Research data are increasingly positioned as central to transparency, cumulative knowledge production, and innovation in educational research, yet the everyday practices through which data are prepared, shared, and reused remain uneven and contested (Raffaghelli, Battistig, et al., 2025; van der Zee & Reich, 2018). This tension is particularly evident in research contexts where data are relational, sensitive, and ethically charged, such as educational research involving learners, institutions, and vulnerable populations (Hernández-Leo et al., 2023). While Responsible Research and Innovation, embedding Open Science (Owen et al., 2012),  provides a powerful normative framework promoting openness, transparency, and reuse, less is known about how these principles are interpreted and enacted in practice by researchers, especially at early career stages (Dai et al., 2018; De-Filippo et al., 2023; Raffaghelli & Manca, 2022). Addressing this gap, this study examines Open Research Data practices through the lens of doctoral researchers, combining a research-based synthesis of prior studies and project evidence with an empirical investigation based on a questionnaire administered within doctoral training workshops on Open Data practices between 2023–2024 and 2025. The questionnaire was iteratively developed and validated through a two-round Delphi study involving international experts in educational research, digital scholarship, and data literacy, ensuring conceptual coherence, construct validity, and practical feasibility (Raffaghelli, 2019).

Furthermore, the study is grounded in a theoretical framework that conceptualises research as a situated, socio-technical activity embedded in contested and problematic digital environments (Jandrić et al., 2024) under transformation, from being open, digital and networked (Veletsianos & Kimmons, 2012) to becoming datafied and platformised (Raffaghelli, Ferrarelli, et al., 2025). Particularly, constitutive elements of scholarly work require dynamic practices of data seeking, generation, processing, sharing, evaluation, and reuse as the base to produce or validate knowledge (Dutton, 2011). This perspective is enriched by scholarship on digital and networked academic practices, which foreground openness, participation, and collaboration, while also acknowledging the infrastructural and political conditions that shape contemporary research within datafied and platformised digital instruments and environments. Within this framework, data practices are thus treated not as neutral technical procedures, but as ethically and institutionally mediated actions that contribute to researchers’ professional identities.

The findings reveal that doctoral researchers largely endorse the values of openness, transparency, and public accountability associated with Open Science, and they report frequent engagement in generating and managing their own research data, often using established tools and producing traditional forms of data representation. At the same time, practices related to data sharing, reuse, and publication in open repositories remain sporadic and marginal. Notably, ethical reflection emerges as a salient and comparatively robust dimension of research activity: respondents report frequent consideration of ethical issues related to data extraction, treatment, and presentation, often exceeding their engagement with formalised frameworks for data quality assessment or open data standards. Importantly, these patterns point to a form of situated data literacy shaped by disciplinary norms, professional socialisation, and asymmetric institutional support structures. Data preparation and sharing emerge as negotiated practices involving judgment, care, and risk assessment, particularly in relation to qualitative data, research involving minors or vulnerable groups, and the secondary use of data by third parties or AI systems whose training data and analytical processes often remain opaque. Learning about data practices is largely sustained through informal and self-directed learning ecologies, relying on peer relationships and self-selected resources rather than formal institutional provision.

Overall, this contribution speaks directly to ongoing debates within Network 12 on the practical challenges of data sharing and reuse in educational research.

The aim of this survey study is to investigate how research data practices are enacted, learned, and negotiated as part of contemporary research activity. The primary research instrument is an online questionnaire designed to capture researchers’ engagement with data across the research lifecycle. Research data practices are operationalised through four practice-based scales, each measured using frequency-based Likert items (1 = Never; 5 = Always): Seeking and Retrieving Research Data (SeekRD). Data acquisition, including the generation of original data, automated extraction of digital data, and the use of Open Data repositories. Managing and Processing Research Data (ManageRD). Data processing activities, use of proprietary and open-source tools, collaboration in data processing with humans or artificial agents (AI), and the production of traditional versus dynamic data visualisations. Sharing Research Data (SharRD). Data dissemination practices in open repositories, institutional or personal platforms, academic social networks, teaching environments, and engagement with civil society. Evaluating Research Quality through Data (EvalRQ). Data quality assessment (e.g. FAIR principles), ethical reflection, use of traditional and emerging research metrics, and critique of existing evaluation regimes. In addition, the questionnaire includes a dedicated section on learning about data practices, structured around three dimensions of researchers’ learning ecologies: activities, resources, and relationships. Demographic and professional variables provide contextualisation by disciplinary field, research experience, and institutional setting. Two-round Delphi study involving seven international experts in educational research, digital scholarship, data literacy, and Open Science was carried out. Experts evaluated theoretical coherence, construct validity, clarity of items, and feasibility. Iterative revisions were implemented following each round, achieving substantial inter-expert agreement (Fleiss’ Kappa ≈ .77–.79). The questionnaire was administered in multiple waves between 2023–2024 and 2025, within doctoral training workshops on Open Data practices. Qualtrics software was used, ensuring anonymity, GDPR compliance, and secure data handling. The workshops where data collection had place were part of structured PhD training cycles and were led by three internationally active scientists and open data advocates from education, genetics, and computer science. This design enabled the collection of data across cohorts and disciplinary cultures, situating responses within authentic learning contexts rather than abstract survey settings. Quantitative data were analysed descriptively to identify configurations and tensions in research data practices and learning ecologies. The analysis foregrounds patterns of activity and constraint rather than normative compliance, aligning with a critical Open Science perspective that treats data practices as situated and negotiated elements of research activity.

The analysis of the dataset (N≈90–129, depending on item completion) reveals a patterned configuration of research data practices characterised by high engagement in data generation and management, and low engagement in data sharing and reuse. Most respondents report frequent involvement in generating their own research data (M=3.33) and managing data using established, often proprietary tools (e.g. Excel, SPSS; M≈3.6–3.7), alongside the production of traditional visualisations. In contrast, practices associated with openness and reuse remain marginal. Publishing data in open repositories, personal or institutional blogs, or open educational environments consistently shows low mean values (M≈1.8–2.1), indicating sporadic or exceptional engagement rather than routine practice. Notably, ethical reflection emerges as a salient dimension of research activity. Respondents report comparatively high frequencies of reflecting on ethical aspects of data extraction, treatment, and presentation (M=3.20), exceeding engagement with formal data quality frameworks such as FAIR principles (M=2.22) or emerging research evaluation metrics (M≈2.0). This suggests that ethical awareness is more embedded in researchers’ everyday practices than formalised open data standards. Taken together, these findings point to a configuration in which researchers are actively working with data as part of their scholarly activity but tend to contain data practices within individual or local research workflows. Openness, sharing, and reuse appear constrained not by lack of awareness, but by structural, institutional, and professional conditions shaping research as an activity, resulting in a cautious and selective enactment of Open Science principles. These findings should be interpreted considering some limitations, including the context-bound nature of the sample and reliance on self-reported practices. Future research should extend this work through longitudinal designs and qualitative inquiry, and inform doctoral training and institutional policies that support Open Research Data as a scaffolded, ethical, and collectively sustained research practice rather than an individual compliance task.

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