Tag Archives: funder update

New NSF Templates – Mirrors of the Research.gov Webform – Now Available

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TL;DR

  • Three new NSF templates – one for SBE Directorate, one for EDU, and a Generic for all other Directorates – are now published in the DMP Tool, along with selected guidance from NSF on how to complete them
  • The content, questions, and options are matched from the Research.gov webtool so people can plan their DMP in advance and get collaboration and feedback
  • Researchers will need to transfer the responses at the end into the webform instead of uploading a PDF
  • We will continue to monitor and update as directorates add more response options to the form

Introduction

Like many in the data management community, we’ve been following changes to data management plans (DMPs) at the National Science Foundation.  Instead of submitting the DMP as a PDF, the DMP is now filled out as a form on Research.gov.  This has both positive and negative implications for proper planning, collaboration, and interoperability, which we talk in more detail about on Upstream.  In this post, we will walk through how we replicated the form into the DMP Tool so people can use it to prepare their DMP in advance of completing the final version on Research.gov.  This way, people can still get personalized guidance and feedback from their organization, and collaborate with other users, before taking the final output to copy over to NSF’s webform.

NSF’s New DMSP Webform

The way the Research.gov form works is that you add specific Data and Research Product Categories, one category at a time (up to 4 total).  Each category accounts for all data of that type collected for a research project.  For example, if a project collects two distinct sets of human MRI data that will be published as separate datasets, that would still go in as one category of “human MRI data.” For each broad data category, the researcher will report on:

  • Which, if any, access policies or limitations that apply (i.e., reasons to not fully share data publicly, such as legal considerations)
  • What data standards and metadata will be used (i.e., the format and standard of the data, such as BIDS)
  • The provenance of the data (i.e., if its an existing resource or new collection)
  • The public archiving location (i.e., the repository it will be stored at, such as OSF)
  • The timeline for public accessibility (which is expected to be time of publication unless there is a justified reason for extending)
  • The duration of data availability (which is expected to be at least 2 years unless there is a justified reason for less), including a confirmation that the retention policy of repository will be adhered to
  • Accountability for data management (i.e., which PI or co-PI is responsible)

Some of these questions always provide a list of standard options to select from, such as the timeline and duration of data availability, while others depend on whether the Directorate has entered options, which such as Data and Research Product Category, Data Standards, and Public Archiving.  At the time of this post, most Directorates have not entered options for those, so they just offer an “Add New” option to write in their own. 

How it Works on the DMP Tool

In the DMP Tool, we don’t have the exact capabilities to perfectly replicate the form, but we have published templates that capture all the information and options in them, with additional guidance pulled from various NSF websites. You can download copies of the templates or start a plan at our Funder Requirements page. All older versions are now unpublished or marked obsolete and will be unpublished soon.

Most applicants will use the “Generic” template that doesn’t provide extra options for data type, standards, and repositories, while those applying to the Directorate for Social, Behavioral and Economic Sciences (SBE) or Directorate for STEM Education (EDU) can use those templates in the tool to see what options those directorates provide.

First, the researcher needs to select how many Data or Research Product Categories they will add.  This allows us to hide the appropriate number of later sections, so they only see questions in the right number of sections for the data categories they would add.  If they select 0, that is similar to checking the box on NSF’s webform that a detailed DMSP is not needed, and they can use the Additional Information box to justify why they don’t need one, in case they want feedback from their organization on that.

Screenshot of the DMP Tool, showing a radio button question asking how many Data and Research Product Categories the user wants to add, with options from 0 to 4 categories. There is a text box below labeled "Additional Information," and a sidebar to the right listing Guidance from NSF with multiple paragraphs about data categories.
Screenshot of the start of the DMP Tool template

For each section, they will first enter the Title and Description of that Data category.  

Screenshot of the DMP Tool, showing a text field question of "Title of Data of Research Product Category" and a text box question with "Description of Data or Research Product Category." The right sidebar has guidance from NSF on what to answer for these questions.
DMP Tool: Title and Description
Screenshot of similar questions from the NSF webform, showing a selection of "Add New" as the category, then a text field question of "Title" and a text box question with "Description."
NSF Webform: Title and Description

Or, for SBE and EDU, they can select from the options for the Title that matches what they’d be shown in the NSF webform, or add their own using the Additional Information box. Note that the Additional Information box does not need to be filled out if a standard option is selected.

Screenshot of the DMP Tool showing the same Title question as previously, except now instead of a text field it is showing a long list of radio buttons to select data types, such as "computer model" and "human EEG."
DMP Tool: Data Types for SBE specifically

Next, they will answer all the follow-up questions for each category.  While we don’t have the ability to add formatting to response options or validate how many are selected (e.g., to make sure people don’t select more than 6 access limitations), we provide all the same options as the tool for people to build their DMP the same way they would in the form.

Screenshot of the DMP Tool, showing a list of checkbox style questions where users can select any number of Access Policies and Limitations for data sharing, including options like "Human Data Protection" and "Resource Limitations" with definitions. There is an Additional Information text box below the question, and a sidebar with NSF guidance to the right.
DMP Tool: Access Policies and Limitations
Screenshot of the same Access Policies and Limitations question from the NSF webform, showing a dropdown box with three options visible on screen, and more available with scrolling.
NSF Webform: Access Policies and Limitations

For SBE specifically, there are some repositories that are only shown if certain Data types are selected earlier.  For example, GitHub is only offered as an option if Bespoke Research Software or Computer Model is the Data or Research Product category.  Since we don’t have the functionality to customize options based on a prior questions, we instead list in the response which it applies to so people can select appropriately.

Screenshot of the DMP Tool, showing a list of Public Archiving options as radio buttons, with options of Databrary, Github, OpenNeuro, OSF, and Add New. The Github and OpenNeuro options also include a list of which data types they apply to. There is an Additional Information text box below the question, and a sidebar with NSF guidance to the right.
DMP Tool: Public Archiving
Screenshot of the Public Archiving question from the NSF webform, showing a selection of repositories to pick from under the heading of Data Sharing Location. In this version, only Databrary, OpenNeuro, OSF, and Add New are showing since it is from an example of Human MRI data and not software, so GitHub is not displayed.
NSF Webform: Public Archiving

The Additional Information box is turned on for every question in case people are using “Add New” or providing extra justification in the form, though it often won’t need to be filled out when standard, expected options are selected.

Each question has Guidance on the right sidebar pulled from NSF policies, the webform itself (i.e., info buttons and help text), the guide to the webform, or notes from DMP Tool about functionality.  Organizations can also publish customizations of the template to add their own specific guidance to the form, or add guidance to Themes that will show up next to relevant questions.

Moving Forward

We hope this is helpful for researchers, especially those who want to get feedback from collaborators, data librarians, or other administrators at their university before submitting their plan to NSF.  It will also allow people to publish their plan, get a DMP ID, and connect future outputs to this plan within the tool.  While it is extra work to transfer responses into Research.gov at the end instead of uploading a PDF, the collaboration and guidance may be worth the extra steps.

We’ll keep an eye on feedback and update as needed.  Please report issues or suggested additions (e.g., if you notice a directorate has new options before we do) to dmptool@ucop.edu.

Updated NIH Data Management and Sharing Plan Now Available in the DMP Tool

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We are pleased to share that the new 2026 Data Management and Sharing Plan (DMSP) format for NIH is now available in the DMP Tool.  While this form is not required for applications until May 25th, NIH has stated that they are already accepting it and encourage people writing new plans to use the new format.

The new template in the tool is currently titled “NIH 2026 Data Management and Sharing Plan (2026 Pilot Format, required starting May 25, 2026).”  The 2023 version (formerly known as NIH-Default DMSP) is still available in the tool as well for now with the new title “NIH DMS Plan Format 2023 Version (Allowed for due dates prior to May 25th, 2026)”, as it is allowed prior to May 25th. However, the 2026 template is the recommended format now and will be the first result returned when users select NIH as their funder.  

The legacy NIH-NIMH template is also being deprecated because the new 2026 NIH DMSP format applies across all NIH Institutes and Centers, including NIMH. On May 25th, the older NIH templates will be marked deprecated and then removed, leaving the 2026 format as the single NIH template option that applies to all NIH applications (though some Institutes may have additional sharing policies to keep in mind when answering the questions). We will update the title of the 2026 format at that time to remove the May 25th date.

Screenshot of the DMP Tool page that starts a new DMP. The primary funding organization field has NIH selected, and the dropdown under "Which DMP template would you like to use?" shows 3 available NIH templates, with the top one highlighted, and the title matching the 2026 version mentioned in the paragraph.
Template options when selecting NIH as the funder for a plan

The template matches the NIH Format as closely as possible and brings in relevant Guidance from NIH policies, help pages, and FAQs to help researchers answer each question appropriately. It also includes sample responses for elements such as Element 4 and Element 6 to help users better understand type of information NIH is expecting.

You can download a copy of the template or start a plan from it at our Funder Requirements page.

NIH DMSP Template Working Group

To curate the guidance, the NIH DMSP Template Working Group, who developed the guidance for the 2023 template in the tool, came back together to work on adding new guidance from this updated form.  The group was again chaired by DMP Tool editorial board member Nina Exner and involved the following members who contributed to meetings and guidance additions:

  • Nina Exner (Chair; DMP Tool Editorial Board Member), Virginia Commonwealth University
  • Mathew Covey, The Rockefeller University
  • Will Dean, Temple University
  • Seonyoung Kim, Washington University in St. Louis, Bernard Becker Medical Library
  • Jim Martin, University of Arizona
  • Genevieve Milliken, University of Nevada, Las Vegas
  • Melissa Ratajeski (DMP Tool Editorial Board member), University of Pittsburgh
  • Lesley Skalla, Duke University Medical Center
  • Amy Yarnell, University of Maryland, Baltimore

Each question has guidance pulled from various NIH policy pages and FAQs to give information on how to answer each question.  There are occasional notes about the tool implementation as well. The group’s goal was to balance giving enough guidance to bring key points right into the tool, but not overwhelming with too much text, so there is a mix of both direct guidance and links to other pages that may give more detailed information.

Screenshot of the DMP Tool showing Element 3 of the NIH template, which is a Yes/No question asking if shared scientific data will be made available for at least as long as required by applicable data repository policies and/or journal policies. On the right side of the screen is a Guidance sidebar showing 2 paragraphs of text of guidance from NIH, including a reminder that institutions are required to keep the data for at least 3 years following closeout of a grant, and a note from the DMP Tool that the repository they select in Element 6 may have additional retention policies to consider for this questions as well.
Example of Element 3 with the guidance sidebar on the right

We at UC3 want to thank all of these members for their work to help get this out in a helpful and timely manner for NIH applicants. Navigating the policy documents and resources to bring the concise but comprehensive guidance to each question took a lot of effort from all members, and we are grateful to their work for making these templates accessible to plan writers.

Additional DMP Tool notes for Administrators

  • Any plans created under the old templates will be unchanged, even once those templates are removed later.  Template updates only impact new plans created after the publication of a template or update.
  • If you would like to add customizations to the NIH 2026 template for your organization, such as additional institutional questions or extra guidance for researchers at your university, see our documentation on customization.
  • If you added customizations to the 2023 NIH-Default DMSP template, they will not roll over to the 2026 template (since it is a brand new template and not just a version update), and your content will become inaccessible in the admin menu once the 2023 template is unpublished.  Please download or copy anything you want from your customization before the end of May 2026.
  • If your institution previously added custom guidance to the legacy NIH-NIMH template, please review and migrate that content to the new 2026 NIH template. Since the 2026 NIH DMS Plan format now applies across all NIH Institutes and Centers, including NIMH, the older NIMH template should no longer be used for new plans and can be removed after your guidance has been transferred. Please download or copy anything you want from your customization before the end of May 2026.
  • If you have any issues or questions around the new template, please reach out to us at dmptool@ucop.edu. 

Other resources on the NIH form

Thank you once again to the working group and everyone who has written guidance to help navigate this new update! We’ll continue to monitor changes and updates from NIH to reflect the most up to date format and guidance.

Notice: In a future post, we will discuss updates related to NSF templates releasing next week.  Our plan is still to mirror them from Research.gov as best we can once we can view them in the tool.  For more on UC3’s thoughts on these changes, see our article What is the Future of Data Management Plans? on the Upstream blog.

What is the Future of Data Management Plans? [X-Post from Upstream]

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Note: This post is a cross-post of an article written for Upstream blog to make sure DMP Tool followers are aware of these important changes.  Please refer to that site as the version of record; DOI: 10.54900/fbq63-61s08

As stated in a prior post, we will be adding the updated NIH and NSF forms to the DMP Tool and expect to have both available by the end of the month.

Over the past decade, there has been an international effort across the research community to make data management and sharing plans (DMSPs, also called DMPs) more than static, narrative documents. Through work on machine-actionable DMPs (maDMPs), shared metadata standards, and integration with research infrastructure, the goal for a growing number of groups around the world has been to make DMPs more structured, more connected, and more meaningful across the research lifecycle.

This work has led to real progress. DMPs are increasingly seen not just as compliance requirements, but as part of a broader ecosystem that connects researchers, institutions, repositories, and funders. The idea that DMPs should be interoperable, reusable, and able to support downstream workflows is now more widely accepted than ever.

At the same time, recent developments from the National Science Foundation (NSF) and the National Institutes of Health (NIH) suggest a shift in how this vision is being implemented. Both agencies are moving away from free-form narrative plans toward more structured formats. NSF has announced that, starting April 27, 2026, their DMPs will be completed directly within Research.gov as a webform, while NIH is introducing a revised template for their DMSPs beginning May 25, 2026 that emphasizes structured responses and simplified inputs.

We have recently outlined these changes in a post on our DMP Tool blog, and in many ways, these changes reflect the direction the community has been advocating for. But they also raise an important question: as DMPs become more streamlined and embedded in funder systems, how do we ensure they remain interoperable, collaborative, and connected to the broader research data ecosystem?

Improvements in the DMP landscape

Many of the recent changes from funders reflect directions that the community has been actively working toward for years. Efforts around maDMPs, shared metadata standards, and stronger connections between planning and outputs have all been grounded in a common goal: to make DMPs more structured, more usable, and more integrated into the research lifecycle. In that context, the move away from free-form narrative plans toward more structured formats is both expected and welcome.

Several aspects of the evolving landscape stand out as particularly positive:

  • Moving toward structured questions helps reduce ambiguity and brings greater consistency to how plans are created and reviewed. 
  • A clearer expectation that data should be shared, with exceptions requiring justification, reinforces a shift from recommendation to norm. 
  • Embedding DMP creation into proposal systems meets researchers where they are and has the potential to reduce administrative burden at the point of application.

There is also a broader opportunity here. More structured plans make it easier to connect DMPs to downstream activities, including tracking data sharing over the course of a project and linking plans to outputs such as datasets, repositories, and related identifiers. These are areas where the community has invested significant effort, through initiatives such as maDMPs, DMP IDs, and tools designed to support more dynamic and reusable integrations.

Taken together, these changes signal real progress. They suggest that funders are not only encouraging data sharing, but also rethinking how planning can better support it in practice.

At the same time, as these ideas move from principle to implementation, new questions begin to emerge. The benefits of structure, simplicity, and integration depend on how well they connect to the broader ecosystem and whether they continue to support meaningful, collaborative planning. These are the areas where the details of implementation will matter most.

Changes at NSF

Recently, NSF has moved toward a structured, webform-based DMP. While the full form has not yet been released, it is expected to include a set of core questions covering familiar elements of data management planning:

  • What kind of data is being shared
  • What concerns limit the sharing of data and why
  • What is the format of the shared data
  • Where will it be shared
  • For how long will it be available
  • What is the source of the data
  • Who is responsible for managing the data

This shift toward structured input is an important development. It brings greater consistency to how plans are created and reviewed and aligns with long-standing efforts to make DMPs more machine-readable and actionable. At the same time, the decision to implement this form within Research.gov introduces a new set of questions about how these plans will connect to the broader research data ecosystem.

maDMPs have been developed with the goal of enabling information to move between systems, supporting workflows that extend beyond the point of proposal submission. As NSF stated in a past Dear Colleague Letter:

A machine-readable document allows a computer program to interpret the DMP, such as to prepare a data repository for an eventual deposit of a large or complicated dataset….A benefit of DMP tools for researchers is that they can generate both a PDF version of the DMP that is suitable for inclusion in a grant proposal and a machine-readable version suitable for sharing with an intended recipient data repository or the researcher’s home institution.

If DMPs are created and maintained entirely within a closed system, without mechanisms such as APIs or support for interoperable formats, it becomes more difficult to realize this vision. Rather than flowing across systems, key information may remain siloed, requiring researchers or institutions to recreate plans in other environments in order to support downstream use. This not only introduces additional effort, but also increases the risk that multiple versions of a plan diverge over time.

There are also implications for the broader infrastructure that has been developing around DMPs. Persistent identifiers such as DMP IDs, along with shared metadata standards developed through efforts like the Research Data Alliance, are intended to support discovery, tracking, and integration across the research lifecycle. If DMPs created in funder systems cannot easily be registered, exported, publicized, or linked to these services, an important layer of connectivity may be lost and some of the core principles of maDMPs are not realized.

Finally, the shift to a funder-hosted form changes how DMPs are created in practice. Data management planning is often a collaborative process, involving researchers, librarians, and institutional support staff. External tools and shared documents make it easier to iterate on plans, incorporate guidance, and ensure alignment with institutional policies and available resources. When plans are created directly within submission systems, that collaborative process can become more difficult, which may reduce opportunities for support and lead to plans that are harder to implement in practice.

NSF’s approach reflects important progress toward more structured and usable DMPs. At the same time, it highlights the importance of ensuring that structure is paired with interoperability, so that DMPs can function not only within funder systems, but across the broader ecosystem they are intended to support.

Changes at NIH

NIH has updated their DMSP template to reflect a different, but equally important, shift in approach. Unlike NSF’s webform, the NIH plan will still be created outside of a submission system for now, allowing researchers to use tools such as the DMP Tool and to collaborate more easily with institutional partners (though some discussions indicate NIH may consider a webform in the future). This supports many of the goals the community has been working toward, including integration with existing tools, the ability to register and reuse plans, and more flexible, collaborative workflows.

The NIH’s emphasis seems to be on creating a streamlined, structured format, which is understandable. By focusing on a small number of core questions, primarily centered on whether data will be shared, where it will be shared, and what outputs are expected, their new template reduces the burden on researchers at the proposal stage and aligns with broader efforts to simplify the DMP process and more easily track compliance with data sharing.

At the same time, this simplification introduces a different kind of tension.

Data management plans are most effective when they prompt researchers to think prospectively about how data will be managed throughout the lifecycle of a project. As stated by NIH regarding the 2023 policy:

Prospectively planning for how scientific data will be managed and ultimately shared is a crucial first step in optimizing the reach of data generated from NIH-funded research. Investigators and institutions are encouraged to consider these crucial elements early in research planning. 

A more minimal template may make it easier to complete a plan, but it may also reduce the extent to which researchers engage with these aspects of planning. When the primary interaction becomes confirming that data will be shared, there is a risk that important details are deferred until later in the project, when options may be more limited and challenges more difficult to address.  Key elements such as metadata, standards, preservation, and access will be less likely to be considered in advance, leaving researchers less positioned to produce data that is usable by others.

There is also a subtle shift in how researchers interact with institutional support. One of the benefits of more detailed DMSPs has been the opportunity for researchers to engage with data librarians and stewards, who bring expertise in policies, repositories, and best practices. A simplified form may reduce the need for that engagement, which lowers burden, but may also reduce access to guidance that helps ensure plans are both compliant and achievable.

NIH’s approach creates a challenge not about interoperability, but about maintaining the role of DMPs as meaningful planning tools. The move toward simplicity is an important step in reducing friction, but it also raises the question of how to preserve the depth of planning that enables effective data sharing in practice.

What we’d like to see

Taken together, these changes from NSF and NIH reflect progress and also highlight an important inflection point. As DMPs become more structured and more embedded in funder workflows, the next question is: how do we ensure they remain connected to the broader ecosystem they are intended to support?

Focus on Interoperability

One area where this alignment becomes especially important is interoperability.

Supporting mechanisms such as APIs, along with the ability to import and export DMPs in structured, machine-readable formats, allows each plan created to connect with institutional tools, repositories, and other parts of the research lifecycle. This would preserve the benefits of webform-based submission, including structured input, integration with proposal systems, and funder-side tracking, while also enabling the kinds of workflows envisioned through machine-actionable DMPs.

In practice, this could support multiple pathways for researchers. Some may choose to complete a plan directly within a funder system, while others may develop it in a tool such as DMP Tool or a similar service and submit it through interoperable formats. Institutions could build integrations that allow DMPs to be shared across systems, reducing duplication of effort and improving consistency between planning and implementation.

More broadly, enabling access to DMPs through APIs would allow the ecosystem to build on them. Institutions could connect plans to grant management systems, track compliance with data sharing commitments, and provide targeted support to researchers working with complex data. Connections to persistent identifiers and other research infrastructure would further strengthen the ability to discover, link, and reuse data over time.

Pre- and post-award versions of DMPs

A second area for consideration is how DMPs are used across different stages of the research lifecycle.

There is a strong case for distinguishing between planning at the proposal stage and planning after funding has been awarded. A lighter-weight, structured plan at the application stage can support review and reduce burden for both applicants and reviewers. At the same time, more detailed planning is often most valuable once a project is funded, when researchers have greater clarity about their data and stronger incentives to ensure their plans are actionable.

This staged approach is already used in other contexts such as Horizon Europe, where an initial statement of intent is followed by a more comprehensive plan developed after funding. Applying a similar model here could balance efficiency with effectiveness: keeping proposal requirements streamlined while ensuring that funded projects benefit from more thorough, collaborative planning.

Such an approach would also better align with institutional support structures. Libraries and data support teams could focus their efforts where they are most impactful, working closely with funded projects to develop plans that reflect available resources, appropriate repositories, and relevant standards. Providing a defined window after funding to complete this work would allow researchers the time and context needed to engage meaningfully with the process.

Taken together, these directions point toward a model where DMPs are both simpler and more connected: easy to create at the point of application, but also interoperable, extensible, and capable of supporting the full research lifecycle.

Conclusion

The recent updates from NSF and NIH mark an important moment in the evolution of data management planning. They reflect many of the directions the community has been working toward, including greater structure, clearer expectations around data sharing, and efforts to reduce burden at the point of application. At the same time, they highlight how much the details of implementation matter.

Data management plans should not be static compliance documents. Their value lies in supporting thoughtful, collaborative planning across the research lifecycle and in connecting that planning to the systems that enable data to be shared, discovered, and reused. When planning becomes more lightweight or more isolated, there is a risk that these connections weaken over time. The impact of that shift may not be immediately visible, but it can emerge later in the form of data that is harder to interpret, less consistently structured, and more difficult to integrate into broader workflows.

Because NSF and NIH play such a key role in the US and global research communities, their approaches are also likely to influence others. This creates both risk and opportunity. If new models emphasize simplicity without connectivity, fragmentation may increase. If they successfully balance structure, interoperability, and meaningful planning, they can help establish a stronger foundation for the next phase of research data infrastructure.

The path forward does not require choosing between reducing burden and supporting richer, more connected planning. The elements needed to do both are already visible: structured, machine-readable inputs; flexibility in how plans are created and shared; interoperability across systems; and a distinction between early-stage commitments and more detailed, post-award planning.

Bringing these elements together would allow DMPs to function as intended: not just as part of the application process, but as living components of the research lifecycle that support data sharing in practice. As these changes continue to evolve, there is an opportunity for funders, institutions, and the broader community to work together to ensure that DMPs remain both usable and meaningful.

Copyright © 2026 Becky Grady, Maria Praetzellis. Distributed under the terms of the Creative Commons Attribution 4.0 License.

Note: This post is a cross-post of an article written for Upstream blog to make sure DMP Tool followers are aware of these important changes.  Please refer to that site as the version of record; DOI: 10.54900/fbq63-61s08

Evolving Data Management Plans: Adapting to news from NSF and NIH

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Like many in the research data management community, we have been closely following updates from the National Science Foundation (NSF) and National Institutes of Health (NIH) about changes to their data management and sharing plans (DMSPs, also known as DMPs).  

For those not aware, both the NSF and NIH are moving away from free-form narrative document DMSPs towards more structured, standardized forms, which can then potentially be embedded directly into their proposal systems. NSF announced that their DMSPs will, starting on April 27th 2026, be completed as a form on Research.gov rather than uploaded as a separate document. NIH is also making a major change to their DMSP template starting May 25th 2026, also moving away from free-form text narrative to mostly Yes/No questions about data sharing, plus a list of expected outputs and their intended repositories and a space to explain any exceptions to data sharing. 

These changes reflect a broader shift on how funders approach data management planning. Rather than narrative documents, DMSPs are becoming structured inputs that can be more easily reviewed, compared, and in some cases tracked over the course of a project.

Community Impact

We are happy to see a move towards structured, machine-actionable questions over free text and reducing burden on researchers applying for grants. However, these changes have the potential to disrupt the way data management and planning is done throughout the research lifecycle. 

  • NSF’s new form may include the standard sections recommended in a DMSP, but the fact that it will only be accessible on Research.gov may make it harder for collaboration between researchers and data librarians to take place.
  • NIH’s form will still be uploaded as a document as far as we are aware, but limiting to mostly Yes/No questions may take away much of the planning that needs to happen before data is collected.

We understand both these updates are new and will undergo evaluation and feedback periods – we look forward to working with NSF and NIH to see how these new forms perform and if there are areas of improvement for the future.

The two main areas the DMP Tool team will be watching is cross-institutional communication and interoperability.  In our experience, researchers and grants teams value personalized university guidance and the ability to collaborate with local data librarians and research IT teams to get feedback on their DMSPs. These new changes will require a shift in the way the community works but may also require further refinement from the agencies.

We also hope to see more investment in interoperability in the future. Locking the DMSP information into a closed system without an API risks creating a new silo of important research information that will make it harder for other researchers to find and track data outputs from funded research. We hope that the agencies look for new ways for researchers to engage with their platforms that enable these types of interoperability and connectivity.  

Adjusting to new workflows 

While the DMP tool team continues to understand the implications of these new workflows, we are also committed to meeting the needs of our communities. Many have reached out to ask specific questions around how we will adjust the tool to work with NSF and NIH’s new approaches.

  • For NSF, as soon as the final version of the Research.gov form is available, we will implement a copy of it into the tool.  People who complete their DMSP for NSF in the DMP Tool will still be able to use its collaboration and guidance for help filling it out, though at the end they will likely need to copy/paste the information into the Research.gov form rather than export as a PDF.  Regardless, the key features that support collaboration and communication will still be available for institutions to use in NSF proposal consultations.
  • For NIH, we have already started work implementing the new form based on the preview provided.  The questions are already entered, and we’re working with members of our DMP Tool Editorial Board to add appropriate guidance, recommendations, and relevant policies to the elements.  As soon as the NIH form is finalized and we have that entered, we will publish it on the DMP Tool so researchers can start to use it for upcoming submissions, and organizations can start adding customizations and extra guidance if they wish.

Stay updated on the latest!  We will message our status and next steps on this blog, our LinkedIn account, and direct emails to all member organization contacts.

Implications on our ongoing platform development

As we described above, in the immediate future, we will continue to support creating DMSPs in the tool for NSF, NIH, and many other US and international funders however they structure their templates.  In parallel, our rebuild work continues on.  We will be taking these new announcements as opportunity to reflect and adjust our priorities and timelines.  We think that many of the new functionalities coming in the new tool fit well with this evolving landscape.  For example, the new tool will support creating a Project that can house multiple related plans and allow uploads of plans created elsewhere.  This could allow, for example, people to upload a copy of the plan they submitted to NSF to the tool, and house related plans within one research project. This allows for support of Data Security Plans, Software Management Plans, and other documents that many universities and field stations now require.  

In the long term, we’re committed to evolving the DMP Tool to meet the needs of the community, even as those needs change.  We will continue to have open conversations about how to properly prioritize and adapt our current efforts for the changes we see coming on the horizon.  

Our core commitment is to serve and promote best practices in data management planning, and that goes beyond the document itself.  We know that our community’s strengths are in the customized guidance, collaboration, and resources that we all bring together from researchers, funders, and universities into one place, and we think that is more valuable than ever.  We will keep you all posted as we address the evolving landscape together! 

Progress Update: Matching Related Works to Data Management Plans

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TL;DR

  • We’re making progress on our plan to match DMPs to associated research outputs.
  • We’ve brought in partners from COKI who have applied machine-learning tools to match based on the content of a DMP, not just structured metadata.
  • We’re getting feedback from our maDMSP pilot project to learn from our first pass.
  • In our new rebuilt tool, we plan to have an automated system to show researchers potential connected research outputs to add to the DMP record.

Have you ever looked at an older Data Management Plan (DMP) and wondered where you could find resulting datasets it mentioned would be shared? Even if you don’t sit around reading DMPs for fun like we do, you can imagine how useful it would be to have a way to track and find published research outputs from from a grant proposal or research protocol.

To make this kind of discovery easier, we aim to make DMPs more than just static documents used only in grant submissions.  By using the rich information already available in a DMP, we can create dynamic connections between the planned research outputs — such as datasets, software, preprints, and traditional papers — and their eventual appearance in repositories, citation indexes, or other platforms.

Rather than linking each output manually to their DMP, we’re using the new structure of our machine actionable data management and sharing plans (maDMSPs) from our rebuild to help automate these connections as much as possible.  By scanning relevant repositories and matching the metadata to information in published DMPs, we can find potential connections that researchers or librarians just have to confirm or reject, without adding the information themselves.  This keeps them in control and helps ensure connections are accurate, while reducing the burden of how much information they have to enter. 

Image from an early version of this in the DMP Tool showing a list of citations for potential marches with buttons to Review and a status column showing them as Approved or Pending
Image from an early version of this in the DMP Tool showing a list of citations for potential marches with buttons to Review and a status column showing them as Approved or Pending

This helps support the FAIR principles, particularly making the data outputs more findable, and helps transform DMPs into useful, living documents that provide a map to a research project’s outputs throughout the research lifecycle.

Funders, librarians, grant administrators, research offices, and other researchers will all benefit from a tracking system like this being available. And thanks to a grant from the Chan Zuckerberg Initiative (CZI), we were able to start developing and improving the technology to start searching across the scholarly ecosystem  and matching to DMPs.  

The Matching Process

AI generated image from Google Gemini of a monkey holding two pieces of paper next to each other

We started with DataCite, matching based on titles, contributors (names and ORCIDs), affiliations, and funders (names, RORs and Crossref funder ids).  Turns out, when you have a lot of prolific researchers, they can have many different projects going on in the same topic area, so that’s not always enough information to to find the dataset from this particular project. We don’t want to just find any datasets or papers that any monkey-researcher has published about monkeys, we want to find the ones that are from this particular grant about monkey behavior.

To help expand the datasets and other outputs we could find, we partnered with the Curtin Open Knowledge Initiative (COKI) to ingest information from OpenAlex and Crossref, and we’re working on including additional sources like the Data Citation Corpus from Make Data Count. COKI’s developers are also applying machine-learning, using embeddings generated by large language models and vector similarity search to compare the text from the title and abstract of a DMP to those descriptive fields within the datasets, rather than just the metadata for authors and funders.  That will help us match if, say, the DMP mentions “monkeys” but the dataset uses the work “simiiformes.”

To confirm the matches, we used pilot maDMSPs from institutions that are part of our projects with our partners at the Association of Research Libraries, funded by the Institute of Museum and Library Sciences and the National Science Foundation.  This process recently yielded a list of 1,525 potential matches to registered DMPs from the pilot institutions. We asked members of the pilot cohort to evaluate the accuracy of these matches, providing us with a set of training data we can use to test and refine our models.  For now we provided the potential matches in a Google Sheet, but in the future with our rebuild we plan to integrate this flow directly in the tool.

Screenshot from one university’s Google Sheet for matching DMP-IDs to research output DOIs, showing some marked as Yes, No, and Unsure for if its a match

Initial Findings

It will take some time for the partners to finish judging all the matches, but so far about half of the potential related works were confirmed as related to the DMP. This means we’ve got a good start and can use the ones that didn’t match to train our model better.  We’ll use those false positives, as well as false negatives gathered from partners, to refine our matching and get better over time.  Since we’re asking the researchers to approve the matches, we’re not too worried about false matches, but we do want to find as many as possible.

This process is still early, but here are some of our initial learnings:

  • Data normalization is an important and often challenging step within the matching process. In order to match DMPs to different datasets, we need to make sure that each field is represented consistently. Even a structured identifier like a DOI can be represented with many different formats across and within the sources we’re searching.  For example, sometimes they might include the full URL, sometimes just the identifier, and some are cut off and therefore have an incorrect ID that needs to be corrected in order to resolve. That’s just one small example, but there are many more that make the cleanup difficult, including normalization of affiliation, funder, grant and researcher identifiers across and within the datasets.  Without the ability to properly parse the information, even a seemingly comprehensive source of data may not be useful for finding matches.
  • Articles are still much easier to find and match than datasets. This is not surprising, given the more robust metadata associated with DOIs for articles that make them easier to find. Data deposited into repositories often does not have the same level of metadata available to match, if a DOI and associated metadata are even available at all.  We’re hoping we can use those articles, which may mention datasets, to find more matches in our next pass.
  • There is not likely to be a magic solution that gets us to completely automate the process of matching a research output to a DMP without changes in our scholarly infrastructure.  Researchers conduct a lot of research in the same topic area, so it’s difficult to know for sure if a paper or dataset came from a DMP, unless they specifically include these references.  There are ways to improve this, such as using DOIs and their metadata to create bi-directional links between funding and their outputs (as opposed to one-directional use of grant identifiers), including in data repositories. DataCite and Crossref are both actively working to build a community around these practices, but many challenges still remain. Because of this, we plan to have the researcher confirm matches before they are added to a record, rather than attempt to add them automatically.

Next Steps

We’re continuing to spend most of our development work on our site rebuild, which is why we’re grateful for our funding from CZI and our partnership with COKI to improve our matching.  Our next step is including information from the Make Data Count Data Citation Corpus, as well as following up on the initial matches once pilot partners finish their determinations.

We hope to have this Related Works flow added to our rebuilt dmptool.org website in the future.  The mockup is below (where we show researchers that we have found potential related works on a DMP, and would then ask them to confirm if it’s related so it can be added to the metadata for the DMP-ID and become part of the scholarly record).  We’ll want to balance confidence and breadth, finding an appropriate sensitivity so that we don’t miss potential matches but also don’t spam people with too many unrelated works.

Mockup of a project block in the new DMP Tool which a red pip and test saying "Related works found"
Mockup of a project block in the new DMP Tool which a red pip and test saying “Related works found”

If you have feedback on how you would want this process to work, feel free to reach out! 

DMPTool Funder Templates Updated

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We are excited to announce the completion of the first project of our newly established DMPTool Editorial board. As of September 2020, the Board has audited 36 funder templates within the DMPTool and updated the templates when necessary to reflect current proposal requirements and ensure all funder related content is up to date.

Template updates mean that admins will now need to transfer any customizations you may have created for these templates (instructions here). 

None of the updates made to templates affect the core requirements of the DMPs and updates largely involve correcting links, resources, and other data management planning requirements. A detailed summary of the changes for each template is below and you can view all templates on the DMPTool Funder Requirements page.

The critical work keeping the DMPTool in line with current funder requirements would not have been possible without the effort, expertise, and excellence of our volunteer Editorial Board and we at the DMPTool are endlessly grateful for their commitment to supporting the tool. Please join us in recognizing their contributions and thanking them for their work supporting our shared infrastructure advancing research data management.

  • Heather L Barnes, PhD, Digital Curation Librarian, Wake Forest University
  • Raj Kumar Bhardwaj, PhD, Librarian, St Stephen’s College, University of Delhi, India
  • Renata G. Curty, PhD, Social Sciences Data Curator, University of California, Santa Barbara
  • Jennifer Doty, Research Data Librarian, Emory University
  • Nina Exner, Research Data Librarian, Virginia Commonwealth University
  • Geoff Hamm, PhD, Scientific Publications Coordinator, Lawrence Berkeley National Laboratory
  • Janice Hermer, Health Sciences Liaison Librarian, Arizona State University
  • Megan O’Donnell, Data Services Librarian, Iowa State University
  • Reid Otsuji, Data Curation Specialist Librarian, University of California, San Diego
  • Nick Ruhs, PhD, STEM Data & Research Librarian, Florida State University
  • Anna Sackmann, Science Data & Engineering Librarian, University of California, Berkeley
  • Bridget Thrasher, PhD, Data Stewardship Coordinator, Associate Scientist III, National Center for Atmospheric Research
  • Douglas L. Varner, Assistant Dean for Information Management / Chief Biomedical Informationist, Georgetown University Medical Center

Together with the Editorial Board, we’ll be working on adding new templates to the tool over the coming months. If you have suggestions for funders to be added please let us know by emailing maria.praetzellis@ucop.edu.

Summary of DMPTool Template Updates

All NSF templates were updated to include links to the updated 2020 Proposal & Award Policies and Procedures Guide (2020 PAPPG). Additional updates are summarized below:

NSF-AGS: Atmospheric and Geospace Sciences

  • Updated link to new 2020 PAPPG
  • Edited question text 

BCO-DMO NSF OCE: Biological and Chemical Oceanography

  • Updated link to new 2020 PAPPG
  • Updated questions & links

NSF-CISE: Computer and Information Science and Engineering 

  • Updated link to 2020 PAPPG. 
  • Added “Additional Guidance on Selecting or Evaluating a Repository” under “Plans for Archiving and Preservation”

NSF-DMR: Materials Research

Department of Energy (DOE): Generic

  • Funder links added for Office of Science, and Energy Efficiency/Renewable Energy instructions

Department of Energy (DOE): Office of Science

  • Funder link added
  • Description updated with additional guidance

Institute of Museum and Library Services (IMLS) 

  • Data Management Plans for IMLS are collected via the IMLS Digital Product Form. Originally the form was broken out into three templates within the DMPTool, however we have streamlined the process and combined them into one, comprehensive, template to more accurately reflect current requirements.

National Aeronautics and Space Administration (NASA)

  • Updated text to match the wording of NASA’s description of an ideal DMP 

USDA

  • Reformatted section 1 to make reading easier.
  • Deleted the compliance/reporting section. This is no longer part of the DMP template as it is related to annual reporting. This information was moved to an Overview phase description.
  • Made the guidance links consistent.

Alfred P. Sloan Foundation

National Oceanic and Atmospheric Administration (NOAA)

  • Updated links

U.S. Geological Survey (USGS)

  • Updated questions and links
  • We are continuing to work with USGS and may have additional updates to this template in the near future. 

Minor NSF template updates + other miscellanea

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In the waning weeks of summer, we accomplished a wide range of DMPTool things. A bulleted summary of mostly template-related updates is below. Admins should take note that the minor National Science Foundation (NSF) template updates resulted in new versions of the 4 templates in question. This means that admins will need to transfer any customizations you may have created for these templates (instructions here). All users will also see a dismissable notification message when you log into the tool (screenshot below). Read on for more details.

TL;DR

  • Some minor NSF template updates: AGS, EAR, CISE, SBE
  • DCC template now available in Brazilian Portuguese
  • DMPTool templates added to protocols.io
  • Final promo materials shipped and order form closed
  • First successful eduGAIN configuration: welcome to Australian National University!

transfer template customization

notification of template changes

Minor NSF template updates
While working on our machine-actionable DMPs grant, we noticed that a handful of NSF entities had issued updates to DMP requirements since our comprehensive template audit in Feb 2018. The four divisions/directorates listed below posted new documents in Apr 2018 with very minor changes from the previous versions. None of the changes affect the core requirements; most involve updated links and resources. A detailed summary of the changes for each template follows and you can view all templates on the DMPTool Funder Requirements page:

NSF-AGS: Atmospheric and Geospace Sciences

NSF-EAR: Earth Sciences

  • updated PDF document with new links
  • updated appendix with list of recommended repositories and other resources

NSF-CISE: Computer & Information Science & Engineering

  • updated links and reformatting on webpage
  • merged redundant questions about data storage reducing total questions from 7 to 6

NSF-SBE: Social, Behavioral & Economic Sciences

  • new PDF document with no substantive changes; mostly reformatting and removed references to specific repositories

DCC template available in Brazilian Portuguese

A big thanks to Vitor Silvério Rodrigues from São Paulo State University (UNESP) for translating the DCC template (defined by our Digital Curation Centre partners) into Brazilian Portuguese! This is the default, best practices template provided when users check the box to indicate that they aren’t applying to a specific funder. Anyone can now download the translated template from the Funder Requirements page. The DMPTool is not localized to automatically serve up the translated template for users who set their language to Brazilian Portuguese, however. In order to create a new plan with the translated version, users should make the following selections in the create plan wizard (regardless of language setting):

  1. Enter a project title
  2. Select São Paulo State University (UNESP) as your organization
  3. Select Digital Curation Centre (DCC) as the “funder”
  4. Click button to create plan

Brazilian Portuguese create plan options

new plan with translated DCC template

DMPTool templates added to protocols.io

Protocols.io is an open repository popular among computational and bioinformatics researchers, yet open to all domains, where all scientific protocols (private or public) can be annotated and discussed on step- or protocol-level. Users can also fork (clone) public protocols and publish modified versions as well as connect protocols to published articles and other research outputs, all in the pursuit of increasing transparency and reproducibility.

Scientific protocols are among the many research outputs that we aim to inventory with machine-actionable DMPs. We often promote the notion that DMPs themselves are essentially protocols (i.e., a description of digital research methods), and should be maintained as such over the course of a project. During conversations with the protocols.io team about our intersecting activities, they suggested that we experiment with enabling researchers to create and maintain DMPs on their platform. So we created a Data Management Plans group with two basic DMP templates for users who might prefer this dynamic platform for documenting their digital protocols to an online wizard that produces a static text file. Go check it out and spread the word!

Final promo materials shipped and order form closed

Everyone who placed orders for DMPTool marketing materials (postcards and stickers) should have received them by now, hopefully in time for workshops and other events to kick off the new academic year. The order form for free materials associated with the launch is now closed. Just a reminder that we provide various promo materials (all CC0) on the website so anyone can produce their own swag and spread the DMPTool gospel.

First successful eduGAIN/SSO configuration!

One of the most popular features of the DMPTool is the ability for participating institutions to configure Shibboleth single signon, thereby enabling their users to sign in easily with institutional credentials. Until recently, we only provided this functionality for members of the US-based InCommon federation. There is now an interfederation service called eduGAIN that connects identity federations around the world. We are pleasantly surprised (since Shib can be a tricky, black-box affair) that we were able to configure our first eduGAIN institution: the Australian National University. We hope for (but cannot promise) similar success stories for other identity federations that participate in eduGAIN. The Australian Access Federation is documenting the process and we’re delighted to welcome ANU to the DMPTool community!

First annual funder template pizza party!

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template editors

As we approach our target release date of Feb 2018 for the DMP Roadmap platform, the DMPTool team has embarked on a major housekeeping effort. A top-to-bottom content review is underway, and last week we began an audit of the funder templates and guidance. Ten participants gathered for an all-day, pizza-fueled event that amounted to a huge template success (but an epic pizza fail, see evidence below). We were so productive and gratified by the opportunity to analyze multiple DMP policies in a group setting that we decided to make it an annual event. Read on for more DMPTool funder template news + migration plans, followed by brief updates on the DMP Roadmap project and machine-actionable DMPs.

DMPTool funder templates

The DMPTool is a hugely popular community resource in part because it serves as a central clearinghouse of information about DMP requirements and guidance for researchers applying for grants from U.S. funding agencies. Migrating the DMPTool data to the new platform provides an opportunity to update and normalize things to maintain this value. [Side note: we’re also adding a “Last updated” field to the DMP Requirements table as an enhancement in the new platform per your feedback.]

At present the tool contains 32 templates for 16 different federal and private funders. This top 10 templates list demonstrates that our users are especially keen on getting support with NSF and NIH grant proposals, although the NEH is #7, and DOE and others aren’t far behind. Some global usage statistics to put these numbers in context: 26.8k users have created 20k plans; and we have 216 participating institutions (mostly U.S. colleges and universities).

funder-template-table

Our goals for the pizza party included: 1) ensuring that template language comes directly from the most recent versions of funder policy documents; and 2) applying themes (more on themes here). Staying up to date with DMP requirements remains a crowdsourced effort spearheaded by data librarians using the Twitter hashtag #OSTPResp and a Google spreadsheet. In the past year, two additional resources entered the scene: a list of public access plans from U.S. federal agencies at CENDI.gov and this lovely SPARC tool. Using these reference materials and some additional internet research, we updated 7 links to policy documents in the current DMPTool platform (NIH-GDS, NEH-ODH, NSF-CHE, NOAA, USDA-NIFA, Joint Fire Science Program, Sloan) and made some revisions to templates in the new platform (mostly formatting). We also identified some templates that require deeper investigation and/or consultation with agency contacts to verify the best way to present DMP requirements; between now and the release date we’ll continue to work on these templates. In addition, Jackie Wilson is contracting with us to finalize the clean-up of templates and guidance (checking links and guidance text provided by funders).

#pizzafail

#pizzafail

By January we aim to have a beta DMPTool-branded version of the new platform ready for training and testing purposes. Stay tuned for a rollout plan in the new year that includes webinars for institutional administrators, with an orientation to templates and themes. Also, please note that we will be disabling template editing functionality on 18 Dec in the current version of DMPTool to maintain the integrity of template data in the new platform. For admin users who wish to make changes to templates and guidance after that date, you can contact the helpdesk, but it would be great if you can keep changes to a minimum. All other functionality in the current DMPTool will remain the same up to the final migration date (adding new users, institutions, creating and editing plans, etc.)

A million thanks to the 2017 template fixing team: Amy Neeser, Joan Starr, Alana Miller, Jackie Wilson, Marisa Strong, Daniella Lowenberg, Perry Willett, John Chodacki, and Stephen Abrams.

DMP Roadmap update

The co-development team is busy building and refining the final MVP features. The usage dashboard is the last new feature left to add. In the meantime, parallel data migration efforts are underway at DCC to move from the existing 28 DMPonline themes to the new set of 14. By January both service teams will be working on new user guides, updating other content, testing and branding. If all continues to go smoothly, we’ll be on track for a DMP Roadmap demo at IDCC in Barcelona (19–22 Feb) and an official code release. Stay tuned!

Machine-actionable DMPs

On the machine-actionable DMP front, there are two items to report:

  1. We’ll be emailing the various DMP lists shortly to encourage everyone to participate in working meetings for the RDA WGs (DMP Common Standards & Exposing DMPs) at the next plenary. For now mark your calendars for 21–23 Mar and join us in Berlin!
  2. Following on a productive session at FORCE2017, we’re finishing a draft of the 10 Simple Rules for Machine-Actionable DMPs that we will circulate soon soon.

As always, we encourage you to contact us to get involved!

New template: DOD

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As far as we can discern, DMPs are not yet a required component of Department of Defense (DOD) grant applications. But in an effort to address numerous user requests for a DOD template, we went ahead and created one based on the draft DOD Public Access Plan issued in Feb 2015, which states:

“This proposed plan is a draft at this point and has not been adopted as part of the DoD regulatory system or as a definitive course of action.”

The (draft) DOD requirements for DMPs are similar to those issued by NSF, NASA, and others so DMPTool users should note the resemblance among these templates. Another similarity is that the DOD plan focuses heavily on access to data underlying published articles. The plan mentions an implementation date at the end of FY 2016 — we will monitor the situation and update the template accordingly. This also presents an opportunity to monitor the new CENDI.gov inventory of public access plans.

Meanwhile, the DOD encourages pilot projects with voluntary submission of articles and data. The Defense Technical Information Center (DTIC) will be responsible for key elements of policy implementation and compliance monitoring (see their prototype DOD Public Access Search for articles that mention DOD funding).

Official news remains pending, but for now we’re happy to provide a draft DOD template for conscientious researchers. If anyone has experience with DOD programs asking for DMPs or related developments, please let us know!

New template: NIJ (DOJ)

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The National Institute of Justice (NIJ) is the research, development, and evaluation agency of the U.S. Department of Justice (DOJ). We created a template to assist NIJ funding applicants with preparing a Data Archiving Plan. This is essentially a 1–2 page DMP submitted with grant proposals: 1) to demonstrate your recognition that data sets resulting from your research must be submitted as grant products for archiving and have budgeted accordingly, and 2) to describe how the data will be prepared and documented to allow reproduction of the project’s findings as well as future research that can extend the scientific value of the original project. The policy also notes that “some amount of grant award funds is typically withheld for submission of research data along with the final report and other products/deliverables.”

In most cases, the NIJ requires grantees to deposit their data in the National Archive of Criminal Justice Data (NACJD), which is hosted by ICPSR. The template contains links to guidelines, best practices, FAQs, and other helpful information provided by the NACJD and ICPSR, including specific instructions pertaining to common types of social science data and software.

While the NIJ is not subject to the OSTP Memo, the requirement to submit a Data Archiving Plan has been in place since 2014. We finally added a template in response to a user request.