DATS, the data tag suite to enable discoverability of datasets

Area of Science:

• Life Sciences
• Bioinformatics
• Data Science

Background:

• Scientific research increasingly generates large, distributed datasets.
• Discovering relevant datasets is a growing challenge for researchers, particularly in the life sciences.
• Existing data repositories lack a standardized method for metadata submission and retrieval.

Purpose of the Study:

• To design and implement a standardized model for dataset metadata.
• To support the development of a data discovery index (DataMed) analogous to PubMed for publications.
• To enhance the discoverability and accessibility of scientific datasets across repositories.

Main Methods:

• Developed the DAta Tag Suite (DATS) model, an international collaborative effort.
• Created a core set of generic metadata elements applicable to any dataset.
• Developed an extended set of elements for specialized data types.
• Made the DATS model platform-independent and available as an annotated schema.org serialization.

Main Results:

• The DATS model provides a standardized framework for dataset metadata.
• The DATS model supports the DataMed data discovery index.
• The schema.org serialization ensures compatibility with major search engines (Google, Microsoft, Yahoo, Yandex).

Conclusions:

• The DATS model significantly improves dataset discoverability in the life sciences.
• DataMed, powered by DATS, aims to be the PubMed for scientific data.
• Standardized metadata facilitated by DATS enhances data accessibility and reuse.