Skills for data governance and management are critical for the wide adoption of Open Science practices and effective use of the data in research, industry, business and other economic sectors. The FAIR (Findable -Accessible -Interoperable -Reusable) data management principles and data stewardship provide a foundation for effective research data management. The 2018 "Turning FAIR into Reality" report and other documents recommend that data skills should be more widely included in university curricula and that a concerted effort should be made to coordinate and accelerate the pedagogy for professional data roles. Throughout Europe and beyond, many organisations, projects and initiatives work on providing training on FAIR data competences. However, wider adoption of the FAIR data culture can be achieved by including FAIR competences into university curricula. This paper presents the ongoing work of the FAIRsFAIR project to develop a Data Stewardship competence framework and to provide recommendations for implementing this framework in university curricula by means of defining the Data Stewardship Body of Knowledge Model Curricula. The proposed approach and identified competences and knowledge topics are supported by a job market analysis. The presented work is actively using the EDISON Data Science Framework as a basis for Data Stewardship competences definition and methodology for linking competences, skills, knowledge, and intended learning outcomes when designing curricula.
The future of scholarly publishing, in particular the growth of Open Access publishing and how it is financially sustained, is hotly debated. Many different models, advantages, and disadvantages are being discussed. Yet, there is little consensus about what a future system should look like or how it should be governed. To describe the ongoing dynamics in scholarly publishing, this paper introduces the multilevel perspective on socio-technical transitions (MLP). This explorative application of the MLP addresses five possible transition pathways: transformation, reconfiguration, substitution, de-alignment & re-alignment, or a sequence. The analysis shows that incumbents survive by adapting to niche innovations and withstanding landscape pressure by innovating themselves, in terms of technology, business models and through acquisitions. In other words, the current trajectory reproduces the oligopoly of incumbents. This means that the sector is, at minimum, on a trajectory of transformation or, potentially, reconfiguration. This diagnosis may change when the dynamics change, for instance if niche innovations and landscape pressure converged stronger on an alternative pathway.
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