Toward FAIR Knowledge Turns in Bioinformatics.

Area of Science:

• Bioinformatics
• Data Science
• Genomics

Background:

• The exponential growth of biological data volume challenges traditional biocuration methods.
• Effective sharing of bioinformatics data is crucial for accelerating scientific discovery.
• Existing data curation processes struggle to keep pace with the increasing scale of research data.

Purpose of the Study:

• To present a novel approach for empowering researchers to curate their own bioinformatics data.
• To enhance the quality and reusability of shared bioinformatics datasets.
• To facilitate efficient data reuse and re-analysis within the research community.

Main Methods:

• Developed a framework to enable data-producing researchers to perform data curation.
• Implemented quality control measures to ensure data readiness for reuse.
• Focused on empowering researchers with the tools and processes for self-curation.

Main Results:

• High-quality, curated bioinformatics data is now available for reuse and re-analysis.
• The approach successfully addresses the limitations of traditional biocuration in handling large data volumes.
• Researchers are empowered to contribute directly to data quality and accessibility.

Conclusions:

• The presented approach effectively enables researchers to curate high-quality shared bioinformatics data.
• This empowers the research community to overcome data volume challenges and accelerate discovery.
• Facilitating direct researcher involvement in curation enhances data utility and promotes collaborative science.