Speaker
Description
Open Research Data (ORD) is fundamental to the transparency and reproducibility of scientific results. It fosters scientific exchange and networking. In line with ORD strategies and funding agencies' requirements, research data should be published as openly as possible. Despite the advantages ORD brings to research, however, the publication of research data can be subject to restrictions. This applies to sensitive personal data, health-related data, synthetic data, and copyright protected data (e.g., software, protected works of literature and art). Additionally, the rapidly increasing research on and with Artificial Intelligence (AI) in intra-, inter- and transdisciplinary studies, particularly in biomedicine and engineering, health and environmental sciences, poses certain risks when working with sensitive data. Therefore, researchers need best practices for their work with sensitive data considering ethical questions before data collection and/or developing of AI as well as throughout the research data life cycle; they also need to be aware of the lack of control and the potential risks when processing unpublished and sensitive research data with AI.
The Data Stewards at the Open Science Department, the University Library of Bern are leveraging de-centralized domain-specific support in research data management for Science; Medicine and Veterinary Medicine; Human Sciences; Business, Economics and Social Sciences; Humanities, Law, and Theology and common topics such as sensitive data and data science. These common topics are relevant across all disciplines at the University of Bern and enhancing research data quality, integrity, and compliance with Findable, Accessible, Interoperable and Reusable (FAIR) and Collective Benefit, Authority and Control, Responsibility and Ethics (CARE) principles and funding agencies requirements.
Coordination of Data Stewards and linking them to other University’s departments, services, and support (e.g., IT Department, Data Science Lab, Core Facilities, the Department of Clinical Research, the Directorate for Teaching and Research, the Research Management Office at the Faculty of Human Sciences, the Vice Rectorate Research and Innovation, and the Legal Services Office); finding a common language and cultural environment; finding the right personnel, who could understand scientific language are the main challenges of the centralized approach compare to de-centralized. These challenges, however, give opportunities to extend networking, domain-specific support, and a common topic for establishing a research data management support competence center, where Data Stewards are becoming the main contact points in research data management in the long-term.
The Data Steward team is developing policies and guidelines for researchers to follow good scientific practices, sufficient data documentation, research data governance, the use of AI, and research data ethics. These guidelines are intended to ensure that the management and publication of sensitive, personal and protected data comply with ethical as well as legal requirements and FAIR and CARE principles.
In our presentation, we will address key aspects of ethics and sensitive data as well as AI guidelines on managing and publishing sensitive, personal and protected data across different disciplines. Firstly, we will show how to manage access to sensitive personal data, health-related data, synthetic data, and non-personal protected research data, which are subject of the Swiss cantonal and federal regulations and recommendations aligning with General Data Protection Regulations, which are relevant for research projects supported by the European Commission. Secondly, we will draw on our approach how to publish unprocessed (raw) data, pseudonymized and synthetic data, anonymized and copyright-protected data to facilitate transparency and excellence of research data in intra-, inter- and transdisciplinary research projects. The third aspect concerns data protection, security, and privacy when personal data is processed with AI. Finally, we will discuss how regional and national heterogeneity in guiding researchers working with sensitive data can be governed and aligned with research data management globally.
