The Data Management Plan (DMP) is not only needed to please the funders.
It minimises the loss of data, improves reuse, and accelerates the process of collaboration because it makes your research process visible at the very beginning. Once you record that process of data collection, storage and sharing, you create a workflow that can be continued even after a single project is over.
The Global Research Society and other communities often talk about reusable workflows to do global research and share useful data management strategies across fields. This guide provides you with a template that is ready to use and adapt, as well as has field-specific differences in clinical, social science, and computing research.
A well-organised DMP ensures your information is clean and your project is on schedule, whether you have a larger team or work alone.
What Reviewers and Funders Expect in a Strong DMP?
There are the basic elements that keep doing their rounds and recurring.
- Whatever your discipline, funders seek some predictable factors.
- Discuss what data will be created, standards and metadata, storage, security, sharing, when and how, and your authorities.
- Access schedules and the conservation strategies are also relevant.
- Maintaining such components concise and thorough manner meets the majority of needs.
Keep It Realistic and Scoped
Some funders limit length.
- The NSF, by way of example, limits data-management and sharing plans to two pages.It is significant to think proportionately.
- You do not have to share sensitive data publicly, yet you must have an access, security, and reuse plan where necessary.
- A realistic DMP employs a realistic approach and demonstrates careful planning.
Copy-Ready DMP Template to Paste into a Proposal
1) Project Snapshot
Name of project, project team, data custodian or contact, project schedule, and investment background.
2) Data Types and Outputs
Both raw and processed data, as well as derived data, will be created.
Indicate file formats, e.g. CSV, JSON, TIFF, FASTQ or MP4.
Calculate data volume and growth rate. Separate scientific data and any supporting documents, such as protocols or instrument specifications.
3) Tools, Software, and Code
Document analysis software, code repositories, versioning plans, and dependencies.
How will you replicate to different environments?
4) Documentation and Metadata
- Write README files describing the structure of folders and content.
- Select metadata schemas that are appropriate to your domain.
- Produce codebooks or variable dictionaries.
Name your documents and organise folders in a way that allows other people to know your data.
5) Storage, Backup, and Security
- Stating your environment of working data, institutional or cloud storage.
- Description of backup frequency and retention.
- Explain access controls in terms of least-privilege.
- Encryption of notes and policies of devices.
6) Ethics, Legal, and IP
- Discuss human subjects safeguards and data sharing.
- Elaborate de-identification plans.
- Select licenses like the CC BY for the shareable data.
- Explain intellectual property rights and any partnership agreements.
7) Sharing, Access, and Timelines
State what you will say and what you will not say.
- Establish the location of your shares, like archives of discipline or institutional sources.
- Indicate the time of sharing, via publication, via an embargo or via quality assurance.
- Define access levels between open and controlled.
8) Preservation and Long-term Archiving
Explain data retention, information preservation formats, repository options, as well as data citation strategies.
9) Roles, Oversight, and Budget
- Allocate tasks for data management.
- Note audit cadence.
- Add in the expenses of storage, time and money of curation and the cost of the repository.
Dimensions in the Field: You may plug into the same template
- Clinical and Biomedical Variation: In case of sensitive health information, reinforce consents and strategise to restrict accessibility.
- Anonymity measures and strong security. State sharing boundaries based on participant rights and regulatory obligations.
- Social Sciences and Qualitative Variation: In interviews, focus groups, or ethnographic information, expect to manage transcripts, develop codebooks, and anonymise.
- Provide secured copies of extracts to be shared and conceal the identities of subjects. Secondary analysis-related information contained in documents.
Differentiation in Computer Science and Engineering
In the case of code-heavy projects, focus on version control, containerization, and dependency management.
- Plan for dataset versioning.
- Think of model cards or data cards, which record the intended uses and constraints.
- Record the compute environment requirements to make them reproducible.
Variation in the Geospatial and Remote Sensing
In GIS data, the coordination systems of documents, sensor records, and processing procedures. Select repositories that maintain spatial metadata and allow relevant access.
Writing Shortcuts and Staying On Track With Funders
Apply a tool to align funds.
- The DMPTool supplies public templates, which correspond to funder requirements.
- The Digital Curation Centre provides resources and checklists in the same way.
- These tools are time-saving and can help you keep up with expectations.
- These resources are often discussed in GRS communities as the initial point of new projects.
Conclusion
A robust data-management plan is useful to your project even after the grant proposal is received. It helps keep your crew on the right path, your data safe, and your future self-capable of locating and reusing your work. Moreover, work with the core template and insert field variations that suit your field. Make your DMP a living document and revise it when new approaches are discovered and more questions are presented.
Data management has become a part of global research practice, rather than an administrative concern, for researchers in the Global Research Society and the wider research community. When your data becomes findable, accessible, interoperable and reusable, you can get it to be of value to more than just a study. That is the FAIR attitude, and it begins with a plan.
