Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury...

Area of Science:

• Biomedical Research
• Data Science
• Neuroscience

Background:

• Increasing demand for open data sharing in biomedical research to improve reproducibility and translation.
• Challenges in developing effective data sharing infrastructures.
• Limitations of general repositories ('data dumps') in promoting FAIR data principles.

Purpose of the Study:

• To report on the development and community adoption of a specialized data-sharing ecosystem for preclinical data.
• To showcase the Open Data Commons for Spinal Cord Injury (ODC-SCI) as a model for other fields.
• To review the community-based design template and adoption metrics of ODC-SCI.

Main Methods:

• Community-based agile design involving multiple stakeholders (researchers, consumers, funders, agencies, journals, industry).
• Focus on curating heterogeneous tabular data from preclinical research (bio-behavior, histopathology, molecular endpoints).
• Development of a specialized neurocommons (ODC-SCI) within a data commons framework.

Main Results:

• ODC-SCI has been well-embraced by its target community.
• Successful development of a specialized data commons for 'long-tail' preclinical data.
• Review of community adoption, data assets, released datasets, and web analytics indicates positive engagement.

Conclusions:

• Specialized data sharing communities offer a viable alternative to general repositories for promoting open and FAIR data.
• ODC-SCI serves as a successful example of a community-driven data commons for preclinical neuroscientific data.
• The ODC-SCI model can potentially be adapted for other research fields facing similar data sharing challenges.