Research data management (RDM) is the process of creating organized, documented, accessible, and reusable research data. It helps with research organization and comprehensibility, making onboarding to projects easier and allowing for ease in communicating research results to colleagues and the public. RDM helps improve research workflows to make them more resilient, efficient, maintainable, and reproducible.
RDM is research self care! The actions taken today to organize your research data will help future you feel more in control of your research project, and make it easier to share your results with your colleagues, companies, and relevant stakeholders in your field.
Taking the extra time now to manage your data will save you an immense amount of time later on in your research!
Want to learn more? Check out our Data 101 LibGuide (https://guides.library.cmu.edu/data101) for more information and recommended practices in research data management!
Managing Your Data Science Projects: Learn Salesmanship, Presentation, and Maintenance of Completed Models by 
Information-Driven Business: How to Manage Data and Information for Maximum Advantage by Did you know that at CMU Libraries, we have several folks who have been professionally trained in RDM education and support across each academic discipline? From writing data management plans, to helping set up an RDM protocol for a lab, we can help you develop strategies for managing your data, no matter what your data looks like.
Whether you are using proprietary data or collecting your own, documenting your research data means ensuring all data used or generated is easy to understand, analyze, and reuse (if applicable). A recommended practice is to consider whether your documentation would address the following situations:
1) If someone from another discipline outside of my own were to look at the data, would the documentation help provide important context to understanding the data?
2) If someone were to look at this data in 20 years, would they be able to understand why and how it analyzed a certain way?
3) If someone wanted to reuse my data, would they know which software to use to replicate my findings?
Types of research data documentation may include, but are not limited to:
It can be incredibly useful to use a consistent filenaming convention when naming your personal research files. Not only does this keep you more organized, but it also makes it easier for others to understand the contents of a file when sharing data. Some recommended practices for filenaming include:
1. Avoid using spaces, periods, and special characters in your filename. It's always a safe bet to stick with using underscores when separating elements of your filename!
Example: xxxx_xxx_xxx.pdf
2. Your filename should give enough context on the contents of the file, and may include elements such as study title, your initials, the date, version number, and any other helpful information. Try to strike a balance between including enough helpful contextual information and keeping your filename short, ideally under 30 characters.
3. Dates should be formatted in ISO 8601 format (YYYYMMDD), the internationally-accepted way to represent date and time.
