/AI-COVID19-interdisciplinarity

Interdisciplinary Research in Artificial Intelligence: Lessons from COVID-19

Primary LanguageR

Publication

Official repository for Interdisciplinary Research in Artificial Intelligence: Lessons from COVID-19

  • Keywords: Team Science, Interdisciplinarity, Artificial Intelligence, COVID-19

Project Description

This project contains a sample of data from CORD-19 concerning AI and COVID-19 with additional information obtained through OpenAlex

Interdisciplinarity metrics
Team AI Team Expertise AI team metric is the fraction of the previous AI publications for each author, averaged over the entire time
PMI (Team) General team metric is the average "recent" disciplinary dispersion (journal distances) of team authors
Balance (Team) Balance measures the evenness of the distribution of categories in the team
Disparity (Team) Disparity quantifies the degree of difference or diversity between elements within a set of the n most recent publications per author in the team
Variety (Team) Variety is the number of different disciplines (or journals) of the n most recent publications per author in the team
Knowledge Share of AI References AI knowledge metric is the fraction of references using AI keywords
PMI (References) General knowledge metric is the average distance among all the journals cited in the references
Balance (References) Balance measures the evenness of the distribution of categories within a set of references
Disparity (References) Disparity quantifies the degree of difference or diversity between elements within a set of references
Variety (References) Variety is the number of different disciplines (or journals) cited

Indicators of "success"
$N_{citations}$ The number of citations
$M_{attention}(i)$ The Altmetric score
$I(i)$ The interdisciplinary spread

Note: $i$ represents a research publication.

Repository structure

  • /DATA contains the data used for the econometric model
    • data_main.csv - csv file
    • Reference&Citations.rar - .rar file containing data_reference.csv & data_citation.csv
    • data_main_senza_preprint.csv - csv file with data_main.csv data but without preprints
  • /CODE contains the code used for the econometric model and to build indexes and metrics
    • Metrics.ipynb - Jupyter Notebok which contains the process for building the metrics and indexes
    • rEconometrics_mainFindings.R - R file which contains the econometric models
    • rEconometrics_robustnessCheck.R - R file which contains the robustness checks