NCBI-Hackathons/Hidden-Figures

A pipeline for inferring gender for acknowledged individuals in scientific literature on a massive scale

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Emerging

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.

No commits in the last 6 months.

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.

academic-research science-policy gender-studies bibliometrics research-impact
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 14 / 25

How are scores calculated?

Stars

7

Forks

3

Language

Jupyter Notebook

License

MIT

Last pushed

Jan 20, 2019

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/nlp/NCBI-Hackathons/Hidden-Figures"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.