pedlr-main-data/README.md

pedlr-main-data

Source data for the pedlr project by Christoph Koch, Ondrej Zika, Rasmus Bruckner, and Nicolas W. Schuck. This data contains anonymized data of participants solving a decision making task specified in Koch, C., Zika, O., Bruckner, R., & Schuck, N. W. Influence of surprise on reinforcement learning in younger and older adults. (https://doi.org/10.31234/osf.io/unx5y).

The task and all analysis code are included in the associated GitHub repository with analysis code: https://doi.org/10.5281/zenodo.10211239. Using the analysis code on this source data creates an derivative dataset available at https://doi.org/10.12751/g-node.tsq6sg that includes all results reported in Koch, C., Zika, O., Bruckner, R., & Schuck, N. W. Influence of surprise on reinforcement learning in younger and older adults. (https://doi.org/10.31234/osf.io/unx5y).

For a description of the repositories structure see a commented folder structure below.

Datalad

This is a datalad repository. For more information on how to use and set up datalad on your machine please see https://www.datalad.org/.

A thorough walkthrough on how to use datalad is given by the datalad handbook. See the installation page in the datalad handbook for more information about setup and configuration of datalad.

Usage

Once you have datalad installed on your machine you can clone the dataset (e.g. via https) using

datalad clone https://gin.g-node.org/koch_means_cook/pedlr-main-data.git

For in integrated usage with the analysis described in the referenced preprint (Koch, C., Zika, O., Bruckner, R., & Schuck, N. W. Influence of surprise on reinforcement learning in younger and older adults. https://doi.org/10.31234/osf.io/unx5y) clone the Github code repository (doi:10.5281/zenodo.10211239), then clone this datalad repository into the code repository and rename the datalad repository folder to data. This will allow the analysis scripts to directly use this dataset.

Large files will not be downloaded automatically. To get them, you can use

datalad get <filename>

Large files that have been downloaded will be 'locked' and therefore read-only. If you wish to write you will need to unlock them using

datalad unlock <filename>

For more information on locked/unlocked files see here.

Content

├── exclusions.tsv         # List of excluded participants
├── LICENSE                # License file (CC-BY-SA 4.0)
├── README.md              # This file
├── 09RI1ZH_exp_data.tsv   # Example file: tab-separated file of participants behavior
└── (...)

License

This data set is published under a CC-BY-SA 4.0 license.