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About catalog

The Behaverse catalog Schema provides a standardized way to describe and organize thematic catalogs of cognitive science and neuroscience datasets.

Motivation​

Research datasets are often more valuable when organized into meaningful catalogs that share characteristics or serve particular research applications. This schema aims to offer a standardized way to describe catalogs of datasets.

Features​

1. Explicit Inclusion Criteria​

The schema requires explicit inclusion_criteria - a list of rules that datasets must meet to be part of the catalog. This ensures transparent, reproducible catalog membership.

Example: A "Multi-Task Studies" catalog might require:

  • Participants completed 2 or more distinct cognitive tasks
  • Tasks measure different cognitive constructs

2. Standards Compatibility with Consistent Naming​

The schema provides consistent snake_case property naming conventions while maintaining compatibility with existing standards like schema.org and Dublin Core. Properties such as description, keywords, and curator map to their schema.org equivalents, while using developer-friendly naming that aligns with common programming conventions.

3. Rich Catalog Metadata​

Catalogs can be described with standard metadata fields:

  • name: URL-friendly identifier
  • pretty_name: Human-readable title
  • description: Comprehensive purpose and scope
  • keywords: Focus areas for discovery
  • curator: People or organizations maintaining the catalog

4. Dataset Membership Tracking​

Catalogs can list their member datasets via:

  • datasets: Array of dataset URLs or DOIs
  • dataset_count: Number of datasets in the catalog

5. Catalog Relationships​

The related_catalogs property enables listing related catalogs to facilitate dataset discovery.

Use Cases​

Research Applications​

  • "Longitudinal Studies" - Datasets with repeated measurements over time
  • "Multi-Task Assessments" - Studies with diverse cognitive tasks
  • "Multimodal Data" - Datasets combining behavioral and neural measures
  • "Developmental Research" - Studies spanning multiple age groups

Population-Focused Catalogs​

  • "Adolescent Mental Health" - Datasets from adolescent populations with mental health measures
  • "Aging and Cognition" - Studies of cognitive aging in older adults
  • "Clinical Populations" - Datasets from specific clinical groups

Methodology-Based Catalogs​

  • "fMRI Studies" - Datasets using functional magnetic resonance imaging
  • "Ecological Momentary Assessment" - Studies using real-time data catalog
  • "Large-Scale Surveys" - Population-level survey datasets

Benefits​

For Data Curators​

  • Transparent criteria: Explicit rules for catalog membership
  • Standardized metadata: Consistent structure across catalogs
  • Relationship tracking: Document connections between catalogs

For Researchers​

  • Improved discovery: Find relevant datasets through thematic catalogs
  • Research context: Understand how datasets relate to each other
  • Quality signals: Curated catalogs provide research validation

For the Ecosystem​

  • Interoperability: Machine-readable catalog definitions
  • Semantic web ready: JSON-LD support for linked data
  • Standards-based: Built on Schema.org DataCatalog vocabulary

Relationship to Standards​

The Catalog Schema builds on:

Namespace​

  • Base namespace: https://behaverse.org/schemas/catalog#
  • Context file: https://behaverse.org/schemas/catalog/context.jsonld

Learn more​

  • Overview — the full property reference (auto-generated from the schema), with a page per property.
  • Examples — worked catalog examples.
  • README on GitHub — full schema details.