According to OpenAire, the research process can generate a wide variety of data outputs including:
| Documents (text, MS Word), spreadsheets |
| Online questionnaires, transcripts, surveys or codebooks |
| Digital audiotapes, videotapes and other digital recording media |
| Scanned photographs or films |
| Transcribed test responses |
| Database contents (video, audio, text, images) |
| Digital models, algorithms, scripts |
| Contents of an application (input, output, logfiles for analysis software, simulations) |
| Documented methodologies and workflows |
| Historical Documents |
Research Data Management (RDM) involves the organisation, storage, preservation, and sharing of data collected and used in a research project. RDM covers the entire research process, from initial planning to long-term archiving and sharing. Research data is essentially the evidence used to inform or support research conclusions. Good RDM practices ensure data is managed according to established guidelines, maximising its academic value.
National and international research funding agencies require the outputs of the research process including research data to be disseminated and preserved on an open access basis. Data Management Plans are now a mandatory component of all grant applications. A data management plan (DMP) is a document outlining how research data will be managed and documented throughout a research project, including its long-term preservation and sharing. It describes the types of data you will create or use, how you will store and back it up, and the conditions for sharing or publishing it. Even if your research is not being funded, a data management plan ensures the integrous, efficient and secure use of research data throughout the research cycle.
Research data isn't always openly shareable due to commercial sensitivity, confidentiality/privacy concerns, ethical considerations, legal restrictions, data ownership/intellectual property rights, as examples. However, alternative access methods exist may be viable, including anonymisation.
The FAIR principles—findability, accessibility, interoperability, and reusability—are now widely acknowledged as best practices for data management. These principles ensure that research data is easily discoverable, accessible to both humans and machines, capable of being integrated with other data, and reusable with proper documentation and licensing. The European Commission stipulates that research data should be “as open as possible, as closed as necessary,” (European Commission, 2018, p.10)
The advantages of effective research data management incorporating a data management plan include:
