This guide is intended to support the Yale-NUS community in effective research data management throughout the data lifecycle of data planning, documenting, storage, sharing, and long-term preservation. This guide has been adapted from NUS Libraries.
Research Data Management (RDM) is "how you look after your data throughout your project. It covers the planning, collecting, organising, managing, storage, security, backing up, preserving, and sharing your data and ensures that research data are managed according to legal, statutory, ethical and funding body requirements" (Whyte, A. & Tedds, J., 2011).
"Digital curation involves maintaining, preserving and adding value to digital research data throughout its lifecycle. The active management of research data reduces threats to their long-term research value and mitigates the risk of digital obsolescence." (Digital Curation Centre)
It is universally acknowledged that researchers are interested in data of all kinds, regardless of origin or type.
Here are some of the recognised definitions of research data:
"Research data, unlike other types of information, is collected, observed, or created, for purposes of analysis to produce original research results." Edinburgh University Data Library Research Data Management Handbook
“Research data means data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or another research output is based. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media”. The Queensland University of Technology Management of Research Data Policy
“The recorded information (regardless of the form or the media in which they may exist) necessary to support or validate a research project’s observations, findings or outputs”. The University of Oxford Policy on the Management of Data Supporting Research Outputs
In addition to research data, research data management also covers managing of research records both during and beyond the life of a project. Examples of such research records include:
Research data represents significant value to researchers and the University, and good stewardship of research data is necessary to validate the outcomes and maintain the integrity of research results.