Research Data Management in Life Science – Gothenburg

Managing research data involves a complex web of decisions. How do you ensure that nothing falls through the cracks, that your data remains secure and organised, and that your project supports FAIR data principles from start to finish? A strong Data Management Plan (DMP) helps researchers think through these questions before problems arise.

This hands-on workshop emphasises data management planning across all phases of a research project, from data collection and documentation to storage, sharing, preservation, and reuse. Participants will gain direct access to the Data Stewardship Wizard (DSW) tool and begin drafting a customised DMP tailored to life science research projects.

New for this year, the workshop will also briefly address the growing role of AI tools in DMP writing. Many researchers can now ask an AI system to generate a DMP in seconds, but a well-written text is not the same as a well-considered plan. We will discuss how AI can support drafting, reflection, and quality control, while also highlighting the risks of generic, incomplete, or misleading answers. The focus will remain on helping researchers understand the decisions behind their DMPs and use available tools responsibly.

Are you confident in how your research project documents, processes, and protects personal data? Life science research often involves complex data flows, multiple actors, and sensitive information, making it essential to understand both the legal requirements and the practical responsibilities involved.

This seminar guides participants through the legal landscape, from the GDPR to relevant Swedish national legislation. Using a practical research scenario, we will clarify essential roles and concepts, including:

• Research principal (forskningshuvudman): Understanding the role and responsibilities of the organisation hosting the research.
• Data controllers vs. processors: Identifying who is responsible for each stage of data processing, including storage, analysis, sharing, and long-term management.
• Sensitive data: Understanding how biomedical research data should be handled, protected, and shared securely, including practical considerations for services such as FEGA Sweden.

New for this year, the seminar will also briefly address how GDPR affects the use of AI tools such as ChatGPT in research. Generative AI can support tasks such as drafting, summarising, coding, documentation, and project planning, but it can also introduce risks when personal data, sensitive data, unpublished research material, or confidential project information are entered into external tools.

We will discuss why AI use is not only a technical question, but also a legal and organisational one: Who is responsible for the processing? What data can be shared with an AI tool? What should never be entered? How do researchers balance the benefits of AI with confidentiality, accountability, and compliance?

The aim is to help researchers make informed decisions when handling personal data in life science research, including in an increasingly AI-supported research environment.

Visit the Interactive Marketplace to explore local and national research data management services in an engaging and informal setting. Meet experts, discover practical support and tools, join interactive discussions, and learn how different services can support your research — all through inspiring stands, conversation-focused posters, and hands-on activities like the Data Management Ferris Wheel.

Interested in contributing to the Marketplace and showcasing your data management support services? Please contact gothenburg@scilifelab.se.

Research depends on code, but without open and FAIR practices, code can be difficult to understand, reproduce, and reuse. Open-source refers to code that is not only available, but also licensed for free use, modification, and sharing. This workshop provides an overview of open and FAIR practices for research code, and provides practical resources at three different levels; beginner, intermediate and advanced. Participants will apply these principles in a hands-on tutorial to improve the openness, transparency, and reusability of their research code.