pubmed-landscape
The landscape of biomedical research
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Download Latest Version (.zip)- __init__.py
- data.py
- exploration.py
- gam.py
- metrics.py
- plotting.py
- scalebars.py
- fig_1_general_embedding.pdf
- fig_1_general_embedding.png
- fig_2_covid.pdf
- fig_2_covid.png
- fig_3_nsc.pdf
- fig_3_nsc.png
- fig_4_high_res.pdf
- fig_4_high_res.png
- fig_4_ml.pdf
- fig_4_ml.png
- fig_5_genders.pdf
- fig_5_genders.png
- fig_6_retracted_papers.pdf
- fig_6_retracted_papers.png
- fig_S10_covid_ablation.pdf
- fig_S10_covid_ablation.png
- fig_S1_summary_data.pdf
- fig_S1_summary_data.png
- fig_S2_interesting_embeddings.pdf
- fig_S2_interesting_embeddings.png
- fig_S3_bert_colored_by_p_n.pdf
- fig_S3_bert_colored_by_p_n.png
- fig_S4_general_embedding_tfidf.pdf
- fig_S4_general_embedding_tfidf.png
- fig_S5_embeddings_grey.pdf
- fig_S5_embeddings_grey.png
- fig_S6_embeddings_and_subregions.pdf
- fig_S6_embeddings_and_subregions.png
- fig_S7_bert_colored_by_length.pdf
- fig_S7_bert_colored_by_length.png
- fig_S8_tsne_BERT_models_1M.pdf
- fig_S8_tsne_BERT_models_1M.png
- fig_S9_tsne_vs_umap_1M.pdf
- fig_S9_tsne_vs_umap_1M.png
- fig_tsne_and_number_of_papers_by_country_v5.pdf
- fig_tsne_and_number_of_papers_by_country_v5.png
- fig_tsne_and_retracted_papers_by_country_v4.pdf
- fig_tsne_and_retracted_papers_by_country_v4.png
- fig_updated_general_embedding_v5.pdf
- fig_updated_general_embedding_v5.png
- 01.2-rgm-data-parse.ipynb
- 02.2-rgm-data-obtain-BERT-embeddings.ipynb
- 03.2-rgm-pipeline-BERT.ipynb
- 04-rgm-analysis-affiliations.ipynb
- 05.2-rgm-pipeline-generate-colors.ipynb
- 09.2-rgm-analysis-covid-19.ipynb
- 15.2-rgm-analysis-retracted-papers.ipynb
- 01-rgm-ls-malteos.ipynb
- 02-rgm-ls-SBERT.ipynb
- 03-rgm-ls-PubMedBERT.ipynb
- 04-rgm-ls-BERT.ipynb
- 05-rgm-pipeline-TFIDF-1M.ipynb
- 06-rgm-pipeline-BERT-models.ipynb
- 07-rgm-comparison-tSNE-UMAP-1M.ipynb
- 08-rgm-SVD-L2-experiment.ipynb
- 09-rgm-TF-IDF-vocabulary-experiment.ipynb
- bert_models.py
- 01-rgm-figure-01.ipynb
- 02-rgm-figure-02.ipynb
- 03-rgm-figure-03.ipynb
- 04-rgm-figure-04.ipynb
- 05-rgm-figure-05.ipynb
- 06-rgm-figure-06.ipynb
- 07-rgm-figure-S1.ipynb
- 08-rgm-figure-S2.ipynb
- 09-rgm-figure-S3.ipynb
- 10-rgm-figure-S4.ipynb
- 11-rgm-figure-S5.ipynb
- 12-rgm-figure-S6.ipynb
- 13-rgm-figure-S7.ipynb
- 14-rgm-figure-S8-countries.ipynb
- 15-rgm-figure-S9-updated-embedding.ipynb
- 16-rgm-figure-S10-updated-countries.ipynb
- 17-rgm-figure-S11.ipynb
- 18-rgm-figure-S12.ipynb
- 19-rgm-figure-S13.ipynb
- 00-rgm-data-download.ipynb
- 01-rgm-data-parse.ipynb
- 02-ls-data-obtain-BERT-embeddings.ipynb
- 03-rgm-pipeline-BERT.ipynb
- 04-rgm-pipeline-TFIDF.ipynb
- 05-rgm-pipeline-generate-colors.ipynb
- 06-rgm-metrics-knn-accuracy.ipynb
- 07-rgm-metrics-knn-recall.ipynb
- 08-rgm-metrics-isolatedness.ipynb
- 09-rgm-analysis-covid-19.ipynb
- 10-rgm-analysis-neuroscience.ipynb
- 11-rgm-analysis-machine-learning.ipynb
- 12-rgm-data-authors-first-names.ipynb
- 13-rgm-prediction-authors-gender.ipynb
- 14-rgm-analysis-authors-gender.ipynb
- 15-rgm-analysis-retracted-papers.ipynb
- 16-rgm-metrics-whitening.ipynb
- 17-rgm-ablation-experiment-covid-19.ipynb
- 18-rgm-analysis-affiliations-2021-baseline.ipynb
- matplotlib_style.txt
- .gitignore
- LICENSE
- README.md
- requirements.txt
- setup.py
# Installation Guide
1. Get the code
git clone https://github.com/berenslab/pubmed-landscape
Downloads the entire project code from GitHub to your computer.
cd pubmed-landscape
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install -e .
Installs the Python libraries listed in requirements.txt (or similar).
If it runs without errors and prints output in the terminal, it worked.
Pulled directly from this repo's README.
// repository documentation
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