-  Home 
        -  Research & Funding 
        -  Research Resources 
        -  NIDDK Data Management & Sharing 
        -  NIDDK DMS Tools & Resources 

NIDDK Data Management & Sharing
-  NIDDK Guidance for Writing a DMS Plan 
-  NIDDK DMS Tools & Resources 

# NIDDK DMS Tools & Resources

NIDDK has created the tools and examples below to assist investigators in developing their Data Management andSharing (DMS) Plan.

**In this section:**
          - DMS Plan Worksheet
          - DMS Plan Examples
          - Data and Metadata Standards
          - Selecting a Data Repository
          - NIDDK DMS Webinar Series
          - Frequently Asked Questions (FAQ)
          - Glossary of DMS Terms

## DMS Plan Worksheet

NIDDK has outlined its expectations for DMS Plans. The DMS Plan Worksheet was developed to assistinvestigators in drafting a DMS Plan by including NIDDK-specific guidance for the National Institute ofHealth (NIH)DMS Plan optionalformat page(DOC, 35.4 KB)**.
NIDDK DMS Plan Worksheet(DOC, 39 KB)****

## DMS Plan Examples

The example DMS Plans provided below are consistent with the expectations of NIDDK’s guidance and the NIH(NOT-OD-21-013 and NOT-OD-21-014). Theexample DMS Plans illustrate the required information and level of detail that should be included for commondata types and research designs.

Researchers are encouraged to review the NIDDK-specific example plans below and adapt the concepts to theirown research, rather than using the plans as a template.
          -  Genomic Data from Human Research Participants    Example  (PDF, 141.48 KB)  
          -  Clinical Data from Human Research Participants    Example  (PDF, 151.85 KB)  
          -  Basic Research from a Non-Human Source    Example  (PDF, 120.93 KB)   
          -  Secondary Data Analysis Example  (PDF, 112.71 KB)  

NIDDK held a webinar that provided an overview of successful approaches to Writing a DMS Plan.

After reviewing the above resources, investigators who have additional questions while drafting their DMSPlan should contact anNIDDK Program Officer.

## Data and Metadata Standards

Details about planned data and metadata should be provided in the DMS Plan.

**The Data and Metadata Standards Examples for DMS Plans** provides examples of data andmetadata standards for common data types generated by NIDDK investigators. It also includes additionalmetadata for select repositories and guidance on how to incorporate data and metadata standards intodeveloping your DMS Plan. These examples serve as a starting point and are not exhaustive.
Data and Metadata Standards Examples for DMSPlans(PDF, 190 KB)****
Additional examples for incorporating metadata information in DMS plans for different data types can be found in NIDDK DMS Plan Examples and the Sample Plans provided by NIH.

Information on data and metadata standards was presented during the NIDDK DMS webinar, “Metadata and Data Standards for NIDDK Research Data**”. This webinar included:
          - An  overview    of metadata and data standards;
          - context for evaluating and finding appropriate and relevant research-specific needs; and
          - the role of metadata and data standards in data comparison.

### Repositories & Data and Metadata Standards

Repositories may have specific requirements for data or metadata standards. Many repositories haveestablished metadata fields (required and optional) and may have templates for depositing data. Reviewingthe requirements when developing the DMS Plan and **prior to collecting data** is critical toensure that the required format, fields, and any preferred terminology are used to avoid issues that couldprevent subsequent deposit and data sharing.

## Selecting a Data Repository

Using an appropriate data repository generally improves the FAIRness (Findability, Accessibility,Interoperability, and Reusability) of the data. Selection of an appropriate data repository isessential to maximize data sharing. NIDDK affirms thedesiredrepository characteristicsestablished by NIH, and strongly encourages the use of existing repositories to the extentpossible for preserving and sharing scientific data.

Investigators need to consider the type of data they will be submitting when selecting a repository.A short justification of the repository selected for each data type must be included.

NIDDK strongly encourages investigators to consider the factors below **in order** whenselecting a repository:
          -     NOFO specified repository (ies).
          -     Organism, domain, or data type-specific repositories.    
          -     Whether controlled access to data is required (e.g., for protection of human subjects’ privacy).    

### Repository Selection Aides 

          - The  Repository Selection Considerations Tool (NIDDK)  (PDF, 293.45 KB)    is intended to assist investigators to align the data types to be    generated with appropriate repositories for submission and sharing.  
          - dkNET  lists available data    repositories used by NIDDK supported researchers and provides tools to help comply with data sharing    requirements.
          - NIH    Repositories for Sharing Scientific Data ,  NIH-supported    repositories and generalist repositories  for a wide range of data types and disciplines.
          - The NIDDK DMS webinar “ Finding    a Repository for Your Data ” provided additional information about NIDDK-research relevant    repositories. The webinar videos linked below provides:                 - an overview of the tools and resources available on the NIDDK Information Network (dkNET)        through  Dr.        Grethe’s presentation    .         
            - data eligibility and acceptance criteria as summarized in a  presentation by Dr. Rodriguez     on the NIDDK Central Repository.
            - how generalist repositories can be leveraged when a domain or data-type specific repository is        not available, as reviewed in the  presentation by        Mr. Chandramouliswaran    . 

## NIDDK DMS Webinar Series

NIDDK hosted a webinar series to provide education and outreach to the NIDDK scientific community about datamanagement and sharing.

#### Writing a DMS Plan

This webinar provided an overview of DMS plan content and highlighted institutional, NIH, and NIDDKresources available to investigators throughout the research study life cycle. The role of researchlibrarians 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. Abrief overview of NIDDK DMS resources and tools for identifying repositories, as well as resourcesavailable through dkNET (NIDDK Information Network) were highlighted. Information about the NIDDKCentral Repository and the Generalist Repository Ecosystems Initiative (GREI) rounded out thesession. 

**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    

## Frequently Asked Questions (FAQ)

The following FAQs are intended to help clarify certain considerations for the NIDDK-specificimplementation of the 2023 NIH DMS Policy.

### Managing and Sharing Scientific Data

#### 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 requirementswithin the DMS Plan submitted with the application. Additional information about general GDS policyis provided on the NIHScientific 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 anappropriate repository, investigators should contact the selected repository to confirm theireligibility 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.

### Updates to DMS Plans

#### 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 thecourse of the award should work proactively with their Program Officer through their institutionalofficial (or with the Scientific Director’s officefor intramural investigators) to obtain review and approval of modifications when anychanges or updates to the DMS Plan are needed. Examples that may require updates to theDMS 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 DMSactivities during the progress report(s), including the final progress report.

The NIH FAQsprovide additional information about the DMS policy agency-wide. Please check back regularlyas new content will be added.

## Glossary of DMS Terms

While not comprehensive, the glossary provides definitions for selected terms related to the 2023 DMS policythat might be unfamiliar or require content-specific definitions. Definitions for additional common datamanagement terminology are available from theDigital Curation Centre and other academicor institution resources.

The information will be updated as additional policy or guidelines are established and as new resourcesare released.