Abstract

Biomedical translational research can benefit from informatics system that support the confidentiality, integrity and accessibility of data. Such systems require functional capabilities for researchers to securely submit data to designated biomedical repositories. Reusability of data is enhanced by the availability functional capabilities that ensure confidentiality, integrity and access of data. A biomedical research system was developed by combining common data element methodology with a service-oriented architecture to support multiple disease focused research programs. Seven service modules are integrated together to provide a collaborative and extensible web-based environment. The modules - Data Dictionary, Account Management, Query Tool, Protocol and Form Research Management System, Meta Study, Repository Manager and globally unique identifier (GUID) facilitate the management of research protocols, submitting and curating data (clinical, imaging, and derived genomics) within the associated data repositories. No personally identifiable information is stored within the repositories. Data is made findable by use of digital object identifiers that are associated with the research studies. Reuse of data is possible by searching through volumes of aggregated research data across multiple studies. The application of common data element(s) methodology for development of content-based repositories leads to increase in data interoperability that can further hypothesis-based biomedical research.

Highlights

  • Translational Medicine (TM) seeks to develop new treatments for diseases with insights towards the improvement of global health

  • Several biomedical informatics applications have been discussed in the literature; for example, Research Electronic Data Capture (REDCap) is a software tool for collecting, storing, creating project specific databases for dissemination of clinical and translational research data[3,4]

  • Analyses of large complex datasets with bioinformatics and image analysis tools, cloud services, application programming interfaces (APIs), and data storage capabilities is supported by the CyVerse infrastructure[8,9]

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Summary

Introduction

Translational Medicine (TM) seeks to develop new treatments for diseases with insights towards the improvement of global health. To achieve this vision, understanding what and why interventions work, and how they can be scaled to benefit the entire population, depends on successful translational biomedical research and data lifecycle management. In overcoming the translational barriers, that biomedical informatics platforms reduce the time it takes for basic research to result in clinical applications[1]. Several biomedical informatics applications have been discussed in the literature; for example, Research Electronic Data Capture (REDCap) is a software tool for collecting, storing, creating project specific databases for dissemination of clinical and translational research data[3,4]. Within the National Institutes of Health (NIH), the Biomedical Translational Research Information System (BTRIS) has supported researchers to bring together data from the NIH Clinical Center and other institutes and centers[13]

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