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Meta-Analysis
. 2022 Sep;17(5):1472-1489.
doi: 10.1177/17456916211053319. Epub 2022 May 17.

The Cooperation Databank: Machine-Readable Science Accelerates Research Synthesis

Giuliana Spadaro  1   2 , Ilaria Tiddi  3 , Simon Columbus  1   4 , Shuxian Jin  1 , Annette Ten Teije  3 ; CoDa Team; Daniel Balliet  1   2
Affiliations
Meta-Analysis

The Cooperation Databank: Machine-Readable Science Accelerates Research Synthesis

Giuliana Spadaro et al. Perspect Psychol Sci. 2022 Sep.

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.

Keywords: cooperation; databank; knowledge representation; meta-analysis; ontologies; social dilemmas.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared that there were no conflicts of interest with respect to the authorship or the publication of this article.

Figures

Fig. 1.
Fig. 1.
The process of developing the Cooperation Databank (CoDa). The steps were performed in the presented order, but there was some feedback and iterations that occurred between specific steps. For example, some changes to the semantic modeling were made using knowledge acquired during the annotation of studies.
Fig. 2.
Fig. 2.
Flow chart of literature search and inclusion of studies in the Cooperation Databank.
Fig. 3.
Fig. 3.
Domain-independent and domain-specific model of the Cooperation Databank (CoDa) applied to one annotated instance. foaf:Person, cdo:DOI, cdo:Paper, and cdo:Study are part of the domain-independent model in the CoDa knowledge graph. Cdo:Treatment and cdo:Observation are part of the domain-specific model. However, some variables included in cdo:Study (e.g., game type) are pertinent only to represent cooperation studies. The figure displays a small selection of variables on which the study was annotated. For the complete list of variables for which studies were annotated, see the Supplemental Material available online.
Fig. 4.
Fig. 4.
Packed bubble chart of the variables used to predict cooperation and for which meta-analyses are possible using the Cooperation Databank (CoDa) with a zoom-in of personality variables. This image displays the Ontology Explorer tool on the CoDa application for users to navigate the variables. The relative size of each bubble represents the relative number of treatments that was annotated with that variable.
See this image and copyright information in PMC

Update of

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