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DMPTool is an easy to use, browser-based resource that helps demystify the process of drafting data management plans. I particularly recommend it for those new to writing data management plans.
Ready to get started? Just visit the DMPTool site and create an account with your Northeastern email address to start building your own plan.
DMPTool gives step-by-step instructions and guidance for building a DMP and allows you to:
→ Create ready-to-use DMPs for specific grant-funding agencies including the NIH, National Endowment for the Humanities Office of Digital Humanities, and more than a dozen NSF directorates
→ Satisfy funder requirements for DMPs.
DMPTool also provides a convenient starting point for DMP writers, because resources such as funding agency guidelines, advice for principal investigators, and even sample DMPs are available on the site.
This short video highlights features of DMPTool 2.
Missed our 2020 Training Series? Watch the recorded webinar below that describes data management plans in less than 20 minutes. Just click on the image below and log in to open up the webinar in a new tab. For questions, comments, and any accessibility or captioning requests, please email me.
If you’ll be working on a grant proposal, chances are you’ll have to write a data management plan (DMP). This session gives insight into what funders are looking for in DMPs, and walks (well, sprints) through the typical components of a DMP, including some examples from successfully funded proposals.
Funding agencies such as the National Institutes of Health and National Science Foundation now require that data management plans (DMPs) be submitted along with grant proposals. We expect that other agencies will follow suit.
What is a data management plan (DMP)?
A DMP documents what a researcher or group will do with the data collected in the course of their project, and upon completion of their research.
What should be included in a DMP?
Specifics vary by discipline, but some common considerations for drafting a data management plan include:
→ Data characteristics - How much data will be generated, and in what file formats? Are these formats proprietary?
→ Data documentation - What file naming conventions and data identifiers will be used to organize data? Is there a standard metadata schema or ontology used in your field for data documentation?
→ Longevity and responsibility - How long should data be retained? What strategies will be used for storage and backup? Who in the research group, department, or university will be responsible for controllling and managing these activities?
→ Sharing, usage and publication - Who may use the data? Have you chosen an archive for your data?
→ Privacy or security considerations - Is any of the data personally identifiable, or might it pose a security risk?
Want to know more? Check out this chart that details common data management plan themes.