Pre-Clinical Common Data Elements for Traumatic Brain Injury Research: Progress and Use Cases

LaPlaca, Michelle C.; Huie, J. Russell; Alam, Hasan B.; Bachstetter, Adam D.; Bayır, Hülya; Bellgowan, Patrick S.F.; Cummings, Diana M.; Dixon, C. Edward; Ferguson, Adam R.; Ferland‐Beckham, Chantelle; Floyd, Candace L.; Friess, Stuart H.; Galanopoulou, Aristea S.; Hall, Edward D.; Harris, Neil; Hawkins, Bridget E.; Hicks, Ramona; Hulbert, L.E.; Johnson, Victoria E.; Kabitzke, Patricia; Lafrenaye, Audrey D.; Lemmon, Vance; Lifshitz, Carrie W.; Lifshitz, Jonathan; Loane, David J.; Misquitta, Leonie; Nikolian, Vahagn C.; Noble-Haeusslein, Linda J.; Smith, Douglas H.; Taylor-Burds, Carol; Umoh, Nsini; Vovk, Olha; Williams, Aaron M.; Young, Matthew P.; Zai, Laila

doi:10.1089/neu.2020.7328

Cited by 24 publications

(25 citation statements)

References 35 publications

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“…The purpose of the ODC-TBI is to establish an infrastructure to facilitate effective data sharing practices within the preclinical TBI research community and expand the data standardization and harmonization guidelines initiated by the NINDS [20,26]. Additionally, the ODC-TBI interface has been developed to address the concerns of preclinical TBI researchers towards data sharing practices [21] and empower the researchers through an intuitive interface. Currently, the ODC-TBI provides guidelines to help researchers format their dataset according to best practices for data interoperability [27,28] and standardize them according to FAIR principles [25] and NINDS-defined CDEs.…”

Section: Resultsmentioning

confidence: 99%

“…The ODC-TBI is a data commons developed for preclinical TBI research designed to (1) enable data sharing within the research community [12], (2) support data standardization guidelines established by the NINDS [20,21], (3) promote FAIR data sharing principles [25], and (4) empower Big Data analytics in preclinical TBI research [10]. Through the ODC-TBI, we leverage the heterogeneity of preclinical TBI research to identify common TBI immune responses across three different preclinical TBI studies that vary time post-injury, age, and treatment.…”

Section: Discussionmentioning

confidence: 99%

“…We also realize the importance of supporting and integrating common terminology such as CDEs to improve all aspects of FAIR data sharing on the ODC-TBI. Indeed, NINDS and the TBI research community have recognized the challenges in data comparison due to the lack of common variable names and definitions; this spurred a concerted multi-center endeavor to identify and define CDEs to be adopted by clinical and preclinical TBI researchers [15,20,21]. We manually aligned variables in 11 datasets described here to NINDS-defined CDEs prior to uploading them to the ODC-TBI.…”

Section: Discussionmentioning

confidence: 99%

“…Data from 11 published studies at UCSF were collected from various data sources and structured according to the Tidy data format [28]. Variable names were aligned to NINDS preclinical TBI CDEs when possible [20,21]. Data was uploaded following the ODC-TBI data upload workflow.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, the language and terminology of collected variables can differ in name and definition between labs. The National Institute of Neurological Disorders and Stroke (NINDS) have released dictionaries of “common data elements” (CDEs), basic units of data that prescribe the data type and standardize the language for variables in an effort to improve the reproducibility of clinical and preclinical TBI research [20,21]. However, there remains an unmet need for open data infrastructures that host preclinical TBI data and for datasets to begin integrating the NINDS-defined CDEs for data sharing and reusability.…”

Section: Introductionmentioning

confidence: 99%

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Open Data Commons for Preclinical Traumatic Brain Injury Research: Empowering Data Sharing and Big Data Analytics

Chou

At²,

Huie³

et al. 2021

Preprint

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Traumatic brain injury (TBI) is a major unsolved public health problem worldwide with considerable preclinical research dedicated to recapitulating clinical TBI, deciphering the underlying pathophysiology, and developing therapeutics. However, the heterogeneity of clinical TBI and correspondingly in preclinical studies have made translation from bench to bedside difficult. Here, we present the potential of data sharing, data aggregation, and multivariate analytics to integrate heterogeneity and empower researchers. We introduce the Open Data Commons for Traumatic Brain Injury (ODC-TBI.org) as a user-centered web platform and cloud-based repository focused on preclinical TBI research that enables data citation with persistent identifiers, promotes data element harmonization, and follows FAIR data sharing principles. Importantly, the ODC-TBI implements data sharing at the level of individual subjects, thus enabling data reuse for granular big data analytics and data-hungry machine learning approaches. We provide use cases applying descriptive analytics and unsupervised machine learning on pooled ODC-TBI data. Descriptive statistics included subject-level data for 11 published papers (N = 1250 subjects) representing six distinct TBI models across mice and rats (implementing controlled cortical impact, closed head injury, fluid percussion injury, and CHIMERA TBI modalities). We performed principal component analysis (PCA) on cohorts of animals combined through the ODC-TBI to identify persistent inflammatory patterns across different experimental designs. Our workflow ultimately improved the sensitivity of our analyses in uncovering patterns of pro- vs anti-inflammation and oxidative stress without the multiple testing problems of univariate analyses. As the practice of open data becomes increasingly required by the scientific community, ODC-TBI provides a foundation that creates new scientific opportunities for researchers and their work, facilitates multi-dataset and multidimensional analytics, and drives collaboration across molecular and computational biologists to bridge preclinical research to the clinic.

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