Data Management Plans
As recognition of the importance of data management and sharing grows, it is increasingly common that funders of research and institutions require a Data Management Plan (DMP) to be created for grants and projects. In the US, many federal funders require DMPs at the time of grant submission including the National Science Foundation (NSF) and the National Institutes of Health (NIH), which recently released a new policy requiring Data Management and Sharing Plans beginning in January 2023. The requirement for DMPs and data sharing is similar with national research funders in many countries as well as with private funders; A few examples include: Canadian Institutes of Health Research and Canadian funders, China, European Research Council, Qatar National Research Fund, Swiss National Science Foundation, Swedish Research Council, UK Medical Research Council, UK Economic and Social Research Council, Australian National Health and Medical Research Council, Wellcome Trust, Moore Foundation, and Gates Foundation). Even if a DMP is not required by a funder or institution, considering plans for data management and sharing proactively at the start of a project is an excellent research practice.
Overall, a DMP asks the researcher to consider how data and associated products of research such as code or other files, will be handled across the life span of a project and beyond. This includes how the data will be stored, secured, accessed, documented, formatted, and versioned. The plan should also include where and when data will be shared, if it will be made publicly available, how it will be licensed for reuse, and how and for how long data will be archived. Both general best practices for data management and archiving, should be considered as well as any discipline-specific practices for file formats, metadata, and documentation that would support discovery and reuse of the data. If your research involves human subjects or other sensitive information, ethics, consent, and de-identification of data should also be addressed.
At academic institutions, support for data management including guidance on creating DMPs is often available from experts such as data librarians, so we recommend checking the websites of the library and the research office at your institution to get assistance.
There are also resources such as DMPTool and DMPonline that track funder requirements for DMPs and offer both examples of DMPs and templates to write your own DMP according to the funder’s requirements.
DMPTool List of Funder Requirements
DMPonline List of Funder Requirements
You may also find our Guide to Best practice for managing your outputs on Figshare helpful.
The use of an established repository (see Re3data for information on more than 2,000 research data repositories), whether discipline-specific, general, or institutional, has many benefits over sharing data via a website or a link to a file in the cloud. Established data repositories follow community standards for metadata, indexing, security, and preservation so that researchers can share their data following these best practices without having to worry about this infrastructure. This allows open data in a repository to be more FAIR (findable, accessible, interoperable, and reusable) and to have a greater impact including to be downloaded, reused, and cited so researchers can get credit for their shared data.
Some funders recommend a variety of data repositories or repository characteristics for researchers to consider (e.g. NIH, DOT, Gates, Wellcome). If there is a repository that is specific to the research discipline or methodology, it is recommended and sometimes even required (e.g. genomic data), that appropriate data be deposited there to facilitate discovery and reuse. However for all other data and research materials for which a discipline-specific repository does not exist or isn’t appropriate, trusted generalist repositories like Figshare are a suitable choice for sharing data responsibly.
Using Figshare for compliance in your data sharing plans
Figshare.com is an appropriate repository for researchers to permanently store the datasets and other materials produced from their research and to include in their data management plans submitted to funders. Figshare’s repository infrastructure is trusted by over 150 research institutions to make their research openly available. Since its founding in 2011, Figshare has provided a way for researchers to openly publish all of the results of their research, including everything from tabular data to images and video to articles to software and code. Figshare makes it easy to share your data in a way that is discoverable and reusable and to get credit for all of your work.
Figshare offers several key advantages for data sharing:
- Persistence - Figshare provides a unique persistent identifier, a DataCite Digital Object Identifier (DOI), for each published item and does not allow deletion of content. Figshare also backs up all public content using Chronopolis (Preservation Policy). Items will be maintained in Figshare for the life of the repository and for a minimum of 10 years (contact us at email@example.com for longer preservation).
- Provenance - Figshare requires descriptive metadata for each item according to community standards and encourages documentation that provides comprehensive context for the data. Figshare endorses the FAIR principles for Findable, Accessible, Interoperable, and Reusable data. Each item in Figshare must include a machine-readable license describing how the data can be reused and funding sources, associated publications, and other references can also be included in the metadata.
- Discoverability - All content in Figshare is indexed so that it is searchable across Figshare, Google, Google Dataset Search or Google Scholar (based on item type), and other databases including Dimensions.
- Impact - Figshare DOIs are citable and trackable and can be reported as research products in funding reviews and biosketches. Views, downloads, Altmetrics, and citations are tracked for each item with citation counts being collected from the full text of journal articles and preprints.
- Flexibility and Ease of Use - Figshare accepts all file formats in order to keep the barriers to data sharing low for researchers. This allows researchers to share the data according to the best practices of their research communities as well as allows flexibility for certain methods or disciplines where specific file types are required for data access or reuse.
- Figshare accepts all file types and previews over 1,000 in the browser. You can add just a single file or thousands of files to an item as well as create collections of related items that support the same paper, project, or grant. We can accommodate single files up to 5TB.
- If you have large files or are uploading many files at once, Figshare provides an FTP uploader and an open API available for both upload and download of files and metadata.
- Figshare offers 20GB of storage and individual file uploads up to 20GB. We also offer Figshare+, our Figshare repository for FAIR-ly sharing big datasets that allows for more storage, larger files, additional metadata and license options, and expert support. There is a one-time cost associated with Figshare+ to cover the cost of storing the data persistently ad infinitum. Find out more about Figshare+ including transparent data publishing charge pricing that can be included in your grant budget or get in touch at firstname.lastname@example.org to plan for data sharing on Figshare+.
Examples and prompts to describe sharing data in Figshare in a Data Management Plan:
- Research data from the project will be deposited in Figshare, an established data repository that makes the results of research discoverable and freely available to view, download, and reuse. Figshare maintains a digital repository infrastructure dedicated to data preservation and continuity of access, data integrity, and security, and has endorsed the TRUST principles for digital repositories.
- State what data will be shared in Figshare (e.g. raw data, processed data, de-identified data, data supporting publications, null results, etc.)
- State when data will be deposited. E.g. Data will be deposited in Figshare and made publicly available at the time a preprint or peer-reviewed publication associated with the data is published. New releases of the data will be published as subsequent versions of the same DOI if additional analyses are shared. Unpublished data including null results will be made open at the end of the award period.
- State what file formats the data will be shared in so that they can be reused.
- State what documentation will be included with the files to provide context and enable reuse such as a README file, data dictionary, or codebook.
- State what other materials will be shared. Other materials supporting the data and data analysis and visualization including (e.g. code, software, images, video, workflows, anything) will also be deposited in Figshare and linked to related datasets.
- Each dataset published in Figshare will be assigned a unique permanent identifier, a DOI (Digital Object Identifier) that is version controlled and can be cited and tracked.
- All files published in Figshare will also include metadata that conforms to community standards for DataCite DOIs. This includes a title, authors, description, keywords, categories, a machine-readable license, item type, funding sources including grant IDs, associated publications, and references.
- State how the data and other materials will be licensed for reuse
- State how the data will be described so that it can be discovered
- State how long the data will be preserved. E.g. Items will be maintained in Figshare for the life of the repository and for a minimum of 10 years. Figshare data is stored redundantly in the cloud and also archived via Chronopolis.
Related Figshare Resources
Guide to sharing NIH-funded research on figshare.com
Guide to sharing NSF-funded research on figshare.com
Best practice for managing your outputs on Figshare
NIH Data Management and Sharing Policy (effective 2023) - Figshare's perspective on policy highlights and guidance on data sharing
How Figshare meets NIH Desirable Characteristics for Data Repositories