In recent years, there have been huge developments in the data landscape on education and learning. This is due to several reasons. First, the ways data can be collected and analysed. In addition, research data infrastructures at both the national and international level have taken a more prominent role and guide researchers throughout the research process with regard to questions relating to data management (Corti et al., 2019). Moreover, there has been a growing understanding that sound educational policy-making requires sound evidence based on high-quality and accessible data. Many research funders now require that data resulting from funded projects will be made available at the end of the project (Logan, Hart & Schatschneider, 2021). At the same time, the legal frameworks have evolved and several countries now allow for more easy data linkages. For example, linking data from the administrations to survey data has become more common (Harron et al., 2017). In this contribution, we draw a picture of the educational research data landscape in five European countries. The aim is threefold: We provide a description of the educational data landscapes across varying contexts, compare them and derive conclusions on what can be learnt from one context to the next. The countries included in the study are England, Norway, France, Sweden and Switzerland. These countries have been selected as contrasting cases in how educational data are administered, provided and how they can be accessed by researchers. The countries also have different legal bases for working with sensitive data and vary in the degree to which data infrastructures are centralized. In order to get a complete view on the educational data landscape in these countries we have carried out interviews with experts from each of the countries. The five countries are compared along the following characteristics: (1) Main data providers (e.g. universities, statistical offices, institutional repositories); (2) Types of available data (level of education, statistical data, learning systems, competencies etc.); (3) Possibilities for data linkage; (5) Laws and Regulations. The analysis shows that there is considerable variation in terms of what data are made available, the procedures, coverage and handling. At the same time, there are some commonalities across countries. The findings allow us to draw conclusions for future directions of the educational data landscape. We make suggestions on what can be learnt from other contexts and what would facilitate high-quality data-driven educational research that could inform educational policy and practice.

Corti, L., Van den Eynden, V., Bishop, L., & Woollard, M. (2019). Managing and sharing research data: A guide to good practice (2nd ed.). London: Sage. Harron, K., Dibben, C., Boyd, J., Hjern, A., Azimaee, M., Barreto, M. L., & Goldstein, H. (2017). Challenges in administrative data linkage for research. Big data & society, 4(2), 2053951717745678. Logan, J. A. R., Hart, S. A., & Schatschneider, C. (2021, 2021/01/01). Data Sharing in Education Science. AERA Open, 7, 23328584211006475. https://doi.org/10.1177/23328584211006475