Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop

Kathleen M. Jagodnik, Simon Koplev, Sherry L. Jenkins, Lucila Ohno-Machado, Benedict Paten, Stephan C. Schurer, Michel Dumontier, Ruben Verborgh, Alex Bui, Peipei Ping, Neil J. McKenna, Ravi Madduri, Ajay Pillai, Avi Ma'ayan

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

The volume and diversity of data in biomedical research have been rapidly increasing in recent years. While such data hold significant promise for accelerating discovery, their use entails many challenges including: the need for adequate computational infrastructure, secure processes for data sharing and access, tools that allow researchers to find and integrate diverse datasets, and standardized methods of analysis. These are just some elements of a complex ecosystem that needs to be built to support the rapid accumulation of these data. The NIH Big Data to Knowledge (BD2K) initiative aims to facilitate digitally enabled biomedical research. Within the BD2K framework, the Commons initiative is intended to establish a virtual environment that will facilitate the use, interoperability, and discoverability of shared digital objects used for research. The BD2K Commons Framework Pilots Working Group (CFPWG) was established to clarify goals and work on pilot projects that address existing gaps toward realizing the vision of the BD2K Commons. This report reviews highlights from a two-day meeting involving the BD2K CFPWG to provide insights on trends and considerations in advancing Big Data science for biomedical research in the United States.

Original languageEnglish
Pages (from-to)49-57
Number of pages9
JournalJournal of Biomedical Informatics
Volume71
DOIs
StatePublished - Jul 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 Elsevier Inc.

Funding

FundersFunder number
National Human Genome Research InstituteU54HG007990

    Keywords

    • Accessibility
    • Big Data
    • FAIR principles
    • Findability
    • Interoperability
    • Reusability

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