Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research.
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ABSTRACT:
SUBMITTER: Chou A
PROVIDER: S-EPMC8985540 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research.
Chou Austin A Torres-Espín Abel A Huie J Russell JR Krukowski Karen K Lee Sangmi S Nolan Amber A Guglielmetti Caroline C Hawkins Bridget E BE Chaumeil Myriam M MM Manley Geoffrey T GT Beattie Michael S MS Bresnahan Jacqueline C JC Martone Maryann E ME Grethe Jeffrey S JS Rosi Susanna S Ferguson Adam R AR
Neurotrauma reports 20220405 1
Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implement ...[more]
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Project description:Over the last 5 years, multiple stakeholders in the field of spinal cord injury (SCI) research have initiated efforts to promote publications standards and enable sharing of experimental data. In 2016, the National Institutes of Health/National Institute of Neurological Disorders and Stroke hosted representatives from the SCI community to streamline these efforts and discuss the future of data sharing in the field according to the FAIR (Findable, Accessible, Interoperable and Reusable) data stewardship principles. As a next step, a multi-stakeholder group hosted a 2017 symposium in Washington, DC entitled "FAIR SCI Ahead: the Evolution of the Open Data Commons for Spinal Cord Injury research." The goal of this meeting was to receive feedback from the community regarding infrastructure, policies, and organization of a community-governed Open Data Commons (ODC) for pre-clinical SCI research. Here, we summarize the policy outcomes of this meeting and report on progress implementing these policies in the form of a digital ecosystem: the Open Data Commons for Spinal Cord Injury (ODC-SCI.org). ODC-SCI enables data management, harmonization, and controlled sharing of data in a manner consistent with the well-established norms of scholarly publication. Specifically, ODC-SCI is organized around virtual "laboratories" with the ability to share data within each of three distinct data-sharing spaces: within the laboratory, across verified laboratories, or publicly under a creative commons license (CC-BY 4.0) with a digital object identifier that enables data citation. The ODC-SCI implements FAIR data sharing and enables pooled data-driven discovery while crediting the generators of valuable SCI data.
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Project description:Traumatic brain injury (TBI) is among the leading causes of death and disability worldwide, with enormous negative social and economic impacts. The heterogeneity of TBI combined with the lack of precise outcome measures have been central to the discouraging results from clinical trials. Current approaches to the characterization of disease severity and outcome have not changed in more than three decades. This prospective multicenter observational pilot study aimed to validate the feasibility of implementing the TBI Common Data Elements (TBI-CDEs). A total of 650 subjects who underwent computed tomography (CT) scans in the emergency department within 24?h of injury were enrolled at three level I trauma centers and one rehabilitation center. The TBI-CDE components collected included: 1) demographic, social and clinical data; 2) biospecimens from blood drawn for genetic and proteomic biomarker analyses; 3) neuroimaging studies at 2 weeks using 3T magnetic resonance imaging (MRI); and 4) outcome assessments at 3 and 6 months. We describe how the infrastructure was established for building data repositories for clinical data, plasma biomarkers, genetics, neuroimaging, and multidimensional outcome measures to create a high quality and accessible information commons for TBI research. Risk factors for poor follow-up, TBI-CDE limitations, and implementation strategies are described. Having demonstrated the feasibility of implementing the TBI-CDEs through successful recruitment and multidimensional data collection, we aim to expand to additional study sites. Furthermore, interested researchers will be provided early access to the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) data set for collaborative opportunities to more precisely characterize TBI and improve the design of future clinical treatment trials. (ClinicalTrials.gov Identifier NCT01565551.).
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Project description:ObjectiveThe integrated Translational Health Research Institute of Virginia (iTHRIV) aims to develop an information architecture to support data workflows throughout the research lifecycle for cross-state teams of translational researchers.Materials and methodsThe iTHRIV Commons is a cross-state harmonized infrastructure supporting resource discovery, targeted consultations, and research data workflows. As the front end to the iTHRIV Commons, the iTHRIV Research Concierge Portal supports federated login, personalized views, and secure interactions with objects in the ITHRIV Commons federation. The canonical use-case for the iTHRIV Commons involves an authenticated user connected to their respective high-security institutional network, accessing the iTHRIV Research Concierge Portal web application on their browser, and interfacing with multi-component iTHRIV Commons Landing Services installed behind the firewall at each participating institution.ResultsThe iTHRIV Commons provides a technical framework, including both hardware and software resources located in the cloud and across partner institutions, that establishes standard representation of research objects, and applies local data governance rules to enable access to resources from a variety of stakeholders, both contributing and consuming.DiscussionThe launch of the Commons API service at partner sites and the addition of a public view of nonrestricted objects will remove barriers to data access for cross-state research teams while supporting compliance and the secure use of data.ConclusionsThe secure architecture, distributed APIs, and harmonized metadata of the iTHRIV Commons provide a methodology for compliant information and data sharing that can advance research productivity at Hub sites across the CTSA network.
| S-EPMC8922196 | biostudies-literature
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Project description:Traumatic brain injury (TBI) is an extremely complex condition due to heterogeneity in injury mechanism, underlying conditions, and secondary injury. Pre-clinical and clinical researchers face challenges with reproducibility that negatively impact translation and therapeutic development for improved TBI patient outcomes. To address this challenge, TBI Pre-clinical Working Groups expanded upon previous efforts and developed common data elements (CDEs) to describe the most frequently used experimental parameters. The working groups created 913 CDEs to describe study metadata, animal characteristics, animal history, injury models, and behavioral tests. Use cases applied a set of commonly used CDEs to address and evaluate the degree of missing data resulting from combining legacy data from different laboratories for two different outcome measures (Morris water maze [MWM]; RotorRod/Rotarod). Data were cleaned and harmonized to Form Structures containing the relevant CDEs and subjected to missing value analysis. For the MWM dataset (358 animals from five studies, 44 CDEs), 50% of the CDEs contained at least one missing value, while for the Rotarod dataset (97 animals from three studies, 48 CDEs), over 60% of CDEs contained at least one missing value. Overall, 35% of values were missing across the MWM dataset, and 33% of values were missing for the Rotarod dataset, demonstrating both the feasibility and the challenge of combining legacy datasets using CDEs. The CDEs and the associated forms created here are available to the broader pre-clinical research community to promote consistent and comprehensive data acquisition, as well as to facilitate data sharing and formation of data repositories. In addition to addressing the challenge of standardization in TBI pre-clinical studies, this effort is intended to bring attention to the discrepancies in assessment and outcome metrics among pre-clinical laboratories and ultimately accelerate translation to clinical research.
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Project description:SummaryIn translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR , a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements.Availability and implementationThe source code is licensed under the Apache 2.0 License and is freely available on GitHub: https://github.com/transmart/SmartR .Contactreinhard.schneider@uni.lu.Supplementary informationSupplementary data are available at Bioinformatics online.
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