Most of the cutting-edge science is built on scientific software, which makes scientific software often as important as traditional scholarly literature. Despite that, the software is not always treated as such, especially when it comes to funding, credit, and citations. Moreover, with the ever-growing number of open-source software tools, it is impossible for many researchers to track tools, databases, and methods in a specific field.
In an effort to automate the process of crediting and identifying relevant and essential software in the biomedical domain, we've developed a machine learning model to extract mentions of software from scientific articles. The input to this model is a text from a scientific article and the output is a list of mentioned software within it.
We applied this model to the CORD-19 full-text articles and stored the output inCORD-19 Software Mentions . Cite as: Wade, Alex D., & Williams, Ivana. (2021). CORD-19 Software Mentions [Data set]. https://doi.org/10.5061/dryad.vmcvdncs0
Python 3.7+ (tested on 3.7.4)
Python packages: pandas, numpy, keras, torch, nltk, sklearn, transformers, os, seqeval, json, time, tqdm, argparse, blink, bs4, re, itertools
Softcite:
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Full corpus (downloaded on February 8, 2021)
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Download the XML file above and place it in the ./data folder.
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XML file processing notebook:
./notebooks/Parse softcite data.ipynb-
Input: ./data/softcite_corpus-full.tei.xml
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Output: ./data/labeled_dfs_all.csv
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Model training notebook:
./notebooks/Train software mentions model.ipynb-
Input: ./data/labeled_dfs_all.csv, ‘allenai/scibert_scivocab_cased’ SciBERT: A Pretrained Language Model for Scientific Text (Beltagy et al., EMNLP 2019)
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Output: ./models/scibert_software_sent
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Performance:
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Download pretrained model 'scibert_software_sent' from: s3://meta-prod-ds-storage/software_mentions_extraction/models and place it in the ./models/ folder.
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Example of how to run the model in inference mode:
./scripts/Software mentions inference mode.ipynb -
Example:
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This model is based on BLINK
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Follow instructions on the github repo to download relevant models/install.
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Example of how to run the model in inference mode:
./scripts/Link text to wikipedia.ipynb -
Example:
- CORD-19 data: More information and download instructions: here
- Save to the ./data/ folder
- Run notebook:
./scripts/Software mentions CORD19.ipynb
Contributions and ideas are welcome! Please see our contributing guide and don't hesitate to open an issue or send a pull request to improve the functionality of this gem.
This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to opensource@chanzuckerberg.com.