Some potential benefits to good data management (from MIT Libraries' Data Management Guide):
What type of data will be produced?
Gather a clear picture of what your data will look like. Is it, for example, numerical data, image data, text sequences or modeling data? Knowing exactly what kind of data you have will inform many decisions you need to make about storage, backups and more. Image data requires a lot of storage space, so you'll want to decide which of your images, if not all, you want to retain, and where such large datasets can be housed. As for backing up your data, your research center or university may have the ability to help you. On the other hand, if you are storing images, you may quickly exceed your institution's limit for backing up individual laboratories or groups.
How much of it, and at what growth rate?
Will it change frequently?
Who is it for?
Who controls it (PI, student, lab, UC, funder)?
How long should it be retained? (e.g. 3-5 years, 10-20 years, permanently)
Not all data needs to be retained indefinitely. Figure out what's important to keep and make sure your plan for those datasets is solid.
Managing your data before you begin your research and throughout its life cycle is essential to ensure its current usability and long-run preservation and access. To do so, begin with a planning process.
Managing your data – Project Start & End Checklists (MIT Data Management Services): Data Management Checklist (PDF) with detailed resources to help researchers set up and maintain robust data management practices for the full life of a project.
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