Research data management has become increasingly important in educational research, driven by the Open Science movement and the widespread adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles (Wilkinson et al., 2016). Despite growing recognition of the importance of open data sharing, significant barriers persist in accessing FAIR data in the educational field (Bayer et al., 2023). One fundamental challenge concerns the technical interoperability of data formats across different statistical software platforms.

Researchers in the social and educational sciences use various software for statistical analysis, including SPSS, Stata, R, SAS, and Python. Each software has proprietary data formats that are only partially compatible, with other solutions. This non-interoperability undermines FAIR principles and creates obstacles to replication studies and data reuse. To meet diverse user needs, data producers must offer multiple formats through redundant work that is error-prone and costly. Moreover, researchers frequently struggle to find suitable data for secondary analysis, with many reporting difficulties related to data compatibility and accessibility (Bayer et al., 2022).

The Open Data Format (ODF) represents a promising solution to these interoperability challenges (Han et al, 2024). Developed through the KonsortSWD consortium within Germany's National Research Data Infrastructure (NFDI), ODF is a non-proprietary, multilingual, metadata-enriched, and zip-compressed data format explicitly designed to meet FAIR principles. The format consists of a CSV file containing the raw data, an XML metadata file structured according to the DDI Codebook standard (Vardigan et al., 2008), and a version file, all packaged in a standardized archive. Import and export packages are currently available for R, Python, and Stata, enabling seamless data exchange across these platforms.

This contribution examines the practical implementation of ODF in the context of national education monitoring in Germany. As part of the ShaReD (Sharing and Reusing Data) cooperation at DIPF | Leibniz Institute for Research and Information in Education, data used in national education reporting is prepared for publication by the end of 2026. These data, drawn from various sources including survey data, administrative data from educational institutions, and official documents, form the empirical foundation for system monitoring and educational governance. The decision to publish these monitoring data in ODF format provides an opportunity to reflect on the format's advantages and limitations in real-world application.

This contribution combines practical experience from data curation work with conceptual reflection on data format interoperability. Within the ShaReD cooperation, data from various sources used in German national education monitoring are collected and harmonized, applying specific selection criteria: availability of time series data and representativeness for the German education system. These data will be published in a unified, stringent format by the end of 2026, with ODF selected as the format of choice. The paper reflects critically on this format decision through several dimensions: First, we examine the rationale for selecting ODF, considering both technical and community-oriented factors. Second, we analyze the advantages ODF offers for FAIR data sharing, including enhanced metadata documentation, cross-platform compatibility, and non-proprietary accessibility. Third, we discuss limitations and implementation challenges, framing these as important caveats for the research community. Finally, we aim to disseminate knowledge about this emerging format to contribute to its adoption and, potentially, its further development. The analysis draws on documentation from the ODF project (Han et al., 2024), practical experiences from data preparation work, and literature on FAIR data principles and research data infrastructure in education.

The Open Data Format represents a promising approach to increasing interoperability in educational research data management. By providing a non-proprietary, standardized format with rich metadata capabilities, ODF addresses key technical barriers to data reuse while supporting FAIR principles. The format's alignment with DDI standards and its inclusion of comprehensive metadata facilitate cross-platform reuse of education data. However, important limitations must be acknowledged. Currently, ODF packages are implemented only for R, Python, and Stata. Users of SPSS and SAS – software platforms still widely used in educational research – cannot directly work with ODF files without conversion. This partial coverage may limit initial adoption and create new interoperability challenges for some research communities. The practical application of ODF in German national education monitoring data provides valuable insights into the format's real-world usability. As research data infrastructures continue developing within NFDI and similar initiatives, standardized formats like ODF could play a crucial role in enabling cumulative, collaborative research across diverse educational contexts. Future work should focus on expanding software support, documenting best practices for ODF implementation, and engaging research communities in format development to ensure broad accessibility and adoption.

Bayer, S., Blask, K., Gnambs, T., Jansen, M., Maehler, D. B., Meyermann, A., & Neuendorf, C. (2023). Data for Psychological Research in the Educational Field: Spotlights, Data Infrastructures, and Findings from Research. Journal of Open Psychology Data, 11(1), 1–9. https://doi.org/10.5334/jopd.105 Bayer, S., Loesch, T., & Hasche, G. (2022). Reusing research data: Researchers' perspectives on potentials and challenges [Conference presentation]. European Conference on Educational Research, Yerevan, Armenia. https://osf.io/zp436 Han, X., Hartl, T., & Wenzig, K. (2024). Introducing Open Data Format: A platform-independent, non-proprietary, metadata-enriched, multilingual data format and its implementation in R and Stata (Working Paper No. 202410). Zenodo. https://doi.org/10.5281/zenodo.14215268 Vardigan, M., Heus, P., & Thomas, W. (2008). Data Documentation Initiative: Toward a standard for the social sciences. International Journal of Digital Curation, 3(1), 107–113. https://doi.org/10.2218/ijdc.v3i1.45 Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18