Summary and Setup

The lesson encompasses best practices around Research Data Management.

The lessons herein are based on the work done by Kristin Briney in “The Research Data Management Workbook1

What is Research Data Management (RDM)

RDM involves practices and choices that simplify the process of finding, comprehending, and utilizing research data for you, your collaborators, and your future self. These practices encompass the entire data lifecycle, from collection and analysis to sharing and reuse. Research is hard enough as it is without having to fight with your files, so the goal of data management is for you to maximize your time doing research instead of spending extra time with file handling.

Why do RDM

Most researchers have spent time, at some point in their careers, digging through their computer to find a specific file that can’t be located. It’s incredibly frustrating and a waste of time and resources, especially if you end up recollecting missing data. The good news is that it is possible to avoid this situation entirely by strategically managing your data better.

Done well, RDM means:

  • Understanding of data and its collection methods, even after a year
  • Quick and easy retrieval of desired files
  • Prevention of data loss, even if your hard drive crashes
  • Clarity around the rights and responsibilities you have over your data
  • Knowledge of how and where to share your data to comply with your funder’s data sharing policy
  • Ability to pick up and easily reuse data from a past project

Key Points

  • RDM is really about helping researchers understand their data
  • RDM helps researchers keep their data safe and backed up
  • RDM helps researchers use their data even after leaving it for a long period
  • RDM allows you to more easily share your research data

  1. Briney, K. (2023). The Research Data Management Workbook. Caltech Library. https://doi.org/10.7907/z6czh-7zx60↩︎