What is the study about?

The digital transformation in research is accompanied by an enormous increase in research data. Large amounts of data and diverse data types in conjunction with increasing computer power mean that data-intensive research is becoming more and more relevant - as is the general topic of research data at universities.

This development has also been reinforced by political initiatives at national and international level: scientific associations, funding bodies and publishers are increasingly requiring the long-term storage and accessibility of the data on which research results are based. The demand for reproducibility of research results in various disciplines also means that the management of research data is becoming increasingly important.

For researchers, however, this sometimes implies a considerable effort in the planning and realisation of their projects. Research support services are intended to support scientists in this process. In order to develop these, a comprehensive knowledge of the research data landscape at the institution in question is indispensable: What types of data are collected, used, produced and disseminated there?

To answer this question, we conducted a case study at WU with two goals in mind: The first goal was to obtain a detailed overview of the research data landscape at WU. To this end, we conducted a quantitative document analysis of one year's research output at WU.

The second goal was to gain insight into the research data practices of the disciplines represented at WU. In semi-structured interviews, we asked researchers from all departments represented at WU about their experiences and needs and the trends in their respective fields.

What is the most exciting aspect of the study for you? Were there any surprising results?

From a methodological perspective, the most challenging question was where to find and analyse the research data or information about it. Since the data or references to it can usually be found in the related publications, the most suitable approach seemed to be using these as data sources. For the document analysis, we used journal articles that were listed in WU's own research information system FIDES. However, this database only contained the citations of the almost 600 articles to be analysed, so we first had to obtain the corresponding digital full texts.

As far as the results are concerned, the high proportion of quantitative research data was not unexpected given the subject areas represented at WU. What was surprising, however, was that we were able to identify research data from practically all academic disciplines – even data that one would not expect to find at WU with its focus on economics and the social sciences, such as medical research data, or data of natural science provenance.

It was also interesting to see that sometimes data from different disciplines, such as quantitative macroeconomic data, was linked with qualitative data from sociological studies. An example of cross-disciplinary reuse of research data is the use of satellite image data as a benchmark dataset for testing a mathematical algorithm.

The semi structured interviews with the researchers not only gave us important insights into the different research cultures, but also showed a very heterogeneous picture regarding data management within the research process. In the areas researching with quantitative methods, the increasing importance of large amounts of data (keyword "big data") was also emphasised. The growing focus on the internet and social media as data sources was also mentioned here.

Why did you decide to make the data openly accessible?

When we conducted the study, there was little comparable work in the scientific literature on the topic of research data management. Therefore, we found it important to not only make the publication itself publicly available, but also the underlying quantitative data. In various disciplines and research communities, making research data available has been part of the research culture for years. It is part of our understanding of research to contribute here – not only to the advancement of knowledge and to the exchange of research results in library and information sciences, but also enabling verifiable and replicable science.

• Thomas Seyffertitz has been working as a field librarian for economics at the University Library of WU Vienna since 2013. His work focuses on research data management and collection management on the topics of finance, mathematics and statistics.