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NIH Data Management and Sharing Policy

This guide supports researchers' efforts to comply with the NIH Data Management and Sharing Policy. It offers step-by-step guidance on how to plan for data sharing and write a data management and sharing plan to meet NIH requirements.

What to Include in Your Plan

General Considerations

The "Standards" element of your data management and sharing plan should include the following details:

  • The common data standards that will be applied to the scientific data and associated metadata to enable interoperability of datasets and resources
  • Whether data standards are formal or informal and how they will be applied
  • The names of formal standards that will be used or an indication that no consensus standards exist
Types of Data Standards

Data standards can take the following forms:

  • Specific and well-defined data formats
  • Data dictionaries
  • Definitions
  • Unique identifiers
  • Metadata schemas, vocabularies, and ontologies
  • Other data documentation

Examples of Data Standards

The following table shows examples of data standards required or encouraged by NIH-supported data repositories. Data repositories typically provide detailed data submission instructions on their websites. These instructions should include data standard expectations for submitters.

Data Standard Type of Standard Applicable NIH Data Repository
Brain Imaging Data Structure (BIDS) Data format OpenNeuro
Digital Imaging and Communications in Medicine (DICOM) Data format Medical Imaging Data Resource Center (MIDRC)
Minimum Information About a Microarray Experiment (MIAME) and Minimum Information About a High Throughput Sequencing Experiment (MINSEQE)* Metadata Gene Expression Omnibus (GEO)
NMR Self-Defining Text Archive and Retrieval Format (NMR-STAR) Data format Biological Magnetic Resonance Data Bank (BMRB)

*MIAME and MINSEQE are part of a collection of metadata standards called Minimum Information for Biological and Biomedical Investigations, which includes standards for reporting multiple types of experiments (e.g., those involving metabolomics, cardiac electrophysiology, genotyping, T-cell assays, metagenomics, immunohistochemistry, RNAi, flow cytometry, etc.).

Where to Find Data Standards

What You Will See in the DMPTool

The DMPTool's section on the "Standards" element of your data management and sharing plan includes only one text box for specifying the standards that will be applied to the data and metadata related to your project.

Accompanying this box is sample language for cases with existing, formal data standards and cases without such standards. The "Standards" section also includes considerable guidance from NIH, JHU, and the DMPTool.

See the "Planning Step 5: Consider Using the DMPTool to Write Your DMS Plan" page of this guide for more about the DMPTool.

The DMPTool's section on standards

Learning Resources on Data Documentation

General Resources
  • Data Best Practices and Case Studies - A guide covering a range of data documentation topics, including file naming, working with spreadsheet data, and versioning. Created by the Stanford Libraries.
  • Documenting Your Research Data - A self-paced online training, which includes modules on special considerations for documenting code, tabular data, geospatial data, and data generated from clinical, biomedical, and public health research. Created by JHU Data Services.
  • Metadata for Effective Research Data Management - A four-page primer on metadata and how to comprehensively generate them. Created by JHU Data Services.
Special Topics
  • File-Level Metadata for Next Generation Sequencing Files - A brief lesson with elements to consider when managing the metadata of next generation sequencing files. This lesson is part of a larger module on data sharing for next generation sequencing, which includes detailed guidance on the dbGaP data submission process. Part of an NIH BD2K grant awarded to the Welch Medical Library.
  • Guide to Writing "readme" Style Metadata - A guide for writing "readme" style metadata, an appropriate strategy for facilitating data reuse when no appropriate standards-based metadata scheme exists. Created by the Research Data Management Service Group at Cornell University.
  • Using File Naming to Organize Research Files - A self-paced, six-module online training with quick tips for naming files and organizing folders. Created by JHU Data Services.