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Repo to store all downstream data for PEDLR.

Christoph Koch 7a8cd440f9 Update 'README.md' 11 bulan lalu
.datalad a3c52f4328 [DATALAD] new dataset 1 tahun lalu
analysis 0097030f16 Pre-release derivatives 11 bulan lalu
figures 1af2e7e440 Overhaul pcrt sig annotations 11 bulan lalu
model_fitting acfdd24fce Modelling results and analysis/results_02 for new bandit init (first three updates kicked out of LL calculation) 1 tahun lalu
parameter_recovery 0097030f16 Pre-release derivatives 11 bulan lalu
posterior_pred_checks 0097030f16 Pre-release derivatives 11 bulan lalu
simulation 2260c8e8f0 Remove deprecated files 11 bulan lalu
.gitattributes ce0657b272 Make sure README.md and LICENSE are not annexed 1 tahun lalu
.gitignore 68a51730cc Add figure pdfs and rename seplr to valence in figs 1 tahun lalu
LICENSE 0f9bfa5964 Add license and readme 1 tahun lalu
README.md 7a8cd440f9 Update 'README.md' 11 bulan lalu
datacite.yml 6fdb6426cf Add information for publishing with DataCite 11 bulan lalu

README.md

pedlr-derivatives

Repo to store all results originating from PEDLR experiment data.

Datalad

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

Usage

For integration with the Github code repository (DOI: 10.5281/zenodo.10211239) clone the pedlr-main-data (DOI: 10.12751/g-node.9gm3lt) as data and this repository as derivatives into the code repository.

Data set structure

├── LICENSE   # License file (CC BY-SA 4.0)
├── README.md # This file
├── analysis  # Dir containing .html files of analysis notebooks
│   ├── data_quality.html   # Data quality assessment
│   ├── demographic.html    # Demographics summary
│   ├── results_01.html     # Main results (behavior)
│   ├── results_02.html     # Main results (modeling)
│   └── (...)
├── figures                 # Dir holding figures .pdf and data
│   └── (...)
├── model_fitting           # Dir holding data for model fitting
│   ├── fit-09RI1ZH_sv-random.tsv   # Example file: fit of all models to choice data (random starting-values)
│   ├── (...)
│   ├── modeldata-09RI1ZH_sv-random.tsv   # Example file: Model choice proabability for each trial (random starting-values)
│   └── (...)
├── parameter_recovery      # Dir holding analysis and data of model and parameter recovery
│   ├── analysis_model_recov.html  # Results model recovery
│   ├── analysis_param_recov.html  # Results parameter recovery
│   ├── modelrecov_base-09RI1ZH_model-rw_randips-TRUE_randbetas-FALSE_randsvs-TRUE_principle-TRUE.tsv # Example file: Fit of all models on data simulated by specified model (random parameters, fixed regression betas, random starting-values)
│   ├── (...)
│   ├── paramrecov_base-09RI1ZH_model-rw_randips-TRUE_randbetas-FALSE_randsvs-TRUE_principle-TRUE.tsv # Example file: Parameter recovery of all models on data simulated by specified model (random parameters, fixed regression betas, random starting-values)
│   ├── (...)
│   ├── recovdata_base-09RI1ZH_model-rw_randips-TRUE_randbetas-FALSE_randsvs-TRUE_principle-TRUE.tsv # Example file: Trialwise information on recovery (random parameters, fixed regression betas, random starting-values)
│   └── (...)
├── posterior_pred_checks             # Dir holding analysis and data of posterior predictive checks
│   ├── analysis_posterior_pred_checks.html    # Analysis notbook of post. pred. checks
│   ├── postpred-09RI1ZH_model-rw.tsv          # Data for post. pred. check for specified model
│   ├── (...)
│   ├── windowrizepred-09RI1ZH.tsv   # Data for post. pred. check (influence of surprise) for spec. model
│   └── (...)
└── simulation
    └── (...)

datacite.yml
Title Pedlr Derivatives
Authors Koch,Christoph;Universität Hamburg, Hamburg, Germany;ORCID:0000-0002-4620-9577
Zika,Ondrej;Max Plank Institute for Human Development, Berlin, Germany;ORCID:0000-0003-0483-4443
Bruckner,Rasmus;Freie Universität Berlin, Berlin, Germany;ORCID:0000-0002-3033-6299
Schuck,Nicolas W.;Universität Hamburg, Hamburg, Germany;ORCID:0000-0002-0150-8776
Description This is the derivatives data set of of the Pedlr project. This contains a datalad repository of all data downstream from the source data (source data available at DOI: 10.12751/g-node.9gm3lt). The code repository used to create all downstream data (from the source data) is available at DOI: 10.5281/zenodo.10211239
License Creative Commons Attribution-ShareAlike 4.0 International Public License (https://creativecommons.org/licenses/by-sa/4.0/deed.en)
References
Funding MPG, M.TN.A.BILD0004
Keywords Reinforcement Learning
Computational Modeling
Aging
Surprise
Resource Type Dataset