The Cooperation Databank: Machine-Readable Science Accelerates Research Synthesis

CoDa Team

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Publishing studies using standardized, machine-readable formats will enable machines to perform meta-analyses on demand. To build a semantically enhanced technology that embodies these functions, we developed the Cooperation Databank (CoDa)—a databank that contains 2,636 studies on human cooperation (1958–2017) conducted in 78 societies involving 356,283 participants. Experts annotated these studies along 312 variables, including the quantitative results (13,959 effects). We designed an ontology that defines and relates concepts in cooperation research and that can represent the relationships between results of correlational and experimental studies. We have created a research platform that, given the data set, enables users to retrieve studies that test the relation of variables with cooperation, visualize these study results, and perform (a) meta-analyses, (b) metaregressions, (c) estimates of publication bias, and (d) statistical power analyses for future studies. We leveraged the data set with visualization tools that allow users to explore the ontology of concepts in cooperation research and to plot a citation network of the history of studies. CoDa offers a vision of how publishing studies in a machine-readable format can establish institutions and tools that improve scientific practices and knowledge.

Original languageEnglish
Pages (from-to)1472-1489
Number of pages18
JournalPerspectives on psychological science : a journal of the Association for Psychological Science
Volume17
Issue number5
Early online date17 May 2022
DOIs
Publication statusPublished - Sept 2022

Bibliographical note

Funding Information:
The pipeline to generate the Cooperation Databank graph from raw data and the code of the Shiny app for R are provided at https://github.com/cooperationdatabank. Further information for users (e.g., video tutorials on how to perform the main activities in the platform) is accessible at https://cooperationdatabank.org/.

Publisher Copyright:
© The Author(s) 2022.

Funding

The pipeline to generate the Cooperation Databank graph from raw data and the code of the Shiny app for R are provided at https://github.com/cooperationdatabank. Further information for users (e.g., video tutorials on how to perform the main activities in the platform) is accessible at https://cooperationdatabank.org/.

FundersFunder number
Horizon 2020 Framework Programme635356

    Keywords

    • cooperation
    • databank
    • knowledge representation
    • meta-analysis
    • ontologies
    • social dilemmas

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