NIDDK DMS Tools & Resources

Writing a DMS Plan

This webinar provided an overview of DMS plan content and highlighted institutional, NIH, and NIDDK resources available to investigators throughout the research study life cycle. The role of research librarians as a resource to help in planning and execution of the DMS plan was emphasized.

Webinar Materials 

• Meeting page
• Meeting summary
• Webinar recording 

Presentations

• Data Management and Sharing (DMS) Overview  (Slides (PDF, 2.02 MB) )
  Jeran Stratford, Ph.D., RTI International
  
  Introduction to the DMS policy, DMS plan content, and NIDDK-specific guidance. Overview of resources available to NIDDK researchers writing a DMS plan.

• Help from a Research Data Management Librarian (Slides (PDF, 737.25 KB) )
  Lisa Federer, Ph.D., National Library of Medicine
  
  Overview of research librarian skills, training, and knowledge, and how research librarians are valuable institutional resources for investigators drafting DMS plans.

• The UCSF Data Science Initiative—An Institutional Hub for DMS Support  (Slides (PDF, 932.04 KB) )
  Ariel Deardorff, M.L.I.S., University of California, San Francisco (UCSF)
  
  A practical example of how data librarians are adapting to support investigators and institutions implementing the new NIH DMS policy. An overview of the resources and services UCSF data librarians are providing to investigators were discussed.

Finding a Repository for Your Data

This webinar provided in-depth information on selecting appropriate repositories for scientific data. A brief overview of NIDDK DMS resources and tools for identifying repositories, as well as resources available through dkNET (NIDDK Information Network) were highlighted. Information about the NIDDK Central Repository and the Generalist Repository Ecosystems Initiative (GREI) rounded out the session. 

Webinar Materials

• Meeting page
• Meeting summary
• Webinar recording 

Presentations

• Finding a Repository for NIDDK Study Data Sets  (Slides (PDF, 1.18 MB) )
  Jeran Stratford, Ph.D., RTI International
  
  NIH criteria for selecting a data repository, different types of data repositories, and NIH and NIDDK resources for selecting an appropriate repository for research data were presented. Repository features to consider when choosing a repository, including access control and use of persistent identifiers, were also discussed. 

• dkNET: Connecting Researchers to Resources  (Slides (PDF, 1.12 MB) )
  Jeffrey Grethe, Ph.D., University of California, San Diego
  
  The dkNET suite of tools and resources available for investigators were discussed including a searchable list of DK-relevant repositories and a repository wizard in development. 

• Submitting Resources to the NIDDK Central Repository  (Slides (PDF, 2.25 MB) )
  Rebecca Rodriguez, Ph.D., NIDDK
  
  The NIDDK Central Repository (NIDDK-CR) is one potential repository for NIDDK research data. Policies related to eligibility for submitting data to the NIDDK-CR, as well as the types of data accepted were reviewed. Important features of the NIDDK-CR that make it a FAIR and trustworthy (according to the CoreTrustSeal parameters) resource were also covered.

• NIH Generalist Repository Ecosystem Initiative (GREI) (Slides (PDF, 1.32 MB) )
  Ishwar Chandramouliswaran, M.S., M.B.A., NIH Office of Data Science Strategy
  
  GREI is comprised of several generalist repositories that operate in important niches in the research data ecosystem and fill certain gaps and needs of investigators in making their data public. The presentation reviewed the GREI mission and how it is meeting the needs of data generators.

Metadata and Data Standards for NIDDK Research Data

This webinar reviewed importance and utility of metadata and data standards in maximizing scientific data value and intersections with the NIH DMS policy. The webinar covered use of metadata and data standards and lessons learned from NIDDK and NIH projects that use standards to enhance data quality and access.

Webinar Materials

• Meeting page
• Meeting summary (PDF, 204.43 KB)
• Webinar recording 

Presentations

• Metadata and Data Standards - What and Why (Slides (PDF, 1.27 MB) )
  Matthew Schu, Ph.D., RTI International
  
  An introduction to metadata and data standards including examples from the Nutrition for Precision Health and All of Us programs.

• Establishing Data Structure to Increase Accessibility (Slides (PDF, 2.81 MB) )
  M. Todd Valerius, Ph.D., Brigham and Women's Hospital, Harvard University
  
  Case study using the ATLAS-D2K program on how complex data is brought together using data harmonization steps including standards and controlled terminologies to enable accessibility.

• Role of Data Standards in Quality and Harmonization (Slides (PDF, 4.26 MB) )
  Sanjay Jain, Ph.D., Washington University in St. Louis
  
  An example using the Kidney Precision Medicine Project on how data standards enable integration of assay data across many different study types and institutions including role of quality and standardization pipelines. Collaborative tools enabling discovery are mentioned.

• Implementation of Data and Metadata Standards – The Added Value (Slides (PDF, 2.16 MB) )
  Kenneth Young, Ph.D., University of South Florida
  
  The Environmental Determinants of Diabetes in the Young (TEDDY) study is a multi-center, multi-national effort that relies on data standards to enable sharing and insight. This presentation uses TEDDY as a case study to show how data standards and data dictionaries facilitate data sharing.

The ‘R’ in FAIR: Data Reuse

This webinar looked at secondary data use, also known as data reuse. Discussion focused on what data contributors can do to increase the reusability of their data and how secondary use of shared data can advance scientific knowledge. Examples of tools researchers can use at the generalist repository FigShare to find data were also presented.

Webinar Materials

• Meeting page
• Meeting summary (PDF, 166.47 KB)
• Webinar recording 

Presentations

• Advancing knowledge through secondary data use (Slides (PDF, 4.45 MB) )
  Vivian Ota Wang, Ph.D., NIH Office of Data Science Strategy
  
  An overview of the role that open data and data sharing play in promoting innovation and scientific advancement, challenges to overcome in reusing data, and some of the ethical, economic, legal, and social implications of data reuse.

• Best practices for secondary data use (Slides (PDF, 3.34 MB) )
  Harold Lehmann, MD, Ph.D., Johns Hopkins University
  
  Reviewed some of the methodological considerations for secondary data use and the potential value of leveraging larger studies and cohorts. Discussion included the role of computational tools and resources in enabling this larger scaled research and in research communication.

• Generalist repositories for sharing and finding data (Slides (PDF, 2 MB) )
  Ana Van Gulick, Ph.D., FigShare
  
  Figshare is a generalist repository that enables the sharing of data in a way that supports FAIR principles. A review of how sharing practices help data reusability including findability is presented along with resources to aid investigators in developing their data management and sharing plans

How does the DMS policy intersect with the Genome Data Sharing Policy?

Genomic Data Sharing (GDS) requirements (NOT-OD-22-198) may apply simultaneously with the new DMS policy. Investigators should address GDS requirements within the DMS Plan submitted with the application. Additional information about general GDS policy is provided on the NIH Scientific Data Sharing FAQ on GDS.

What are the eligibility criteria for depositing data in repositories?

Eligibility criteria to deposit data vary by repository. While selecting an appropriate repository, investigators should contact the selected repository to confirm their eligibility to submit data and that their data type(s) are accepted.

Repository Selection Considerations Tool (NIDDK) (PDF, 293.45 KB)

Does a contract or proof of agreement with the repository need to be included in the DMS Plan?

No.

What do I need to consider if my proposed research project will involve Artificial Intelligence (AI) technologies?

NIH Office of Science Policy has issued a resource to assist the research community in understanding how NIH policies guide artificial intelligence (AI)-related research. The purpose of the resource is to illustrate the applicability of existing policies and guidance to research involving AI technologies. The resource can be accessed at: https://osp.od.nih.gov/policies/artificial-intelligence.

Is it possible to update the DMS Plan during the course of the award?

Yes, investigators who need to make changes to any element(s) of the DMS Plan during the course of the award should work proactively with their Program Officer through their institutional official (or with the Scientific Director’s office for intramural investigators) to obtain review and approval of modifications when any changes or updates to the DMS Plan are needed. Examples that may require updates to the DMS Plan may include, but are not limited to, the following:

• If the type(s) of data generated change(s).
• A different data repository(ies) is(are) chosen for submission.
• The sharing timeline changes.

How often should DMS Plan progress be reported?

• Investigators should include an update on progress made towards fulfillment of the DMS activities during the progress report(s), including the final progress report.
• NIH will issue new DMS Research Performance Progress Report questions that align with the NIH Final Policy on Data Management and Sharing, to include updates on the status of data sharing, repositories and unique identifiers for data that have been shared. See NOT-OD-24-123 for up-to-date instructions and forms (when available).