Tools for Common Data Element Adoption in the Pan-Neurotrauma Data Commons (PANORAUMA)

University Of California, San Francisco, San Francisco CA

Investigators

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Abstract

Project Summary Neurotrauma (trauma to the spinal cord and brain) affects over 2.5 million individuals in the US, with an annual economic impact of $80 billion in medical and socioeconomic costs. Despite improved patient management in the last decades, there are limited viable options to promote neurological recovery. Spinal cord injury (SCI) and traumatic brain injury (TBI) result in multifaceted syndromes spanning heterogeneous data sources and multiple scales of analysis. In addition, these injuries often occur at various sites within the central nervous system, with graded severities producing heterogeneous injuries with diverse outcome trajectories. Making sense of this complexity requires pooling data across multiple injury severities, types, and scales of analysis ranging from molecular, anatomical, physiological, and behavioral levels. Large-scale data resources and big-data tools have the potential to help. By pooling and harmonizing diverse data at the individual level, it becomes possible to make neurotrauma data “Findable, Accessible, Interoperable, and Reusable” (FAIR). FAIR neurotrauma data can be harnessed using modern data workflows and analytics, directing novel discovery and accelerating translation. Moreover, FAIR data can set the stage for widespread adoption of artificial intelligence (AI) and machine learning (ML), and it is at the core of NIH Strategic Plan for Data Science and AI/ML-readiness. Researchers and data scientists can use FAIR neurotrauma data to drive novel discoveries and build robust reproducibility and translation tools, such as data processing software and new analytical workflows and pipelines. The overarching objective of the Pan-Neurotrauma data commons parent project is to build a Pan- Neurotrauma (PANORAUMA) data commons infrastructure. The award aims at improving the efficiency, quality, and sustainability of the community-driven Open Data Commons for Spinal Cord Injury (odc-sci) and Traumatic Brain Injury (odc-tbi) by centralizing their operations and governance. In order for to optimize interoperability and reusability, it is important for researchers to share data that has been aligned to Common Data Elements (CDEs). This process can often be tedious, frustrating, and difficult, leading to researchers to not adopt these data standards. We have begun work on software tools that will streamline this process to facilitate ease-of-use and compliance with CDEs. For this supplement, we propose to: 1) Enhance our Common Data Element Mapper (‘CDEM’) to make the creation of customized datasets templates more intuitive, 2) Augment the CDEM tool capability by providing researchers assistance in aligning their unique data elements through guided suggestions, and 3) Engage with the growing Panorauma community to promote user adoption of the CDEM tool and adherence to CDEs.