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A Case for Data Commons: Toward Data Science as a Service.

Robert L Grossman1, Allison Heath1, Mark Murphy1

  • 1University of Chicago.

Computing in Science & Engineering
|October 17, 2017
PubMed
Summary
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Data commons integrate data, storage, and computing resources with essential services and tools for research data management and analysis. Operating these large-scale data commons provides valuable insights into their architecture and implementation.

Area of Science:

  • Computer Science
  • Data Science
  • Research Infrastructure

Background:

  • Research data management requires integrated infrastructure for efficient data handling.
  • Interoperability is crucial for enabling data sharing and collaborative research.
  • Existing data repositories often lack integrated computational resources and core services.

Purpose of the Study:

  • To describe the architecture of data commons.
  • To share lessons learned from operating large-scale data commons.
  • To promote the development of interoperable research resources.

Main Methods:

  • Architectural design of data commons.
  • Operational analysis of existing large-scale data commons.
  • Identification of core services, tools, and applications.

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Main Results:

  • Data commons successfully collocate data, storage, and computing infrastructure.
  • Core services and commonly used tools facilitate data management, analysis, and sharing.
  • Lessons learned provide practical guidance for data commons implementation.

Conclusions:

  • Data commons represent a robust model for creating interoperable research resources.
  • The described architecture and operational insights are valuable for the research community.
  • Effective data commons enhance research productivity and data accessibility.