Understanding Research Data Management Systems

Research Data Management (RDM) is a combination of systems, methods & processes used & carried out in a systematic manner to ensure that research data is findable & reusable. Learn more about RDM here!

Understanding Research Data Management Systems

Research data management is a combination of systems, methods, and processes that are used and carried out in a systematic, coherent, and structured manner to ensure that research data is findable, accessible, interoperable, and reusable. It covers the collection, management, retention, and sharing of research data throughout the research lifecycle. This includes data management planning at the grant application and development stage of research, day-to-day data management during the research project, and long-term data retention and exchange after completion of research and publication of results. In the last decade, additional measures have been introduced to regulate how data is shared, preserved, accessed, and reused.

Libraries and research data management teams help researchers from the proposal stages to the realization of RDM plans. Digital is only one of the latest players in the data sphere; however, thanks to templates and structured data, ELNs allow researchers to easily establish metadata standards and quickly provide all the information needed to reuse their data. Operating systems also support embedding metadata in this way, such as Microsoft Document Properties. Data consolidation is not necessary; however, numerical data is much easier to replicate than qualitative data.

Software can “process numbers today faster than ever” (Antonius, 200). Open data benefits strong citations by improving their value and impact even after the project or research is completed. Digital object identifiers (DOIs) can be used to make data easily traceable on the Internet. Funders generally don't like to store research data they funded in personal repositories or elsewhere without authorization.

Therefore, research data management plans have become mandatory in grant applications. Researchers are expected to plan how and where to store their data, which backup systems to use, the best file format, the data license, and possible embargo periods. Germany has launched the Priority Initiative to coordinate the digitalisation of science and has established the German Council for Scientific Information Infrastructures to support the development of infrastructures for the management of research data. Archived data must be linked to a permanent identifier such as a DOI (digital object identifier), which can be referenced in publications. Spichtinger and Siren (201) define research data as “recorded factual material commonly accepted in the scientific community as necessary to validate research results, including data sets used to support academic publications”.