Navigation | AeonLabs Main Index >> Open Scientific Research >> Real-time validation of Experimental Data Origins: A Swarm of DAQ devices able to Deliver Unique Experimental Data using Blockchain-like Fingerprint ID to a Data Repository
Real-time validation of Experimental Data Origins: A Swarm of DAQ devices able to Deliver Unique Experimental Data using Blockchain-like Fingerprint ID to a Data Repository
Change Language
Last update: 01-04-2024
Vision
(2007)
I can already see students in a laboratory all dressed up and equipped with their tablets and smart device kits, all sending the experimental data to a public data repository while at the same time receiving experimental data from other students. All inside that tablet cooperating and without the need to get to know each other.
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continue reading here.
The problem
Current scientific methods use traditional dataloggers (DAQ) to collect and measure experimental data. This means collected data many times is stored in a paper format, and most of the time in a conventional CSV Excel data file. This is prone to errors and even worse, forgery of experimental data. To this date, no dataloggers can automate experimental data acquisition in a scientific experiment, making it less transparent and less trustworthy.
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continue reading here.
The idea
This Sci. research presents an innovative method for experimental data acquisition and management of collected data in real time and is compatible with any open environment. The proposed smart DAQ device prototype has the minimum hardware characteristics to handle data measurements collected from sensors locally connected to it, store it on a local CSV or SQLite database file, and finally connect and synchronize data measurements collected with a data repository hosted remotely on a Dataverse.
These Smart DAQ devices are of type "Internet of Everything" (IoE) Smart Devices and are able to connect with each other using swarm intelligence. The main purpose is to increase data integrity and trustworthiness among DAQ devices connected and on all experimental data collected during an experiment or research project.
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In everyday science at a laboratory these Smart DAQ devices are able to connect among each other, in a swarm-like manner, and when doing so, increase experimental data trustworthiness and authenticity in an experiment part of a research project or experimental campaign. Setting up a Swarm network of smart DAQ devices not only increases the quality of research results, by tagging each individual piece of experimental data collected from each individual sensor, with a unique data fingerprint ID (hash) at the exact same moment of data collection, broadcast it to other nearby smart DAQ devices and finally do data upload to a repository where a new, additional data fingerprint is added to existing ones (generated locally). This way is maintained and guarantees data collection integrity locally, from the laboratory, until the moment is received and stored in a data repository in a cloud server.
(...)
continue reading here.
A paper is currently being written, in an open-environment format
Available as a preprint draft document at Elsevier's SSRN platform. https://ssrn.com/abstract=4210504 .
See its revision history and ongoing writing works here for how this kind of smart electronics can be connected for fail-safe data and redundancy (and as an IoE DAQ device) on any experimental setup.
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The PCB hardware has now the status of fully tested and fully working.
Currently, coding the firmware for the first public beta stable release.
Most recent update
To read about the most recent update click here.
To view a list of work scheduled to be done on all these open hardware smart data acquisition devices see this page here.
Click to view on Youtube
The OEM version of the firmware code can be found in the folder firmware code. It has by default OTA updates, meaning the smart data acquisition device automatically updates itself when newer updated versions are made available here.
This code uses my own ESP32 C++ class libraries to expedite the development of the code of ESP32 microcontrollers. The repository is located here for anyone to use.
The Android App under development is able to connect these smart data acquisition devices and can be found here on the following repository "Mobile App for managing LDAD Smart DAQ devices".
In parallel is being written a C library to expedite API integration on smart DAQ devices or elsewhere. Follow the link to its repository:
https://github.com/aeonSolutions/OpenScience-Dataverse-API-C-library
Dataverse API in another coding language
Goto dataverse.org for another coding language that best suits your coding style and needs. Currently, there are client libraries for Python, Javascript, R, Java, and Julia that can be used to develop against Dataverse Software APIs
https://guides.dataverse.org/en/5.12/api/client-libraries.html
My presentation at the monthly September 2023 dataverse.org meeting, to the team of programmers behind the #datavserse open source code about real-time experimental data and #dataverse, in particular to propose adding the functionality to receive live, real-time, experimental data directly from a smart data acquisition device into a #dataverse repository.
See this presentation on dataverseTV here: https://dataverse.org/dataversetv
View on Youtube my presentation about Real-time experimental data and Dataverse
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The Dataverse Project is an open source web application to share, preserve, cite, explore, and analyze research data. It facilitates making data available to others and allows you to replicate others' work more easily. Researchers, journals, data authors, publishers, data distributors, and affiliated institutions all receive academic credit and web visibility. |
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figshare is a repository where users can make all of their research outputs available in a citable, shareable and discoverable manner |
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In the Wiki, the reader can find a list of Open Science projects worth mention. And if you know one of interest, share it here too, for everyone to see and use.
see a list here of open science projects that utilize these open hardware solutions for data acquisition. In the photo below, to the left, the smart DAQ is installed on an acrylic case and screwed with plastic screws to an acrylic base with the same cross-section area as the specimen to be tested. The acrylic base can be bought here. And the acrylic case here.
The photo above, to the right, is one of many specimens I purposely fabricated to research the self-sensing properties of asphalt mixed with a known content of carbon fibers. This is a 10cm cylinder specimen and on the top is already set up my own design smart #DAQ (get it here on my GitHub ) with the ability to upload LIVE experimental data to a #dataverse.
Proof of Concept
To test and validate proposed smart DAQ PCB electronics and its firmware as a solution for LIVE experimental data measurements on any test specimen part of an experimental campaign, This PCB electronics is being used to measure a predefined set of variables/parameters to further study several asphalt mixtures with known carbon fiber weight content in the asphalt matrix. Below is a YouTube link to an unedited short video showing one of the experimental setups.
Click to view on Youtube
See a list with the hardware specifications for the 12bit pcb on the WiKi.
In everyday science at a laboratory this smart DAQ is able to:
- connect to all kinds of 3.3V digital sensors
- connect to all kinds of sensors compatible with the I2C protocol (max 118 sensors simultaneously)
- measure voltage in the range of [0;3.3V]
- measure electrical resistance [0; 10^6] Ohm
- do temperature and humidity compensation on all measurements
- has a voltage reference sensor for improved accuracy on ADC measurements
- has a motion sensor to know if anyone moved a specimen during an experiment
- can be powered using 4.2V LiPo batteries
- 12-bit Smart Data Acquisition Devices (8)
- 16-bit Smart Data Acquisition Device (1)
- 24-bit Smart Data Acquisition Devices (2) (soon . stay tuned.)
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Bug reports and pull requests are welcome on GitHub at https://github.com/aeonSolutions/OpenScience-Dataverse-API-C-library. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct. Don't forget to read AeonLabs's Wiki before using any code or electronics available here on GitHub. Thank you.
Please make sure tests pass before committing, and to add new tests for new additions.
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