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Researchers can now find 19 infectious disease data resources suitable for National Institutes of Health Data Management and Sharing Plans. This study identifies resources and highlights challenges in data submission requirements for effective data sharing.

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Area of Science:

  • Infectious disease research
  • Data science
  • Bioinformatics

Background:

  • The National Institutes of Health (NIH) Data Management and Sharing (DMS) Policy mandates DMS Plans for grant applications.
  • Researchers face difficulties identifying suitable data resources for fulfilling DMS Policy requirements.
  • The National Institute of Allergy and Infectious Diseases (NIAID) seeks to assist researchers in preparing DMS Plans.

Purpose of the Study:

  • To compile a comprehensive list of infectious, allergic, and immune-mediated disease (IID) data resources.
  • To identify IID data resources that support data submission for NIH DMS Plans.
  • To characterize the features and submission requirements of these IID data resources.

Main Methods:

  • Reviewed online resources and consulted subject matter experts to identify IID data resources.
  • Developed a questionnaire based on NIH recommendations and best practices to assess data resources.
  • Characterized 58 IID data resources, focusing on those suitable for data submission.

Main Results:

  • Identified 303 data resources, with 58 specifically focused on IID data, primarily 'omics', clinical, and biological assay data.
  • Found 19 IID data resources suitable for data submission, with varying requirements for registration, approvals, and network membership.
  • Most resources offered open access, metadata, and analytical tools, but inconsistencies in submission requirements and data management practices were noted.

Conclusions:

  • A subset of 19 IID data resources are suitable for researchers preparing NIH DMS Plans.
  • Challenges exist regarding inconsistent data submission requirements and data management practices across resources.
  • Enhancing transparency and standardization of data resources is crucial for effective data sharing and researcher support.