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Source data and code for a Scopus-based bibliometric study for female first authorship of Artificial Intelligence Research in Africa

Type de record:

datasetsoftware
Créateur:
SakHouBonFat
Hôte:avatar

To cite the work: Folorunso, S., Turki, H., Dossou, B., & Salami, F. (2025). African Female Authorship in Artificial Intelligence Research: A Scopus-based Bibliometric Study. In The 6th International Workshop on Democracy and AI (DemocrAI@IJCAI 2025).

Source data:

  • scopus (1).csv, scopus (2).csv, and scopus (3).csv: Output of the data retrieval from Scopus.
  • dataset.csv: Final dataset after resolving first names to their corresponding gender using genderize.io.

Source code:

  • Data Processing.ipynb: Creating the final dataset by merging the initial data and assigning gender to the first names of the scientists.
  • Data_Analysis.ipynb: Generating tables and figures for the Paper.

Visit

figshare.com

Tags

Gender studies not elsewhere classifiedApplied computing not elsewhere classifiedArtificial intelligence not elsewhere classifiedHuman information interaction and retrievalInformation governance, policy and ethicsInformetricsArtificial IntelligenceBibliometricsGender RepresentationAfrican Female Scientists

Licenses

CC BY 4.0

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