Improving the discoverability, accessibility, and citability of omics datasets: a case report

Area of Science:

• Bioinformatics
• Genomics
• Data Science

Background:

• Omics datasets are valuable for research but lack consistent infrastructure for reuse.
• Current systems hinder discoverability, accessibility, and citability of published omics data.
• Nuclear receptor signaling transcriptomic data served as a model system.

Purpose of the Study:

• To develop a model for improving the discoverability, accessibility, and citability of published omics datasets.
• To create a framework for enhancing the reuse of transcriptomic data.
• To promote data sharing and integration within the biomedical research community.

Main Methods:

• Retrieved primary transcriptomic datasets from archives.
• Processed data for metadata enrichment and gap filling, creating secondary datasets.
• Developed web pages for data mining, discovery, and citation integration.
• Established automated processes for linking datasets across multiple platforms using digital object identifiers.

Main Results:

• Created reprocessed and reannotated derivative datasets.
• Implemented responsive web pages for enhanced data mining and discovery.
• Integrated single-click citation functionality with reference managers.
• Embedded digital object identifier-driven links to secondary datasets in journals and databases.

Conclusions:

• The model significantly improves the visibility and usability of omics datasets.
• Enhanced data accessibility fosters cross-community research and hypothesis generation.
• Standardized infrastructure is crucial for maximizing the value of biomedical big data.