Conference Agenda

This paper explores the concept of Mindful Data Stewardship, an approach that integrates principles of mindfulness and awareness into data governance practices. While data stewardship traditionally focuses on compliance, data quality, and lifecycle management (Plotkin, 2020), a mindful perspective emphasizes the importance of mindful relationships—the human-centered connections that stewards build with data, stakeholders, and institutional values. Drawing from the broader discourse on mindfulness in librarianship (Moniz et al., 2015) and education (Sherretz, 2011), this approach highlights the role of attentiveness, personal reflection, and relational engagement in data stewardship.

At the core of this approach is the recognition that data stewards are, above all, facilitators. Their primary competence is not only technical expertise but also the ability to foster dialogue, guide processes, and support the diverse needs of researchers and institutions. Facilitation is deeply connected to the skill of listening, particularly mindful listening, which enables stewards to better understand and respond to the needs of their colleagues s. Mindfulness practices such as body scans and meditation cultivate this skill by training practitioners to listen—not only to their own thoughts and sensations but also to those of others. By developing a heightened awareness through mindfulness, data stewards can enhance their ability to create responsive and user-centered data services.

The first part of the paper introduces the conceptual foundations of mindful data stewardship, drawing from information science and mindfulness studies. Rather than focusing solely on navigating complex data ecosystems, this section examines how mindfulness can enhance data stewardship as a service-oriented role. Informed by contemplative perspectives on information work (Latham, Hartel, & Gorichanaz, 2020), we argue that data stewards, by cultivating empathy, and attentiveness, can design and manage mindful data services that lead to deeper understanding and communication. This aligns with the Framework for Information Literacy for Higher Education (ACRL, 2016), which underscores the importance of metacognition and reflective practices in developing expertise.

The second part presents a case study and return of experience based on a workshop designed to integrate mindful practices into the vocational training of future data stewards. The workshop encouraged participants (n = 16) to engage in mindfulness exercises before, during, and after the sessions to cultivate awareness in their daily work. Pre- and post-workshop questionnaires were used to assess participants’ attitudes and evaluate the impact of these practices. The findings, which will be presented in the paper, provide insights into the benefits and challenges of embedding mindfulness into data stewardship.

This paper contributes to the discourse on responsible research data management by proposing a relationship-driven, human-centered framework for data stewardship. By incorporating mindfulness into data governance, we advocate for an approach that moves beyond procedural compliance to allow service-oriented engagement. As the field of data stewardship continues to evolve, mindful relationships can serve as a guiding principle for building trust, improving communication, and ensuring that data management practices align with broader human and institutional values.

Successful skills transfer requires that learning objectives are defined in such a way that they accurately describe the intended learning gain. For this purpose, the Learning Objective Matrix for the teaching of Research Data Management (RDM) has been developed.

The matrix formulates learning topics and objectives for all RDM-relevant skills for four different target groups: undergraduates, graduates, PhD candidates, and Data Stewards. The subject areas included are: RDM-basics, working with data, data documentation, metadata assignment, archiving, publication, and re-use of research data, legal and ethical aspects as well as cross-cutting topics, in particular didactics.

The Learning Objective Matrix was originally developed by the University Kiel and the DINI/nestor Research Data Working Group's subgroup “Training/Further Education” and was first published in September 2022. Editorial adjustments led to version 2 in June 2023 (Petersen et al., 2023). In addition, members of NFDI4Health contributed to the English translation of version 2. This was followed by a major community-driven revision in 2024, starting with a community meeting involving the Data Literacy Alliance (DALIA) and the Section “Training & Education” of the German National Research Data Infrastructure (NFDI). An editorial team then integrated the community proposals. The resulting version 3 is scheduled for release in the Spring of 2025. The Learning Objective Matrix has benefited enormously in the past years from the collaboration between different interest groups and stakeholders. The high level of demand is also reflected in the number of users: all versions have 11,392 views, 9,224 downloads and many citations both in the context of academic libraries like Grunwald-Eckhardt et al. (2022), Leimer et al. (2023), and Blumesberger et al. (2024), but also in the broader academic context (Riedel et al., 2024; Slowig et al., 2023).

For the third version, in addition to new and revised content and learning objectives, extensive accompanying material has been developed including application scenarios, a how-to-use-guide, and a glossary of terms used in the matrix. The glossary contains more than 50 terms and is unique in its scope in the field of Research Data Management. It is also presented with SKOS labeling to enable controlled, structured and machine-readable access for the semantic web.

The presentation will show practical applications of the Learning Objective Matrix for planning RDM training in academic libraries. We discuss how different actors and stakeholders can benefit from the Learning Objective Matrix. We identify how the learning objectives can be applied to different target groups and how the different levels of competence differ. The Learning Objective Matrix thrives on the further development of RDM competence with the participation of academic libraries.

References

Blumesberger, S., Eberhard, I., Hafeneder, E., Novotny, G., & Torggler, E. (2024). Handbuch Repositorienmanagement: Grundlagen – Anwendungsfelder – Praxisbeispiele (S. 596 S., 4000 kB) [Application/pdf]. University of Graz. https://doi.org/10.25364/9783903374232

Grunwald-Eckhardt, L., Mersmann, J., Schuray, A., Golsch, L., Hickmann, J., & Strötgen, R. (2022). Bausteine Forschungsdatenmanagement: 2022, 1Forschungsdatenmanagement etablieren – Bestehende Service-Angebote und geplante Erweiterungen [Application/pdf]. https://doi.org/10.17192/BFDM.2022.1.8363

Leimer, S., Hendriks, S., Korte, L., Stegemann, J., Stock, S. A., Timm, H., & Rehwald, S. (2023). Research Data Management Curriculum of the Research Data Services at the University Library Duisburg-Essen. Proceedings of the Conference on Research Data Infrastructure, 1. https://doi.org/10.52825/cordi.v1i.209

Petersen, B., Engelhardt, C., Hörner, T., Jacob, J., Kvetnaya, T., Mühlichen, A., Schranzhofer, H., Schulz, S., Slowig, B., Trautwein-Bruns, U., Voigt, A., & Wiljes, C. (2023). Lernzielmatrix zum Themenbereich Forschungsdatenmanagement (FDM) für die Zielgruppen Studierende, PhDs und Data Stewards. https://doi.org/10.5281/ZENODO.8010617

Riedel, C., Wiepke, A., & Lucke, U. (2024). Die Lernzielmatrix zur Evaluierung von DMPs in der Hochschullehre. https://doi.org/10.5281/ZENODO.11388258

Slowig, B., Blümm, M., Förstner, K. U., Lindstädt, B., Müller, R., & Lanczek, M. (2023). Zertifikatskurs „Forschungsdatenmanagement“ als Blaupause für die FDM-bezogene Kompetenzentwicklung im Rahmen der NFDI. Proceedings of the Conference on Research Data Infrastructure, 1. https://doi.org/10.52825/cordi.v1i.268

Based on the ACRL Framework [1], this study focuses on assessing students' self-efficacy regarding certain aspects of the "Information Creation" frame to determine their ability to evaluate the processes involved in creating information, as well as their effectiveness in meeting information needs by verifying the authenticity and quality of sources.

The study included a sample of 230 Education students from the University of Granada. A quantitative methodology was implemented using an ad hoc questionnaire. Participants reported an overall self-efficacy score in content creation above 7.3 points (SD=1.36) on a Likert scale from 1 to 10. It is worth noting that 70.9% of participants admit to using artificial intelligence (AI) to create content, with ChatGPT being their preferred platform (68.3%). Additionally, 42.2% say they share information in a restricted manner, with Instagram being the most used social network (95.7%). We observed an association between AI use and the average self-efficacy reported in information creation.

In conclusion, on the one hand, there is evidence of a gap in students’ perceived self-efficacy regarding certain aspects related to information creation (formats, media, data) that are more characteristic of media and data literacy. On the other hand, there is a significant impact of AI management on students’ perceived self-efficacy in the information creation process. Both findings reinforce the need to integrate three types of literacies (information, media, and data) into a holistic framework, which is essential for the education of university students in today's digital society, characterized by the massive use of data.

Artificial intelligence (AI) has had profound effects on society. Although it is rapidly evolving, its use in the workplace is still in its infancy and it is difficult to predict the impact – both beneficial and harmful – it will have on work and workers. Empirical data on the implications of AI are still scarce, with mixed evidence, mostly anecdotal (OECD, 2023). However, understanding how AI affects society and the workplace is imperative in order to capitalize on the opportunities it offers (Brynjolfsson & McAfee, 2014). Is AI a tool of worker empowerment or one of disempowerment?

A useful thought tool for analysing AI and its effects are Marshall McLuhan’s tetrads of media effects. A tetrad examines (i) what the new medium enhances or intensifies; (ii) what is rendered obsolete or displaced; (iii) what does it retrieve; and (iv) into what does it ‘flip’ or ‘reverse’? As argued by Turner (2015: 7), “[w]hen all four questions are considered in depth […] we have a better understanding of the medium and can address its negative effects”. We suggest that tetrads are still relevant for understanding technologies, the world of work, the nature of consciousness and the significance of self-awareness (George, 2024). McLuhan’s theories provide a useful social constructivist reading of society and technology as mutually shaping phenomena and of technology as the output of social processes in which humans (workers) have agency (Balka, 2000: 73). In supplying a critical approach to technology and work, McLuhan’s ideas are especially pertinent to critical AI literacy as a framework for understanding the technologies and the questions of (agentic) power, which are a central concern with AI (Hirvonen, 2024). This understanding allows us the possibility to anticipate and (partially) influence the transformative changes that technologies bring, delivering for all not just the few.

The objective of this research is to identify expert perspectives on transformative impact of AI on the workplace and workers’ agency to achieve decent work as well as its implications for AI literacy instruction. Qualitative research methodology will be employed. A focus group, with the participation of Croatian trade unionists from sectors where AI is widely adopted and integrated (e.g. healthcare and the creative industries), and interviews with an international pool of trade union experts on AI will be conducted to apply McLuhan’s tetrads to their experiences and their expectations of the effects of AI in the workplace, with a specific focus on transformations of labor practices, workplace equity and workers’ rights, as well as information practices and required competencies.

Two research questions arise: RQ1: What social implications can the tetrads indicate about AI influence that unionists identify and consider in terms of workplace transformation?; and RQ2: What lessons can we glean from unionists’ responses about the way a critical approach to AI literacy instruction should be structured?

This research will contribute an original approach by applying McLuhan’s tetrads to examine the transformation of the workplace under AI, offering alternative perspectives for understanding its impact on worker agency and the future of work.

References

Balka, E. (2000). Rethinking ‘the medium is the message’. Media International Australia, 94(1), 73−87.

Brynjolfsson E., McAfee A. (2014). The second machine age. New York, NY: W. W. Norton.

George, M. (2024). Is McLuhan still relevant for information and communication sciences? Some reflections on the topic. In Konferencija doktoranada (pp. 20−22). Zagreb: Filozofski fakultet Sveučilišta u Zagrebu-FF-press.

Hirvonen, N. (2024). Information literacy after the AI revolution. Journal of Information Literacy, 18(1), 47−54.

OECD. (2023). OECD employment outlook 2023: Artificial intelligence and the labour market. Paris: OECD.

Turner, A. (2015). McLuhan in the library. Art Libraries Journal, 40(1), 5−10.