Longitudinal research data are fundamental to advancing educational research, yet their structure, accessibility, and reusability vary widely across countries and institutional settings, with profound implications for researchers’ capacity to conduct longitudinal and comparative studies (Pavolini et al., 2025). The Horizon Europe project LINEup (Longitudinal Data for INequalities in Education) addresses this challenge by providing the first systematic mapping of national and regional longitudinal datasets on student learning outcomes in primary and secondary education (ISCED 1–3) across Europe. The mapping identifies what longitudinal data exist, where they are located, how they are documented, and to what extent they can be reused for research on educational inequalities.

Existing literature shows that educational inequalities emerge early and persist across key transitions, shaped by institutional arrangements and contextual conditions (Sirin, 2005; Van de Werfhorst & Mijs, 2010; Jackson & Jonsson, 2013; Chmielewski, 2019) - a pattern confirmed by recent reviews of longitudinal studies in Europe (Campos et al., 2025; Kampylis et al., 2024; Symonds et al., 2025). Understanding these dynamics requires evidence capable of tracing how gaps develop and evolve over time (Gangl, 2010; Skopek & Passaretta, 2021). Yet longitudinal data in Europe remain unevenly available, inconsistently documented, and difficult to access.

The study adopts a conceptual understanding of data as socio-technical objects: their analytical value depends not only on content but also on infrastructures, documentation practices, governance models, and access conditions (Leonelli, 2020). Drawing on Open Science principles, the project examines how metadata quality, management structures, and transparency shape research possibilities. Despite growing demand for stronger longitudinal evidence, researchers still face barriers related to insufficient documentation, heterogeneous variable definitions, lack of harmonisation, opaque access procedures, and uneven integration of administrative data - constraints that hinder the analysis of how inequalities in education arise, widen, or narrow over time.

Recent scholarship on open research data underscores the need for transparent documentation, interoperable metadata standards, and governance frameworks that enable data reuse (Borgman, 2015; Leonelli, 2020; Wilkinson et al., 2016). Yet in education, progress remains uneven. The LINEup project contributes to this debate by offering an empirically grounded model for structuring metadata to enhance comparability, support harmonisation, and enable researchers to assess datasets’ analytical potential.

We developed a shared identification and documentation protocol applied across 32 European countries (EU Member States, EEA associated countries, Switzerland, and the United Kingdom), resulting in a systematic analysis of 103 longitudinal datasets meeting strict inclusion criteria. The mapping reveals substantial variation in dataset quality, periodicity, sample size, measurement comparability, socio-demographic coverage, linkage possibilities, and access openness. The findings depict a fragmented landscape: some countries maintain well-established infrastructures, while others display discontinuities, limited documentation, or restricted access.

Building on this empirical work, the paper introduces the LINEup Education Data Explorer, an open, user-oriented digital infrastructure designed to enhance the findability, transparency, and reusability of longitudinal educational datasets. The Explorer consolidates country- and dataset-level metadata, documentation links, classification variables, and access procedures into a searchable platform grounded in FAIR principles. It aims to lower entry barriers for researchers and educational stakeholders, increase the visibility of existing data sources, and support cross-country research. By transforming a static mapping into a dynamic infrastructure, the Explorer provides a replicable model for data documentation and reuse in educational research. It enables comparative insights into European data ecosystems, supports reflections on harmonisation feasibility, and offers a practical tool for planning future longitudinal data collections.

The study adopts a multi-stage research design integrating systematic data mapping, metadata analysis, and digital infrastructure development. This approach reflects the project dual nature: it is both an investigation of longitudinal datasets and an exercise in building shared infrastructure that enhances the transparency, comparability, and reusability of educational data across Europe. The methodological strategy unfolds across three components: 1. Mapping methodology – The mapping followed a multi-step procedure combining three evidence sources: a systematic literature review, an expert questionnaire, and web searches. The review (Kampylis et al., 2024) identified 77 datasets, 43 meeting the inclusion criteria. The questionnaire gathered 193 responses from scholars across 28 countries and identified 70 datasets. Desk research conducted by partners, complemented by exchanges with Horizon Europe projects, provided 116 datasets, bringing the total to 229. Each dataset was assessed against common criteria: longitudinal or repeated cross-sectional design; ISCED 1–3 coverage; presence of learning outcomes; national or regional representativeness; and at least one wave after 2014. Eligible datasets were documented using a standardised form capturing structural characteristics, variables, linkage possibilities, and access conditions. A multi-layer validation protocol – internal checks, partner cross-review, and final consistency assessment – ensured comparability. Through this process, 103 datasets met all criteria and were retained. 2. Analytical framework – Dataset characteristics were analysed along five dimensions: • How: longitudinal architecture (design type, continuity, periodicity, unit of analysis). • What: information collected, including learning outcomes and student, household, teacher, and school variables. • Where: geographical scope and representativeness. • When: temporal depth, number of waves, educational stages. • Access: microdata openness, documentation quality, and linkability. This framework enabled systematic cross-country comparison of documentation quality, measurement coherence, socio-demographic coverage, temporal structure, and access conditions. It also supported the identification of structural gaps and complements the classification scheme developed in the validation phase. 3. Development of the Education Data Explorer – The Explorer was developed as a lightweight, extensible and open-access digital infrastructure integrating metadata from the mapping. The process followed iterative, user-centred design principles. Metadata were harmonised through controlled vocabularies and classification categories, supporting three information layers: dataset overviews, analytical dimensions, and variable-level metadata. Interface prototyping and testing informed improvements in navigation and usability. Search and filtering functions were developed to address different user needs, while the beta version incorporates mechanisms for refinement. The platform does not host microdata but provides access information and directs users to official sources, thereby supporting responsible reuse

The study shows that the landscape of longitudinal educational data in Europe is simultaneously rich and uneven, characterised by substantial variation in documentation practices, accessibility conditions, and contextual and socio-demographic coverage. The systematic identification and validation of 103 datasets reveal both promising infrastructures - often supported by long-standing administrative systems - and persistent gaps, particularly in countries where data collections are discontinuous, poorly documented, or inaccessible to external researchers. These asymmetries shape what kinds of questions can be asked, which inequalities can be studied, and how robustly comparative analyses can be carried out. By translating the mapping results into an open and continuously evolving digital platform, the LINEup Education Data Explorer addresses a critical barrier to cumulative scientific knowledge: the difficulty of locating, comparing, accessing, and evaluating longitudinal datasets. The Explorer functions as a FAIR-oriented environment that lowers the transaction costs of dataset discovery, clarifies access conditions, and enables researchers to assess the suitability of different data sources for longitudinal designs. At the same time, it makes visible the structural limitations that constrain research - whether due to inconsistent documentation, restricted accessibility, or insufficient contextual variables - thereby providing actionable insights for policymakers and data owners. The findings underscore the importance of investing in interoperable metadata standards, transparent governance frameworks, and long-term data infrastructures capable of supporting equity-oriented research. Enhanced documentation and open infrastructures do not replace the need for high-quality microdata, but they significantly expand the conditions under which existing data can be reused, combined, and analysed. More broadly, the study illustrates how Open Science principles can be operationalised in education research through practical tools that reinforce transparency, comparability, and researcher autonomy. By integrating methodological rigour with infrastructural innovation, LINEup contributes to a more coherent European data ecosystem and strengthens the foundations for robust, cross-country research on educational inequalities.

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