Research data are collected during research and used as a source for analysis. This data can occur in many forms, e.g.: measurement data of experiments (e.g., from equipment), illustrations, geographical data, survey results, models, physical samples, audio recordings (e.g., interviews), and sources (e.g., archival research, literature research, notebooks).

 

Data management is important for several reasons. Good data management ensures that participants are protected, and that the data is of high quality, safely stored and appropriately used. It also makes sure that the research complies with law and policy, is efficient, ethical, and reproduceable, the research impact is increased, and that FAIR principles apply. The FAIR principle was published in 2016 and is the result of the work of an international collective of researchers. The basic guidelines of the principle are the following: 1) data is findable, in other words, easy to locate; 2) data is accessible, by both humans and computers in appropriate circumstances; 3) data is interoperable, or in other words understandable by everyone, and can be used in any data management system (SURF, Google Drive, etc.); and 4) data is reusable: it is possible to use data in future research and processing, such that new studies can efficiently build on the collected data. 

 

You can find definitions for commonly used data jargon in the Tilburg University LibGuide.