At the broadest level, Data Management is a series of decisions about how you will create, store, publish and preserve the material created during a research project. In a practical sense Data Management can mean very different things depending on your subject area, the kinds of material and software you work with and the stage your project is at. This guide is designed to help you get the most out of your research by keeping your data safe and well-organised and improve the quality of your publications.
There are many reasons to take data management seriously, and many benefits for doing so.
Data produced from academic research is valuable output, just like journal articles and books
Data that is not managed is likely to be lost permanently
UCL and funding agencies have policies on data management requirements that researchers are expected to comply with
Good research data management improves reproducibility, validation and integrity of research
Sharing of research data enables re-use by other research groups and increases the value of the time and effort invested in generating the data in the first place
What is considered "Data" ?
Data can be defined in a variety of ways, depending on the discipline and the context. Here, we consider a wide definition which covers (but is not limited to!):
Research notebooks, detailing progress of research and experiments
Responses to surveys and questionnaires
Software, code, algorithms and models
Measurements from laboratory or field equipment
Images (such as photographs, films, scans of documents)
Methods, protocols and experimental procedures
Databases of collected information
A corpus of writings
Audio and video recordings
Physical samples and objects
Referencing and managing information training sessions