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Data reporting quality and semantic interoperability increase with community-based data elements (CoDEs). Analysis of
Anushka Sheoran1, Kenneth A Fond2, Lex Maliga Davis2
1Department of Neuroscience, University of California San Diego, San Diego, CA, USA.
Experimental Neurology
|December 8, 2024
Summary
The Open Data Commons for Spinal Cord Injury (SCI) community-based data elements (CoDEs) improve data sharing. However, flexible standards lead to harmonization issues, highlighting a need for refined data standards in SCI research.
Area of Science:
- Biomedical Informatics
- Data Science
- Neuroscience Research
Background:
- Data interoperability is essential for scientific advancement, particularly in complex fields like spinal cord injury (SCI) research.
- Establishing common data elements (CoDEs) and adhering to FAIR data principles are key strategies to enhance data sharing and reuse.
- The Open Data Commons for Spinal Cord Injury (ODC-SCI) developed CoDEs to promote interoperability within the SCI research community.
Purpose of the Study:
- To evaluate the adoption and adherence of ODC-SCI's community-based data elements (CoDEs) within the SCI research community.
- To identify challenges and inform future development of data standards for improved data interoperability in SCI research.
- To analyze researchers' reporting habits and their impact on data harmonization.
Main Methods:
- Systematic analysis of 39 public SCI datasets against 17 required CoDEs.
- Evaluation of data reporting variations and adherence to specified data structures.
- Identification of researcher reporting patterns, including formatting and naming conventions.
Main Results:
- Variations were observed between reported data and the structure specified by CoDEs across analyzed datasets.
- Enforcement of data standards positively correlated with improved reporting rates of CoDEs variables.
- Different variables necessitate varying levels of curation for semantic equivalence, and researcher flexibility in standards adoption can impede harmonization.
Conclusions:
- While ODC-SCI's semi-formal approach to data standards is user-friendly, its flexibility can create harmonization challenges.
- A need exists for tailored data standards based on study types (e.g., human vs. derivative studies) to address specific implementation issues.
- This study provides a foundational analysis of reporting behaviors to guide the refinement and facilitation of future data standards in SCI research.
