Digitalization has profoundly reshaped data production in educational research. Technology-based assessments now generate large volumes of log-files holding process-oriented data, while at the same time expectations regarding transparency, reproducibility, and data reuse have increased across the social sciences. Against this background, educational researchers face a dual challenge: managing technically complex data such as log files and embedding these data within broader infrastructures and governance frameworks that enable responsible sharing and reuse. This symposium brings together four complementary contributions that address these challenges across different stages of the research data lifecycle.

Three of the contributions focus specifically on log data from technology-based large-scale assessments, examining issues of documentation, data quality, and data protection. The fourth contribution deliberately broadens the perspective by examining educational research data more generally within a national social science repository. Together, the presentations situate log data within the wider ecosystem of educational research data practices rather than treating them as a standalone methodological innovation.

The first contribution, by Breitwieser addresses the challenge of making log data reusable through adequate documentation. Log data are inherently context-dependent: Individual events can only be interpreted in relation to the digital system that generated them. Documentation is crucial in helping secondary researchers to understand this relation. Drawing on examples from international large-scale assessments and digital learning environments, the presentation illustrates why conventional documentation formats are often insufficient. It compares alternative strategies, such as enhanced system descriptions and log-data replays, and concludes with practical recommendations and proposed minimum standards for documenting log data in a way that supports transparency, reproducibility, and secondary use.

The second contribution, by Borisova, examines data protection challenges associated with sharing and reusing log data under the General Data Protection Regulation (GDPR). With the shift to online testing, large-scale assessments increasingly collect detailed behavioral traces that may constitute personal data even after direct identifiers are removed. Drawing on experiences from TIMSS 2023, the presentation discusses challenges related to transparency, purpose limitation, and re-identification risk. It illustrates how legal requirements can be addressed through improved participant information, technical safeguards, and contractual measures, while still enabling scientifically meaningful data access.

The third contribution, by Sibberns, critically examines the widespread assumption that machine-generated data are inherently clean and error-free. Based on empirical experiences from ILSA log-file processing, the presentation demonstrates how technical failures, programming inconsistencies, and administrative deviations introduce systematic errors into log data. It discusses methods for identifying such issues and compares strategies for handling them, emphasizing that preprocessing decisions have substantive analytical consequences and must therefore be explicitly documented.

The fourth contribution, by Heers, shifts the focus from specific data types to research data infrastructures. Using metadata from SWISSUbase, Switzerland’s national social science data repository, the presentation profiles educational research datasets in terms of data types, institutional affiliations, and regional distribution. These patterns are compared with publicly available information on education research outputs to identify gaps and areas of underrepresentation. The analysis highlights structural and cultural barriers that continue to limit data sharing in education research and discusses how repositories can address these challenges through tailored support, guidance on ethical and practical issues, and incentives aligned with FAIR principles.

Taken together, the symposium highlights that the scientific value of both log data and educational research data more broadly depends on coordinated attention to data quality, documentation, legal compliance, and infrastructural support. By combining log-data–specific perspectives with a repository-based analysis, the symposium aims to foster a more integrated and reflexive approach to research data practices in large-scale educational studies.

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