Open Cognitive Science Data

URL: https://nimh-dsst.github.io/OpenCogData/
Category: Themed directories
Tags: datasets, cognitive-science, research
License: MIT License (DSST / NIMH)

Overview

Open Cognitive Science Data (OpenCogData) is an open, curated collection of publicly available cognitive task datasets. It focuses on behavioral and related data from cognitive science research and serves as a directory linking out to underlying data repositories (e.g., OSF, GitHub) and associated papers.

Features

• Curated dataset directory

  • Aggregates publicly available behavioral and cognitive task datasets.
  • Organized as an online archive with individual entries per study.

• Study-level entries

  • Each entry typically includes:
    • Study citation (authors and year).
    • Study title.
    • Link to the published paper (e.g., via DOI).
    • Link to the dataset (e.g., OSF, GitHub).
    • Brief textual summary/abstract-like description of the study’s aims and findings.

• Example included studies (2023)

  • Duncan et al. (2023)
    “The electrophysiological markers of statistically learned attentional enhancement: Evidence for a saliency-based mechanism”

    • Paper link: https://doi.org/10.1162/jocn_a_02066
    • Data link: https://osf.io/v7yhc

  • Ester & Weese (2023)
    “Temporally dissociable mechanisms of spatial, feature, and motor selection during working memory-guided behavior”

    • Paper link: https://doi.org/10.1162/jocn_a_02061
    • Data link: https://osf.io/dm73j

  • Li, J., et al. (2023)
    “Context-specific effects of violated expectations: ERP evidence”

    • Paper link: https://doi.org/10.1016/j.cognition.2023.105628
    • Data link: https://osf.io/cpwbt

  • McEllin et al. (2023)
    “Action planning and execution cues influence economic partner choice”

    • Paper link: https://doi.org/10.1016/j.cognition.2023.105632
    • Data link: https://osf.io/yebvt

  • Xiang et al. (2023)
    “Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks”

    • Paper link: https://doi.org/10.1016/j.cognition.2023.105609
    • Data link: https://github.com/yyyxiang/responsibility_attribution

• Site structure and navigation

  • Home: overview and latest/featured datasets.
  • Archives: browse the collection of datasets by date or listing.
  • Tags: browse datasets organized by tags/topics.
  • About: information about the project’s purpose and context (not fully shown in the excerpt).
  • Contribute: guidance on how to add or suggest datasets (details not in the excerpt, but implied by the page name).

• Technical/implementation details

  • Built as a static site powered by Hexo.
  • MIT-licensed content, under DSST/NIMH.

Data Types and Domain Focus

• Behavioral and cognitive task datasets.
• Emphasis on cognitive science domains such as:
  • Attention and statistical learning.
  • Working memory and action planning.
  • Language and predictive processing (e.g., ERP in sentence comprehension).
  • Social and economic decision-making (e.g., partner choice, responsibility attribution).

Usage

• Acts as a directory; data are hosted on external repositories (e.g., OSF, GitHub) linked from each study’s page.
• Suitable for:
  • Reuse and reanalysis of cognitive science datasets.
  • Methods development, replication studies, and teaching resources.

Pricing

• No pricing information is provided. The site and its listings are presented as an open, MIT-licensed resource.