NCBI-Hackathons/Hidden-Figures
A pipeline for inferring gender for acknowledged individuals in scientific literature on a massive scale
This tool helps researchers and academic policy analysts understand gender representation within scientific collaborations by analyzing acknowledgments in biomedical research articles from PubMed Central. It takes raw article data and outputs insights into who is acknowledged, their inferred gender, and the nature of their contributions. Anyone studying equity, diversity, and inclusion in scientific research would find this valuable.
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Use this if you need to analyze large-scale patterns of gender representation and types of contributions within scientific acknowledgments to inform policies or academic studies.
Not ideal if you need to identify the precise, individual contributions of specific people or organizations for a single paper, or if you need to infer gender for languages other than English.
Stars
7
Forks
3
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 20, 2019
Commits (30d)
0
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