/99608f92.e26845b4},
  abstract = {To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user. Data science is thus much more than data analysis, e.g., using techniques from machine learning and statistics; extracting this value takes a lot of work, before and after data analysis. Moreover, data privacy and data ethics need to be considered at each phase of the life cycle.Keywordsanalysis, collection, data life cycle, ethics, generation, interpretation, management, privacy, storage, story-telling, visualization},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FX2ANGB3/Wing - 2019 - The Data Life Cycle.pdf;/Users/rca2t1/Dropbox/Zotero/storage/R4ZJXP5J/3.html}
}

@book{winklerSearchingSkiesLegacy1997,
  title = {Searching the {{Skies}}: {{The Legacy}} of the {{United States Cold War Defense Radar Program}}},
  shorttitle = {Searching the {{Skies}}},
  author = {Winkler, David Frank and Command, Air Com Headquarters Air Combat and Force, United States Air},
  year = {1997},
  publisher = {{Headquarters Air Combat Command}},
  abstract = {Originally published in 1997, this hitherto hard-to-find study examines the impact that construction of radar stations and command facilities had on the American landscape. With accompanying black and white photographs throughout, the author explores patterns, themes, and trends that created, influenced, and formed the backdrop to the Cold War defense radar program. This study provides an in-depth look at the radar systems, a state by state listing of the infrastructure that supported the systems, and an extensive bibliography. This historic content can be used to understand and evaluate properties associated with America's detection and command and control system.},
  googlebooks = {iXrfAAAAMAAJ},
  isbn = {978-1-907521-91-1},
  langid = {english}
}

@article{wirthCRISPDMStandardProcess1999,
  title = {{{CRISP-DM}}: {{Towards}} a {{Standard Process Model}} for {{Data Mining}}},
  author = {Wirth, R{\"u}diger and Hipp, Jochen},
  year = {1999},
  abstract = {The CRISP-DM (CRoss Industry Standard Process for Data Mining) project proposed a comprehensive process model for carrying out data mining projects. The process model is independent of both the industry sector and the technology used. In this paper we argue in favor of a standard process model for data mining and report some experiences with the CRISP-DM process model in practice.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/K427SX4Z/Wirth and Hipp - CRISP-DM Towards a Standard Process Model for Dat.pdf}
}

@article{wittInstitutionalRepositoriesResearch2008,
  title = {Institutional Repositories and Research Data Curation in a Distributed Environment},
  author = {Witt, Michael},
  year = {2008},
  journal = {Library Trends},
  volume = {57},
  number = {2},
  pages = {191--201},
  publisher = {{Johns Hopkins University Press}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/CHBLVJ2V/Witt - 2008 - Institutional repositories and research data curat.pdf;/Users/rca2t1/Dropbox/Zotero/storage/XGNNKYHZ/summary.html}
}

@misc{wladawsky-bergerDataScienceEmergence2013,
  title = {Data {{Science}} - the {{Emergence}} of a {{New Discipline}}},
  author = {{Wladawsky-Berger}, Irving},
  year = {2013},
  month = apr,
  journal = {Irving Wladawsky-Berger},
  abstract = {Data Science is emerging as a hot new profession and academic discipline. Data Scientist: the Sexiest Job of the 21st Century is the title of a recent Harvard Business Review article. Its authors, Tom Davenport and D. J. Patil, define...},
  howpublished = {https://blog.irvingwb.com/blog/2013/04/data-science-the-potential-emergence-of-a-new-discipline.html},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/TKV87U4G/data-science-the-potential-emergence-of-a-new-discipline.html}
}

@article{wladawsky-bergerWhyWeNeed2014,
  title = {Why {{Do We Need Data Science When We}}'ve {{Had Statistics}} for {{Centuries}}? -- {{WSJ Blog}}},
  shorttitle = {Why {{Do We Need Data Science When We}}'ve {{Had Statistics}} for {{Centuries}}?},
  author = {{Wladawsky-Berger}, Irving},
  year = {2014},
  month = may,
  journal = {Dow Jones Institutional News},
  publisher = {{Dow Jones \& Company, Inc.}},
  abstract = {Data Science is emerging as one of the hottest new professions and academic disciplines in these early years of the 21st century. A number of articles have noted that the demand for data scientists is racing ahead of supply. People with the necessary skills are scarce, primarily because the discipline is so new. But, the situation is rapidly changing, as universities around the world have started to offer different kinds of graduate programs in data science. This year, for example, New York University is offering two new degrees--a general Master in Data Science, and a more domain-specific Master in Applied Urban Science and Informatics.},
  copyright = {Copyright \textcopyright{} 2014, Dow Jones \& Company, Inc.},
  langid = {english},
  keywords = {Computers/Electronics,Computing,International Business Machines Corp.,Technology}
}

@misc{wladawsky-bergerWhyWeNeed2014a,
  title = {Why {{Do We Need Data Science When We}}'ve {{Had Statistics}} for {{Centuries}}?},
  author = {{Wladawsky-Berger}, Irving},
  year = {2014},
  month = may,
  journal = {WSJ},
  abstract = {Because while statistics can explain something happening now, data science aims to discover and extract actionable knowledge that can be used to make decisions and predictions, writes Guest Columnist Irving Wladawsky-Berger. And now, "given our newfound ability to gather valuable data on almost any topic," he writes, data science "can now apply to softer disciplines like the health and social sciences."},
  langid = {american},
  keywords = {DATA SCIENCE,DATA SCIENTIST}
}

@misc{WOCEFindingsAtlantic,
  title = {{{WOCE}} Findings on the {{Atlantic}} Now Available as a Printed {{Atlas}}},
  abstract = {The World Ocean Circulation Experiment (WOCE) is the most extensive endeavor to investigate and measure the world ocean in our history. ``It offers us a tremendous amount of data on temperatures, salinity, nutrient concentrations and water transport  in the ocean,'' explains Viktor Gouretski from the Integrated Climate Data Center (ICDC). Together with colleagues from Hamburg and Moscow, he has now completed the third part of the WOCE Atlas in printed form: the most important findings on the Atlantic Ocean.},
  howpublished = {https://www.clisap.de/clisap/about-us/news/woce-findings-on-the-atlantic-now-available-as-a-printed-atlas/},
  langid = {british},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UZT2873S/woce-findings-on-the-atlantic-now-available-as-a-printed-atlas.html}
}

@article{wolfeEpidemiologicInvestigationHealth1985,
  title = {An Epidemiologic Investigation of Health Effects in {{Air Force}} Personnel Following Exposure to Herbicides and Associated Dioxins},
  author = {Wolfe, William H. and Lathrop, George D. and Albanese, Richard A. and Moynahan, Patricia M.},
  year = {1985},
  month = jan,
  journal = {Chemosphere},
  volume = {14},
  number = {6},
  pages = {707--716},
  issn = {0045-6535},
  doi = {10.1016/0045-6535(85)90178-X},
  abstract = {In 1979 the United States Air Force (USAF) made the commitment to conduct an epidemiologic study of the possible health effects from chemical exposure in Air Force personnel who conducted aerial herbicide dissemination missions in Vietnam (Operation RANCH HAND). The purpose of this epidemiologic investigation is to determine whether long-term health effects exist and can be attributed to occupational exposure to herbicides. This study uses a matched cohort design in a nonconcurrent prospective setting, incorporating mortality, morbidity, and follow-up studies. This paper presents the results of the analysis of health information on 2706 Ranch Handers and comparison individuals obtained by questionnaire and 2269 Ranch Handers and comparison individuals undergoing an extensive physical examination. There is insufficient evidence to support a cause and effect relationship between herbicide exposure and adverse health in the Ranch Hand group at this time. The study has disclosed numerous medical findings, mostly of a minor or undetermined nature, that require detailed follow-up.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/FIJIP5PF/Wolfe et al. - 1985 - An epidemiologic investigation of health effects i.pdf;/Users/rca2t1/Dropbox/Zotero/storage/3U7TUZEF/004565358590178X.html}
}

@misc{woodieDataScientistsWhat2016,
  title = {For {{Data Scientists}}, {{What}}'s in a {{Name Really Matters}}},
  author = {Woodie, Alex},
  year = {2016},
  month = mar,
  journal = {Datanami},
  abstract = {Shakespeare once pondered the nature of names, pointing out that "a rose by any other name would smell as sweet." For data scientists, the meaning behind},
  howpublished = {https://www.datanami.com/2016/03/29/data-scientists-whats-name-really-matters/}
}

@article{woodieTrackingDataScience2016,
  title = {Tracking the {{Data Science Talent Gap}}},
  author = {Woodie, Alex},
  year = {2016},
  month = mar,
  journal = {Datanami},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/UCCALJ53/tracking-data-science-talent-gap.html}
}

@misc{woodsBitlyHilaryMason2012,
  title = {Bitly's {{Hilary Mason}} on "{{What}} Is {{A Data Scientist}}?"},
  shorttitle = {Bitly's {{Hilary Mason}} on "{{What}} Is {{A Data Scientist}}?},
  author = {Woods, Dan},
  year = {2012},
  month = mar,
  journal = {Forbes},
  abstract = {Mason divides data science into two halves. The one half is analytics. The other half is the invention of new techniques to draw insights from data that were not possible before.},
  chapter = {Enterprise \& Cloud},
  howpublished = {https://www.forbes.com/sites/danwoods/2012/03/08/hilary-mason-what-is-a-data-scientist/},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/JACGA93K/hilary-mason-what-is-a-data-scientist.html}
}

@misc{woodsEMCGreenplumSteven2011,
  title = {{{EMC Greenplum}}'s {{Steven Hillion}} on {{What Is}} a {{Data Scientist}}?},
  author = {Woods, Dan},
  year = {2011},
  month = oct,
  journal = {Forbes},
  abstract = {As the mounds of data pile up from every direction, businesses are going to be differentiated increasingly by how they use that data. The essential skill set needed for the enterprise of the 21st century is that of the ``data scientist,'' a role dedicated to understanding and making use of data to help a business or other organization. Steven Hillion is vice president of analytics at EMC Greenplum explains his vision for the role.},
  chapter = {Data Driven},
  howpublished = {https://www.forbes.com/sites/danwoods/2011/10/11/emc-greenplums-steven-hillion-on-what-is-a-data-scientist/},
  langid = {english}
}

@misc{woodsIBMAnjulBhambhri2012,
  title = {{{IBM}}'s {{Anjul Bhambhri}} on {{What Is}} a {{Data Scientist}}?},
  author = {Woods, Dan},
  year = {2012},
  month = feb,
  journal = {Forbes},
  abstract = {To continue my series of articles on ``What is a Data Scientist?'' I interviewed Anjul Bhambhri, vice president of development for big data projects at IBM, about her vision for creating business value from big data. ``The enterprises that will achieve a competitive edge and win will have a blend of a healthy data-science culture, enterprising data scientists who can bend the ear of C-level decision makers, and the right combination of technology that will surface the data that make sense in the context of the business,'' says Bhambhri.},
  chapter = {Data Driven},
  howpublished = {https://www.forbes.com/sites/danwoods/2012/02/16/ibms-anjul-bhambhri-on-what-is-a-data-scientist/},
  langid = {english}
}

@misc{woodsLinkedInDanielTunkelang2011,
  title = {{{LinkedIn}}'s {{Daniel Tunkelang On}} "{{What Is}} a {{Data Scientist}}?"},
  shorttitle = {{{LinkedIn}}'s {{Daniel Tunkelang On}} "{{What Is}} a {{Data Scientist}}?},
  author = {Woods, Dan},
  year = {2011},
  month = oct,
  journal = {Forbes},
  abstract = {Daniel Tunkelang, principal data scientist at LinkedIn, joined LinkedIn in December 2010 and oversees the data science team, which analyzes terabytes of data to produce products and insights that serve LinkedIn's members. In this article he explains his definition of a data scientist.},
  chapter = {Data Driven},
  howpublished = {https://www.forbes.com/sites/danwoods/2011/10/24/linkedins-daniel-tunkelang-on-what-is-a-data-scientist/},
  langid = {english}
}

@misc{woodsLinkedInMonicaRogati2011,
  title = {{{LinkedIn}}'s {{Monica Rogati On}} "{{What Is A Data Scientist}}?"},
  shorttitle = {{{LinkedIn}}'s {{Monica Rogati On}} "{{What Is A Data Scientist}}?},
  author = {Woods, Dan},
  year = {2011},
  month = nov,
  journal = {Forbes},
  abstract = {To continue our series on the emerging role of the data scientist in today's data-driven organizations, we spoke with Monica Rogati, Senior Data Scientist at LinkedIn, for her views on the subject.},
  chapter = {Data Driven},
  howpublished = {https://www.forbes.com/sites/danwoods/2011/11/27/linkedins-monica-rogati-on-what-is-a-data-scientist/},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ILL39BEZ/linkedins-monica-rogati-on-what-is-a-data-scientist.html}
}

@misc{woodsTableauSoftwarePat2011,
  title = {Tableau {{Software}}'s {{Pat Hanrahan}} on "{{What Is}} a {{Data Scientist}}?"},
  shorttitle = {Tableau {{Software}}'s {{Pat Hanrahan}} on "{{What Is}} a {{Data Scientist}}?},
  author = {Woods, Dan},
  year = {2011},
  month = nov,
  journal = {Forbes},
  abstract = {We continue our series on the emerging role of the data scientist, by talking to Pat Hanrahan, chief scientist and co-founder of Tableau Software. Hanrahan received a PhD in biophysics from the University of Wisconsin in 1985 and joined the faculty at Princeton University in 1989. In the late 1980s he worked for Digital Equipment Corp. and directed the 3D computer graphics laboratory at the New York Institute of Technology. At Stanford University since 1996, he is now the CANON Professor of Computer Science and Electrical Engineering.},
  chapter = {Data Driven},
  howpublished = {https://www.forbes.com/sites/danwoods/2011/11/30/tableau-softwares-pat-hanrahan-on-what-is-a-data-scientist/},
  langid = {english}
}

@misc{woodsWhatDataScientist2012,
  title = {What {{Is}} a {{Data Scientist}}?: {{Michael O}}'{{Connell}} of {{TIBCO Spotfire}}},
  shorttitle = {What {{Is}} a {{Data Scientist}}?},
  author = {Woods, Dan},
  year = {2012},
  month = jan,
  journal = {Forbes},
  abstract = {We continue our series, ``What Is a Data Scientist?'' by speaking with Michael O'Connell, senior director of analytics of TIBCO Spotfire, TIBCO Software Inc. O'Connell is working at TIBCO to create real-time data distribution and analysis platforms that help users get to the heart of the question more quickly, and with better outcomes of value for the business. In this role, O'Connell has arrived at several maxims that can help the data scientist become most valuable to the business, and at practices that can best create a data-driven culture at an organization.},
  chapter = {Data Driven},
  howpublished = {https://www.forbes.com/sites/danwoods/2012/01/25/what-is-a-data-scientist-michael-oconnell-of-tibco-spotfire/},
  langid = {english}
}

@misc{woodsWhatDataScientist2012a,
  title = {What {{Is}} a {{Data Scientist}}?: {{Michael Rappa}}, {{Institute}} for {{Advanced Analytics}}},
  shorttitle = {What {{Is}} a {{Data Scientist}}?},
  author = {Woods, Dan},
  year = {2012},
  month = mar,
  journal = {Forbes},
  abstract = {NC State's Michael Rappa on What is a Data Scientist?: ``Any time you put `science' in a label, science invokes certain very real concepts within it, like the notion of public knowledge and peer review and so forth,'' Rappa says. ``And it's safe to say that the `data scientist' in places like Silicon Valley aren't publishing their algorithms and putting them out to peer review scrutiny and making it all public knowledge. And within the university environment we're naturally a little bit more sensitized to the notion of what a `science' is.''},
  chapter = {Data Driven},
  howpublished = {https://www.forbes.com/sites/danwoods/2012/03/05/what-is-a-data-scientist-michael-rappa-north-carolina-state-university/},
  langid = {english}
}

@article{WorldMostValuable2017,
  title = {The {{World}}'s {{Most Valuable Resource Is No Longer Oil}}, but {{Data}}},
  year = {2017},
  month = may,
  journal = {The Economist},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/X8QIQD8M/the-worlds-most-valuable-resource-is-no-longer-oil-but-data.html}
}

@article{wuConvergencePropertiesEM1983,
  title = {On the {{Convergence Properties}} of the {{EM Algorithm}}},
  author = {Wu, C. F. Jeff},
  year = {1983},
  month = mar,
  journal = {Annals of Statistics},
  volume = {11},
  number = {1},
  pages = {95--103},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0090-5364, 2168-8966},
  doi = {10.1214/aos/1176346060},
  abstract = {Two convergence aspects of the EM algorithm are studied: (i) does the EM algorithm find a local maximum or a stationary value of the (incomplete-data) likelihood function? (ii) does the sequence of parameter estimates generated by EM converge? Several convergence results are obtained under conditions that are applicable to many practical situations. Two useful special cases are: (a) if the unobserved complete-data specification can be described by a curved exponential family with compact parameter space, all the limit points of any EM sequence are stationary points of the likelihood function; (b) if the likelihood function is unimodal and a certain differentiability condition is satisfied, then any EM sequence converges to the unique maximum likelihood estimate. A list of key properties of the algorithm is included.},
  langid = {english},
  mrnumber = {MR684867},
  zmnumber = {0517.62035},
  keywords = {curved exponential family,EM algorithm,GEM algorithm,incomplete data,maximum likelihood estimate},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RCHSJG3H/Wu - 1983 - On the Convergence Properties of the EM Algorithm.pdf;/Users/rca2t1/Dropbox/Zotero/storage/TPM8A3KP/DPubS.html}
}

@misc{wuStatisticsDataScience1997,
  title = {Statistics = {{Data Science}}?},
  author = {Wu, C. F. Jeff},
  year = {1997},
  annotation = {Slides to Wu's inaugural lecture for his appointment to the H. C. Carver Professorship at the University of Michigan.},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/QQIKSMWT/Wu - 1996 - Statistics = Data Science.pdf}
}

@article{yajimaJapaneseClassificationSociety1990,
  title = {Japanese {{Classification Society}} ({{JCS}})},
  editor = {Yajima, Keiji},
  year = {1990},
  month = oct,
  journal = {International Federation of Classification Societies Newsletter},
  volume = {1},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/6XUC8YBB/Murtagh and Yajima - 1990 - Japanese Classification Society (JCS).pdf}
}

@article{yanFirstCourseData2019,
  title = {A {{First Course}} in {{Data Science}}},
  author = {Yan, Donghui and Davis, Gary E.},
  year = {2019},
  month = may,
  journal = {Journal of Statistics Education},
  volume = {27},
  number = {2},
  pages = {99--109},
  publisher = {{Taylor \& Francis}},
  issn = {null},
  doi = {10.1080/10691898.2019.1623136},
  abstract = {Data science is a discipline that provides principles, methodology, and guidelines for the analysis of data for tools, values, or insights. Driven by a huge workforce demand, many academic institutions have started to offer degrees in data science, with many at the graduate, and a few at the undergraduate level. Curricula may differ at different institutions, because of varying levels of faculty expertise, and different disciplines (such as mathematics, computer science, and business) in developing the curriculum. The University of Massachusetts Dartmouth started offering degree programs in data science from Fall 2015, at both the undergraduate and the graduate level. Quite a few articles have been published that deal with graduate data science courses, much less so dealing with undergraduate ones. Our discussion will focus on undergraduate course structure and function, and specifically, a first course in data science. Our design of this course centers around a concept called the data science life cycle. That is, we view tasks or steps in the practice of data science as forming a process, consisting of states that indicate how it comes into life, how different tasks in data science depend on or interact with others until the birth of a data product or a conclusion. Naturally, different pieces of the data science life cycle then form individual parts of the course. Details of each piece are filled up by concepts, techniques, or skills that are popular in industry. Consequently, the design of our course is both ``principled'' and practical. A significant feature of our course philosophy is that, in line with activity theory, the course is based on the use of tools to transform real data to answer strongly motivated questions related to the data.},
  keywords = {Curriculum design,Data science life cycle,Data visualization,Exploratory data analysis},
  annotation = {\_eprint: https://doi.org/10.1080/10691898.2019.1623136},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/7HMZNXI7/Yan and Davis - 2019 - A First Course in Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/AH2F66NB/10691898.2019.html}
}

@misc{yauDataNewHot2009,
  title = {Data Is the {{New Hot}}, {{Drop-dead Gorgeous Field}}},
  author = {Yau, Nathan},
  year = {2009},
  month = aug,
  journal = {FlowingData},
  abstract = {We all know this already, but it's nice to get some backing from The New York Times every now and then. In this NYT article, that I'm sure has spread to every statistician's email\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/W7VELTU9/data-is-the-new-hot-drop-dead-gorgeous-field.html}
}

@misc{yauGoogleChiefEconomist2009,
  title = {Google's {{Chief Economist Hal Varian}} on {{Statistics}} and {{Data}}},
  author = {Yau, Nathan},
  year = {2009},
  month = feb,
  journal = {FlowingData},
  abstract = {I keep saying the sexy job in the next ten years will be statisticians. People think I'm joking, but who would've guessed that computer engineers would've been the sexy job of the\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/Y6C25BJY/googles-chief-economist-hal-varian-on-statistics-and-data.html}
}

@misc{yauRiseDataScientist2009,
  title = {Rise of the {{Data Scientist}}},
  author = {Yau, Nathan},
  year = {2009},
  month = jun,
  journal = {FlowingData},
  abstract = {Photo by majamarko As we've all read by now, Google's chief economist Hal Varian commented in January that the next sexy job in the next 10 years would be statisticians. Obviously, I wh\ldots},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/VXVUI2A3/rise-of-the-data-scientist.html}
}

@article{youngbloodVitalSignsGood2005,
  title = {Vital {{Signs Good}} for {{Heart-Monitor Firm}}},
  author = {Youngblood, Dick},
  year = {2005},
  month = may,
  journal = {Star Tribune},
  address = {{Minneapolis, MN}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/RPWBUPNC/teaserdocument.html}
}

@book{youngForbesGreatestTechnology1998,
  title = {Forbes {{Greatest Technology Stories}}: {{Inspiring Tales}} of the {{Entrepreneurs}} and {{Inventors Who Revolutionized Modern Business}}},
  shorttitle = {Forbes {{Greatest Technology Stories}}},
  author = {Young, Jeffrey S. and Facs, MD, Jeffrey S. Young and Staff, Inc, Forbes},
  year = {1998},
  month = sep,
  publisher = {{John Wiley \& Sons}},
  abstract = {Here is the fascinating story of the making of the high-tech business revolution and the birth of the Digital Age. Journalist Jeffrey Young chronicles six decades of unbridled technological innovation, taking you inside today's business empires and introducing you to the dreamers, the schemers, the entrepreneurs, and the inventors who built them, including: * Millionaire-playboy turned visionary-CEO Thomas Watson, Jr.-how he helped pioneer the business computing industry in the early 1950s and made IBM its undisputed master for the next three decades *  Fast-talking hippie-entrepreneur Steve Jobs and engineering genius Steve Wozniak-how they went from penniless "wireheads" to high-tech multimillionaires overnight *  Bill Gates-whose talent for turning other people's creative innovations into his own marketplace success was at the heart of Microsoft's brilliant strategy-a tactic Gates perfected at a very tender age *  How a handful of visionaries turned a Cold War communications system into the global phenomenon called the Internet  Packed with all the excitement of scientific discovery and nail-biting suspense of entrepreneurial brinksmanship, Forbes Greatest Technology Stories is must reading for every business professional.  Forbes (r) is a registered trademark of Forbes Inc. Its use is pursuant to a license agreement with forbes Inc.},
  googlebooks = {zeKnJilSyx8C},
  isbn = {978-0-471-24374-8},
  langid = {english},
  keywords = {Business \& Economics / Entrepreneurship,Business \& Economics / Industries / General,Business \& Economics / Management,Business \& Economics / Skills}
}

@book{yovitsSelfOrganizingSystems1960,
  title = {Self-{{Organizing Systems}}},
  author = {Yovits, Marshall C. and Cameron, Scott},
  year = {1960},
  keywords = {Cameron; Scott; Eds}
}

@book{yuComputationalIntelligentData2016,
  title = {Computational {{Intelligent Data Analysis}} for {{Sustainable Development}}},
  author = {Yu, Ting and Chawla, Nitesh and Simoff, Simeon},
  year = {2016},
  month = apr,
  publisher = {{CRC Press}},
  abstract = {Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present},
  googlebooks = {AGXNBQAAQBAJ},
  isbn = {978-1-4398-9595-5},
  langid = {english},
  keywords = {Business \& Economics / Statistics,Computers / Databases / Data Mining,Computers / Programming / Games,Nature / Environmental Conservation \& Protection}
}

@misc{yuLetUsOwn2014,
  title = {Let Us Own {{Data Science}}},
  shorttitle = {Institute of {{Mathematical Statistics}} | {{IMS Presidential Address}}},
  author = {Yu, Bin},
  year = {2014},
  month = oct,
  journal = {Institute of Mathematical Statistics},
  abstract = {IMS Presidential Address.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/23YWM4IW/ims-presidential-address-let-us-own-data-science.html}
}

@article{zhangDevelopmentDataScience2017,
  title = {The {{Development}} of {{Data Science Education}} in {{China}} from the {{LIS Perspective}}},
  author = {Zhang, Jilong and Fu, Anna and Wang, Hao and Yin, Shenqing},
  year = {2017},
  journal = {International Journal of Librarianship},
  volume = {2},
  number = {2},
  pages = {3--17},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/WBKCWQZI/Zhang et al. - 2017 - The Development of Data Science Education in China.pdf;/Users/rca2t1/Dropbox/Zotero/storage/IKSBDKS7/29.html}
}

@article{zhouThreePerspectivesData2003,
  title = {Three {{Perspectives}} of {{Data Mining}}},
  author = {Zhou, Zhi-Hua},
  year = {2003},
  journal = {Artificial Intelligence},
  volume = {143},
  number = {1},
  pages = {139--146},
  publisher = {{Elsevier}},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/ACDBIWFI/Zhou - 2003 - Three Perspectives of Data Mining.pdf;/Users/rca2t1/Dropbox/Zotero/storage/3FFISBCD/S0004370202003570.html}
}

@inproceedings{zhuDataExplosionData2009,
  title = {Data {{Explosion}}, {{Data Nature}} and {{Dataology}}},
  booktitle = {Brain {{Informatics}}},
  author = {Zhu, Yangyong and Zhong, Ning and Xiong, Yun},
  editor = {Zhong, Ning and Li, Kuncheng and Lu, Shengfu and Chen, Lin},
  year = {2009},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  pages = {147--158},
  publisher = {{Springer}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-642-04954-5_25},
  abstract = {The essence of computer applications is to store things in the real world into computer systems in the form of data, i.e., it is a process of producing data. Some data are the records related to culture and society, and others are the descriptions of phenomena of universe and life. The large scale of data is rapidly generated and stored in computer systems, which is called data explosion. Data explosion forms data nature in computer systems. To explore data nature, new theories and methods are required. In this paper, we present the concept of data nature and introduce the problems arising from data nature, and then we define a new discipline named dataology (also called data science or science of data), which is an umbrella of theories, methods and technologies for studying data nature. The research issues and framework of dataology are proposed.},
  isbn = {978-3-642-04954-5},
  langid = {english},
  keywords = {Brain Data,Cloud Computing,Data Nature,Internet Data,Real Nature},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/3BLDTQF3/Zhu et al. - 2009 - Data Explosion, Data Nature and Dataology.pdf}
}

@article{zhuDataScience2015,
  title = {Towards {{Data Science}}},
  author = {Zhu, Yangyong and Xiong, Yun},
  year = {2015},
  month = may,
  journal = {Data Science Journal},
  volume = {14},
  number = {0},
  pages = {8},
  publisher = {{Ubiquity Press}},
  issn = {1683-1470},
  doi = {10.5334/dsj-2015-008},
  abstract = {The CODATA\&nbsp;Data Science Journal\&nbsp;is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data.All data is in scope, whether born digital or converted from other sources.},
  copyright = {Authors who publish with this journal agree to the following terms:    Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a  Creative Commons Attribution License  that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.  Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.  Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See  The Effect of Open Access ).  All third-party images reproduced on this journal are shared under Educational Fair Use. For more information on  Educational Fair Use , please see  this useful checklist prepared by Columbia University Libraries .   All copyright  of third-party content posted here for research purposes belongs to its original owners.  Unless otherwise stated all references to characters and comic art presented on this journal are \textcopyright, \textregistered{} or \texttrademark{} of their respective owners. No challenge to any owner's rights is intended or should be inferred.},
  langid = {english},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/GF4XVW7Y/Zhu and Xiong - 2015 - Towards Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/KAK2IAG5/print.html}
}

@article{zhuDefiningDataScience2015,
  title = {Defining {{Data Science}}},
  author = {Zhu, Yangyong and Xiong, Yun},
  year = {2015},
  month = jan,
  journal = {arXiv:1501.05039 [cs]},
  eprint = {1501.05039},
  eprinttype = {arxiv},
  primaryclass = {cs},
  abstract = {Data science is gaining more and more and widespread attention, but no consensus viewpoint on what data science is has emerged. As a new science, its objects of study and scientific issues should not be covered by established sciences. Data in cyberspace have formed what we call datanature. In the present paper, data science is defined as the science of exploring datanature.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computers and Society,Computer Science - Databases},
  file = {/Users/rca2t1/Dropbox/Zotero/storage/6YYDM5XL/Zhu and Xiong - 2015 - Defining Data Science.pdf;/Users/rca2t1/Dropbox/Zotero/storage/4L54NBVW/1501.html}
}