Research data management (RDM) is about handling research data effectively and appropriately throughout the life of a research project and beyond. It refers to all aspects of creating, storing, sharing and archiving data and is an essential aspect of conducting responsible research.
It includes planning for data management at the grant application stage or before the start of a project, managing data on a day-to-day basis over the lifetime of a project, and sharing and preserving data for the long term after the completion of a project.
Research data is any information that has been collected, observed, generated or created to validate original research findings. Although usually digital, research data also includes non-digital formats such as laboratory notebooks and sketchbooks.
Some examples of research data:
Data may be raw or primary (e.g. direct from measurement or collection) or derived from primary data for subsequent analysis or interpretation (e.g. cleaned up or as an extract from a larger data set), or derived from existing sources where the rights may be held by others.
In addition to research data, the following accompanying research records may also be important to manage during and beyond the life of a project:
Managing research data brings many benefits, not only to the project but to future researchers and wider society.
Good data management practice:
Effective data management is carried out for the entire lifecycle of the data, from the point of creation through to dissemination, publication and archiving.
(Source: UC Santa Cruz University Library)