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Data discovery with DATS: exemplar adoptions and lessons learned.

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Summary
This summary is machine-generated.

The Data Tag Suite (DATS) model enhances dataset discoverability online. Evaluation of DATS across diverse data sources led to implementation guidelines and identified needs for improved data indexing.

Keywords:
data discoverydata modelmetadatasearch engine

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Area of Science:

  • Data Science
  • Bioinformatics
  • Information Science

Background:

  • The Data Tag Suite (DATS) is a data model designed to standardize dataset description, indexing, and discovery.
  • It utilizes schema.org for web discoverability and powers DataMed, a prototype data discovery index.
  • Previous experience with DataMed involved indexing over 60 diverse repositories.

Purpose of the Study:

  • To evaluate the fitness of the DATS model for describing diverse datasets.
  • To refine DATS based on practical application and user feedback.
  • To identify best practices and future development paths for DATS.

Main Methods:

  • Mapping three additional diverse data sources to the DATS model.
  • Involving data representatives and experts in the mapping process.
  • Gathering feedback from users and implementers of DATS.

Main Results:

  • DATS demonstrated fitness for describing new, diverse data sources.
  • Implementation guidelines and best practices for DATS were developed.
  • Specific needs for dataset indexing, particularly for clinical and observational data, were identified.

Conclusions:

  • The DATS model is effective for enhancing dataset discoverability.
  • Practical application and feedback are crucial for model evolution.
  • Further optimization is needed to address complex data types like clinical information.