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The continuous development of new digital research methods and the increasing amount of available research data enable the application of such methods in more and more research fields. In this context, new research questions can be developed and existing research questions can be addressed for the first time or in a new way with the help of different data-driven analyses. However, the necessary digital and data-related skills are often not available where they would make this possible. Accordingly, there is great potential to anchor data-driven work more broadly and deeply in various scientific fields, which can be exploited by strengthening (subject-specific) data expertise. This can also contribute to cultural change in these subjects. This is in line with the goals formulated in the German government's data strategy to significantly increase data literacy in science, among others, and to establish a data culture as well as to promote responsible data use and leverage innovation potential. Increasing data literacy and promoting data-based innovations are also central components of the Federal Ministry of Education and Research's (BMBF) Research Data Action Plan, which is anchored in the German government's data strategy. Building data literacy in science is also essential for working on and with the data infrastructures emerging at national and European level (National Research Data Infrastructure [NFDI] and European Open Science Cloud [EOSC]).
Numerous recently launched measures to teach data competencies at universities start at the undergraduate level. In contrast, not enough attention is paid to young scientists. However, promoting the teaching of data competencies to young scientists is worthwhile in many respects: among young scientists there is not only a great openness to new methods and possibilities of data-driven research, but also a lot of exchange via conferences and networks. Last but not least, young scientists provide a significant amount of teaching. Increasing the data competencies of young scientists can therefore not only provide better answers to current research questions, but at the same time carry these competencies into the broader community and pass them on.
Accordingly, this funding announcement aims to broaden and deepen the data literacy of young scientists at universities and non-university research institutions in the diverse subjects of the scientific landscape by linking specialized data science skills with subject-specific knowledge. Priority is given to subjects in which data skills are not yet available (to the same extent).
By combining subject-specific research questions with specialized data science methods, the funding implements the above-mentioned goals in a concrete measure. To this end, research projects are funded that are suitable for increasing data competencies in subjects in which these competencies or certain data science methods are not yet established or only established to a selective extent, but in which new scientific findings can be expected.
The added value of projects in which data methods are used innovatively is only really great when new findings from individual projects are carried into the respective subject community and also used there. The present funding guideline therefore aims to support research projects by young scientists in which data competencies are built up, which are then sustainably anchored in the respective subject. In particular, through cooperation and exchange between actors with a subject-related focus on the one hand and a data-related focus on the other, long-term cooperation structures are to be established and a sustainable implementation of data science analyses in different subject-related contexts in the respective subjects is to be advanced.
In this way, Germany should be further strengthened internationally as a center of science, and the international connectivity of data-based science should be ensured. At the same time, it is to be expected that an increase in the data competencies of young scientists across research disciplines can also increase the innovation potential in industry if some of the young scientists contribute the corresponding competencies to cooperation projects with companies or if young scientists move to industry.
On the basis of the present open-topic announcement, the BMBF will fund research projects involving scientists who are at the doctoral or postdoctoral stage or who head a junior research group. The funding is intended to answer research questions with the help of data analyses that have not yet been established in the relevant specialist culture. For this purpose, collaborations should be entered into with partners within or outside the applicant institution who can demonstrate distinctive competencies in data science methods. The project description must show that this exchange would not have taken place without the funding. In addition, it must be convincingly demonstrated how the newly acquired data competencies will be used beyond the funded project to ensure anchoring of the methodology that is new to the discipline.
Innovative use of existing data is to be funded, but not the collection of new data sets or the construction of databases. The application must describe the data with the help of which the analyses will take place.
This does not mean that the data must already be in a form where analysis can begin immediately; however, the focus of the project should be on data analysis and data preparation should be minimal.
Funding is provided for projects on research questions that can be answered innovatively with the help of data analysis. To this end, cooperation between data-related and subject-related actors is to be established in order to expand and deepen the data competencies of young scientists on the basis of concrete research projects. To this end, collaborations are to be newly established in order to apply previously unused data analyses or data science methods in a new subject-related context. The partners of the collaboration must be known at the time of application, but should not yet be collaborating for the purpose of applying data science methods in the respective subject area. It must be clear from the project application that data processing and analysis will not be carried out in the sense of a service by the participant(s) with already existing data competencies. Rather, these tasks are to be performed jointly by both partners in close exchange. In this way, data competencies are to be built up on the one hand and a deeper understanding of possible use cases is to be created on the other.
Funding is provided for projects whose scientific relevance and data-methodological suitability for the research question give rise to expectations of impulses for research in the respective subject. Ideally, this means that a method that can also be applied to other topics is used to address a question that has so far only been answered to an insufficient extent. The methods used do not necessarily have to be developed from scratch, but they must not yet be established in the relevant community.
2.1.1 The added value generated by the planned project for the specialist community must be presented in a coherent and comprehensible manner. In particular, it must be shown how research questions can be addressed that have not yet been answered or have been answered only inadequately.