http://dl.gi.de/handle/20.500.12116/36547 2026-07-21T13:38:49Z 2026-07-21T13:38:49Z Lefebvre, Armel Bakhtiari, Baharak Spruit, Marco http://dl.gi.de/handle/20.500.12116/36552 2021-06-21T12:23:08Z 2020-01-01T00:00:00Z

Exploring research data management planning challenges in practice Lefebvre, Armel; Bakhtiari, Baharak; Spruit, Marco Research data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP.

2020-01-01T00:00:00Z Berčič, Katja Kohlhase, Michael Rabe, Florian http://dl.gi.de/handle/20.500.12116/36550 2021-06-21T12:23:08Z 2020-01-01T00:00:00Z

(Deep) FAIR mathematics Berčič, Katja; Kohlhase, Michael; Rabe, Florian In this article, we analyze the state of research data in mathematics. We find that while the mathematical community embraces the notion of open data, the FAIR principles are not yet sufficiently realized. Indeed, we claim that the case of mathematical data is special, since the objects of interest are abstract (all properties can be known) and complex (they have a rich inner structure that must be represented). We present a novel classification of mathematical data and derive an extended set of FAIR requirements, which accomodate the special needs of math datasets. We summarize these as deep FAIR . Finally, we show a prototypical system infrastructure, which can realize deep FAIRness for one category (tabular data) of mathematical datasets.

2020-01-01T00:00:00Z Heuer, Andreas http://dl.gi.de/handle/20.500.12116/36549 2021-06-21T12:23:08Z 2020-01-01T00:00:00Z

Research Data Management Heuer, Andreas Article Research Data Management was published on February 1, 2020 in the journal it - Information Technology (volume 62, issue 1).

2020-01-01T00:00:00Z Hasselbring, Wilhelm Carr, Leslie Hettrick, Simon Packer, Heather Tiropanis, Thanassis http://dl.gi.de/handle/20.500.12116/36553 2021-06-21T12:23:08Z 2020-01-01T00:00:00Z

From FAIR research data toward FAIR and open research software Hasselbring, Wilhelm; Carr, Leslie; Hettrick, Simon; Packer, Heather; Tiropanis, Thanassis The Open Science agenda holds that science advances faster when we can build on existing results. Therefore, research data must be FAIR (Findable, Accessible, Interoperable, and Reusable) in order to advance the findability, reproducibility and reuse of research results. Besides the research data, all the processing steps on these data – as basis of scientific publications – have to be available, too. For good scientific practice, the resulting research software should be both open and adhere to the FAIR principles to allow full repeatability, reproducibility, and reuse. As compared to research data, research software should be both archived for reproducibility and actively maintained for reusability. The FAIR data principles do not require openness, but research software should be open source software. Established open source software licenses provide sufficient licensing options, such that it should be the rare exception to keep research software closed. We review and analyze the current state in this area in order to give recommendations for making research software FAIR and open.

2020-01-01T00:00:00Z