Research Data Management and Data Stewardship Competences in University Curriculum

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.

I. Introduction

The growing importance of data in the modern data driven economy, research and industry, requires special attention to including data management and governance related topics in university education. The future specialists should understand the role and value of data in research and industry and be able to derive actionable value from data collected from research, technological process or business/social activity, and be able to use open data and public data. Modern data-driven research and industry require new types of specialists capable of supporting all stages of the data lifecycle from data production to data processing and actionable results delivery, visualisation and reporting, which can be jointly defined as the Data Science professions family [1], [2].