A Data Management Plan (DMP) is both a useful guide to help focus your thinking about data management for your research, and a required piece of most publicly funded research grants. Requirements for what a DMP must include vary by funder, and your grant may have some additional requirements that are unique to the section of the organization funding it, or the nature of the grant.
These are the general components of a data management plan:
Most public funding agencies require a DMP of some sort in the grant application process. You can find information about the data policies of funders at SPARC (the Scholarly Publishing and Academic Resources Coalition).
Find your funder and look for the Data Management Planning section to see what kind of DMP is required and other sections for more detail on what should be included. For quick reference, to NIH and NSF policies see below.
What kind of data is going to be recorded in your study?
There are lots of possible kinds of data, any recorded observation can be considered data, whether it’s produced by a finely-tuned sensor or on pen and paper. Some examples are: tabular, experimental, microscope images, and physical samples. Give some context for where the data is coming from and how you are recording it.
What format is the data in?
There are also lots of possible formats your data might be in, and we encourage you to use open formats that can be opened by lots of programs not just proprietary software (more info in the Standards section). Note what formats you are using.
A DMP is both to show that you are going to follow good procedures to your funders, but also a chance to think about those procedures before you start, and hopefully avoid mistakes!
What format(s) will your data be collected and stored in?
Explain what the data will be, when it is going to be collected, and why you are choosing these formats. Open formats are better than propietary ones, because more programs can open adn edit open formats. For instance, for storing tabular or spreadsheet data, .csv files are preferred over .xslx files (from Excel), because .csv files can be opened by text editors and spreadsheet programs. DataONE has some best practices and information on file formats.
What volume of data (MB/GB/TB) do you expect to collect?
Include estimates of raw data, processed data and other outputs.
What metadata will you provide to augment and inform your data?
Plan to include documentation on your methodology, analysis, a data dictionary, and hardware and software used.
The Research Data Alliance has a directory of metadata standards you can use as a template.
If you have questions about storing your data correctly and safely, contact ITS: Privacy@temple.edu
Explain how the data will be stored during the research project.
If there are different policies at different sites or stages of the project, explain them each.
If you are collecting Personal Health Information (PHI), how are you ensuring that it is stored securely and participants privacy is assured?
Note how data will be backed up and who will be responsible for that.
Explain how the data will be shared after publication, for instance in a subject-specific or institutional repository. If PHI, or other ethical issues, are involved, how will that inform your data sharing plan?
Make clear who owns the copyright and intellectual property rights to the data.
Similar to storage and security, how will the data be stored and shared securely and safely?
Who can reuse it and how?
How will the data be formatted and shared to make it useful for re-use.?
Are you using non-proprietary data formats (.csv, for example)? Are you including instructions on how to interpret the data?
Explain how the data will be preserved for future use.
Are you depositing the data in a repository that will provide preservation services?
How are you preserving physical data specimens and records?
Are you requesting money in your grant to cover preservation costs?
It's better to ask for money up front, then have to scramble for access to good preservation (required by many funders) of your data at the end of the project.
Data management planning isn't just a the document required for your grant proposal. It's a plan for you and your lab as you collection and analyze data. Here are some best practices for dealing with data on a daily basis.