Improving Data Quality in Neurotrauma Data Sharing: The Open Data Commons Minimal Data Standards and a Data Tool for...

Area of Science:

• Neuroscience
• Data Science
• Biomedical Informatics

Background:

• The Open Data Commons for Traumatic Brain Injury (ODC-TBI) and Spinal Cord Injury (ODC-SCI) facilitate data sharing within the neurotrauma community.
• Manual data preparation and revision processes for ODC datasets are time-consuming and complex.
• A need exists for streamlined workflows to ensure data quality and adherence to ODC standards.

Purpose of the Study:

• To introduce the ODC Minimum Data Standards (MDS) and the ODC Data Quality App (ODCdqa).
• To simplify and automate the data curation and revision process for ODC datasets.
• To identify common data quality issues and inform future ODC platform development.

Main Methods:

• Developed the ODC Data Quality App (ODCdqa), an open-source, web-based tool for automated data quality checks.
• Applied the ODCdqa to analyze all publicly available datasets on ODC-SCI and ODC-TBI platforms.
• Established exclusion criteria for datasets lacking data dictionaries or having header inconsistencies.

Main Results:

• Out of 124 datasets, 119 (84 ODC-SCI, 35 ODC-TBI) were analyzed using ODCdqa.
• A decreasing trend in failed quality checks was observed over time for both ODC-SCI and ODC-TBI.
• Regular usage of MDS and ODCdqa by curators and users indicated successful integration into the workflow.

Conclusions:

• The ODCdqa effectively automates initial data quality assessments, enabling users to prepare datasets for the ODC platform more efficiently.
• The tool aids in ensuring datasets meet ODC specifications, reducing manual revision cycles.
• Ongoing development of ODCdqa will incorporate additional checks and features to support the evolving ODC platform.