Digital Research Data

We already talked about the characteristics of research data, the research data lifecycle, the research data workflow and the definition of research data management in the introduction. The following text is meant as additional reading material to facilitate deeper understanding of these topics.

What are research data?

This concept considers only digital research data. There is no fixed definition of research data. Kindling and Schirmbacher provided one of the first definitions in 2013: “By digital research data we mean […] all digitally available data that are generated during or as a result of the research process”. [Kindling, et al. 130] In 2015, the German Research Foundation (DFG) adopted the “Guidelines on the Handling of Research Data”, in which research data is defined as follows: “Research data might include measurement data, laboratory values, audiovisual information, texts, survey data, objects from collections, or samples that were created, developed or evaluated during scientific work. Methodical forms of testing such as questionnaires, software and simulations may also produce important results for scientific research and should therefore also be categorised as research data.” (DFG)

The DARIAH project has developed a research data definition for the humanities and cultural studies that includes: “all those sources/materials and results collected, written, described and/or evaluated in the context of a research and research question in the field of human and cultural sciences, and in machine-readable form for the purpose of archiving, citation and for further processing.” (DARIAH-DE)

Depending on the subject area, research data may vary significantly. The characteristics of research data strongly depend on the context (conditions of generation, methods used, perspective). Since they can be very heterogeneous, a further subdivision is not reasonable, therefore one usually speaks merely of “research data”.

Research data lifecycle

The research data lifecycle presents the steps necessary to map the process of a research project with regard to the research data. In particular, according to the UK Data Archive, the lifecycle includes the collection, processing, analysis, archiving, access and re-use of research data (see Figure 1: Research Data Lifecycle).

The Research Data Lifecycle

Fig. 1: Research Data Lifecycle, according to UK Data Service (for didactic reasons, depiction differs from source)

Research data workflow

Based on the research data lifecycle, a research data workflow describes in more detail the individual processing steps of research data depending on the selected software and the required infrastructures and services. A process-oriented perspective is adopted, which allows the data creators to depict the data transfer and conversion necessary between data processing and analysis, for example. Responsibilities (roles and stakeholders) are also defined in a workflow.

This text is an excerpt (p. 34 to 37) from Biernacka, Katarzyna, Bierwirth, Maik, Buchholz, Petra, Dolzycka, Dominika, Helbig, Kerstin, Neumann, Janna, Odebrecht, Carolin, Wiljes, Cord, & Wuttke, Ulrike. (2020). Train-the-Trainer Concept on Research Data Management (3.0). Zenodo.