Overview of the studies

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Select studies

Choose inclusion criteria to select studies.

Use one of our selection examples:

*Please allow some time for the data to update.

Or make your own selection:

Explore your selection

Click on the rows in the table to de-select effect sizes

APA references (.rtf)

Bibtex references (.bib)

Raw dataset (.csv)

Meta-analytic models

In order to do this, you need to select some data first.

Go back to data overview and select at least one specific IV Treatment.

Choose your model


Choose additional moderators (mods.) for your analysis

Add your study for meta-analysis

Studies will be added to meta-analysis

Add your study for Meta-analysis

Study Metadata

Effect Size

Don't know your effect size? Here's a guide.

Treatment 1

Treatment 2


Download data

Selected dataset (.csv)

Meta-regression models

In order to do this, you need to select some data first.

Go back to data overview and select at least one specific IV Treatment.

Choose your model


Choose additional moderators (mods.) for your analysis

Add your study for meta-regression

Studies will be added to meta-regression

Click to add another study for meta-regression

Add your study for Meta-regression

Study Metadata

Use our calculation sheet to compute logit-transformed cooperation and variance.

Treatment 1


Download data

Selected dataset (.csv)

Analyze citations

The network below shows papers (nodes) and their citations (links). A link between nodes represents a citation. Larger nodes are more highly cited papers.

Communities in the network are identified based on their modularity, i.e., the density of the links intra- and inter-communities, using the Louvain community detection algorithm (Blondel et al., 2008).

Users can choose to display the citation network for specific range of years, papers with a specified number of citations, and color the nodes according to several criteria (e.g., community, game type).

The citation network can take a few seconds to load.

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, P10008.

Ontology overview

Explore the ontology of Independent Variables

The image shows all the Independent Variables (IV) that were used to annotate studies and their treatments. The size of the bubbles indicate how many treatment were annotated with the IV. Click on a chosen circle to zoom in and see the subclasses, and click on the external circles to zoom out. For a more specific description of the IVs, you can refer to the table below.

Independent Variables details

Below a description of the independent variables, their moderators, and their possible values. For further details, see also https://data.cooperationdatabank.org/ .

Add Study

You can use this form to manually add studies to the Cooperation Databank, including their effect sizes and sample/study characteristics. These studies can be published or unpublished. At the moment, the Cooperation Databank only includes studies that observe cooperation in a Prisoner’s Dilemma, Public Goods Dilemma, and Resource Dilemma. The submitted information about each study will be reviewed by an Editorial Board and subsequently added to the Databank.

Add Study Metadata

Add Treatments

Defining Treatments:

Each study can include one or more effect sizes. To allow flexibility to compute effect sizes, we request that you specify treatments in your study (i.e., the manipulated or measured variables used to predict cooperation).

A treatment can be characterized by:
  (a) one or more variables that takes different levels (or values) within a study (e.g., gender: male; group size: 4).
  (b) a single variable correlated with cooperation (e.g., age, personality trait, or expectations of partner cooperation)

Below, we request that you specify the different treatments in your study that can be used as input to create effect sizes. We recommend watching this tutorial for further instructions and relevant examples.

For example, a study could manipulate both (1) the presence and absence of punishment and (2) the presence and absence of communication (e.g., a 2 x 2 between subjects design). This would create four treatments that can be specified below. If the study also included a measure of a personality variable that was related to cooperation, then this would be defined as an additional treatment.

Below, you can add and define treatments by selecting one or more variables that specify the treatment, e.g., a punishment treatment and communication treatment. When you select a specific Generic Independent Variable that specifies the treatment, you will be asked to assign values to several more Specific Independent Variables.

For each treatment you can report the requested statistics, including a sample size, mean, standard deviation, Proportion of Cooperation, the Lowest choice, the Highest choice, Between Subjects vs. Within Subjects design, Pearson’s correlation.

Note: If you do not find a general or specific IV among the listed ones, you can still annotate the treatments by selecting “Add description” in the list and describe what variables were manipulated or measured.
The CoDa editorial board will review this content and either re-annotate it according to the current codebook or add this variable to the list of the new ones to be implemented in the future.

Add Treatment1

Add another treatment for this variable
Add another variable

Add Sample Characteristics

Please report other relevant characteristics of the study (e.g., related to the sample or the game structure). This information will assist the editors in evaluating your study, and can be used for search criteria and as variables in meta-analyses (e.g., moderator variables).

Check our codebook for the list of all the Sample Characteristics, their values, and their definitions

Add Study Characteristics

Check our codebook for the list of all the Study Characteristics variables, their values, and their definitions

Add Overall cooperation

Submit your study



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