research project data management plan

Using the Library

• Teaching & publication support
• Ask a Librarian

Research Data Management

• Research Data Management at UVA
• About Research Data Management
• Data Privacy and Human Subjects
• File Management Best Practices
• Metadata and Documentation
• Data Storage, Backup and Security
• Data Citations
• Licensing Data & Code
• Data Rights and Policies
• Open Science and Open Data

Your Research Data Management Team

Research data management librarian.

Senior Research Data Management Librarian

Introduction

A Data Management & Sharing Plan (DMSP), also referred to as a Data Management Plan (DMP), is a formal document that outlines what you will do with your data during the active phase of the research project and after the project ends.  This document may also be called a Data Management Plan (DMP) depending on the funding agency.  The National Institutes of Health (NIH) refer to it as a DMSP, whereas the National Science Foundation refers to it as a DMP.  We use the term DMSP because it suggests both data management and sharing.

DMSPs are typically two-page documents. Most US federal funders and many private foundations require DMSPs to be submitted with their funding applications. Whether you are participating in a funded research project or not, writing a DMSP will help you think about the practices, people, and resources needed to manage your data.

Although funder DMSP requirements may vary, in most cases you will be asked to describe your data, other research products, and relevant software, address metadata, standards, documentation, storage, preservation, and any ethical, legal or other restrictions, and define roles and responsibilities. If applicable, you will need to discuss compliance with federal regulations protecting human subjects and privacy.

The DMP Tool can help with writing your data management plan. It provides customized DMP forms with guidance and examples based on your research funder and research institution selections. You may use this tool to request feedback from collaborators and the UVA Library Data Management Team. If you want to request a consultation or ask a question, email us at [email protected] .

Below are resources to help you write your plan.

• DMP Tool Create Data Management Plans that meet requirements and promote your research.
• Using the DMPTool DMPTool Tutorial
• UVA HSL NIH DMSP Guidance If you are applying to the National Institutes of Health (NIH) for funding, the UVA Health Sciences Library (HSL) provides guidance on six elements required for a NIH Data Management and Sharing Plan (DMSP).
• Checklist for a Data Management Plan. v.4.0 This Digital Curation Centre checklist provides guidance and questions to consider for Storage and Backup and Ethics and Legal Compliance in addition to other aspects of research data management. more... less... DCC. (2013). Checklist for a Data Management Plan. v.4.0. Edinburgh: Digital Curation Centre
• Research Data Management and Sharing This course provides learners with an introduction to research data management and sharing. Topics include practices for the planning, organization, documentation, storage and security, preserving and sharing of data. This course is located on Coursera.org and is offered by The University of North Carolina at Chapel Hill and The University of Edinburgh. Course materials are available upon enrollment.

Data, Standards, and Documentation

Data and other products of research

Describe the data and other products of research to be produced from the research project. Include details about the source (e.g., sensor readings, survey results), forms (e.g., numeric, text, images, audio, video), file types (e.g., csv, txt, png, flac, mp4), and data volume. Also, indicate whether the data will change or grow in size after the research project is finished and data is submitted to a repository and if any specific software is required to analyze the data.

If you are using existing data for secondary analysis, describe the content, source and requirements for obtaining and using that data. If the existing data will be combined with data to be generated from your research project, explain the relationship between the data sets.

• DMP Tool Guidance: Types of Data The DMPTool Help Types of Data section addresses data sources, forms, stability, volume, and file formats.

Funders and data repositories may require specific metadata standards or shared vocabularies to make data easier to find. Relevant data standards may also refer to areas beyond metadata or shared vocabularies, such as file formats for data exchange, guidance for data collection, or requirements for data protection. To find standards appropriate for your discipline, see Choosing and Using Metadata Standards .

Documentation

Describe what documentation will be included with your data to make it understandable. Documentation may be given at three levels. Project level documentation includes the purpose of the study, research questions, hypothesis, methodology, instruments, and measurements used. File and database level documentation describes the datasets and supporting documentation. Variable level documentation defines the variables and values, particularly coded values.

For more details on metadata and documentation, see Metadata and Documentation .

Preservation

Adapted Timbuktu Manuscript image by Mark Fischer with a Creative Commons Attribution-ShareAlike 2.0 license.

You will need to find a place to store your data when it is still being collected, processed, and analyzed at the research institution. Be prepared to discuss how much storage will be needed, how often data will be backed up, how data will be recovered, access control for research team members, secure data transmission from the field, and handling of information with varying degrees of sensitivity. For more details about storage, see Data Storage, Backup, and Security .

Most funders require submission of data to a repository when your research project is finished if there are no restrictions that prohibit submission. For data that will be submitted, indicate which repository will receive your research data. If the funder has its own repository or a preferred repository, use that repository. If not, your discipline may have a commonly used repository. If so, use that repository. If not, use LibraData , the UVA institutional data repository.

When choosing a repository, consider the National Science and Technology Council guidance on Desirable Characteristics of Data Repositories for Federally Funded Research. Also discuss plans for long-term retention of data at your research institution. For more details, see Data Sharing and Preservation .

Find out if your funder allows costs associated data submission in your grant budget.  Some data repositories charge fees to cover curation and preservation costs. Even if you deposit your data to a repository that does not charge a fee, consider resources you may need for preparing the data for submission, submitting the data, and responding to questions and requests from the repository.

• Desirable Characteristics of Repositories for Federally Funded Research This guidance is provided by the National Science and Technology Council. It includes three main sections about organizational infrastructure, digital object management, and technology. There is a Table listing Additional Considerations for Repositories Storing Human Data.
• UVA Dataverse (LibraData) The LibraData homepage.
• Federally Funded Data Repository Characteristics Discusses alignment of LibraData with Federally Funded Data Repository Characteristics

Access and Reuse Restrictions

Data access and reuse may be restricted because of privacy protection requirements, data rights, or other reasons. Restrictions on data access or reuse should be addressed in your DMSP. Describe in your DMSP what you are going to share as well as what you are not sharing, and/or what you are sharing but only through proper de-identification and/or data use agreements.

If you have created new data from human subjects, you may need to redact your data to avoid re-identifying individuals. LibraData, the UVA data repository, requires that depositors remove any sensitive or confidential information from their data submissions. You may also determine that access to some or all your newly created data should be restricted because of data sensitivity or intellectual property claims. When submitting your data to a repository, you might have the option to recommend public access, which allows data sets to be downloaded by anyone, or restricted access, which requires researchers to request permission to access the data. You or the repository might also require other researchers might to sign a data use agreement.

If you are using existing data from a repository, a data use agreement may require that the data be shared only with certain members of your research team because of privacy requirements and then destroyed after a specified time period. If you are using existing data from a vendor through a UVA library subscription, the license probably will not allow reuse of the data for those not affiliated UVA.

Data redaction, data use agreements and vendor licenses are common examples of data access and reuse limitations. However, in some cases there may be other factors involved that restrict data access, such as informed consent language or federal, Tribal, or state laws, regulations, or policies. If you want a consultation or have questions, please contact email us at [email protected] .

For information about US human subjects and privacy laws and UVA Institutional Review Boards (IRBs) for compliance with federally mandated research guidelines, see Data Privacy and Human Subjects .

For information about intellectual property related to data and UVA ownership policies, see Data Rights and Policies .

• UVA IRB-SBS/Researcher Guide/Data The IRB-SBS Researcher Guide Data section covers topics, such as Protecting Privacy, Secondary Use of Existing Data, and Record Keeping – Retention of Research Records and Destruction of Data.
• What can be deposited in LibraData? See this FAQ for instructions to remove any confidential or sensitive information from data before submission.

Roles and Responsibilities

Indicate who is responsible responsible for overseeing data management and sharing activities and updating the DMSP. Include details about these activities (e.g., data collection, documentation, quality control, analysis, archiving and sharing) and identify who will be performing them. It may be useful to refer to this section of the DMSP when determining data and documentation access for team members.

• << Previous: About Research Data Management
• Next: Data Privacy and Human Subjects >>
• Last Updated: Aug 6, 2024 4:13 PM
• URL: https://guides.lib.virginia.edu/RDM
• 434-924-3021
• [email protected]
• UVA Shannon Library P.O. Box 400113 160 McCormick Road Charlottesville, VA 22904

About the Library

• Staff Directory
• Fellowships
• Library Use Policies
• Off-Grounds Access
• ITS Computing Accounts
• Accessibility Services
• Emergency Information
• UVA Privacy Policy
• Tracking Opt-out

Other Sites

• Cavalier Advantage
• Library Staff Site

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• PLoS Comput Biol
• v.11(10); 2015 Oct

Ten Simple Rules for Creating a Good Data Management Plan

William k. michener.

College of University Libraries & Learning Sciences, University of New Mexico, Albuquerque, New Mexico, United States of America

Introduction

Research papers and data products are key outcomes of the science enterprise. Governmental, nongovernmental, and private foundation sponsors of research are increasingly recognizing the value of research data. As a result, most funders now require that sufficiently detailed data management plans be submitted as part of a research proposal. A data management plan (DMP) is a document that describes how you will treat your data during a project and what happens with the data after the project ends. Such plans typically cover all or portions of the data life cycle—from data discovery, collection, and organization (e.g., spreadsheets, databases), through quality assurance/quality control, documentation (e.g., data types, laboratory methods) and use of the data, to data preservation and sharing with others (e.g., data policies and dissemination approaches). Fig 1 illustrates the relationship between hypothetical research and data life cycles and highlights the links to the rules presented in this paper. The DMP undergoes peer review and is used in part to evaluate a project’s merit. Plans also document the data management activities associated with funded projects and may be revisited during performance reviews.

As part of the research life cycle (A), many researchers (1) test ideas and hypotheses by (2) acquiring data that are (3) incorporated into various analyses and visualizations, leading to interpretations that are then (4) published in the literature and disseminated via other mechanisms (e.g., conference presentations, blogs, tweets), and that often lead back to (1) new ideas and hypotheses. During the data life cycle (B), researchers typically (1) develop a plan for how data will be managed during and after the project; (2) discover and acquire existing data and (3) collect and organize new data; (4) assure the quality of the data; (5) describe the data (i.e., ascribe metadata); (6) use the data in analyses, models, visualizations, etc.; and (7) preserve and (8) share the data with others (e.g., researchers, students, decision makers), possibly leading to new ideas and hypotheses.

Earlier articles in the Ten Simple Rules series of PLOS Computational Biology provided guidance on getting grants [ 1 ], writing research papers [ 2 ], presenting research findings [ 3 ], and caring for scientific data [ 4 ]. Here, I present ten simple rules that can help guide the process of creating an effective plan for managing research data—the basis for the project’s findings, research papers, and data products. I focus on the principles and practices that will result in a DMP that can be easily understood by others and put to use by your research team. Moreover, following the ten simple rules will help ensure that your data are safe and sharable and that your project maximizes the funder’s return on investment.

Rule 1: Determine the Research Sponsor Requirements

Research communities typically develop their own standard methods and approaches for managing and disseminating data. Likewise, research sponsors often have very specific DMP expectations. For instance, the Wellcome Trust, the Gordon and Betty Moore Foundation (GBMF), the United States National Institutes of Health (NIH), and the US National Science Foundation (NSF) all fund computational biology research but differ markedly in their DMP requirements. The GBMF, for instance, requires that potential grantees develop a comprehensive DMP in conjunction with their program officer that answers dozens of specific questions. In contrast, NIH requirements are much less detailed and primarily ask that potential grantees explain how data will be shared or provide reasons as to why the data cannot be shared. Furthermore, a single research sponsor (such as the NSF) may have different requirements that are established for individual divisions and programs within the organization. Note that plan requirements may not be labeled as such; for example, the National Institutes of Health guidelines focus largely on data sharing and are found in a document entitled “NIH Data Sharing Policy and Implementation Guidance” ( http://grants.nih.gov/grants/policy/data_sharing/data_sharing_guidance.htm ).

Significant time and effort can be saved by first understanding the requirements set forth by the organization to which you are submitting a proposal. Research sponsors normally provide DMP requirements in either the public request for proposals (RFP) or in an online grant proposal guide. The DMPTool ( https://dmptool.org/ ) and DMPonline ( https://dmponline.dcc.ac.uk/ ) websites are also extremely valuable resources that provide updated funding agency plan requirements (for the US and United Kingdom, respectively) in the form of templates that are usually accompanied with annotated advice for filling in the template. The DMPTool website also includes numerous example plans that have been published by DMPTool users. Such examples provide an indication of the depth and breadth of detail that are normally included in a plan and often lead to new ideas that can be incorporated in your plan.

Regardless of whether you have previously submitted proposals to a particular funding program, it is always important to check the latest RFP, as well as the research sponsor’s website, to verify whether requirements have recently changed and how. Furthermore, don’t hesitate to contact the responsible program officer(s) that are listed in a specific solicitation to discuss sponsor requirements or to address specific questions that arise as you are creating a DMP for your proposed project. Keep in mind that the principle objective should be to create a plan that will be useful for your project. Thus, good data management plans can and often do contain more information than is minimally required by the research sponsor. Note, though, that some sponsors constrain the length of DMPs (e.g., two-page limit); in such cases, a synopsis of your more comprehensive plan can be provided, and it may be permissible to include an appendix, supplementary file, or link.

Rule 2: Identify the Data to Be Collected

Every component of the DMP depends upon knowing how much and what types of data will be collected. Data volume is clearly important, as it normally costs more in terms of infrastructure and personnel time to manage 10 terabytes of data than 10 megabytes. But, other characteristics of the data also affect costs as well as metadata, data quality assurance and preservation strategies, and even data policies. A good plan will include information that is sufficient to understand the nature of the data that is collected, including:

• Types. A good first step is to list the various types of data that you expect to collect or create. This may include text, spreadsheets, software and algorithms, models, images and movies, audio files, and patient records. Note that many research sponsors define data broadly to include physical collections, software and code, and curriculum materials.
• Sources. Data may come from direct human observation, laboratory and field instruments, experiments, simulations, and compilations of data from other studies. Reviewers and sponsors may be particularly interested in understanding if data are proprietary, are being compiled from other studies, pertain to human subjects, or are otherwise subject to restrictions in their use or redistribution.
• Volume. Both the total volume of data and the total number of files that are expected to be collected can affect all other data management activities.
• Data and file formats. Technology changes and formats that are acceptable today may soon be obsolete. Good choices include those formats that are nonproprietary, based upon open standards, and widely adopted and preferred by the scientific community (e.g., Comma Separated Values [CSV] over Excel [.xls, xlsx]). Data are more likely to be accessible for the long term if they are uncompressed, unencrypted, and stored using standard character encodings such as UTF-16.

The precise types, sources, volume, and formats of data may not be known beforehand, depending on the nature and uniqueness of the research. In such case, the solution is to iteratively update the plan (see Rule 9 ).

Rule 3: Define How the Data Will Be Organized

Once there is an understanding of the volume and types of data to be collected, a next obvious step is to define how the data will be organized and managed. For many projects, a small number of data tables will be generated that can be effectively managed with commercial or open source spreadsheet programs like Excel and OpenOffice Calc. Larger data volumes and usage constraints may require the use of relational database management systems (RDBMS) for linked data tables like ORACLE or mySQL, or a Geographic Information System (GIS) for geospatial data layers like ArcGIS, GRASS, or QGIS.

The details about how the data will be organized and managed could fill many pages of text and, in fact, should be recorded as the project evolves. However, in drafting a DMP, it is most helpful to initially focus on the types and, possibly, names of the products that will be used. The software tools that are employed in a project should be amenable to the anticipated tasks. A spreadsheet program, for example, would be insufficient for a project in which terabytes of data are expected to be generated, and a sophisticated RDMBS may be overkill for a project in which only a few small data tables will be created. Furthermore, projects dependent upon a GIS or RDBMS may entail considerable software costs and design and programming effort that should be planned and budgeted for upfront (see Rules 9 and 10 ). Depending on sponsor requirements and space constraints, it may also be useful to specify conventions for file naming, persistent unique identifiers (e.g., Digital Object Identifiers [DOIs]), and versioning control (for both software and data products).

Rule 4: Explain How the Data Will Be Documented

Rows and columns of numbers and characters have little to no meaning unless they are documented in some fashion. Metadata—the details about what, where, when, why, and how the data were collected, processed, and interpreted—provide the information that enables data and files to be discovered, used, and properly cited. Metadata include descriptions of how data and files are named, physically structured, and stored as well as details about the experiments, analytical methods, and research context. It is generally the case that the utility and longevity of data relate directly to how complete and comprehensive the metadata are. The amount of effort devoted to creating comprehensive metadata may vary substantially based on the complexity, types, and volume of data.

A sound documentation strategy can be based on three steps. First, identify the types of information that should be captured to enable a researcher like you to discover, access, interpret, use, and cite your data. Second, determine whether there is a community-based metadata schema or standard (i.e., preferred sets of metadata elements) that can be adopted. As examples, variations of the Dublin Core Metadata Initiative Abstract Model are used for many types of data and other resources, ISO (International Organization for Standardization) 19115 is used for geospatial data, ISA-Tab file format is used for experimental metadata, and Ecological Metadata Language (EML) is used for many types of environmental data. In many cases, a specific metadata content standard will be recommended by a target data repository, archive, or domain professional organization. Third, identify software tools that can be employed to create and manage metadata content (e.g., Metavist, Morpho). In lieu of existing tools, text files (e.g., readme.txt) that include the relevant metadata can be included as headers to the data files.

A best practice is to assign a responsible person to maintain an electronic lab notebook, in which all project details are maintained. The notebook should ideally be routinely reviewed and revised by another team member, as well as duplicated (see Rules 6 and 9 ). The metadata recorded in the notebook provide the basis for the metadata that will be associated with data products that are to be stored, reused, and shared.

Rule 5: Describe How Data Quality Will Be Assured

Quality assurance and quality control (QA/QC) refer to the processes that are employed to measure, assess, and improve the quality of products (e.g., data, software, etc.). It may be necessary to follow specific QA/QC guidelines depending on the nature of a study and research sponsorship; such requirements, if they exist, are normally stated in the RFP. Regardless, it is good practice to describe the QA/QC measures that you plan to employ in your project. Such measures may encompass training activities, instrument calibration and verification tests, double-blind data entry, and statistical and visualization approaches to error detection. Simple graphical data exploration approaches (e.g., scatterplots, mapping) can be invaluable for detecting anomalies and errors.

Rule 6: Present a Sound Data Storage and Preservation Strategy

A common mistake of inexperienced (and even many experienced) researchers is to assume that their personal computer and website will live forever. They fail to routinely duplicate their data during the course of the project and do not see the benefit of archiving data in a secure location for the long term. Inevitably, though, papers get lost, hard disks crash, URLs break, and tapes and other media degrade, with the result that the data become unavailable for use by both the originators and others. Thus, data storage and preservation are central to any good data management plan. Give careful consideration to three questions:

• How long will the data be accessible?
• How will data be stored and protected over the duration of the project?
• How will data be preserved and made available for future use?

The answer to the first question depends on several factors. First, determine whether the research sponsor or your home institution have any specific requirements. Usually, all data do not need to be retained, and those that do need not be retained forever. Second, consider the intrinsic value of the data. Observations of phenomena that cannot be repeated (e.g., astronomical and environmental events) may need to be stored indefinitely. Data from easily repeatable experiments may only need to be stored for a short period. Simulations may only need to have the source code, initial conditions, and verification data stored. In addition to explaining how data will be selected for short-term storage and long-term preservation, remember to also highlight your plans for the accompanying metadata and related code and algorithms that will allow others to interpret and use the data (see Rule 4 ).

Develop a sound plan for storing and protecting data over the life of the project. A good approach is to store at least three copies in at least two geographically distributed locations (e.g., original location such as a desktop computer, an external hard drive, and one or more remote sites) and to adopt a regular schedule for duplicating the data (i.e., backup). Remote locations may include an offsite collaborator’s laboratory, an institutional repository (e.g., your departmental, university, or organization’s repository if located in a different building), or a commercial service, such as those offered by Amazon, Dropbox, Google, and Microsoft. The backup schedule should also include testing to ensure that stored data files can be retrieved.

Your best bet for being able to access the data 20 years beyond the life of the project will likely require a more robust solution (i.e., question 3 above). Seek advice from colleagues and librarians to identify an appropriate data repository for your research domain. Many disciplines maintain specific repositories such as GenBank for nucleotide sequence data and the Protein Data Bank for protein sequences. Likewise, many universities and organizations also host institutional repositories, and there are numerous general science data repositories such as Dryad ( http://datadryad.org/ ), figshare ( http://figshare.com/ ), and Zenodo ( http://zenodo.org/ ). Alternatively, one can easily search for discipline-specific and general-use repositories via online catalogs such as http://www.re3data.org/ (i.e., REgistry of REsearch data REpositories) and http://www.biosharing.org (i.e., BioSharing). It is often considered good practice to deposit code in a host repository like GitHub that specializes in source code management as well as some types of data like large files and tabular data (see https://github.com/ ). Make note of any repository-specific policies (e.g., data privacy and security, requirements to submit associated code) and costs for data submission, curation, and backup that should be included in the DMP and the proposal budget.

Rule 7: Define the Project’s Data Policies

Despite what may be a natural proclivity to avoid policy and legal matters, researchers cannot afford to do so when it comes to data. Research sponsors, institutions that host research, and scientists all have a role in and obligation for promoting responsible and ethical behavior. Consequently, many research sponsors require that DMPs include explicit policy statements about how data will be managed and shared. Such policies include:

• licensing or sharing arrangements that pertain to the use of preexisting materials;
• plans for retaining, licensing, sharing, and embargoing (i.e., limiting use by others for a period of time) data, code, and other materials; and
• legal and ethical restrictions on access and use of human subject and other sensitive data.

Unfortunately, policies and laws often appear or are, in fact, confusing or contradictory. Furthermore, policies that apply within a single organization or in a given country may not apply elsewhere. When in doubt, consult your institution’s office of sponsored research, the relevant Institutional Review Board, or the program officer(s) assigned to the program to which you are applying for support.

Despite these caveats, it is usually possible to develop a sound policy by following a few simple steps. First, if preexisting materials, such as data and code, are being used, identify and include a description of the relevant licensing and sharing arrangements in your DMP. Explain how third party software or libraries are used in the creation and release of new software. Note that proprietary and intellectual property rights (IPR) laws and export control regulations may limit the extent to which code and software can be shared.

Second, explain how and when the data and other research products will be made available. Be sure to explain any embargo periods or delays such as publication or patent reasons. A common practice is to make data broadly available at the time of publication, or in the case of graduate students, at the time the graduate degree is awarded. Whenever possible, apply standard rights waivers or licenses, such as those established by Open Data Commons (ODC) and Creative Commons (CC), that guide subsequent use of data and other intellectual products (see http://creativecommons.org/ and http://opendatacommons.org/licenses/pddl/summary/ ). The CC0 license and the ODC Public Domain Dedication and License, for example, promote unrestricted sharing and data use. Nonstandard licenses and waivers can be a significant barrier to reuse.

Third, explain how human subject and other sensitive data will be treated (e.g., see http://privacyruleandresearch.nih.gov/ for information pertaining to human health research regulations set forth in the US Health Insurance Portability and Accountability Act). Many research sponsors require that investigators engaged in human subject research approaches seek or receive prior approval from the appropriate Institutional Review Board before a grant proposal is submitted and, certainly, receive approval before the actual research is undertaken. Approvals may require that informed consent be granted, that data are anonymized, or that use is restricted in some fashion.

Rule 8: Describe How the Data Will Be Disseminated

The best-laid preservation plans and data sharing policies do not necessarily mean that a project’s data will see the light of day. Reviewers and research sponsors will be reassured that this will not be the case if you have spelled out how and when the data products will be disseminated to others, especially people outside your research group. There are passive and active ways to disseminate data. Passive approaches include posting data on a project or personal website or mailing or emailing data upon request, although the latter can be problematic when dealing with large data and bandwidth constraints. More active, robust, and preferred approaches include: (1) publishing the data in an open repository or archive (see Rule 6 ); (2) submitting the data (or subsets thereof) as appendices or supplements to journal articles, such as is commonly done with the PLOS family of journals; and (3) publishing the data, metadata, and relevant code as a “data paper” [ 5 ]. Data papers can be published in various journals, including Scientific Data (from Nature Publishing Group), the GeoScience Data Journal (a Wiley publication on behalf of the Royal Meteorological Society), and GigaScience (a joint BioMed Central and Springer publication that supports big data from many biology and life science disciplines).

A good dissemination plan includes a few concise statements. State when, how, and what data products will be made available. Generally, making data available to the greatest extent and with the fewest possible restrictions at the time of publication or project completion is encouraged. The more proactive approaches described above are greatly preferred over mailing or emailing data and will likely save significant time and money in the long run, as the data curation and sharing will be supported by the appropriate journals and repositories or archives. Furthermore, many journals and repositories provide guidelines and mechanisms for how others can appropriately cite your data, including digital object identifiers, and recommended citation formats; this helps ensure that you receive credit for the data products you create. Keep in mind that the data will be more usable and interpretable by you and others if the data are disseminated using standard, nonproprietary approaches and if the data are accompanied by metadata and associated code that is used for data processing.

Rule 9: Assign Roles and Responsibilities

A comprehensive DMP clearly articulates the roles and responsibilities of every named individual and organization associated with the project. Roles may include data collection, data entry, QA/QC, metadata creation and management, backup, data preparation and submission to an archive, and systems administration. Consider time allocations and levels of expertise needed by staff. For small to medium size projects, a single student or postdoctoral associate who is collecting and processing the data may easily assume most or all of the data management tasks. In contrast, large, multi-investigator projects may benefit from having a dedicated staff person(s) assigned to data management.

Treat your DMP as a living document and revisit it frequently (e.g., quarterly basis). Assign a project team member to revise the plan, reflecting any new changes in protocols and policies. It is good practice to track any changes in a revision history that lists the dates that any changes were made to the plan along with the details about those changes, including who made them.

Reviewers and sponsors may be especially interested in knowing how adherence to the data management plan will be assessed and demonstrated, as well as how, and by whom, data will be managed and made available after the project concludes. With respect to the latter, it is often sufficient to include a pointer to the policies and procedures that are followed by the repository where you plan to deposit your data. Be sure to note any contributions by nonproject staff, such as any repository, systems administration, backup, training, or high-performance computing support provided by your institution.

Rule 10: Prepare a Realistic Budget

Creating, managing, publishing, and sharing high-quality data is as much a part of the 21st century research enterprise as is publishing the results. Data management is not new—rather, it is something that all researchers already do. Nonetheless, a common mistake in developing a DMP is forgetting to budget for the activities. Data management takes time and costs money in terms of software, hardware, and personnel. Review your plan and make sure that there are lines in the budget to support the people that manage the data (see Rule 9 ) as well as pay for the requisite hardware, software, and services. Check with the preferred data repository (see Rule 6 ) so that requisite fees and services are budgeted appropriately. As space allows, facilitate reviewers by pointing to specific lines or sections in the budget and budget justification pages. Experienced reviewers will be on the lookout for unfunded components, but they will also recognize that greater or lesser investments in data management depend upon the nature of the research and the types of data.

A data management plan should provide you and others with an easy-to-follow road map that will guide and explain how data are treated throughout the life of the project and after the project is completed. The ten simple rules presented here are designed to aid you in writing a good plan that is logical and comprehensive, that will pass muster with reviewers and research sponsors, and that you can put into practice should your project be funded. A DMP provides a vehicle for conveying information to and setting expectations for your project team during both the proposal and project planning stages, as well as during project team meetings later, when the project is underway. That said, no plan is perfect. Plans do become better through use. The best plans are “living documents” that are periodically reviewed and revised as necessary according to needs and any changes in protocols (e.g., metadata, QA/QC, storage), policy, technology, and staff, as well as reused, in that the most successful parts of the plan are incorporated into subsequent projects. A public, machine-readable, and openly licensed DMP is much more likely to be incorporated into future projects and to have higher impact; such increased transparency in the research funding process (e.g., publication of proposals and DMPs) can assist researchers and sponsors in discovering data and potential collaborators, educating about data management, and monitoring policy compliance [ 6 ].

Acknowledgments

This article is the outcome of a series of training workshops provided for new faculty, postdoctoral associates, and graduate students.

Funding Statement

This work was supported by NSF IIA-1301346, IIA-1329470, and ACI-1430508 ( http://nsf.gov ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

MIT Libraries logo MIT Libraries

Write a data management plan

A data management plan (DMP) will help you manage your data, meet funder requirements, and help others use your data if shared.

Alternatively, you can use the questions below and any specific data management requirements from your funding agency to write your data management plan. Additional resources for creating plans  are also provided below.

• What’s the purpose of the research?
• What is the data? How and in what format will the data be collected? Is it numerical data, image data, text sequences, or modeling data?
• How much data will be generated for this research?
• How long will the data be collected and how often will it change?
• Are you using data that someone else produced? If so, where is it from?
• Who is responsible for managing the data? Who will ensure that the data management plan is carried out?
• What documentation will you be creating in order to make the data understandable by other researchers?
• Are you using metadata that is standard to your field? How will the metadata be managed and stored?
• What file formats will be used? Do these formats conform to an open standard and/or are they proprietary?
• Are you using a file format that is standard to your field? If not, how will you document the alternative you are using?
• What directory and file naming convention will be used?
• What are your local storage and backup procedures ? Will this data require secure storage?
• What tools or software are required to read or view the data?
• Who has the right to manage this data? Is it the responsibility of the PI, student, lab, MIT, or funding agency?
• What data will be shared , when, and how?
• Does sharing the data raise privacy, ethical, or confidentiality concerns ?  Do you have a plan to protect or anonymize data, if needed?
• Who holds intellectual property rights for the data and other information created by the project? Will any copyrighted or licensed material be used? Do you have permission to use/disseminate this material?
• Are there any patent- or technology-licensing-related restrictions on data sharing associated with this grant? The Technology Licensing Office (TLO) can provide this information.
• Will this research be published in a journal that requires the underlying data to accompany articles?
• Will there be any embargoes on the data?
• Will you permit re-use , redistribution, or the creation of new tools, services, data sets, or products (derivatives)? Will commercial use be allowed?
• How will you be archiving the data? Will you be storing it in an archive or repository for long-term access? If not, how will you preserve access to the data?
• Is a discipline-specific repository available? If not, consider depositing your data into a generalist data repository . Email us at [email protected] if you’re interested in discussing repository options for your data.
• How will you prepare data for preservation or data sharing? Will the data need to be anonymized or converted to more stable file formats?
• Are software or tools needed to use the data? Will these be archived?
• How long should the data be retained? 3-5 years, 10 years, or forever?

Additional resources for creating plans

• Managing your data – Project Start & End Checklists (MIT Data Management Services) : Checklist (PDF) with detailed resources to help researchers set up and maintain robust data management practices for the full life of a project.
• ezDMP : a free web-based tool for creating DMPs specific to a subset of NSF funding requirements.
• Guidelines for Effective Data Management Plans and Data Management Plan Resources and Examples (ICPSR) : Framework for creating a plan and links to examples of data management plans in various scientific disciplines
• Example Plans (University of Minnesota)
• NSF (by the DART project) : assessment rubric and guidance
• NIH (by FASEB)

See other guides to data management for additional guidance on managing data and select information related to particular formats or disciplines.

Study at Cambridge

About the university, research at cambridge.

• Undergraduate courses
• Events and open days
• Fees and finance
• Postgraduate courses
• How to apply
• Postgraduate events
• Fees and funding
• International students
• Continuing education
• Executive and professional education
• Courses in education
• How the University and Colleges work
• Term dates and calendars
• Visiting the University
• Annual reports
• Equality and diversity
• A global university
• Public engagement
• Give to Cambridge
• For Cambridge students
• For our researchers
• Business and enterprise
• Colleges & departments
• Email & phone search
• Museums & collections
• Data Management Guide
• Creating your data

• Data Management Guide overview
• Creating your data overview
• Organising your data
• Accessing your data overview
• Looking after your data
• Sharing your data
• Choosing a software licence
• Electronic Research Notebooks overview
• Support overview
• Resources and support at Cambridge overview
• Data Management Plan Support Service
• DMP Pilot for PhDs
• External support
• Data Repository overview
• Upload your data
• Depositor's checklist
• Guidance on the data submission process
• Data Policies overview
• University of Cambridge Research Data Management Policy Framework overview
• Cambridge data-related policies
• Funders Policies overview
• News overview
• Data Champions overview
• Data Champion list
• Data Champion Community
• Data Champions Cartoons
• Alumni Data Champions
• 2024 Call for Data Champions
• Events overview
• Love Data Week 2024
• Past Events
• Contact Us overview
• Our Governance
• Request a Meeting
• Data Management Plan
• Research Data Management
• Choosing Formats
• Intellectual Property Rights
• Data Protection and Ethics
• Accessing your data
• Electronic Research Notebooks

Crafting your data management plan

Most  research funders encourage researchers to think about their research data management activities from the beginning of the project. This will often mean a formal plan for managing data (a 'data management plan').

However, even informally setting out your plans and project guidelines can make your life much easier. If you want to be able to reuse your data or manage collaboration with colleagues, it helps to plan for that from the beginning. Decisions you make about which software to use, how to organise, store and manage your data, and the consent agreements you would have to negotiate, will all affect what is possible to do, and what data is shareable in the future.

Planning ahead for your data management needs and activities will help ensure that:

• you have adequate technological resources (e.g. storage space, support staff time)
• your data will be robust and free from versioning errors and gaps in documentation
• your data is backed up and safe from sudden loss or corruption
• you can meet legal and ethical requirements
• you are able to share your finalised data publicly, if you and/or your funder desires
• your data will remain accessible and comprehensible in the near, middle, and distant future.

What do research funders expect?

Most funders expect you to prepare a data management plan when applying for a research grant. Additionally, some funders, for example the Medical Research Council ( MRC ), will require you to regularly review your data management plan and make all necessary amendments while managing your grant. The Economic and Social Research Council (ESRC) provides comprehensive guidelines on how treating personal and sensitive data, as well as on obtaining consent for data collection from participants. The information on funder requirements is available here .

Where do I start?

Much of research data management is simply good research practice so you will already be some way down the line. Data plans are just a way of ensuring (and/or showing) that you have thought about how to create, store, backup, share and preserve your data. 

The Digital Curation Centre ( DCC ) has produced an interactive online tool to help researchers create data management plans: DMPOnline . The website records all major UK/European funder requirements, and it automatically tailors the data management plan template to the needs of your funder. You can log in to DMPonline using your Raven account (to do this, simply select the University of Cambridge as your institution, and you will be re-directed to the Raven log-in interface). Data plans that you create are easily exportable to a desired file type (Word, Excel, pdf), so you can simply add them to your grant applications.

What should I include in my data plan?

The best way to start is to look for what your funder expects you to cover in your Data Management Plan. You can either check this on your funder's website or by using the DMPonline tool, which is populated with funder's template and will guide you through your funder's requirements.

Who can help with data planning at the University of Cambridge?

The University has a range of support staff who can help you create a data management plan, including:

• your departmental or college IT staff
• subject and departmental librarians
• your funder - some funders, for example, the Economic and Social Research Council (ESRC), offer support in preparation of data management plans

No matter who you ask for support, please get in touch early, so there is enough time for support staff to help. 

Simple data management plan template

Have a look at our simple data management plan template here - if your funder does not provide guidance on data plans, this might be a good starting point.

Related links

• DMPonline - tool to create data management plans
• ESRC - support for data management plans

About this site :

This site is managed by the Research Data Team.

If you have any questions about this site, please e-mail us directly

The project is a joint initiative of Cambridge University Library and the Research Operations Office .

Privacy policy

© 2024 University of Cambridge

• Contact the University
• Accessibility
• Freedom of information
• Privacy policy and cookies
• Statement on Modern Slavery
• Terms and conditions
• University A-Z
• Undergraduate
• Postgraduate
• Research news
• About research at Cambridge
• Spotlight on...

Writing data management plans

• Requirements and resources
• DMP service

What is a DMP/DMSP?

A data management plan (DMP) or data management and sharing plan (DMSP) is a written document that describes:

• the data you expect to acquire or generate during the course of a research project,
• how you will manage, describe, analyze, and store those data, and
• what mechanisms you will use at the end of your project to share and preserve your data.

You may have already considered some or all of these issues with regard to your research project, but writing them down helps you:

• formalize the process,
• identify areas of your plan that need improvement,
• provide you with a record of what you intend(ed) to do and an easy reference during the project,
• make it easier for everyone in your research group to understand their roles and the data management processes that will be used for the research project.

A DMP is a living document

Research is all about discovery, and the process of doing research sometimes requires you to shift gears and revise your intended path.

Your DMP is a living document that you may need to alter as the course of your research changes. Remember that any time your research plans change, you should review your DMP to make sure that it still meets your needs.

Data management is best addressed in the early stages of a research project, but it is never too late to develop a data management plan.

• Next: Requirements and resources >>
• Last Updated: Jul 7, 2023 8:37 AM
• URL: https://guides.library.stanford.edu/dmps

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• CAREER FEATURE
• 13 March 2018

Data management made simple

• Quirin Schiermeier

You can also search for this author in PubMed   Google Scholar

When Marjorie Etique learnt that she had to create a data-management plan for her next research project, she was not sure exactly what to do.

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

Nature 555 , 403-405 (2018)

doi: https://doi.org/10.1038/d41586-018-03071-1

See Editorial: Everyone needs a data-management plan

Related Articles

The FAIR Guiding Principles for scientific data management and stewardship

Tales of a migratory marine biologist

Career Feature 28 AUG 24

Nail your tech-industry interviews with these six techniques

Career Column 28 AUG 24

How to harness AI’s potential in research — responsibly and ethically

Career Feature 23 AUG 24

Inside China’s race to lead the world in nuclear fusion

News Feature 28 AUG 24

The cool technologies that could protect cities from dangerous heat

News Feature 27 AUG 24

Can South Korea regain its edge in innovation?

Nature Index 21 AUG 24

Global Faculty Recruitment of School of Life Sciences, Tsinghua University

The School of Life Sciences at Tsinghua University invites applications for tenure-track or tenured faculty positions at all ranks (Assistant/Ass...

Beijing, China

Tsinghua University (The School of Life Sciences)

Tenure-Track/Tenured Faculty Positions

Tenure-Track/Tenured Faculty Positions in the fields of energy and resources.

Suzhou, Jiangsu, China

School of Sustainable Energy and Resources at Nanjing University

ATLAS - Joint PhD Program from BioNTech and TRON with a focus on translational medicine

5 PhD positions for ATLAS, the joint PhD Program from BioNTech and TRON with a focus on translational medicine.

Mainz, Rheinland-Pfalz (DE)

Translational Oncology (TRON) Mainz

Alzheimer's Disease (AD) Researcher/Associate Researcher

Xiaoliang Sunney XIE’s Group is recruiting researchers specializing in Alzheimer's disease (AD).

Changping Laboratory

Supervisory Bioinformatics Specialist CTG Program Head

The National Library of Medicine (NLM) is a global leader in biomedical informatics and computational health data science and the world’s largest b...

Bethesda, Maryland (US)

National Library of Medicine, National Center for Biotechnology Information

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

An official website of the United States government

Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

•   Facebook
•   Twitter
•   Linkedin
•   Digg
•   Reddit
•   Pinterest
•   Email

Latest Earthquakes |    Chat Share Social Media  

Data Management Plans

Planning for a project involves making decisions about data resources and potential products. A Data Management Plan (DMP) describes data that will be acquired or produced during research; how the data will be managed, described, and stored, what standards you will use, and how data will be handled and protected during and after the completion of the project. 

Data Management Plan Checklist

This USGS checklist provides guidance in what must be considered in developing a DMP for all new USGS projects.

Science Center Data Management Strategy

To help standardize or provide guidance on DMPs, a science center or funding source may choose to document their own Data Management strategy. Click the link below for a template for developing a Science Center Data Management Strategy.

Table of Contents

This page is a guide to help you develop a DMP. Find answers to frequently asked questions, checkout templates and DMP examples, learn about tools for creating DMPs, and understand USGS DMP requirements.

• Getting started
• Frequently Asked Questions
• Templates and Examples

Reviewing Data Management Plans

Related training modules.

• What the U.S. Geological Survey Manual Requires

Getting Started  

The resources in this section will help you understand how to develop your DMP. The checklist outlines the minimum USGS requirements. The FAQ and DMP Writing Best Practices list below will help you understand other important considerations when developing your own DMP. To help standardize or provide guidance on DMPs, a science center or funding source may choose to document their own Data Management strategy. Click here for a template for developing a  Science Center Data Management Strategy [DOCX] .

DMP Writing Best Practices

• Create a DMP prior to initiating research as required by USGS policy.
• Consider available DMP tools and templates, along with their intended use.
• Write DMP content that is descriptive of the project's data acquisition, processing, analysis, preservation, publishing, and sharing (public access) of products as described by the USGS Science Data Lifecycle.
• Identify any proprietary or sensitive data in the DMP prior to data acquisition or collection to legally justify the need to withhold them from public access if necessary.
• Define roles and responsibilities for management, distribution and ownership of data and subsequent metadata or, if available, reference existing Memoranda of Understanding, Memoranda of Agreement, and/or Data Sharing agreements.
• Add content to supplement a DMP template provided by a funding source if that template does not allow you to fully describe your project, data assets, and products and the required investments needed for any software (developed or purchased) and any hardware that are needed to support the research.
• Establish a schedule for reviewing and updating a DMP in combination with project events such as funding approval, project review, and publication.
• Ensure DMP content contains a level of detail that enables stakeholders (funders, project staff, and repository managers) to understand the reality of the project activities.
• Ensure that DMP content and outlined procedures reflect USGS Fundamental Science Practices (FSP) requirements and Science Center guidance.

Frequently Asked Questions  

The following FAQ's were developed to extend the information provided by the USGS Fundamental Science Practices  DMP FAQ Page . This list also presents exemplary solutions from USGS science centers that are currently in practice.

Note: Always refer to specific guidance that may be provided by your funding source or science center to understand their requirements first and foremost.

Where does a data management plan fit into my project workflow?

Business practices that affect project workflows vary among science centers and funding sources; however, in general terms, DMP creation should occur between the proposal stage and accepted funding stage of the project. SM 502.6 requires "The project work plan (SM 502.2) for every research project funded or managed by the USGS must include a data management plan prior to initiation of the project." Below are example project workflow diagrams showing when a DMP is required to be completed; however, you should use the workflow established by your center or program, if applicable. The DMP may need to be updated at various other project milestones. WARC Example Alaska Science Center Example  

What happens when my funding source requires use of a different DMP template?

A DMP developed to meet the requirements of a funding source is usually acceptable if it captures, at a minimum, the same information as the science center format. Deficiencies should be addressed as an addendum to the funding source DMP.  

Who uses a DMP?

There are numerous users of a DMP. The author uses the DMP to plan how data will be handled throughout its lifecycle, updating the document throughout the project. Additionally, the author uses a DMP to capture and record relevant information in a timely manner that can be used later on for other requirements such as metadata. Project staff use the DMP to help understand roles and responsibilities of various team members, especially in teams involving partners from different organizations. Data managers and communication teams can use the information to ensure that preservation and data sharing activities are done appropriately.

Funding sources can use DMPs to promote transparent, high quality, and discoverable products. Lastly, in the event of a Freedom of Information Act (FOIA) request, your FOIA officer can use the DMP as substantiating material. The DMP, considered part of a formally agreed upon project work plan, legally establishes who is responsible for providing free public access to the data and what data are proprietary if they are used by the USGS.  

How do I know what DMP content to complete or update at each stage of my project?

You may need to develop your DMP throughout your project to maintain accurate and useful content. Understanding the USGS Science Data Lifecycle will help you develop DMP content; however, specific guidance may also be provided by your funding source or science center.

A Single Document with Color Coding The  National Regional Climate Adaptation Science Centers template  uses a color coded approach within a single document. Fields shaded gray are not required for proposals. If a project is funded, all fields are required.  

Why does my DMP seem similar to other project documentation?  

DMPs are focused on the data-related aspects of the project and work together with other descriptive project documents such as a proposal, project plan, or BASIS+ entry. Often DMPs contain planning, roles and responsibilities sections that collect similar information to that found in other documents, but this "duplicate" content is necessary for anyone outside of your project to understand your DMP.  

How can I manage all of my project and data documentation including a DMP?

There are many ways to organize and store DMP files. It's most important that you simply develop a consistent strategy. Organization and naming conventions can be associated with other useful elements of a project such as project IDs, project stages, fiscal year, or any combination. Storage options to consider include databases, single files, or folders of content. Online data management and documentation tools can also affect the management of your documents. You may choose to create content or use forms that can be loaded and stored in the software tool.

The Great Lakes Science Center and the Northern Rocky Mountain Science Center (NOROCK) are two examples of centers that conceptualize project documentation as a bundle, where a project folder comprises many documents and forms that describe the project and data. The bundle includes documents such as a Study Plan, a DMP, and a metadata questionnaire. NOROCK additionally uses SharePoint to house research documentation (proposals, Project Work Plans, DMPs, etc.). Document sets have managed metadata. Automated workflows help to streamline the review and approval process, as well as facilitate records management.

Templates and Examples  

Below is a selection of DMP templates provided by USGS science centers and programs. Each template was designed with specific needs and use cases in mind. When developing your own or choosing an existing DMP template consider your own project needs.

* Some DMPs listed below are for educational purposes only and are subject to change. Please contact your USGS center or program for more information on their specific DMP requirements and process.

USGS Powell Center

• Proposal evaluation
• Understand data and IT support needs
• Download Data Management Plan [DOCX]  (Template)

National and Regional Climate Adaptation Science Centers

• Share and manage data and information products
• DMP Template [DOCX]  (Template)
• Data Input Existing Collection [PDF]  (Example)
• Biochar Cost-Benefit assessment tool [PDF]  (Example)

USGS California Water Science Center

• Example Microsoft questionnaire form
• Coordination needed with center data manager team to develop actual DMP
• Example Form [PDF] * (Template)

USGS Wetlands and Aquatic Research Center

• Example google questionnaire form
• Bathythermograph [PDF]  ( Note: This example is not a DMP from this center )

USGS Coastal and Marine Geology Program

• Example database form

USGS Fort Collins Science Center

• Example template
• Example Template [PDF] * (Template)

USGS Water Mission Area

• Excel-based Data Management Planning tool (DMTool)
• Example template  maintained by the Office of Quality Assurance
• Template is used by the WMA. Template should be used by researchers in Water Science Centers if the Center does not have a DMP template containing the minimum elements outlined in Survey Manual Fundamental Science Practices ( SM 502.6, Section 4 ) and on this page.

Tools  

Below is a selection of tools available to USGS staff. Each tool was designed with specific needs and use cases in mind. 

• DMP Tool -  https://dmptool.org/
• DMPEditor -  https://my.usgs.gov/dmpeditor/
• ezDMP -  https://ezdmp.org  (for writing NSF DMPs)
• Microsoft Word Templates - See CASC template above as an example
• Microsoft Forms - See California Water Science Center and Wetland and Aquatic Research Center templates as examples

An important aspect of data management planning is having someone knowledgeable about data management and USGS policies review a project's DMP to flag any potential oversights or challenges before they become an issue. The USGS Data Management Working Group has developed a USGS Data Management Plan Review Checklist to help facilitate these types of reviews.

• Planning for Data Management Part 2: Using the DMPTool to Create Data Management Plans

What the  U.S. Geological Survey Manual  Requires:  

Effective October 1, 2016 the USGS Survey Manual chapter  SM 502.6 - Fundamental Science Practices: Scientific Data Management Foundation , requires the project work plan ( SM 502.2 ) for every research project funded or managed by the USGS must include a data management plan prior to initiation of the project.

SM 502.6 further specifies, a data management plan will include standards and intended actions as appropriate to the project for acquiring, processing, analyzing, preserving, publishing/sharing, describing, and managing the quality of, backing up, and securing the data holdings.

For more information about data management planning as it pertains to the USGS policy, visit the  Fundamental Science Practices FAQs: Data Management Planning .

References  

• Chatfield, T., Selbach, R. February, 2011. Data Management for Data Stewards. Data Management Training Workshop. Bureau of Land Management (BLM).
• DataONE Data Management Skillbuilding Hub .
• Digital Curation Centre.  Checklist for a Data Management Plan .
• UK Data Archive.  Data Management Costing Tool and Checklist .
• Inter-university Consortium for Political and Social Research (ICPSR).  Guide to Social Science Data Preparation and Archiving. [PDF]
• UK Data Archive.  Plan to Share.

Page last updated 6/21/21.

Ask Yale Library

My Library Accounts

Find, Request, and Use

Help and Research Support

Visit and Study

Explore Collections

Research Data Management: Plan for Data

• Plan for Data
• Organize & Document Data
• Store & Secure Data
• Validate Data
• Share & Re-use Data
• Data Use Agreements
• Research Data Policies

What is a Data Management Plan?

Data management plans (DMPs) are documents that outline how data will be collected , stored , secured , analyzed , disseminated , and preserved over the lifecycle of a research project. They are typically created in the early stages of a project, and they are typically short documents that may evolve over time. Increasingly, they are required by funders and institutions alike, and they are a recommended best practice in research data management.

Tab through this guide to consider each stage of the research data management process, and each correlated section of a data management plan.

Tools for Data Management Planning

DMPTool is a collaborative effort between several universities to streamline the data management planning process.

The DMPTool supports the majority of federal and many non-profit and private funding agencies that require data management plans as part of a grant proposal application. ( View the list of supported organizations and corresponding templates.) If the funder you're applying to isn't listed or you just want to create one as good practice, there is an option for a generic plan.

Key features:

Data management plan templates from most major funders

Guided creation of a data management plan with click-throughs and helpful questions and examples

Access to public plans , to review ahead of creating your own

Ability to share plans with collaborators as well as copy and reuse existing plans

How to get started:

Log in with your yale.edu email to be directed to a NetID sign-in, and review the quick start guide .

Research Data Lifecycle

Additional Resources for Data Management Planning

• << Previous: Overview
• Next: Organize & Document Data >>
• Last Updated: Sep 27, 2023 1:15 PM
• URL: https://guides.library.yale.edu/datamanagement

Site Navigation

P.O. BOX 208240 New Haven, CT 06250-8240 (203) 432-1775

Yale's Libraries

Bass Library

Beinecke Rare Book and Manuscript Library

Classics Library

Cushing/Whitney Medical Library

Divinity Library

East Asia Library

Gilmore Music Library

Haas Family Arts Library

Lewis Walpole Library

Lillian Goldman Law Library

Marx Science and Social Science Library

Sterling Memorial Library

Yale Center for British Art

SUBSCRIBE TO OUR NEWSLETTER

@YALELIBRARY

Yale Library Instagram

Accessibility       Diversity, Equity, and Inclusion      Giving       Privacy and Data Use      Contact Our Web Team    

© 2022 Yale University Library • All Rights Reserved

• AUT Library
• Library Guides
• Research Support

Managing your research data

• Writing a data management plan
• Research data & data management

Research data management plan

Aut dmp tool, other guides and checklists.

• Collecting data
• Managing data
• Sharing data

A data management plan  ( DMP ) is a formal document that outlines how you will handle your data both during your research, and after the project is completed.  This ensures that data are well-managed in the present, and prepared for preservation in the future.  A DMP  is often required in grant proposals.

A research data management plan is a  living document and should be reviewed and updated regularly.

WSG format for a data management plan mentioned in the video.

Your DMP should include the a brief description about your project and how data will be managed: 

• Roles and responsibilities
• Ethics and policies/guidelines compliance 
• Types of data, data format and documentation
• Data storage, file backup and security
• Access, sharing and archiving 

AUT guidelines and policies

• Principles, policies and codes
• Data management guidelines - AUT ethical guidelines and confidentiality requirements
• AUT guide for drafting a data management plan

The  AUT Data Management Planning Tool  makes use of a platform developed and hosted by University of California Digital Library. By using this tool you will create a data management plan based on current AUT data management guidance.

Plans can be drafted on DMPTool and once complete are downloadable in PDF form for your own records. Settings in the tool allow you to control whether your plan is private, institutionally viewable or open to public view.

The questions and structure of the DMPTool have been customised for AUT researchers as part of a joint project between AUT Library and the University Research Office. If you would like to give constructive feedback on the tool please contact:  [email protected]

Sign in to AUT DMPTool:

• Enter your AUT email to the sign in box
• On the next screen, click  Sign in with Institution (SSO)

Important:  To access the AUT Template, you must select 'No funder associated with this plan or my funder is not listed' on the Create Plan page.

• Video - Creating a Data Management Plan (DMP) - Curtin University
• ANDS guide for Data management plans By Australian National Data Service.
• Example DMPs Examples on the Digital Curation Centre website (UK).
• Data management costing tool and checklist  - UK Data Service
• DDC guidance  - The Digital Curation Centre (DCC) UK
• Digital Creation Centre (DCC), Edinburgh
• UK Data Archive
• << Previous: Research data & data management
• Next: Collecting data >>
• Last Updated: Nov 21, 2023 11:28 AM
• URL: https://aut.ac.nz.libguides.com/RDM

• Data Management Plans

What is a Data Management Plan (DMP)?

For nih (recently updated), what about other government agencies, writing a plan, having a plan reviewed, what generally goes into a dmp, anticipate the storage, infrastructure, and software needs of the project, create or adopt standard terminology and file-naming practices, set a schedule for your data management activities, assign responsibilities, think long-term.

A DMP (or DMSP, Data Management and Sharing Plan) describes what data will be acquired or generated as part of a research project, how the data will be managed, described, analyzed, and stored, and what mechanisms will be used to at the end of your project to share and preserve the data. 

One of the key advantages to writing a DMP is that it helps you think concretely about your process, identify potential weaknesses in your plans, and provide a record of what you intend to do. Developing a DMP can prompt valuable discussion among collaborators that uncovers and resolves unspoken assumptions, and provide a framework for documentation that keeps graduate students, postdocs, and collaborators on the same page with respect to practices, expectations, and policies. 

Data management planning is most effective in the early stages of a research project, but it is never too late to develop a data management plan.

How can I find out what my funding agency requires?

Most funding agencies require a DMP as part of an application for funding, but the specific requirements differ across and even within agencies. Many agencies, including the NSF and NIH, have requirements that apply generally, with some additional considerations depending on the specific funding announcement or the directorate/institute. 

Here are some resources to help identify what you’ll need:

• DMP Requirements  (PAPPG, Chapter 2, Proposal prep instructions)
• Data sharing policy  (PAPPG, Chapter 11, Post-award requirements)
• Links to directorate-specific requirements  
• Data Management and Sharing Policy Overview
• Research Covered Under the Data Management and Sharing Policy
• Writing a Data Management and Sharing Plan
• Final NIH Policy for Data Management and Sharing
• NOTE : Some specific NIH Institutes, Centers, or Offices have additional requirements for DMSPs. For example, applications to NIMH require a data validation schedule. Please check with your institute and your funding announcement to ensure all aspects expected are included in your DMSP.
• Data Sharing Policies
• General guidance and examples
• Data Sharing and Management Policies for US Federal Agencies

Need help figuring out what your agency needs? Ask a PRDS team member !

Where can I get help with writing a DMP?

With recent and upcoming changes to the research landscape, it can be tricky to determine what information is needed for your Data Management (and Sharing) Plan. As a Princeton researcher, you have several ways of obtaining support in this area

You have free access to an online tool for writing DMPs: DMPTool . You just need to sign in as a Princeton researcher, and you’ll be able to use and adapt templates, example DMPs, and Princeton-specific guidance. You can find some helpful public guidance on using DMPTool created by Arizona State University.

You are also welcome to schedule an appointment with a member of the PRDS team. While we are unable to write your DMP for you, we are happy to review your funding call and guide you through the information you will need to provide as part of your DMP

PRDS also offers free and confidential feedback on draft DMPs. If you would like to request feedback, we require:

• Your draft DMP (either via email [[email protected]] or by selecting the “Request Feedback” option on the last page of your DMP template in the DMPTool ).
• Your funding announcement.
• Your deadline to submit your grant proposal.

NOTE: Reviewing DMPs is a process and may involve several rounds of edits or a conversation between you and our team. The timeline for requesting a DMP review is as follows:

• Single-lab or single-PI grants:  no fewer than 5 business days ; 
• Complex, multi-institution grants, including Centers:  no fewer than 10 business days . 

We will make every effort to review all DMPs submitted to us, however, we cannot guarantee a thorough review if submitted after our requested time frame.

Details will vary from funder to funder, but the  Digital Curation Centre’s Checklist for a Data Management Plan  provides a useful list of questions to consider when writing a DMP:

• What data will you collect or create?       Type of data, e.g., observation, experimental, simulation, derived/compiled      Form of data, e.g., text, numeric, audiovisual, discipline- or instrument-specific      File Formats, ideally using research community standards or open format (e.g., txt, csv, pdf, jpg)
• How will the data be collected or created?
• What documentation and metadata will accompany the data?
• How will you manage any ethical issues?
• How will you manage copyright and intellectual property rights issues?
• How will the data be stored and backed up during research?
• How will you manage access and security?
• Which data should be retained, shared, and/or preserved?
• What is the long-term preservation plan for the dataset?
• How will you share the data?
• Are any restrictions on data sharing required?
• Who will be responsible for data management?
• What resources will you require to implement your plan?

Additional key things to consider

Consider the types of data that will be created or used in the project. For example, will your project…

• generate large amounts of data?
• require coordinated effort between offsite collaborators?
• use data that has licensing agreements or other restrictions on its use?
• Involve human or non-human animal subjects?

Answers to questions like these will help you accurately assess what you’ll need during the project and prevent delays during crucial stages. 

Decide on file and directory naming conventions and stick to them. Document them (either independently or as part of a standard operating procedure (SOP) document) so that any new graduate students, post-docs, or collaborators can transition smoothly into the project.

Plan and implement a back-up schedule onto shared storage in order to ensure that more than one copy of the data exists.  Periodic file and/or directory clean-ups will help keep “publication quality” data safe and accessible.

Make it clear who is responsible for what. For example, assign a data manager who can check that backup clients are functional, monitor shared directories for clean-up or archiving maintenance, and follow up with project members as needed.

Decide where your data will go after the end of the project. Data that are associated with publications need to be preserved long-term, and so it’s good to decide early on where the data will be stored (e.g. a discipline or institutional repository) and when and how it will get there. Other data may need this level of preservation as well. PRDS can help you find places to store your data and provide advice about what kinds of data to plan to keep.

• Events and Training
• Getting Started
• Guides for Good Data Management
• Data Curation
• Data Ownership
• Large Data Resources
• Data Use Agreements
• Human Subjects
• Finding Datasets
• Non-Human Animal Subjects
• Data Security
• File Organization
• READMEs for Research Data
• Research Computing Resources
• Collaboration Options
• Help with Statistics
• Publisher Requirements
• Data Repositories
• Princeton Data Commons
• Getting Started as a Princeton Data Commons Describe Contributor
• Publishing Large Datasets
• Using Globus with Princeton Data Commons (PDC)
• Storage Options
• Tools and Resources

We use cookies on reading.ac.uk to improve your experience, monitor site performance and tailor content to you.

Read our cookie policy to find out how to manage your cookie settings.

This site may not work correctly on Internet Explorer. We recommend switching to a different browser for a better experience.

Writing a data management plan

Every research project that involves the collection and use of research data should have a data management plan (DMP). This is a structured document describing:

• what data will be collected or used in the course of a research project;
• how the data will be managed on a day-to-day basis;
• how relevant data will preserved for the long term and made available for re-use by others on completion of the research and publication of findings.

A DMP can enable you and your project team to work efficiently, to identify requirements and manage risks, and to apply appropriate solutions.

You may have been required to submit a DMP as part of a grant application , and this can be a useful starting point for project planning, but the DMP that is a practical instrument for your project will still need to be developed.

We provide a post-award data management guide (pdf) to help PIs and project teams address a project’s data management requirements when a project is being set up.

NERC and EC-funded researchers should be aware of post-award DMP requirements:

• NERC-funded PIs must contact the NERC Data Centre nominated on their Outline Data Management Plan within three to six months of the start date of the grant, in order to develop a full DMP for the project in discussion with them;
• Horizon 2020 funded projects (including ERC grants) that have opted in to the Open Research Data Pilot are required to submit a DMP deliverable within the first six months of the grant.

Planning tools

You should use a DMP template to structure your plan and ensure you cover all aspects of data management. When writing the DMP for use in the project, you may find a generic template more useful than the funder-specific one used for the grant application, as funders' DMP templates may not cover all aspects of data management. These template tools are recommended:

• DMPonline is an online data management planning tool. It provides funder-specific templates for use in grant applications, but you can also select the 'No funder' option when you create a new plan to generate a standard template that is suitable for any project. Plans can be saved, shared with co-applicants, commented and edited, and exported in a variety of formats.
• The Digital Curation Centre (DCC) provides a Checklist for a Data Management Plan . This is broken down into sections, with guidance to help you address all relevant requirements.
• For postgraduate students we provide a PGR Data Management Plan template, with detailed guidance and a review checklist that can be used by supervisors.

For projects with substantial and/or complex data management requirements and costs, an activity-based costing approach may be helpful in preparing the budget. A costing tool is provided by the UK Data Service for this purpose. Although the tool is primarily aimed at researchers in the social sciences, the activity-based approach can be easily applied in any discipline.

What does a data management plan involve?

A DMP will generally cover the following:

• the context of data collection : i.e. the research project, and relevant policies and contracts, e.g. funders' and institutional policies on research data, a collaboration agreement or a PhD industrial sponsorship agreement;
• data collection, storage and processing : the data to be collected, methods of collection and processing and instruments to be used, quality control procedures, solutions for storage, backup and organization of data, and relevant formats and standards;
• documentation and metadata : information that will be created and linked to the data to identify them, document the methods by which they were created, and provide you and other people with the means to understand and use the data;
• ethics and legal compliance : measures for the management of data in order to comply with any research ethics and Data Protection Act requirements, including actions to be taken to facilitate data sharing on publication of findings, e.g. securing appropriate consent, anonymising datasets;
• intellectual property rights : who owns the data that will be collected and used in the research, how ownership affects data sharing, and what permissions may need to be sought for data sharing;
• preservation and sharing : what data will be preserved over the long term, what data repositories or other services will be used to preserve and share data, and on what terms data will be made accessible;
• responsibilities and resources : how roles and responsibilities for data management will be allocated, what resources will be required, and what additional costs will be incurred, such as data storage costs, or charges for data archiving.

Tips for writing a Data Management Plan

• Always create a DMP for the project - even if data management seems simple and straightforward. You will find there is a lot more to even basic data management once you start thinking it through.
• If the research is a team project, develop the DMP as a team. The DMP should have an owner (e.g. the project PI) and be developed with the input of everyone in the team who is involved in data management.
• Use a template or checklist to structure the plan and break it down into logical parts. If you have already written a DMP as part of a grant application for the project, this can provide a basis, but you will need to add more practical detail. A standard/generic template, e.g. the DMPonline no-funder template (see above), or the University's PGR Data Management Plan , may be useful.
• Add as much detail as you need. The important thing is that the DMP is a practical resource for the project. This may involve recording considerable detail, e.g. about file organisation and naming, data processing instructions, etc. Imagine what would happen if a key member of the team left and someone else had to take over their data management responsibilities. What exactly would they need to know?
• Make data sharing central to the plan. Your DMP should identify the repository or repositories in which your data will be deposited for long-term preservation and public sharing at the end of the project, and you should factor into your plan time towards the end of the project to prepare data for archiving.
• If you will be collecting data from research participants, make sure that your data management will comply with research ethics and data protection requirements. Ensure that your recruitment and consent procedures maximise opportunities for future data sharing.
• If the research is undertaken in collaboration or partnership with other organisations, clarify issues relating to intellectual property rights and public sharing of data. Most research contracts have standard IP and Publication clauses, vesting ownership of IP in the originating party or parties, and requiring any party to give appropriate notice to the other parties of any intended publication.
• Use the DMP: don't just file it and forget it. Ensure team members are aware of and follow the plan, and review the plan on a regular basis, updating it as the project progresses. It could be a standing item on the agenda for project team meetings.
• If you need help writing your project DMP, or would like your DMP to be reviewed, contact the Research Data Manager.

Robert Darby , Research Data Manager

[email protected]

0118 378 6161

A data management plan (DMP) is a document which defines how data handled throughout the lifecycle of a project—that is, from its acquisition to archival.

While these documents are typically used for research projects to meet funder requirements, they can be leveraged within a corporate environment as well to create structure and alignment between stakeholders.

Since DMPs highlight the types of data that will be used within the project and addresses the management of it throughout the  data lifecycle , stakeholders, such as governance teams, can provide clear feedback on the storage and dissemination of sensitive data, such as personally identifiable information (PII), at the onset of a project. These documents allow teams to avoid compliance and regulatory pitfalls, and they can serve as templates on how to approach and manage data for future projects.

Learn the building blocks and best practices to help your teams accelerate responsible AI.

Register for the white paper on AI governance

A data management plan typically has five components:

1. A statement of purpose  2. Data definitions   3. Data collection and access  4. Frequently asked questions (FAQs)   5. Research data limitations 

Each of these focus areas enables research agencies and research funders (or perhaps your data management team) to assess the amount of risk associated with a given project. The data management plan also addresses how to manage that risk. For example, if sensitive data is used within a project, is it appropriate to re-use that data for future projects? Depending on the sensitivity of that data, it may not be appropriate, or it may require additional user consent.   

Each component of a data management plan focuses on a particular piece of information, we’ll delve more into each one.

1. Statement of purpose:  This explains why the team needs to acquire specific types of data over the course of the project. It should clearly outline the question that the team is attempting to answer with this dataset.

2. Data definitions:  Data descriptions help end users and their audiences understand naming conventions and their correspondence with specific datasets. Some of this information may also be held within the metadata, typically labeling data by its data sources and file formats. Creating and abiding by pre-defined metadata standards throughout the data acquisition process will also ensure a more consistent collection and smoother integration process.

3. Data collection and access:  This section of a DMP highlights how data will be collected, stored, and accessed from a data repository. It will likely address the data source of any existing data or the approach that will be taken to create new data, such as an experiment. It should also contain information around the timing of data—i.e. how often it will be updated and over what period of time. The type and timing of the data will generally inform its storage and access to third-parties. For example, unstructured data will require a  non-relational  system versus a  relational  one, and larger datasets will require more compute power compared to smaller ones. There also may be restrictions around data sharing due to privacy or intellectual property rights. Since project stakeholders will expect that sensitive data, such as personally identifiable information (PII), is treated with the upmost care and security, it’s important for data owners to be clear about their data management practices, particularly in this area. This will include answers to questions around the data’s long-term preservation, such as data archiving or data re-use. For data that is not sensitive in nature, there will be an expectation to provide a pathway for third parties to access raw data and research results.

4. Frequently Asked Questions:  This section can be considered a “catch-all” for other popular questions within data management projects, such as sharing plans, citation preferences, and data backup methods. Researchers or data owners may to highlight any digital object identifiers (DOI) for owners of adjacent or related projects. Additionally, if project owners are archiving data, they’ll also need to address the length of the archive’s existence. Will it live for one year, five years, or perhaps indefinitely?

5. Research data limitations:  This section addresses upfront limitations with the dataset, which will limit its ability to generalize more broadly to populations. For example, data may be focused on a specific demographic, such as a geography, gender, race, age group, et cetera.

Data management plans are predominantly used in more academic settings, particularly for federal government funded programs, such as the National Institutes of Health (NIH) and National Science Foundation (NSF), but corporations can also leverage them in either their research or data governance functions. While academics and researchers need to comply with funder requirements in grant applications, many research institutions create a DMP tool to provide participants with the relevant template for their research project. Data governance teams within organizations can set up similar protocols to ingest data requests from stakeholders advocating for new data initiatives.

Grant applications

Researchers in both private and public sectors look to different funding agencies to sponsor research and innovation initiatives. DMPs mitigate risk for both parties, ensuring that data owners have assessed the value as well as their own personal responsibility (i.e. security and disaster recovery measures) to research data management.

Data governance initiatives Data management plans are also incredibly helpful for new data initiatives in business settings, assisting all stakeholders in understanding the importance of new data sources and how it can tie to business outcomes. As developments within  hybrid cloud ,  artificial intelligence , the internet of things (IoT), and  edge computing  continue to spur the growth of big data, enterprises will need to find ways to manage the complexity of it within their data systems.

Read the free report to learn how data management on a unified platform for data, analytics and AI can accelerate time to insights.

Learn the best practices to ensure data quality, accessibility, and security as a foundation to an AI-centric data architecture.

Scale AI workloads for all your data, anywhere, with IBM watsonx.data, a fit-for-purpose data store built on an open data lakehouse architecture.

Research Data Management: Data Management Plan

• What is Research Data Management
• What is Research Data
• Research Data Cycle
• Funder Requirements
• RDM Policy & Legislation
• Library Support for Research Data Management
• RDM Training
• Data Sharing & Publishing
• Data Citing
• Research Data & Research Data Management
• Data Storage

About Data Management Plans

What is a data management plan (dmp).

A data management plan (DMP) is a written document that describes the data you expect to acquire or generate during a research project, how you will manage, describe, analyze, and store those data, and what mechanisms you will use at the end of your project to share and preserve your data.

You may have already considered some or all of these issues about your research project, but writing them down helps you formalize the process, identify weaknesses in your plan, and provide you with a record of what you intend(ed) to do. Data management is best addressed in the early stages of a research project, but it is never too late to develop a data management plan.

Creating a Data Management Plan

Research is all about discovery, and doing research sometimes requires you to shift gears and revise your intended path. Your DMP is a living document that you may need to alter as the course of your research changes. Remember that any time your research plans change, you should review your DMP to ensure it meets your needs.

What should be covered in the Data Plan?

The framework below, adapted from one developed by the   Inter-University Consortium for Political and Social Research (ICPSR) , shows one approach to the elements of a data management plan.

DMPTool @NWU

Online tool for creating a dmp @nwu.

North-West University Libraries provides access to the online  Data Management Planning (DMP) Tool . The DMPTool includes data management plan templates and a wealth of information and assistance to guide you through creating a ready-to-use DMP for your specific research project and funding agency. 

We can review your data management plan and make suggestions. We are also happy to verify whether your intended use of the Dayta Ya Rona Digital Repository, as described in your plan, matches up with the Dayta Ya Rona services we provide.

DMP submission

Once your data management plan is complete, you will include it with the rest of your proposal to the funding agency. North-West University Research's Office has further information on proposal development and submission.

• << Previous: RDM Policy & Legislation
• Next: Library Support for Research Data Management >>
• Last Updated: Jul 31, 2024 2:52 PM
• URL: https://libguides.nwu.ac.za/research-data-management

• University home
• For business
• Alumni and supporters
• Our departments
• Visiting us

Data management plans

• Costing data management
• Planning for sensitive data

A data management plan (DMP) describes how the research data will be managed throughout the research lifecycle. This includes what research data will be created and/or collected, how it will be managed during the project, and how it will be shared and preserved at the end of the project. It should typically also describe any potential legal or ethical issues that need to be addressed. A DMP is a dynamic document, and should be updated as a project progresses and it should be reviewed regularly as your data needs change.

Data management plans in funding bids

The University’s  Open Access Research and Research Data Management Policy states that all research proposals must include a DMP.

Additionally, most research funders require you to complete a DMP as part of the application process, although the format and content of these plans can differ between funders. A number of bids have been rejected on the basis of weak DMPs, so make sure you give yourself enough time to put together a good quality document.

The table below highlights the funder requirements for DMPs at the grant application stage, but some funders, including the Natural Environment Research Council, require more detailed data management plans to be submitted after a project has started.

Funder DMP Requirements

Writing a data management plan

DMPonline , developed by the Digital Curation Centre (DCC), is a helpful tool for writing DMPs. DMPonline contains data management plan templates for all of the major research funders, and provides guidance and advice on what to include. DMPonline also has the DCC DMP checklist and example DMPs, illustrating the content and level of detail required. Guidance on using DMPonline can be found in the DMPonline User Guide .

Although each DMP is individual, they should all include the same basic information:

• What data will be created?
• How will the data be documented and described?
• How will you manage ethics and intellectual property rights?
• What are the plans for data sharing?
• What is the strategy for long-term preservation and sustainability?

The Research Data Management team are happy to discuss your DMP with you and to provide feedback and comments on your draft DMP. To help you get started, the table below covers the elements that typically make up a DMP. It contains a list of points that you should think about, along with providing suggestions on possible wording.

Typical elements of a DMP

Research Data Management

• Data Storage and Backup
• Data Inventory and Data Dictionaries
• File Naming and Date Formatting
• Standards for Data Management
• NIH Data Management and Sharing Requirements
• Data Management Plans
• UConn File Storage
• Scientific Data Repositories
• Preservation
• Open Science
• Data Management Education & Books
• Data Related Policies at UConn

What is a Data Management Plan?

A data management plan (DMP) is a written document that describes the data you expect to acquire or generate during the course of a research project, how you will manage, describe, analyze, and store those data, and what mechanisms you will use at the end of your project to share and preserve your data (Source: Stanford University Libraries)

A DMP is required as part of your grant  proposal by many funders, BUT  p lanning for good data management, data sharing, and data preservation should be a part of your project plan and integrated throughout the workflow. Plan for this at the same time you write your DMP. To learn more about best practices for good research and data organization, and writing DMPs:

• See the information in this guide  
• Use the Data Management General Guidance from the DMP Tool  
• Come to our  Research Data Management 101: Data Management Essentials workshops  

We can review your Data Management Plans. Please email [email protected] for more information.

Disclaimer for Data Management Plan Review Services : A review of a data management plan by a staff member of the UConn Library is for informational purposes only. A review of a data management plan consists of a set of recommendations that the author(s) of that data management plan may or may not follow. Under no circumstances do the recommendations in a review of a data management plan automatically lead to a grant’s approval based on that data management plan.

• DMP Tool Create data management plans that meet institutional and funder requirements.

What is in a DMP?

While the basic format of a Data Management Plan will be similar from funder to funder, each funder may have specific sections or information that they require. 

The DMPTool has templates and requirements for many funders available on their site. As they state, " Templates for data management plans are based on the specific requirements listed in funder policy documents. The DMPTool maintains these templates, however, researchers should always consult the program officers and policy documents directly for authoritative guidance. Sample plans are provided by a funder or another trusted party."

DMPTool Public Templates are available along with sample plans. 

The sections of most DMPs will include information similar to this: 

• Types of data   - what are you producing or collecting?
• Formats and standards  - What file formats will the data be in? Is there a particular type of text format or a standard recording measure for your data?
• Roles and responsibilities  - Who in the lab will do what? Who will be collecting the data? Is that the same person that will analyze it? Will there be members of the project from outside your institution?
• Dissemination  of results   - Will you be publishing an article? How else will your results be shared?
• Data sharing, public access and reuse  - Will you deposit your data in a disciplinary repository? Will it be available for public reuse?
• Privacy, confidentiality, security, intellectual property rights  - If there are many people in your lab, who will have access to what parts of the data? How will you keep the lab as a whole secure? Who owns the rights to the data? It could be the PI or the institution in some cases, and this is important to know. 
• Archiving data, samples, and other research products, and for on-going access to these products through their lifecycle of usefulness to research and education.    - where will this information be stored long-term after the project ends? Will you be able to access it in five years if someone requests your data? 
• DMPTool Public templates

Using the DMPTool

The DMP Tool is a resource that provides templates that meet institutional and funder requirements around data management and sharing.  As new requirements are implemented, the DMP Tool tracks them.  This tool can be a useful guide for thinking about what information you should collect when planning data management and sharing.

UConn is affiliated with this tool, and using it can make creating your DMP easier, but you are under no obligation to do so.  If you want to use the institutional profile on the DMP Tool, simply choose to sign via your institution on the DMPTool sign in page. 

• << Previous: NIH Data Management and Sharing Requirements
• Next: UConn File Storage >>
• Last Updated: Jun 20, 2024 1:40 PM
• URL: https://guides.lib.uconn.edu/datamanagement

Recommended pages

• New staff intranet
• Student Welcome
• Online registration
• MyUoB mobile app
• Campus maps
• Lecture timetables
• Study spaces
• Student digital services
• Student support
• Student printing
• Car parking
• Room bookings
• Core systems
• Staff development

Data management plans

What is a data management plan.

A data management plan (DMP), sometimes also referred to as Technical Plan or Data Access Plan, is a document that describes how data will be collected, organised, managed, stored, secured, backed-up, preserved, and where applicable, shared. It is an important tool that facilitates and supports the creation of FAIR data .

The DMP is intended to be a living document which is updated as the project progresses.

Warning: we have been made aware that UKRI may be changing the format required for data management plans in new applications. DMPOnline are liaising with UKRI to ensure templates are updated, but in the meantime, please check the requirements on your application.

What exactly is “research data”?

Following the definition provided in the University's Research Data Management Policy , “Research data are the evidence that underpins the answer to the research question, and can be used to validate findings regardless of its form.” Thus, data covers quantitative and qualitative statements, raw data from measurements and derived data – either cleaned or extracted from your primary dataset or derived from an existing source. 

Do I need to write a data management plan?

If your research uses or creates data, yes. The University of Birmingham’s Research Data management Policy states that

“3.4 All funded research must be supported by a DMP or protocol that explicitly addresses data capture, management, integrity, confidentiality, retention, ownership, sharing and publication. This may be either a DMP submitted to the research funder as part of a research application or a document developed via the University’s DMP system after the project receives funding. Unfunded research which is likely to generate Research Data should also be supported by a DMP.”

If you are funded, there might be additional requirements that you need to meet. The main funder requirements are summarised in the data policy section . 

How do I write a data management plan?

We recommend you to use the DMPOnline   tool to create your data management plan, as it provides you with a variety of templates tailored to funder requirements as well as a basic template if you are unfunded. Guidance on using DMPOnline is available in our Introduction to using DMPonline video  (duration 7:11), and the guide to creating a DMP using DMPOnline .

If you are creating a DMP to support a research grant application, you should check whether the funder requires the use of a specific template. Your College Research Support Office can advise.

For PGR students and researchers where no funder requirement applies, the University provides a standard template. You can access it via DMPonline  or download a Word version (DOCX file - 64KB) .

Examples of data management plans

The  RIO journal  allows you to formally publish your data management plan. See their examples of published DMPs.

The Digital Curation Centre provides a collection of  example DMPs and guidance .

DMP Review Service

The Scholarly Communications Team has recently launched a Data Management Plan Review Service for Research Staff . We will review drafted data management plans before grant applications submissions and provide feedback.

 Please allow at least two weeks for our review.

 If you would like us to review your data management plan (DMP), email us a draft of your plan to: [email protected] . Also, make sure that you have included the name of the Principal Investigator, a summary of the project and the associated Funder.

DMPs for PGRs

• College of Arts and Law
• College of Engineering and Physical Sciences
• College of Life and Environmental Sciences
• College of Medicine and Health
• College of Social Sciences

Professional Services

• Academic Services
• Campus Services
• Development and Alumni Relations
• Executive Support
• External Relations
• Human Resources
• IT Services
• Legal Services
• Research Strategy and Services

• How to complete an outputs management plan

We expect the researchers we fund to manage their research outputs in a way that will achieve the greatest health benefit.

• Share with Facebook
• Share with X
• Share with LinkedIn
• Share with Email

These guidelines provide an overview of things to consider as you develop your outputs management plan, in line with our policy on data, software and materials management and sharing and our policy on intellectual property .

Which research outputs are included  

Your outputs management plan should set out your approach for maximising the value of the following types of outputs:

• datasets generated by your research
• original software created in the course of your research
• new materials you create – like antibodies, cell lines and reagents
• intellectual property (IP) such as patents, copyright, design rights and confidential know-how.

Research papers and scholarly monographs must be published in line with our open access policy . These don’t need to be addressed in your outputs management plan.

You will need to submit your plan as part of your grant application. Wellcome staff, the advisory committees and/or peer reviewers will assess the appropriateness and adequacy of your outputs management plan when considering your application.

As part of the end-of-grant reporting process, we will consider the extent to which outputs have been managed and shared in line with our expectations – taking into account your outputs management plan and recognising that this may have been revised as your research progressed.

Choosing the right route: output sharing or IP and commercialisation  

Outputs may be shared with end-users (openly or otherwise) or be made available commercially by licensing for a fee. 

Your outputs management plan should set out which approach is most likely to maximise the adoption and use of the output by the wider research community and the resulting health benefit.

For example, if creating a new software tool, an open approach might be appropriate if others could make immediate and sustained use of it, (for example under a GNU General Public Licence or other licence approved by the Open Source Initiative ). 

However, a commercial approach might be better if you need further funding or a commercial partner to develop, market, distribute or support the ongoing use of the software.

You should also consider whether the output would have greater value to the research community if it was incorporated into an existing commercial product or an existing open resource, rather than making it available as a standalone product.

What to include in your plan  

In your application, your plan should be:

• clear and concise . Don’t repeat methodological detail included elsewhere in your grant application
• proportionate to the scale of the outputs generated and their likely level of value to researchers and other users
• focused specifically on how outputs will be identified, managed and used to advance potential health benefits
• structured to address the key issues outlined below.

You should have a flexible and dynamic approach to outputs management. Output management plans should be living documents. You should review and adapt your plan as your research progresses so your outputs deliver the greatest health benefit.

Timely publication of results in peer-reviewed journals and presentations at conferences are important forms of dissemination, but they are not equivalent to outputs sharing. An intention to publish does not constitute an acceptable outputs management plan.

Examples of output management plans

Read some real examples of what we consider to be good output management plans [PDF 172KB] .

The examples are written by researchers working in a range of areas and at different career stages:

• Senior researcher – neuroimaging data sharing
• Senior researcher – genomic data sharing
• Clinician – controlled access to sensitive data
• PhD student – population modelling data sharing
• Early-career researcher – cardiology data and software sharing
• Mid-career humanities and social sciences researcher – sharing of transcripts and fieldnotes.

If your plan relates to more than one type of output, please identify the different types it covers.

Your plan should address the following, where relevant:

1. Data and software outputs  

The data and software outputs your research will generate and/or re-use show.

Consider and briefly describe:

• the types of data and software the proposed research will generate
• which data and software will have value to other research users and could be shared.

We recognise that in some cases it may not be appropriate for researchers to share data and software outputs (for example, for ethical or commercial reasons). If you don’t intend to share outputs, you must justify your reasons.

Software should be shared in a way that allows it to be used effectively, and we encourage you to provide appropriate and proportionate documentation for the user community.

We encourage you to share null and negative findings and data, as well as data supporting new findings, where this may have value to the community. This helps to avoid unnecessary waste and duplication.

When existing data/resources are being re-used as part of the funding activity, you should consider:

• how existing data/resources will be accessed 
• if there are any constraints on re-use of existing data
• how data provenance will be documented.

The metadata and documentation that will accompany the outputs Show

Data should be shared in line with recognised data standards, where these exist, and in a way that maximises opportunities for data linkage and interoperability. FAIRSharing is one directory of available data standards.

You should:

• provide sufficient, high quality metadata to allow the dataset to be discovered, interpreted and used by others
• adopt agreed best practice standards for metadata provision, where these are in place.

When you intend to share your data and software Show

You must specify the timescale for sharing datasets and software, using any recognised standards of good practice in your research field.

Researchers have the right to a reasonable (but not unlimited) period of exclusive use of the research data and software they produce.

As a minimum, you should make the data and software underpinning research articles available to other researchers at the time of publication , providing this is consistent with:

• any ethics approvals and consents that cover the data
• reasonable limitations required for the appropriate management and exploitation of IP.

You should make sure that these articles include a statement explaining how other researchers can access the data, software or materials. See our guidance on complying with our open access policy .

Where research data relates to a public health emergency, quality-controlled data must be shared as rapidly and openly as possible. This is in line with the joint statement on data sharing in public health emergencies and GLOPID-R principles for data sharing in public health emergencies .

We encourage researchers to consider opportunities for timely and responsible pre-publication sharing of datasets and software. Where appropriate, you may use publication moratoria to enable pre-publication sharing with other researchers, while protecting your right to first publication. 

Any restrictions on data and software use should be reasonable, transparent and in line with established best practice in the respective field.

Where your data and software will be made available Show

You should deposit data in recognised data repositories for particular data types where they exist, unless there’s a compelling reason not to do so. The FAIRSharing and Re3Data sites provide lists of data resources, and Wellcome Open Research maintains a curated list of approved repositories  suitable for Wellcome-funded research.

Where there is no recognised subject area repository available, we encourage researchers to use general community repositories and resources, such as Dryad , FigShare , the Open Science Framework  or Zenodo .

If you intend to create a tailored database resource or to store data locally, you should ensure that you have the resources and systems in place to curate, secure and share the data in a way that maximises its value and guards against any associated risks.

You need to consider how data held in this way can be effectively linked to and integrated with other datasets to enhance its value to users.

For software outputs, use a hosting solution that exposes them to the widest possible number of users. GitHub allows revision control and collaborative hosting of project code for software development, with associated archiving of each release in Zenodo. A suitable revision control system and issue tracker should be in place before programming work begins. This should be available for all members of the research team.

How your data and software will be accessible to others Show

Your plan should set out clearly:

• how potential users will be able to discover, access and re-use data or software outputs
• any associated terms or conditions.

Enabling discovery

Where a data or software resource is being developed as part of a funded activity, you should take reasonable steps to ensure that potential users are:

• made aware of its availability
• updated on significant revisions and releases.

Your plan should outline your approach for maximising the discoverability of your data or software.

We encourage all researchers to use digital object identifiers (DOIs) or other persistent identifiers for their data and software outputs, to enable their re-use to be cited and tracked. 

The DataCite initiative provides a key route through which DOIs are assigned to datasets. Many repositories assign DOIs on deposition.

Where appropriate, you may also publish an article describing dataset or software output to help users discover, access and reference the resource. You can use venues such as Scientific Data , Giga Science  and Wellcome Open Research .

Access procedures for data

Where a managed data access process is required – for example, where a study involves identifiable data about research participants – the access mechanisms should be proportionate to the risks associated with the data. They must not unduly restrict or delay access.

You must describe any managed access procedures in your outputs management plan. It should be consistent and transparent and documented clearly on your study website.

Depending on the study, you may want to establish a graded access procedure where less sensitive data – for example, anonymised and aggregate data – is made readily available, and more sensitive datasets have a more stringent assessment.

Where a Data Access Committee is needed to assess data access requests, the committee should include individuals with appropriate expertise who are independent of the project.

The Expert Advisory Group on Data Access has set out key principles for developing data access and governance mechanisms, to which applicants should refer.

Open software and database licences

If you’re sharing your output through a repository, the terms by which you do so are likely to be set by the repository itself. If you’re sharing directly with the research community, you need to consider the most appropriate way to do so, for example by an appropriate open licence or public domain dedication.

For data, we recommend Creative Commons licences such as CC0 or CC BY. For software, the Open Source Initiative provides access to a range of open software licences, such as the GNU General Public Licence, Apache Licence, and the MIT Licence. Where possible, you should select one of these standard licences (rather than using a bespoke licence).

You must make sure it‘s clear which licence has been applied, so that users can see whether the data or software is accessible and on what terms.

Whether limits to data and software sharing are required Show

For some research, delays or limits on data sharing may be necessary to safeguard research participants or to ensure you can gain IP protection.

Restrictions should be minimised as much as possible and set out clearly in your outputs management plans, if required.

Safeguarding research participants

For research involving human subjects, data must be managed and shared in a way that’s fully consistent with the terms of the consent under which samples and data were provided by the research participants.

For prospective studies, consent procedures should include provision for data sharing in a way that maximises the value of the data for wider research use, while providing adequate safeguards for participants. For more information about consent for data sharing, go to the UK Data Service . Procedures for data sharing should be set out clearly, and current and potential future risks explained to participants.

When designing studies, you must make sure that you protect the confidentiality and security of human subjects, including through appropriate anonymisation procedures and managed access processes.

Clinical trials

For clinical trials you should mention specifically how you will share individual-level patient data. This should include:

• a plan to seek patient informed consent that allows data to be shared in the way outlined
• the level of identification risk and method of de-identification you will adopt
• the repository you plan to use
• any managed access arrangements, such as a data access committee.

Intellectual property (IP)

Delays or restrictions on data or software sharing may be appropriate to protect and use IP in line with our policy on intellectual property and patenting . If this applies, you should only share data or software when it no longer jeopardises your IP position or commercialisation plans.

Your proposed approach for identifying, protecting and using IP should be set out as described in the IP section of this guidance below.

2. Research materials  

What materials your research will produce and how these will be made available show.

Your plan should identify any significant materials you expect to develop using Wellcome funding, which could be of potential value as a resource to other researchers.

You should identify in your plan how the materials will be made available to potential users. For example, by:

• depositing in a recognised collection such as ECACC
• licensing to a reputable life science business partner who can handle advertising, manufacture, storage and distribution.

If the material is highly specialised and the potential number of users is so small that commercial partners cannot be found, distributing samples yourself to other researchers who have asked for them, may be an acceptable plan. However, where possible, you should find a more sustainable long-term solution that doesn’t put an undue burden on you or your institution.

When dealing with commercial entities, you should retain the right to produce the research materials yourself, and to license others to do so, if your chosen commercial partner is unable or unwilling to continue supplying them to the research community.

Whilst your institution may generate reasonable revenue from commercialising research materials, the primary driver should not be revenue generation. You should ensure that your research materials are made available to the wider research community and thereby advance the development of health benefits.

3. Resources required  

You should consider what resources you may need to deliver your plan and outline where dedicated resources are required.

Examples of resources you can ask for include:

People and skills Show

• support for one or more dedicated data manager or data scientist (full- or part-time)
• data and software management training for research or support staff that are needed to deliver the proposed research.

We don’t usually consider costs for occasional or routine support from institutional data managers or other support staff.

Storage and computation Show

• any dedicated hardware or software that is required to deliver your proposed research
• the cost of accessing a supercomputer or other shared facilities.

We would usually expect costs associated with routine data storage to be met by the institution. We will only consider storage costs associated with large or complex datasets which exceed standard institutional allowances.

Access Show

• the reasonable costs of operating an access committee or other data access mechanism over the lifetime of the award
• the costs of preparing and sharing data, software or materials with users (and whether cost-recovery mechanisms will be used)
• the costs of ingesting secondary data, code or materials from users
• costs associated with accessing data, software or materials from others researchers that you need to take forward your proposed research.

Deposition and preservation of data, software and materials Show

If no repository is suitable, we may consider ingestion costs for institutional repositories.

We don’t usually consider estimated costs for curation and maintenance of data, code and materials that extend beyond the lifetime of the award. But we’re willing to discuss how we can help support the long-term preservation of very high-value outputs on a case-by-case basis.

4. Intellectual property  

What ip your research will generate show.

Your plan should describe any significant IP that is likely to arise during your research. You should identify what processes you have in place to identify and capture this IP, as well as any unanticipated discoveries or inventions that result from your work.

How IP will be protected Show

You should describe if and how you will protect significant Wellcome-funded IP. For example, if you’re registering a patent or design, you should briefly outline the territories in which you’ll do this.

Publication of details relating to an invention can limit or entirely destroy the potential to patent and commercialise the invention in the future. If you think that patentable Wellcome-funded IP will arise (or when unanticipated IP has arisen), you should explain how you’ll make sure that publications don’t affect your ability to secure and make suitable use of patent protection to advance health benefits.

How IP will be used to achieve health benefits Show

Wellcome sees IP as a tool which can be used to advance health benefits. You should therefore focus on:

• the benefits your use of the IP will bring to the wider research community
• how this will benefit health.

If your research output is particularly relevant to humanitarian or developing world issues, your plan should specifically address how:

• the output can best be made available for use internationally to address those issues
• your IP strategy will allow this.

Where Wellcome-funded IP comprises a patentable invention, we expect in most cases that it will be protected by filing a patent application. This should be done at a time which maximises the prospects of achieving the desired health benefits, even if this requires a delay to publication. You should only publish details of a potentially patentable invention (without having first sought patent protection) where:

• a market assessment has been carried out and there is no credible prospect of a patent for that invention being commercialised now or in the near future.
• a deliberate decision not to patent the invention (and not to allow anyone else to patent) has been taken for policy reasons. Publication instead of patenting in this case should clearly benefit the wider research community and support the delivery of health benefits. Discuss this with your institution if you’re unsure. Contact Wellcome for advice before publication if you’re still unsure.

Revenue generation should only be a secondary consideration. The primary driver for any commercialisation must be to advance health benefit, even if your employer may generate revenue from commercialising Wellcome-funded IP.

More information  

• FAIRsharing – a curated and searchable portal of data standards, databases, and policies in the life sciences and other scientific disciplines.
• Digital Curation Centre – the UK's leading centre of expertise in data curation. The DCC provides a range of resources and training opportunities for the UK higher education sector, and has developed an online tool for developing data management plans in line with funder requirements.
• Medical Research Council guidance and resources – the MRC has developed detailed practical guidance for researchers on data sharing.
• re3data.org – a global registry of research data repositories across different academic disciplines.
• UK Data Archive – an internationally recognised repository of digital research data in the social sciences and humanities, with associated guidance and services for researchers.
• Wellcome Sanger Institute data sharing guidance – the Wellcome Sanger Institute has a policy setting out the principles that underlie data sharing at the Institute, with associated guidance for researchers.
• TGHN Data sharing toolkit – specifically for clinical data management and sharing advice and resources.

Software Show

• Software Sustainability Institute – the UK’s leading source of expertise in research software sustainability. SSI offers training and advice targeting the specific concerns of research software, including a series of online guides on best practice in software development, licensing, repositories and using a software management plan.
• Software Carpentry – since 1998, Software Carpentry has been teaching basic software skills to researchers in science, engineering, and medicine. They run a worldwide training programme, and provide open access material for self-instruction.
• eScience Lab – eScience Lab host a suite of tools designed to support the creation of e-science laboratories. The tools have been adopted by a large variety of projects and institutions.
• Open Source Initiative – OSI provide the definition for open source software and maintain a list of licences that comply with that definition.
• GitHub and SourceForge – GitHub is the current preferred repository for software collaboration, code review, and code management for open source projects. SourceForge has also been heavily used within the research community for open source development in the past.

Materials Show

Public Health England – culture collections Public Health England is the custodian of four unique collections that consist of expertly preserved, authenticated cell lines and microbial strains of known provenance – namely the European Collection of Authenticated Cell Cultures (ECACC), the National Collection of Type Cultures (NCTC), the National Collection of Pathogenic Viruses (NCPV) and the National Collection of Pathogenic Fungi (NCPF).

Addgene Addgene is a global, nonprofit repository created to help researchers share and access plasmids.

Contact us  

Contact our information officers if you have a question about funding.

Phone us on +44 (0)20 7611 5757

Related content  

• Disability-related support for applicants
• Discretionary Awards: funding outside of Wellcome’s schemes and funding calls
• Embedding lived experience expertise in mental health research
• Funding scheme application deadlines
• How to prepare for a Wellcome funding interview
• How to write an application for funding
• Open Researcher and Contributor ID (ORCID)
• Roles and responsibilities of people involved in Wellcome funding applications

Pardon Our Interruption

As you were browsing something about your browser made us think you were a bot. There are a few reasons this might happen:

• You've disabled JavaScript in your web browser.
• You're a power user moving through this website with super-human speed.
• You've disabled cookies in your web browser.
• A third-party browser plugin, such as Ghostery or NoScript, is preventing JavaScript from running. Additional information is available in this support article .

To regain access, please make sure that cookies and JavaScript are enabled before reloading the page.

• How we are governed
• Constituencies
• PMNCH Strategy, Annual Reports and Workplans
• PMNCH history
• Secretariat
• Academic, Research and Training Institutes
• Adolescents and Youth
• Donors and Foundations
• Global Financing Mechanisms
• Health-Care Professional Associations
• Inter-Governmental Organizations
• Non-Governmental Organizations
• Partner Governments
• Private Sector
• United Nations Agencies
• Working Groups
• Governance Reform
• Executive Committee

• Focus areas
• Maternal, newborn and child health (including stillbirths)
• Sexual and reproductive health and rights
• Adolescent health and well-being
• Knowledge Synthesis
• Partner Engagement
• Campaigns and Outreach

• PMNCH E-Blast
• Headlines »

• Tools and toolkits
• Publications
• Knowledge summaries
• Humanitarian and fragile settings
• All publications
• Economics and financing
• Climate change

• Becoming a partner
• Constituency focal points
• PMNCH Request for Proposals and Job Board
• Partner social media assets
• Digital Advocacy Hubs

• News and events /

WHO: Scientist (Sexual and Reproductive Health and Research)

Focussing on adolescent sexual and reproductive health and rights the incumbent will:

(1) Serve as the lead and senior technical and scientific reference in adolescent sexual and reproductive health and rights among the peer community, Member States, HRP UN cosponsors and other UN Agencies, WHO Regional and Country Offices, and other stakeholders and will provide authoritative advice on adolescent sexual and reproductive health and rights when requested.

(2) Oversee, develop and implement a research agenda and research priorities, informed by contemporary global health challenges, the scientific expertise and literature, recommendations of HRP advisory bodies, input from Member States and cosponsoring agencies, colleagues, managers and other stakeholders. Based on research priorities developed, the incumbent will ensure the development and establishment of strategic and operational plans for research and human capital development in this area.

(3) Lead and oversee research activities, including:(i) the conceptualization and development of research protocols, (ii) identification and recruitment of national partner research institutions, (iii) conceptualization and production of research agreements, (iv) organization of scientific meetings, (v) coordinate the facilitation of training workshops, (vi) conducting research site monitoring visits, (vii) collaborations with data management companies or organizations, (viii) oversight of the network of institutions participating in the research projects, (ix) analysis, write-up and publication of results, (x) evaluation of processes and results in preparation for subsequent research projects.

(4) Coordinate the strengthening of the capacity of research centres to ensure high-quality research that is compliant with WHO Institutional scientific and ethical standards.

(5) Synthesize evidence, carry out and commission systematic reviews of the literature in the field, input to evidence-based clinical and managerial guidelines.

(6) Ensure the writing and preparation of technical progress reports to governing and advisory bodies, WHO reporting requirements, and donors.

(7) Ensure adequate publication, conceptualize and draft manuscripts in the scientific literature of research outputs, and lead related communications activities to strengthen the dissemination, adaptation and use of WHO and HRP outputs by WHO Member States, working in close collaboration with the HRP communications team and the WHO Department of Communications.

(8) Build and maintain strong partnerships with governments, NGOs, private sector, and other stakeholders to generate and use evidence for increased access to comprehensive sexual and reproductive health and rights.

(9) Lead fundraising activities for increased financial support in this area of work.

(11) Carry out other tasks, as required by the Unit Head and Director.

• Project management

What does a product marketing manager do?

Georgina Guthrie

August 28, 2024

Creating a product is only half the battle. It may look like some companies, like Apple , hit a home run on great design alone. But behind the scenes, there’s a team of marketing pros researching their target audience, finding a niche for the product, and creating killer messaging that makes the product irresistible to its end users. 

When fail rates are so staggeringly high (95% or thereabouts), putting some thought into the marketing side of things isn’t just wise — it’s essential. And such an important job calls for a highly specialized role. Enter the product marketing manager, or PMM for short. Let’s take a closer look at this unique and varied role.

What is a product marketing manager?

The product marketing manager leads the charge in promoting a product to its end users.

They know customer needs and industry trends inside-out. Armed with this knowledge, they skillfully position the product in its market niche, shaping its messaging and pricing strategies in a way that maximizes sales potential.

As well as being product cheerleaders, they are cross-functional collaborators. They bridge the gap between the dev team and end users, translating techy product details into customer-centric benefits and customer needs into actionable features.

In a nutshell, they help bring a product to market and lead it to success. They define the product’s proposition, positioning, and messaging, making sure each component pulls its weight and delights end users.

What are some typical product marketing manager goals?

Let’s take a look at the broader goals that drive their daily activities.

• Market penetration and growth: PMMs want to boost the product’s market share. This process includes analyzing competitors , running in-depth market research, learning about market trends, getting to know the various users, and developing strategic plans to position the product — including targeting high potential segments/markets with irresistible messaging.
• Customer acquisition and retention: PMMs focus on keeping existing customers and attracting new ones. This involves a blend of analytics (they use tools like Net Promoter Score [NPS] to gauge customer satisfaction and loyalty), and refining strategies to boost retention.
• Revenue generation and ROI: PMMs work with sales teams to boost product sales and smash revenue targets.
• Brand awareness and positioning: PMMs help make the product both visible and well regarded by creating strong brand messaging. They also manage the PR side of things as part of this.
• Sales support and enablement: PMMs equip sales teams with the tools they need to do the job well (like CRM systems). They help create strong go-to-market strategies, launch campaigns, and gather feedback through win-loss interviews to improve their strategies.
• Linchpin duties: They work with a range of teams and people to keep every effort aligned with business goals.
• Continuous improvement and innovation: PMMs stay ahead by keeping up with market trends, customer feedback, and technology. They automate repetitive tasks, freeing them up to work on strategic initiatives.

What does a PMM not do?

PMMs concentrate on marketing strategies and execution, but not on the techy specifications or development processes — product managers and engineering teams take care of this. 

They’re not involved with direct sales, either. Instead, they lend a hand to the sales team by offering training, tools, info, and creating sales collateral. Drawing these clear boundaries of responsibility helps each team focus on their strengths.

What are some typical product marketing manager responsibilities?

The above goals translate into various tasks. A blend of strategic planning and tactical execution, the role is a varied one. 

• They develop a go-to-market strategy: This involves market research to understand customer needs and behaviors. 
• They suss out the competition: They find opportunities for differentiation. 
• They position the brand/product: They create positioning statements and messaging frameworks that clearly show off the uniqueness of the product.
• They offer support during lift-off: PMMs are heavily involved in the execution phase. They collab with a range of teams (development, sales, and customer support primarily), to make sure the product hits those market needs. 
• They create marketing materials: Product brochures, web content, and sales presentations all fall under their remit. They also help plan marketing campaigns across digital, social, email, and the event space. 
• They manage the product life cycle: They monitor product performance metrics , gather customer feedback , and analyze market trends to shape future updates. Doing this on a rolling basis keeps the product relevant. 
• Market research : They get to know customer needs and market trends. This involves collecting and analyzing data to spot trends and opportunities. 
• Positioning and messaging : They develop strong, clear messaging that showcases the product’s uniqueness and value. 
• Collaboration with product management : Working closely with product managers, they ensure product features are aligned with business roadmaps and market requirements. This keeps marketing efforts in sync.

How does the product marketing manager work with the team?

They work in tandem with product managers to really understand the product roadmap , then team up with the marketing department to turn those insights into irresistible marketing messages and PR. They also work with the sales team, helping them do their part in selling the product with training and sales enablement materials. 

Essentially, PMMs support a range of people to make sure they showcase the product’s value as best they can. 

Where do they report?

They’ll typically check in with the head of marketing or the chief marketing officer (CMO). 

In some places, they might report to a senior product manager or director of product marketing, depending on the company’s structure. This helps them stay in tune with wider business goals while they chew over the techy details with development teams. 

How does the PMM work with the wider organization?

PMMs make sure every department understands the product’s value proposition and strategic goals. This coordination keeps any marketing plans both feasible and effective. By facilitating open communication across the organization, PMMs help everyone work towards shared objectives, and the product’s market success. 

Do you really need a product marketing manager?

It largely depends on the size of your business and complexity of your offering. 

For companies with a broad product portfolio or those in super competitive markets, having a PMM can be a big help. They bring specialized skills that help you launch and sustain your product. They also take some of the load off your marketing team, as well as help break up silos between the engineering nerds and the sales people.

For smaller companies or startups, the roles of product management and marketing might blend until the company grows big enough to justify a dedicated PMM. 

In these cases, a PMM could bring you a strategic advantage by focusing on customer needs and marketing insights, which means other team members can concentrate on their core responsibilities.

A typical day in the life of a product marketing manager

‘Typical day’ is something of a misnomer, since the PMM’s role encompasses a range of diverse tasks. But generally speaking, it’ll involve a mix of strategic planning, cross-functional collaboration, and marketing activities. Here’s a general overview to give you a flavor. 

Rise and shine! 

The day often kicks off with a marketing review. They’ll dive into analytics and metrics, which helps the team work out how well the campaign is doing. From there, they’ll adjust the strategy if needed. A quick team meeting or stand-up might follow where people have the chance to chat about the day’s priorities.

Mid-morning

Next, they’ll typically team up with product managers and development teams. This time might include attending meetings where they’re filled in on product progress, upcoming features, timelines, and so on. They might also take part in brainstorming sessions to get ideas for refining the product positioning and messaging.

Their focus might shift to content creation, campaign planning and execution. This will involve writing product copy (or working with the copywriters who do), developing marketing collateral, creating sales presentations, and coordinating with the design team on visual assets. At some point, they’ll also stop for lunch. 

Late afternoon

After lunch, they might say hi to the sales team, offering training and gathering feedback on customer interactions. Their goal with these interactions is to make sure everyone’s  well-equipped with the latest product info and marketing materials to really help the product shine in the eyes of those who use it. They might also review customer feedback themselves back at their desk, running win-loss interviews, and analyzing market trends.

Almost home time. The day might end with strategic planning and reflection. PMMs will look back on the day’s accomplishments, and prep for upcoming deadlines and tasks. They might also dedicate some time to professional development, like reading industry news, attending webinars, or networking with peers.

How to become a product marketing manager

You’ll want a combination of education, experience, and a specific skill set (which you can develop if you don’t have the required talents already). Here’s a step-by-step guide.

1. Education

Start with a bachelor’s degree in marketing, business, or a related field. A solid educational foundation equips you with essential marketing and business know-how. Advanced degrees, like MBAs, aren’t necessary — but they can give your qualifications a boost and open up more opportunities. It also shows employers you really mean business (no pun intended).

2. Get relevant experience

Entry-level positions in marketing, sales, or product management are great starting points. Look for roles that offer exposure to market research, campaign management, and cross-functional collaboration — all of which form the basis of the fully-fledged PMM role.

3. Develop those essential skills

Focus on honing skills critical to product marketing: strategic thinking, communication, data analysis, and project management, to name the big hitters. Proficiency in digital marketing tools, CRM systems, and analytics platforms is also a must. Consider getting yourself certifications in digital marketing, product management, or market research to bolster your skill set.

4. Networking

It’s who you know! Build a professional network through industry events and online communities. Networking can give you insider insights, not to mention mentorship opportunities and job leads. Engage with pros in the field to learn about best practices and stay ahead of industry trends.

5. Seek out mentorship and guidance

Speaking of networking — find mentors who are experienced PMMs or marketing pros. They’ll be able to offer guidance, share their experiences, help you navigate your career path, and give you valuable industry connections. Of course, not everyone will have the ability to do this, but a polite email might just land you the mentor you want.

6. Stay updated with industry trends

Keep on top of the latest trends in product marketing and buyer behavior. Reading industry publications and taking part in professional development courses can help you stand out from the crowd.

7. Look for advancement opportunities

As you gain experience and develop your skills, look for opportunities to take on more. This could range from leading marketing campaigns, to managing product launches or stepping into a supervisory role. Showing off your ability to drive results and lead projects puts you in good stead for advancement into a PMM role. 

Essential skills you need to become a product marketing manager

To be a great PMM, you need that sweet-spot blend of techy know-how, strategic thinking, and great interpersonal skills. 

• Analytical ability: PMMs need to dive into market data and make sense of it. This includes customer feedback, and performance metrics to scout trends and opportunities.
• Communication skills: PMMs need strong written and verbal abilities so they can create irresistible messaging. They also need to communicate well with a range of disciplines and backgrounds.
• A strategic head: They have to develop long-term strategies that fuel business goals. And to do this, they also need to understand the competitive landscape, and use what they know to plan ahead.
• Project management know-how: Juggling multiple projects, coordinating efforts, and efficiently allocating resources is a must.
• Customer empathy: They need a solid understanding of customer needs to guide product improvements and marketing strategies.
• Adaptability: They need agility to adjust strategies in response to changing market conditions.
• Creativity: Out-of-the-box thinking is a must for developing unique marketing campaigns and solving challenges.
• Great collaboration: PMMs need strong interpersonal skills to build relationships and encourage cross-team collaboration with product, sales, and customer support teams.
• Technical proficiency: Familiarity with digital marketing tools, CRM systems, and analytics software for streamlined processes and data analysis is a must.
• Qualitative and quantitative skills: Ability to gather and interpret both qualitative and quantitative data , including customer feedback and statistical analysis.

Product management tools keep the team on track 

Efficiency is a top priority for PMMs. And few things help you juggle multiple tasks like product management software . 

With Backlog, our own tool, you can set milestones, assign tasks, and manage multiple timelines all from one place. And because it’s cloud-based, everyone — from external stakeholders to the CEO — can log in, track progress, leave comments and collaborate. Plus, thanks to automation features, the tool shoulders some of the more repetitive tasks, leaving PMs free to focus on strategic initiatives and ultimately drive better results for their products and their company.

Mastering project management with Kanban flow

Product Owner vs. Product Manager: understand the differences

Subscribe to our newsletter.

Learn with Nulab to bring your best ideas to life

• Artificial Intelligence
• Generative AI
• Cloud Computing
• CPUs and Processors
• Data Center
• Edge Computing
• Enterprise Storage
• Virtualization
• Enterprise Buyer’s Guides
• Internet of Things
• Network Management Software
• Network Security
• United States
• Newsletters
• Foundry Careers
• Terms of Service
• Privacy Policy
• Cookie Policy
• Copyright Notice
• Member Preferences
• About AdChoices
• E-commerce Links
• Your California Privacy Rights

Our Network

• Computerworld

Google Ireland bid to build new data center rejected

Dublin county council cites insufficient renewable energy plans as key reason for turning it down..

A bid by Google Ireland to build a third data center at Grange Castle Business Park in south Dublin has been rejected by local politicians, in part  due to what was described as “the lack of significant on-site renewable energy” to power the proposed facility.

According to an article that appeared Tuesday in the Irish Times, the planning refusal by South Dublin County Council, “comes amid a growing backlash against data centres here amid a spike in the amount of energy they use at a time when Ireland is struggling to meet its targets to cut carbon emissions.”

In June, Google submitted a proposal that called for the creation of a 72,400 square meter data storage facility that will “incorporate data halls with associated support areas, a high voltage compound, offices and staff facilities, a loading area, mechanical and electrical yards, internal and external utilities, security fence and gates, landscaping including acoustic screening, … stormwater attenuation ponds, additional internal vehicle and pedestrian infrastructure, together with ancillary buildings and site infrastructure.”

Jeremy Roberts, senior research director at Info-Tech Research Group, said the rejection of the bid is not surprising.

“The world is increasingly digital, and the digital and physical worlds intersect at the data center where organizations big and small host their IT infrastructure,” he said. “Data centers are notoriously power-hungry, and given the imminent threat of climate change and the importance of sustainability, it is not always clear that new data centers will get the green light.

 The fact that the decision was made by a local council is proof, he said, that “there is no such thing as ‘the government.’ There are multiple competing centers of power that have varying influence and interest in any project. A state government might disagree on the details with a national government, while the municipal folks might not approve zoning changes. A utility might have a different opinion as well. All of these factors can complicate any application to build a data center, and their perspectives should be well understood.”

Some other points to consider, said Roberts, include:

• Grid capacity : Energy, he said, “must be generated. It is not always possible to support data centers with existing capacity, especially as data center power consumption spikes with AI.” 
• Carbon footprint : How power is generated matters, he pointed out. ”If the source of energy is clean or renewable, like wind or nuclear, a data center can be an excellent addition. If you are burning coal to power the data center, that can conflict with carbon neutrality and other goals for companies and governments.”
• Local climate : Cooling is expensive, he said, “so consider placing a data center somewhere more favorable weather-wise. It is probably more expensive to cool a data center in Arizona than it is in Norway.”
• Proximity to things that matter : Having staff nearby to manage the facility, and proximity to end users to control latency and other performance needs are also two big considerations for data center placement, he said, adding that “data centers should also be placed in a structured fashion so that a cloud provider can create resiliency in the event of a natural disaster or other localized interruption.”
• Laws and regulations : “Different jurisdictions have different requirements, and compliance may be complicated if a provider chooses to host services in an area with an active regulatory state,” he said. 

Related reading:

• Data center construction skyrockets as vacancies drop
• Gartner: AI to drive 10% jump in spending on data center systems
• Data center cooling: Pros and cons of air, liquid and geothermal systems

Related content

Vmware explore 2024: latest news and insights, fujitsu, osaka university develop quantum circuit generator to hasten pace to commercialization, vmware cloud foundation gains ai model store, other updates, newsletter promo module test.

Paul Barker is a freelance journalist whose work has appeared in a number of technology magazines and online with the subject matter ranging from cybersecurity issues and the evolving world of edge computing to information management and artificial intelligence advances.

More from this author

Ai test bed proposal for doe a good first step: analyst, small step towards green data center power tested in dublin, should enterprises be concerned about intel’s crashing cpu flaw, cisco expected to follow dell with layoff announcement: report, google’s scientists lift lid on effingo at sigcomm 2024, launch puts china firmly in the communications satellite game, hpe-juniper merger faces antitrust inquiry in uk, show me more, cisco ties appdynamics to microsoft azure for cloud app management.

Broadcom’s vision for VMware highlights private clouds, private AI

Nvidia fixes chip glitch, says production back on schedule for Q4

Has the hype around ‘Internet of Things’ paid off? | Ep. 145

Episode 1: Understanding Cisco’s Converged SDN Transport

Episode 2: Pluggable Optics and the Internet for the Future

How to use the Grep command

How to see how many days passed since the beginning of the Linux epoch

How to use the pv command

Samantha Putterman, PolitiFact Samantha Putterman, PolitiFact

Leave your feedback

• Copy URL https://www.pbs.org/newshour/politics/fact-checking-warnings-from-democrats-about-project-2025-and-donald-trump

Fact-checking warnings from Democrats about Project 2025 and Donald Trump

This fact check originally appeared on PolitiFact .

Project 2025 has a starring role in this week’s Democratic National Convention.

And it was front and center on Night 1.

WATCH: Hauling large copy of Project 2025, Michigan state Sen. McMorrow speaks at 2024 DNC

“This is Project 2025,” Michigan state Sen. Mallory McMorrow, D-Royal Oak, said as she laid a hardbound copy of the 900-page document on the lectern. “Over the next four nights, you are going to hear a lot about what is in this 900-page document. Why? Because this is the Republican blueprint for a second Trump term.”

Vice President Kamala Harris, the Democratic presidential nominee, has warned Americans about “Trump’s Project 2025” agenda — even though former President Donald Trump doesn’t claim the conservative presidential transition document.

“Donald Trump wants to take our country backward,” Harris said July 23 in Milwaukee. “He and his extreme Project 2025 agenda will weaken the middle class. Like, we know we got to take this seriously, and can you believe they put that thing in writing?”

Minnesota Gov. Tim Walz, Harris’ running mate, has joined in on the talking point.

“Don’t believe (Trump) when he’s playing dumb about this Project 2025. He knows exactly what it’ll do,” Walz said Aug. 9 in Glendale, Arizona.

Trump’s campaign has worked to build distance from the project, which the Heritage Foundation, a conservative think tank, led with contributions from dozens of conservative groups.

Much of the plan calls for extensive executive-branch overhauls and draws on both long-standing conservative principles, such as tax cuts, and more recent culture war issues. It lays out recommendations for disbanding the Commerce and Education departments, eliminating certain climate protections and consolidating more power to the president.

Project 2025 offers a sweeping vision for a Republican-led executive branch, and some of its policies mirror Trump’s 2024 agenda, But Harris and her presidential campaign have at times gone too far in describing what the project calls for and how closely the plans overlap with Trump’s campaign.

PolitiFact researched Harris’ warnings about how the plan would affect reproductive rights, federal entitlement programs and education, just as we did for President Joe Biden’s Project 2025 rhetoric. Here’s what the project does and doesn’t call for, and how it squares with Trump’s positions.

Are Trump and Project 2025 connected?

To distance himself from Project 2025 amid the Democratic attacks, Trump wrote on Truth Social that he “knows nothing” about it and has “no idea” who is in charge of it. (CNN identified at least 140 former advisers from the Trump administration who have been involved.)

The Heritage Foundation sought contributions from more than 100 conservative organizations for its policy vision for the next Republican presidency, which was published in 2023.

Project 2025 is now winding down some of its policy operations, and director Paul Dans, a former Trump administration official, is stepping down, The Washington Post reported July 30. Trump campaign managers Susie Wiles and Chris LaCivita denounced the document.

WATCH: A look at the Project 2025 plan to reshape government and Trump’s links to its authors

However, Project 2025 contributors include a number of high-ranking officials from Trump’s first administration, including former White House adviser Peter Navarro and former Housing and Urban Development Secretary Ben Carson.

A recently released recording of Russell Vought, a Project 2025 author and the former director of Trump’s Office of Management and Budget, showed Vought saying Trump’s “very supportive of what we do.” He said Trump was only distancing himself because Democrats were making a bogeyman out of the document.

Project 2025 wouldn’t ban abortion outright, but would curtail access

The Harris campaign shared a graphic on X that claimed “Trump’s Project 2025 plan for workers” would “go after birth control and ban abortion nationwide.”

The plan doesn’t call to ban abortion nationwide, though its recommendations could curtail some contraceptives and limit abortion access.

What’s known about Trump’s abortion agenda neither lines up with Harris’ description nor Project 2025’s wish list.

Project 2025 says the Department of Health and Human Services Department should “return to being known as the Department of Life by explicitly rejecting the notion that abortion is health care.”

It recommends that the Food and Drug Administration reverse its 2000 approval of mifepristone, the first pill taken in a two-drug regimen for a medication abortion. Medication is the most common form of abortion in the U.S. — accounting for around 63 percent in 2023.

If mifepristone were to remain approved, Project 2025 recommends new rules, such as cutting its use from 10 weeks into pregnancy to seven. It would have to be provided to patients in person — part of the group’s efforts to limit access to the drug by mail. In June, the U.S. Supreme Court rejected a legal challenge to mifepristone’s FDA approval over procedural grounds.

WATCH: Trump’s plans for health care and reproductive rights if he returns to White House The manual also calls for the Justice Department to enforce the 1873 Comstock Act on mifepristone, which bans the mailing of “obscene” materials. Abortion access supporters fear that a strict interpretation of the law could go further to ban mailing the materials used in procedural abortions, such as surgical instruments and equipment.

The plan proposes withholding federal money from states that don’t report to the Centers for Disease Control and Prevention how many abortions take place within their borders. The plan also would prohibit abortion providers, such as Planned Parenthood, from receiving Medicaid funds. It also calls for the Department of Health and Human Services to ensure that the training of medical professionals, including doctors and nurses, omits abortion training.

The document says some forms of emergency contraception — particularly Ella, a pill that can be taken within five days of unprotected sex to prevent pregnancy — should be excluded from no-cost coverage. The Affordable Care Act requires most private health insurers to cover recommended preventive services, which involves a range of birth control methods, including emergency contraception.

Trump has recently said states should decide abortion regulations and that he wouldn’t block access to contraceptives. Trump said during his June 27 debate with Biden that he wouldn’t ban mifepristone after the Supreme Court “approved” it. But the court rejected the lawsuit based on standing, not the case’s merits. He has not weighed in on the Comstock Act or said whether he supports it being used to block abortion medication, or other kinds of abortions.

Project 2025 doesn’t call for cutting Social Security, but proposes some changes to Medicare

“When you read (Project 2025),” Harris told a crowd July 23 in Wisconsin, “you will see, Donald Trump intends to cut Social Security and Medicare.”

The Project 2025 document does not call for Social Security cuts. None of its 10 references to Social Security addresses plans for cutting the program.

Harris also misleads about Trump’s Social Security views.

In his earlier campaigns and before he was a politician, Trump said about a half-dozen times that he’s open to major overhauls of Social Security, including cuts and privatization. More recently, in a March 2024 CNBC interview, Trump said of entitlement programs such as Social Security, “There’s a lot you can do in terms of entitlements, in terms of cutting.” However, he quickly walked that statement back, and his CNBC comment stands at odds with essentially everything else Trump has said during the 2024 presidential campaign.

Trump’s campaign website says that not “a single penny” should be cut from Social Security. We rated Harris’ claim that Trump intends to cut Social Security Mostly False.

Project 2025 does propose changes to Medicare, including making Medicare Advantage, the private insurance offering in Medicare, the “default” enrollment option. Unlike Original Medicare, Medicare Advantage plans have provider networks and can also require prior authorization, meaning that the plan can approve or deny certain services. Original Medicare plans don’t have prior authorization requirements.

The manual also calls for repealing health policies enacted under Biden, such as the Inflation Reduction Act. The law enabled Medicare to negotiate with drugmakers for the first time in history, and recently resulted in an agreement with drug companies to lower the prices of 10 expensive prescriptions for Medicare enrollees.

Trump, however, has said repeatedly during the 2024 presidential campaign that he will not cut Medicare.

Project 2025 would eliminate the Education Department, which Trump supports

The Harris campaign said Project 2025 would “eliminate the U.S. Department of Education” — and that’s accurate. Project 2025 says federal education policy “should be limited and, ultimately, the federal Department of Education should be eliminated.” The plan scales back the federal government’s role in education policy and devolves the functions that remain to other agencies.

Aside from eliminating the department, the project also proposes scrapping the Biden administration’s Title IX revision, which prohibits discrimination based on sexual orientation and gender identity. It also would let states opt out of federal education programs and calls for passing a federal parents’ bill of rights similar to ones passed in some Republican-led state legislatures.

Republicans, including Trump, have pledged to close the department, which gained its status in 1979 within Democratic President Jimmy Carter’s presidential Cabinet.

In one of his Agenda 47 policy videos, Trump promised to close the department and “to send all education work and needs back to the states.” Eliminating the department would have to go through Congress.

What Project 2025, Trump would do on overtime pay

In the graphic, the Harris campaign says Project 2025 allows “employers to stop paying workers for overtime work.”

The plan doesn’t call for banning overtime wages. It recommends changes to some Occupational Safety and Health Administration, or OSHA, regulations and to overtime rules. Some changes, if enacted, could result in some people losing overtime protections, experts told us.

The document proposes that the Labor Department maintain an overtime threshold “that does not punish businesses in lower-cost regions (e.g., the southeast United States).” This threshold is the amount of money executive, administrative or professional employees need to make for an employer to exempt them from overtime pay under the Fair Labor Standards Act.

In 2019, the Trump’s administration finalized a rule that expanded overtime pay eligibility to most salaried workers earning less than about $35,568, which it said made about 1.3 million more workers eligible for overtime pay. The Trump-era threshold is high enough to cover most line workers in lower-cost regions, Project 2025 said.

The Biden administration raised that threshold to $43,888 beginning July 1, and that will rise to $58,656 on Jan. 1, 2025. That would grant overtime eligibility to about 4 million workers, the Labor Department said.

It’s unclear how many workers Project 2025’s proposal to return to the Trump-era overtime threshold in some parts of the country would affect, but experts said some would presumably lose the right to overtime wages.

Other overtime proposals in Project 2025’s plan include allowing some workers to choose to accumulate paid time off instead of overtime pay, or to work more hours in one week and fewer in the next, rather than receive overtime.

Trump’s past with overtime pay is complicated. In 2016, the Obama administration said it would raise the overtime to salaried workers earning less than $47,476 a year, about double the exemption level set in 2004 of $23,660 a year.

But when a judge blocked the Obama rule, the Trump administration didn’t challenge the court ruling. Instead it set its own overtime threshold, which raised the amount, but by less than Obama.

Support Provided By: Learn more

Educate your inbox

Subscribe to Here’s the Deal, our politics newsletter for analysis you won’t find anywhere else.

Thank you. Please check your inbox to confirm.

This site uses cookies to optimize functionality and give you the best possible experience. If you continue to navigate this website beyond this page, cookies will be placed on your browser. To learn more about cookies, click here .