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Creating an NIH Data Management and Sharing Plan

This guide presents information on the NIH Data Management and Sharing Policy, which requires submission of a Data Management and Sharing Plan (DMSP) for all NIH-supported research.

Documenting Data Types

To document in your Data Management and Sharing Plan (DMSP) the types of data that are anticipated to be created in your project, provide your answers to the following questions:

  • What is your data's modality?: In the context of the NIH DMSP, modality refers to the high-level type of data you will be collecting, such as imaging, genomic, text sequences, modeling data, mobile, survey, etc.
  • In what format will your data be collected?: Format refers to the type of files, generally denoted by file extension, that your project will create, such as CSV, TSV, XML, JSON, fMRI files, SAV, SAS, DTA
  • How much data will be collected?: Number of study participants, anticipated number of generated files, etc.
  • What is the level of anticipated data aggregation?: Will individual-level data be used for the study, or aggregates of groups of data? Will only aggregated data be shared?
  • To what level will the data be processed?: Overall data processing for the project can be described, and also level of processing for data to be shared.
  • Which data will be shared?: Based on aggregation and processing considerations above, describe which data can and will be shared at the project's end. For example, this can be only de-identified data, only de-identified subsets supporting publication, etc. Compliance can consist of sharing a subset of the project's data based on legal, ethical, and policy-based data privacy requirements. Rationale for the level of sharing possible must be provided.
  • What metadata and other standard documentation will help others understand the data?: Many metadata standards can be used to describe your datasets, from the most general (Dublin Core, DataCite) to NIH-endorsed Common Data Elements, to standards that are highly specified to a field of study, such as the MIAME and MINSEQE standards.
    • To find domain-specific metadata standards, try searching these standards catalogs:
    • In addition, if other documentation such as data dictionaries or README files is necessary to understand preserved and shared data, describe these files as well.

Domain-specific standards resources from University of Michigan Library's Research Data Management (Health Sciences) Research Guide,