Research data management (or RDM) encompasses the organization, storage, preservation, and sharing of data collected and used in a research project. It involves the everyday management of research data during the lifetime of a research project (for example, using consistent file naming conventions). It also involves decisions about how data will be preserved and shared after the project is completed (for example, depositing the data in a repository for long-term archiving and access).
There are many reasons for managing research data, including:
- Increasing research efficiency
- Improving research integrity
- Making research outputs more visible
- Enabling collaboration
- Complying with funder and journal policies
Fundamentally, the idea is to manage a research project such that the research conclusions can be independently verified. This might include what is commonly thought of as “data” (e.g. numeric measurements, etc.), but it depends on the discipline. For example, text, images, code, and archival collections (and more) may also be used. So, another way to think about data is as “evidence.” The “evidence” that supports research conclusions should be managed in such away that the original researcher or another can locate that evidence and understand how it contributed to the overall research.
Our website includes resources for organizing, storing, preserving, and sharing data, and our monthly Data Nudge is a quick and easy way to learn about good data management practices.