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Feat: update readme and reduce query.tsv list
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awmulyadi authored Jul 24, 2024
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26 changes: 14 additions & 12 deletions README.md
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Expand Up @@ -21,13 +21,13 @@ Let's dive in and set up your own literature survey adventure!
### Getting Started
Follow these simple steps to set up your literature survey website:

1. Click on the ```Use this template``` button to create your own repository based on this template.
1. Click on the ```Use this template``` button to create your own repository based on this template. If you would like to access your repository via URL, please make created repository public.

2. Open up the ```app/data/test.tsv``` file in your favorite code editor or Excel.
2. Open up the ```app/data/query.tsv``` file in your favorite code editor or Excel.

3. Under the `Title` column, give titles to your topics. Under the `Use` column, write <kbd>1</kbd> if you want to use the article for recommendations or <kbd>0</kbd> if you just want to display the article. Under the URL column, specify the correspondng URLs to Semantic Scholar article.
3. Under the `Title` column, give titles to your topics. Under the `Use` column, write <kbd>1</kbd> if you want to use the article for recommendations or <kbd>0</kbd> if you just want to display the article. **Under the URL column, specify the corresponding URLs to Semantic Scholar (https://www.semanticscholar.org/) article.** Submit your edited file by clicking on `Commit changes...`. In the `Commit message` add a prefix by using one of these keywords **feat:, fix: or chore:**, e.g., "feat: added one paper". After committing changes to the `main` branch directly, the workflows should start automatically.

4. Create a venv:
4. (Optional) Create a venv:
```
> python -m venv env
# On Windows
Expand All @@ -37,32 +37,34 @@ Follow these simple steps to set up your literature survey website:
> source env/bin/activate
```

5. Install all the necessary requirements:
5. (Optional) Install all the necessary requirements:
```
> pip3 install -r requirements.txt
```

6. It's time to fetch some literature! Run the ```literature_fetch_recommendation_api.py``` script to grab the recommended articles from Semantic Scholar:
6. (Optional) It's time to fetch some literature! Run the ```literature_fetch_recommendation_api.py``` script to grab the recommended articles from Semantic Scholar:
```
> cd app/code
> python3 literature_fetch_recommendation_api.py
```

7. Now, fire up MkDocs locally to view the recommended articles:
7. (Optional) Now, fire up MkDocs locally to view the recommended articles:
```
> mkdocs serve
```
Head over to the localhost link that pops up in your terminal.

8. This repository includes a `mkdocs-deploy.yml` [workflow](https://github.com/VirtualPatientEngine/literatureSurvey/blob/main/.github/workflows/mkdocs-deploy.yml) that uses GitHub Actions to automatically execute the specified script and deploy the literature survey system as a [GitHub Pages website](https://virtualpatientengine.github.io/literatureSurvey/). Feel free to edit to based on your project needs or use it as it is.
8. This repository includes a `mkdocs-deploy.yml` [workflow](https://github.com/VirtualPatientEngine/literatureSurvey/blob/main/.github/workflows/mkdocs-deploy.yml) that uses GitHub Actions to automatically execute the specified script **once a week** and deploy the literature survey system as a [GitHub Pages website](https://virtualpatientengine.github.io/literatureSurvey/). Feel free to edit to based on your project needs or use it as it is.

> To host your literature survey system online, you must place the YML file in the `.github/workflows/` folder. Once you have pushed you code to GitHub, under the [Actions](https://github.com/VirtualPatientEngine/literatureSurvey/actions) tab, you'll find the ongoing `mkdocs-deploy.yml` workflow. Once this workflow finishes, head over to the [Settings/Pages](https://github.com/VirtualPatientEngine/literatureSurvey/settings/pages) tab. From there, choose `Deploy from a branch` in the Source section. Under the Branch subsection, select `gh-pages` and root from the dropdown menus, then click `Save`.
> To host your literature survey system online, you must place the YML file in the `.github/workflows/` folder. Once you have pushed you code to GitHub, under the [Actions](https://github.com/VirtualPatientEngine/literatureSurvey/actions) tab, you'll find the ongoing `mkdocs-deploy.yml` workflow (this might take even 1h or more depending on the current workload of compute servers and length of the publication list). Once this workflow finishes, head over to the [Settings/Pages](https://github.com/VirtualPatientEngine/literatureSurvey/settings/pages) tab. From there, choose `Deploy from a branch` in the Source section. Under the Branch subsection, select `gh-pages` and root from the dropdown menus, then click `Save`.
9. Change <kbd>site_url</kbd>, <kbd>theme:/logo:</kbd>, <kbd>repo_url</kbd>, and <kbd>repo_name</kbd> in ```base.yml``` to the values related to your project.
9. Under `About` section of your repository, head to the gear symbol and check the box `Use your GitHub Pages website` and `Save changes`. You will see an URL to your literature survey repository under `About` section of the `Code` tab.

10. If you'd like to edit the home page of the website, head over to `docs/index.md` to make the changes.
10. Change <kbd>site_url</kbd>, <kbd>theme:/logo:</kbd>, <kbd>repo_url</kbd>, and <kbd>repo_name</kbd> in ```base.yml``` to the values related to your project.

11. (Optional) Edit custom.css if you'd like to change the styling of web pages.
11. If you'd like to edit the home page of the website, head over to `docs/index.md` to make the changes.

12. (Optional) Edit custom.css if you'd like to change the styling of web pages.

### Bugs? Feature Requests?
If you encounter any bugs or have brilliant ideas for new features, please head over to the [Issues](https://github.com/VirtualPatientEngine/literatureSurvey/issues) and let us know.
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Topic Use URL
Time-series forecasting 1 https://www.semanticscholar.org/paper/A-Survey-on-Graph-Neural-Networks-for-Time-Series%3A-Jin-Koh/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/Graph-Guided-Network-for-Irregularly-Sampled-Time-Zhang-Zeman/455bfc515eb279cc09023faa1f78c6efb61224ba?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/Taming-Local-Effects-in-Graph-based-Spatiotemporal-Cini-Marisca/e2a83369383aff37224170c1ae3d3870d5d9e419?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/Sparse-Graph-Learning-from-Spatiotemporal-Time-Cini-Zambon/0d01d21137a5af9f04e4b16a55a0f732cb8a540b?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/Graph-Deep-Learning-for-Time-Series-Forecasting-Cini-Marisca/ccea298edb788edf821aef58f0952c3e8debc25a?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/Large-Language-Models-Are-Zero-Shot-Time-Series-Gruver-Finzi/123acfbccca0460171b6b06a4012dbb991cde55b?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/Graph-Mamba%3A-Towards-Long-Range-Graph-Sequence-with-Wang-Tsepa/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/A-decoder-only-foundation-model-for-time-series-Das-Kong/f45f85fa1beaa795c24c4ff86f1f2deece72252f?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/UniTS%3A-Building-a-Unified-Time-Series-Model-Gao-Koker/bcbcc2e1af8bcf6b07edf866be95116a8ed0bf91?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/Unified-Training-of-Universal-Time-Series-Woo-Liu/4a111f7a3b56d0468f13104999844885157ef17d?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/Time-LLM%3A-Time-Series-Forecasting-by-Reprogramming-Jin-Wang/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277?utm_source=direct_link
Time-series forecasting 1 https://www.semanticscholar.org/paper/Tiny-Time-Mixers-(TTMs)%3A-Fast-Pre-trained-Models-of-Ekambaram-Jati/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6?utm_source=direct_link
Time-series forecasting 0 https://www.semanticscholar.org/paper/Self-Supervised-Contrastive-Pre-Training-For-Time-Zhang-Zhao/648d90b713997a771e2c49f02cd771e8b7b10b37?utm_source=direct_link
Time-series forecasting 0 https://www.semanticscholar.org/paper/Domain-Adaptation-for-Time-Series-Under-Feature-and-He-Queen/5bd2c0acaf58c25f71617db2396188c74d29bf14?utm_source=direct_link
Time-series forecasting 0 https://www.semanticscholar.org/paper/AZ-whiteness-test%3A-a-test-for-signal-uncorrelation-Zambon-Alippi/c3c94ccc094dcf546e8e31c9a42506302e837524?utm_source=direct_link
Time-series forecasting 0 https://www.semanticscholar.org/paper/Graph-state-space-models-Zambon-Cini/279cd637b7e38bba1dd8915b5ce68cbcacecbe68?utm_source=direct_link
Symbolic regression 1 https://www.semanticscholar.org/paper/Discovering-governing-equations-from-data-by-sparse-Brunton-Proctor/5d150cec2775f9bc863760448f14104cc8f42368?utm_source=direct_link
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Symbolic regression 1 https://www.semanticscholar.org/paper/Data-driven-discovery-of-coordinates-and-governing-Champion-Lusch/3c9961153493370500020c81527b3548c96f81e0?utm_source=direct_link
Symbolic regression 1 https://www.semanticscholar.org/paper/Chaos-as-an-intermittently-forced-linear-system-Brunton-Brunton/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2?utm_source=direct_link
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Neural ODEs 0 https://www.semanticscholar.org/paper/Graph-Coupled-Oscillator-Networks-Rusch-Chamberlain/a50a2a191c98dfe045ac2139495ee80ff1338e47?utm_source=direct_link
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Physics-based GNNs 0 https://www.semanticscholar.org/paper/Discovering-Symbolic-Models-from-Deep-Learning-with-Cranmer-Sanchez-Gonzalez/643ac3ef063c77eb02a3d52637c11fe028bfae28?utm_source=direct_link
Physics-based GNNs 0 https://www.semanticscholar.org/paper/Rediscovering-orbital-mechanics-with-machine-Lemos-Jeffrey/2232751169e57a14723bfffb4ab26aa0e0e3839a?utm_source=direct_link
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Latent space simulators 1 https://www.semanticscholar.org/paper/Extended-dynamic-mode-decomposition-with-dictionary-Li-Dietrich/80744010d90c8ede052c7ac6ba8c38c9de959c6e?utm_source=direct_link
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