Researchers are increasingly encouraged by various stakeholders such as academic journals, professional associations, and research funding agencies to make research processes as transparent as possible, enable reproducible research results, and share their (research) data FAIRly and openly with others. Such requirements can be challenging, as not all researchers are familiar with the concepts of FAIR and open data. At the same time, existing tools to foster the creation of FAIR data – such as templates for data management plans (DMP) – vary to a great extent and rarely provide guidance.

The project Domain-Data-Protocols for Empirical Educational Research (DDP-Education) aims to address this issue by developing a standardized tool to create DMPs. Funded by the German Federal Ministry of Education and Research, it brings together twelve German research institutions with diverse areas of expertise on educational research to develop  Domain Data Protocols (in short DDPs) within a funding period of June 2019 - May 2022. The primary goal of a DDP is to standardize and facilitate the generation of research data in empirical educational research according to the FAIR principles (Wilkinson et al. 2016). The resulting research data will be available to third parties as openly as possible in the sense of Open Science.

Based on the concept of Science Europe (Science Europe 2018), DDPs are open, standardized, and referenceable data protocols, serving as a ‘model’ DMP for a specific research domain, i.e. educational research in our case. DDPs are for the benefit of various stakeholders. First, they assist researchers in doing excellent data management, preparing project proposals and funding applications, and offering support for data archiving and sharing. By describing activities to manage data, DDPs also simplify the budgeting of such activities. Second, they enable replication of results by the research community and the reuse of data by others in new (research) contexts. Third, DDPs simplify review processes regarding data management, reducing the efforts of examining funding applications and reports on data management by implementing standardized procedures. Finally, DDPs foster data ingest in data repositories and archives by assisting researchers in creating FAIR data.

Applying the FAIR principles to the specific needs of educational science as part of social, behavioural, and economic science data is a considerable challenge, among other things, because of highly diverse data types that often contain confidential information regarding individuals (Betancort Cabrera et al. 2020). The development of DDPs is challenging, as their structure needs to be flexible to cover different types of data and methods. In addition, they should enable researchers to reflect their individual project-specific requirements. DDPs, therefore, consist of different modules, referring to, for example, data collection, documentation, legal issues, and data sharing. Each of these modules consists of different elements defining a minimum set of requirements on what FAIR data look like and includes use cases, standards, relevant regulations, and further resources to assist with data management practices.

Moreover, the technical implementation of a DDP can be realized with the Research Data Management Organizer (RDMO) (RDMO 2022). Having integrated a DDP in RDMO, researchers can create DMP and organize data management during their research project. The goal is to use this tool as a standard for transferring research data to research data centers and creating DMP for grant applications to be established.

Finally, the project provides benchmarks for estimating the costs of research data management and subsequent use of the data based on certain key points such as type and scope of the project (Hodson et al. 2018; Perry & Netscher 2022).

A DDP consists of 8 modules targeting topics such as research ethics, data organisation, legal aspects, long-term preservation, and data sharing. In turn, each of these modules contains 7 basic elements, defining a minimum set of requirements for creating FAIR data. It includes use cases and examples to implement the minimum requirements, standards, relevant rules, regulations, and further resources on related data management practices. A DDP basic module including project information and inventory list supplements each module. In this respect, a DDP extends a typical DMP to include standardized, discipline-specific questions, and response options. A glossary provides additional assistance in working with the DDP. The data should ultimately be stored in a trusted archive or repository and made available to third parties for reuse. Much data in the educational sciences is sensitive (e. g. disclosive or confidential) and, therefore, often not available open access. Here, in accordance with the Open Research Data Pilot and the “A” in FAIR (which stands for “accessible”), research data should follow the principle "as open as possible, as closed as necessary" (European Commission Directorate-General for Research & Innovation 2016). Multiple feedback circles helped us to develop and technically implement the DDPs, including, for example, coordination meetings for qualitative and quantitative data types, requirements workshops with external experts, development of relevant use cases, interviews with experts from funding institutions, evaluation workshops with research data managers, development of a concept of non-survey specific elements, working meetings with external international experts as well as internal consultations with the aim of identifying the first cost-relevant factors of research data management and development of technical integration and processing the DDP in RDMO.

The DDP project contributes considerably to current efforts undertaken in making data FAIR. It provides a standardized tool to create DMP and establishes a FAIR tool to foster research and innovation. Thereby, the project ensures the connectivity of the DDPs. On the one hand, it enables the development of further DDPs for educational research. On the other hand, DDPs serve as prototypes for other research disciplines, ensuring cross-domain interoperability. Integrating DDPs into the RDMO tool offers scientists an intuitive and standardized form of research data management in all project phases. In addition, third-party funding applications that have arisen via the DDP offer funding agencies an excellent opportunity to compare them and make more balanced funding decisions based on the DDP criteria. Moreover, the project provides approximate values for the costs of the RDM based on estimations. Considering the unique needs of data in educational sciences regarding sensitivity, the DDPs were subject to legal and ethical review. With the end of the project, the DDP will be maintained by the German Network of Educational Research Data (VFDB), an association of the largest German educational research institutions. The VFDB also ensures the connection to the consortium KonsortSWD as part of the national research data infrastructure (NFDI) and thus the German counterpart to the European Open Science Cloud (EOSC). Therefore, this provides an excellent opportunity to transfer the DDPs to other social science disciplines. DDPs translated into English might be a blueprint for developing DDPs for international communities. International projects with multiple partners would benefit from DDPs as they provide the basis for a joint research DMP. This, in turn, increases the quality and the reuse of the data. The underlying networking concept with the relevant professional societies pushes the awareness of the DDPs among researchers.

Betancort Cabrera, N., Bongartz, E. C., Dörrenbächer, N., Goebel, J., Kaluza, H. & Siegers, P. (2020). White Paper on implementing the FAIR principles for data in the social, behavioural, and economic sciences, RatSWD. RatSWD Working Paper Series: 274. https://doi.org/10.17620/02671.60 European Commission Directorate-General for Research & Innovation (2016): H2020 Programme. Guidelines on FAIR Data Management in Horizon 2020. Version 3.0. 26 July 2016. https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf (last accessed: 21.01.2022) Hodson, S., Jones, S., Collins, S. Sandra, Genova, F., Harrower, N., Laaksonen, L., Mietchen, D., Petrauskaité, R, & Wittenburg, P. (2018). Turning FAIR data into reality. Interim report from the European Commission Expert Group on FAIR data. doi: 10.5281/zenodo.1285272 Perry, A. & Netscher, S. (2022): Measuring the Time Spent on Data Curation. Journal of Documentation. [forthcoming]. RDMO Research Data Management Organizer. (2022). https://rdmorganiser.github.io/en/ (last accessed 24.01.2022) Science Europe. (2018). Science Europe Guidance Document Presenting a Framework for Discipline-specific Research Data Management. http://www.scienceeurope.org/media/nsxdyvqn/se_guidance_document_rdmps.pdf (last accessed 21.1.2022) Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J., Bonino da Silva, S., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C.T., Finkers, R., …Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3. doi: 10.1038/sdata.2016.18