- What is a data management plan?
- When do I need one?
- How do I create one?
- Beyond checking the box…
…Thus, if an application describes a data-sharing plan, NIH expects that plan to be enacted. If progress has been made with the data-sharing plan, then the grantee should note this in the progress report. In the final progress report, if not sooner, the grantee should note what steps have been taken with respect to the data-sharing plan.
In the case of noncompliance (depending on its severity and duration) NIH can take various actions to protect the Federal Government’s interests. In some instances, for example, NIH may make data sharing an explicit term and condition of subsequent awards.
Starting with the October 1, 2003 receipt date, investigators submitting an NIH application seeking $500,000 or more in direct costs in any single year are expected to include a plan for data sharing or state why data sharing is not possible.
The final DMS Policy does not create a uniform requirement to share all scientific data. Unlike a requirement for submission of Plans, which can be implemented across various funding mechanisms and types of research with little variation, appropriate data sharing is likely to be varied and contextual. Through the requirement to submit a Plan, researchers are prospectively planning for data sharing, which we anticipate will increasingly lead researchers to integrate data sharing into the routine conduct of research.
Accordingly, we have included in the final DMS Policy an expectation that researchers will maximize appropriate data sharing when developing Plans. The final DMS Policy retains the Draft Policy’s factors (i.e., ethical, legal, or technical) that may necessitate variations in the extent of scientific data preservation and sharing, and researchers should convey such factors in their Plans.
We believe this will provide the necessary flexibility for researchers to accommodate the substantial variety in research fields, projects, and data types that this expectation will encompass.
The recorded factual material commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings, regardless of whether the data are used to support scholarly publications.
Scientific data do not include laboratory notebooks, preliminary analyses, completed case report forms, drafts of scientific papers, plans for future research, peer reviews, communications with colleagues, or physical objects, such as laboratory specimens.
We encourage investigators to consider, while developing their Plans, how to address data management and sharing in the informed consent process, such that prospective participants will understand what is expected to happen with their data
“[s]hared scientific data should be made accessible as soon as possible, and no later than the time of an associated publication, or the end of the award/support period, whichever comes first.”
The final DMS Policy strongly encourages the use of established repositories to the extent possible. This reflects NIH’s preference that scientific data be shared and preserved through repositories, rather than kept only by the researcher or institution and provided on request, with the recognition that this is not always a practical or even a preferred approach.
Personnel costs required to perform the types of data management and sharing activities described in the final Supplemental Information are allowable. Regarding the availability of data beyond the end of the project, which is crucial to achieving the goals of the DMS Policy, the final Supplemental Information clarifies that fees for long-term data preservation and sharing are allowable…
but funds for these activities must be spent during the performance period, even for scientific data and metadata preserved and shared beyond the award period. NIH funds cannot legally be spent after the award period.
Sharing scientific data accelerates biomedical research discovery, in part, by enabling validation of research results, providing accessibility to high-value datasets, and promoting data reuse for future research studies.
This talk was produced on 2021-10-01 in RStudio using R Markdown. The code and materials used to generate the slides may be found at https://github.com/psu-psychology/2021-10-01-odds-data-mgmt.
Information about the R Session that produced the code is as follows:
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