The Swiss Open Research Data (ORD) Strategy, developed collaboratively by swissuniversities, the ETH Domain, the Swiss National Science Foundation (SNSF), and the Swiss Academies of Arts and Sciences, provides a national framework to advance open research data practices while balancing openness with necessary restrictions. The strategy outlines guiding principles to support researchers and institutions in complying with Open Science provisions, and it is complemented by an Action Plan, published in 2022, which sets out concrete measures for implementation, including clearly defined roles and responsibilities for all stakeholders. A key component of the strategy is the establishment of a collaborative governance framework to ensure coordination, sustainability, and effective execution of ORD initiatives across the country. In parallel, efforts are underway to create secure environments that allow certified users to access sensitive data under controlled conditions, ensuring confidentiality and compliance with ethical and legal standards.
National Open Research Data Strategy (ORD) and Action Plan
Abstract
Country | Switzerland |
Start date | 2021 |
Annual Budget | n/a |
Responsible organisation(s) | Academic cooperation |
Target group(s) | |
Policy instrument type | Staregy and Action Plan |
The ORD Strategy complements the existing Swiss National Strategy on Open Access. It was initiated on the basis of the ORD Agreement between the State Secretariat for Education, Research and Innovation (SERI), Swiss universities, the Swiss National Science Foundation (SNSF), the Swiss Federal Institute of Technology Zurich (ETHZ) and the Swiss Federal Institute of Technology Lausanne (EPFL).
Additionally, Data Anonymization Tools and Guidelines have been developed: - Swiss Data Anonymization Competence Centre (Swiss Anon): Swiss Anon specializes in creating anonymized datasets through statistical techniques and the generation of synthetic datasets that mirror the properties of actual data. This enables the secure sharing of information and the development and testing of AI models while ensuring privacy protection.
- FORS Guides on Data Anonymization: The Swiss Centre of Expertise in the Social Sciences (FORS) has published comprehensive guides addressing both quantitative and qualitative data anonymization. These guides provide theoretical, legal, and practical perspectives, offering researchers detailed methodologies and best practices for anonymizing data. - EPFL Data Protection Guidelines: The École Polytechnique Fédérale de Lausanne (EPFL) has developed guidelines on data protection in research projects, including sections on anonymization versus pseudonymization. These guidelines help researchers understand the differences and apply appropriate techniques to protect personal data.
To ensure that sensitive data is shared responsibly, aligning with legal and ethical standards.
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