NIDDK DMS Tools & Examples
NIDDK has created the tools and examples below to assist investigators in developing their Data Management and Sharing (DMS) Plan.
In this section:
- DMS Plan Worksheet and Examples
- Data Preservation, Access, and Associated Timelines: Selecting a Data Repository
- Glossary of DMS Terms
- Frequently Asked Questions (FAQ)
DMS Plan Worksheet and Examples
NIDDK has outlined its expectations for DMS Plans. The DMS Plan Worksheet was developed to assist investigators in drafting a DMS Plan by including NIDDK-specific guidance for the National Institute of Health (NIH) DMS Plan optional format page (PDF, 105 KB) .
(DOC, 39 KB)
In addition, several example DMS Plans are provided that are consistent with the expectations of NIDDK and the NIH (NOT-OD-21-013 and NOT-OD-21-014). The example DMS Plans illustrate the required information and level of detail that should be included for common data types and research designs. The NIH has provided additional example DMS Plans. Researchers are encouraged to review the NIDDK-specific DMS Guidance and adapt the concepts outlined in these example plans to their own research, rather than using the plans as a template. Investigators who have additional questions while drafting their DMS Plan should contact an NIDDK Program Officer.
Genomic Data from Human Research Participants
Example (PDF, 146.8 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)
Data standards, vocabularies, ontologies, and terminologies for a proposed study are dependent on the data types being collected. A list of potential Data Standards, Vocabularies, and Ontologies, as currently being collected in relevant repositories, is provided below.
|Category||Data Standards, Vocabularies, Ontologies|
|Adverse Event||Common Terminology Criteria for Adverse Events (CTCAE)|
|Clinical Terms||International Classification of Diseases (ICD)|
|SNOMED (SNOMED CT)|
|Clinical Standards (FDA requirement)||Clinical Data Interchange Standards Consortium (CDISC)|
|Drug (FDA)||National Drug Codes (NDC)|
|Imaging||Digital Imaging and Communications in Medicine (DICOM)|
|Measurement||Logical Observation Identifier Name and Codes (LOINC)|
|Regulatory||Medical Dictionary for Regulatory Activities Terminology (MedDRA)|
The specific metadata and associated documentation for a proposed study will vary by scientific area, study design, type of data collected, and characteristics of the dataset. Metadata may include methodology and procedures used to collect the data, data labels, definition of variables, and any other information necessary to reproduce and understand the data. Details about planned metadata should be provided in the DMS Plan as outlined in the Standards guidelines.
Data Preservation, Access, and Associated Timelines: 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 is essential to maximize data sharing. NIDDK affirms the desired repository characteristics established by NIH, and strongly encourages the use of existing repositories to the extent possible 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 when selecting a repository:
- FOA requirement (e.g., NIDDK-funded, large, multi-site clinical studies should submit data to the NIDDK Central Repository).
- Organism, domain, or data type-specific repositories.
- Whether controlled access to data is required (e.g., for protection of human subjects’ privacy).
The NIDDK Repository Selection Considerations Tool is intended to assist investigators to align the data types to be generated with appropriate repositories for submission and sharing.
(PDF, 267.25 KB)
Glossary of DMS Terms
While not comprehensive, the glossary provides definitions for selected terms related to the 2023 DMS policy that might be unfamiliar or require content-specific definitions. Definitions for additional common data management terminology are available from the Digital Curation Centre and other academic or institution resources.
|Code||In the context of data management, this may include computer code or scripts used in the collection, manipulation, processing, analysis, or visualization of data but may also include software developed for other purposes.|
|Controlled Access||Data that are made available under stringent, secure conditions. Typically, confidential or sensitive data.|
|De-identified data||Health information that does not identify an individual and where there is no reasonable basis to establish that the information can be used to identify an individual. De-identification mitigates privacy risks to individuals, supporting the secondary use of data.|
|FAIR Principles||Acronym for four key qualities of managing digital assets: Ensuring that they are "Findable, Accessible, Interoperable, and Reusable." Originally published in "The FAIR Guiding Principles For Scientific Data Management and Stewardship" in Scientific Data (2016).|
|Machine-readable data||Structured data that can be easily processed by a computer. Making data machine readable often requires cleaning and preprocessing raw research data.|
|Metadata||Documentation or information about a data set. It may be embedded in the data itself or exist separately from the data. Metadata may describe the ownership, purpose, methods, organization, and conditions for use of data, technical information about the data, and other information. Many metadata standards exist across a broad range of disciplines and applications.|
|Open access||Freely available material that has few or no copyright or licensing restrictions|
|Persistent Unique Identifier (PID)||A string of letters and numbers used to distinguish between and locate different objects, people, or concepts. Persistent identifiers support interoperability across different platforms and provide a reliable way to track citations and reuse. This identifier can be used to link one or more datasets belonging to the same study, which may be stored in multiple locations or repositories. Examples of PIDs include Digital Object Identifiers (DOIs), ORCID IDs, GUIDs, Handles, and Archival Resources Keys (ARKs).|
A facility that manages the appraisal of, preservation of, and accessibility to materials
on a long-term or permanent basis.
An Institutional Repository typically contains content produced by the institution that hosts the service.
Frequently Asked Questions (FAQ)
The following FAQs are intended to help clarify certain considerations for the NIDDK-specific implementation of the 2023 National Institute of Health (NIH) Policy on Data Management and Sharing (DMS).
Managing and Sharing Scientific Data
What are the eligibility criteria for depositing data in repositories?
Eligibility criteria to deposit data vary by repository. While selecting an appropriate repository (investigators are encouraged to use the NIDDK Repository Selection Consideration Tool), investigators should contact the selected repository to confirm their eligibility to submit data and that their data type(s) are accepted.
NIDDK Repository Selection Considerations Tool (PDF, 267.25 KB)
Does a contract or proof of agreement with the repository need to be included in the DMS Plan?
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 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.
The NIH FAQs provide additional information about the DMS policy agency-wide. Please check back regularly as new content will be added.
The information will be updated as additional policy or guidelines are established and as new resources are released.