Research data management concerns organising and processing of data, from its entry to the research cycle through to the dissemination and archiving of valuable results. Research data management is an essential part of the research process, and aims to make the research process and data use responsible and efficient, as well as meet the expectations and requirements of the university, research funders, and legislation.
The environment for research data management and sharing is continuously evolving. Good data management is fundamental for high quality research data and therefore research excellence, but it is also important for facilitating data sharing and ensuring the sustainability, accessibility and reuse of data in the long-term.
If research data are well organised, documented, preserved and accessible, and their accuracy and validity are controlled at all times, the result is high quality data, efficient research and saving of time and resources. Thus, researchers themselves benefit from well-conducted data management.
Research data management concerns how you:
- Create data and plan for its use
- Organise, structure and name data
- Keep it – make it secure, provide access, store and back it up
- Share with collaborators, publish and get cited
Research data management and data life cycle
The type of the research data depends on the discipline, research topic and type of the research project. These things also affect how you will manage your data. For your research, you may collect the data from the start or reuse data someone else has already produced. In your data management plan (DMP), describe the data types shortly, e.g. by explaining if you are going to produce interview recordings, writings collected from the research participants or measurements with some instruments.
In the beginning of the research process, you have to create a research plan and a data management plan related to it. Data management plan concerns only the aspects of handling the data, not the research itself. You must carefully check the laws and ethical aspects, inform research participants and ask for consents when handling personal data. Plan informing the research participants in a way that data management and sharing are possible in a way you prefer. Agree the data management details with research partners and update your data management plan always when necessary.
You need to plan in detail how to collect data and then conduct the data collection. Data should be documented, described and stored so that they remain usable and protected during the research and after it. To keep your data usable and informative, you must describe your data, which means producing metadata. After data collecting, you analyse your data and report your findings e.g. in a journal article.
Finally, if possible, you can share your data for others to be reused or archive them according to your research organisation’s or collaborator’s guidelines. Data lifespan continues and if they are openly available, data can be used for new purposes, such as for research, teaching, studying or for commercial purposes.
Data reuse and citation
Data reuse means use of existing research data for new purposes. You can search existing data from data repositories and portals (see Reuse of open research data). Reusing data helps to save time and money because you don’t have to do everything from the beginning if useful data already exist. So, consider this option as well.
If you are planning to reuse existing data, this research data must be cited as all other sources used in research. Data reference should consist of following elements:
- Creator, title, host organisation, publication time and/or date and persistent identifier.
- Useful additional elements are: version, resource type, license status, ORCID, embargo information.
- Data repositories and archives usually have guidelines for data citation. Also publishers can have their own guidelines how to refer to data in journals.
Think: What kind of research data do you produce and what kind of problems/challenges you consider to relate to your research data and their management?
CSC: Data management and storage. CSC’s data management and storage web page introduces data services and offers useful information about data management.
Data management guidelines. Finnish Social Science Data Archive.
Data management checklist. Fairdata.fi
Responsible Research. Coordination for Open Science, Publication Forum, The Committee for Public Information (TJNK), Finnish National Board on Research Integrity TENK and the Responsible Research Articles.