<p>A DMP is a living document that describes how your research outputs will be generated, stored, used, and shared. It can change and evolve throughout your research project but it is key to efficient data management.</p>
What data will you collect and how? List the file formats you will use to collect, process, and present your data. It’s also important to consider:
Raw data: data collected from the source
Processed data: a version of that data that has been modified for analysis or visualization
Final data: the data ready to be shared in a publication or repository
What information is needed for the data to be read and interpreted in the future? Here, you should consider all the information you'll need to describe the data and provide context for your work.
What consents do you require? What copyright/ IP might be involved? Here, you’ll need to think about whether you have worked with human participants (or animals or plants) and whether you have worked with third-party data.
How will you store the data and ensure it is backed up? There are multiple factors to take into consideration, including where the data will be stored; how many copies will be made; and if your institution provides automatic backup services.
How will you preserve the data, and for how long? This could be based on any obligations to retain certain data, the potential reuse value, what is economically viable to keep, and any additional effort required to prepare the data for data sharing and preservation.
How will you share the data? The methods used to share data, such as repositories, will be dependent on a variety of factors including, the type, size, complexity, and sensitivity of data. We will explore this later in the guide.
Who will be responsible for the various data tasks throughout the research project, such as data capture, metadata production, data quality, storage and backup, data archiving, and data sharing.
The Digital Curation Centre and OpenAIRE provides lots of support and resources for developing DMPs.