The `mpib_ecomp_derivatives` dataset. https://gin.g-node.org/sappelhoff/mpib_ecomp_sourcedata

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README.md

The mpib_ecomp_derivatives dataset

This dataset contains the derivatives from the eComp experiment, conducted at the Max Planck Institute for Human Development (MPIB) in 2021, by Stefan Appelhoff and colleagues.

It is hosted on GIN: https://gin.g-node.org/sappelhoff/mpib_ecomp_derivatives/

All important details are reported in the original paper for the project:

The source data (with more extensive documentation) are available here:

The raw data organized in Brain Imaging Data Structure (BIDS; https://bids.neuroimaging.io/) format are available here:

The experiment code used during data collection can be found here:

The analysis code for this project can be found here:

Download

The full data can be downloaded using one of these two methods:

  1. Via datalad
    1. install: http://handbook.datalad.org/en/latest/intro/installation.html
    2. clone: datalad clone https://gin.g-node.org/sappelhoff/mpib_ecomp_derivatives
    3. go to root of dataset: cd mpib_ecomp_derivatives
    4. get all data: datalad get *
    5. if you want to work on the files or edit them, you may need to run datalad unlock *
  2. Via the GIN client
    1. install: https://gin.g-node.org/G-Node/Info/wiki/GIN+CLI+Setup
    2. clone: gin get sappelhoff/mpib_ecomp_derivatives
    3. go to root of dataset: cd mpib_ecomp_derivatives
    4. get all data: gin download --content
    5. if you want to work on the files or edit them, you may need to run gin unlock *

Or click on the small "download icon" on the right side above the list of files in the repository overview on GIN. This will allow you to see the GIN documentation on how to download data.

Using this dataset

If you use this dataset in your work, please consider citing it as well as the references describing it.

See the LICENSE below, as well as the datacite.yml file.

License

This data is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://opendatacommons.org/licenses/pddl/1.0/

See also the human readable summary at: https://opendatacommons.org/licenses/pddl/summary/

Please see the LICENSE file for details.

Contact

datacite.yml
Title The mpib_ecomp_derivatives dataset
Authors Appelhoff,Stefan;Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany;ORCID:0000-0001-8002-0877
Hertwig,Ralph;Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany;ORCID:0000-0002-9908-9556
Spitzer,Bernhard;Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany;ORCID:0000-0001-9752-932X
Description This dataset contains derived data. See the README.md file for further information.
License Open Data Commons Public Domain Dedication and License (PDDL) v1.0 (https://opendatacommons.org/licenses/pddl/1-0/)
References Appelhoff S, Hertwig R, Spitzer B (2022) EEG-representational geometries and psychometric distortions in approximate numerical judgment. PLoS Comput Biol 18(12): e1010747. https://doi.org/10.1371/journal.pcbi.1010747 [doi:10.1371/journal.pcbi.1010747] (IsSupplementTo)
Appelhoff, Stefan. (2022). eComp Experiment Code (2022.1.0). Zenodo. https://doi.org/10.5281/zenodo.6411319 [doi:10.5281/zenodo.6411313] (IsReferencedBy)
Appelhoff, Stefan. (2022). eComp Analysis Code (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6411288 [doi:10.5281/zenodo.6411287] (IsSupplementTo)
Appelhoff S, Hertwig R, Spitzer B (2022) The mpib_ecomp_dataset (BIDS). G-Node. https://doi.org/10.12751/g-node.jtfg5d [doi:10.12751/g-node.jtfg5d] (IsReferencedBy)
Appelhoff S, Hertwig R, Spitzer B (2022) The mpib_ecomp_sourcedata dataset. G-Node. https://doi.org/10.12751/g-node.lir3qw [doi:10.12751/g-node.lir3qw] (IsReferencedBy)
Funding Max Planck Institute for Human Development
Keywords cognitive neuroscience
decision-making
numerical cognition
sequential sampling
value distortions
compression
anti-compression
EEG
electroencephalography
eyetracking
representational similarity analysis
RSA
multivariate pattern analysis
MVPA
computational modeling
Resource Type Dataset