Data management plans for secondary use of health data

https://doi.org/10.5281/zenodo.17708638 What we will explore today •How ELIXIR resources support good data management planning practice •Key considerations when working with health and sensitive data •Seven areas that help you plan your project effectively •Take-home pointers for applying Data Management Plans The ELIXIR resources we will focus on today ●Provide practical, domain-tested guidance for complex health and life sciences data ●Work for different backgrounds (data management, clinical, technical, policy, ethics) ●Connect FAIR, governance and good research practice ●Reduce guesswork through clear, structured steps ●Support alignment across teams and institutions ELIXIR resources offer practical, well-tested support for working with complex health data ●RDMkit explains what good practice looks like and has many tips and tricks ●Data Stewardship Wizard helps you decide how to apply it in your project, with lots of useful guidance per question The images on the next slides are taken from the ELIXIR RDM guidelines page. Please visit this pages for the resources and user journeys portrayed, including links Area 4: Date use restrictions ●What: Conditions that apply to using, reusing or sharing data, including consent scope, data use ontology (DUO) codes, institutional rules and permitted-use categories ●Why: Clear restrictions prevent misuse, ensure compliance and define what is allowed in secondary use projects ●How: ○Identify consent limitations or permitted-use categories ○Clarify data use agreements, data transfer rules and governance constraints ○Record all restrictions early so they guide processing, sharing and publication ●Links (from RDMkit): ○Ethical aspects → How to navigate ethical constraints ○Human data → What consent and use limitations apply ○Licensing → Which conditions govern reuse Area 5: Metadata and documentation ●What: Describing data in a structured, consistent way so they can be understood and reused by others (and by you later on) ●Why: Good metadata reduces ambiguity, improves integration and supports quality and reproducibility ●How: ○Capture descriptive, structural and administrative metadata at the moment of collection ○Use templates and controlled terms to keep documentation consistent ○Maintain versioning and record changes over time ●Links (from RDMkit): ○Documentation and metadata → How to document data effectively ○Data organisation → How to structure and name data consistently ○Data provenance → How to record processing context ○Data quality →How to ensure completeness and accuracy Area 6: Standards, ontologies, workflows and provenance ●What: Using common formats, terminologies and workflows to ensure interoperability and traceability across systems and datasets ●Why: Consistent standards and recorded provenance make integration, automation and reproducibility possible ●How: ○Adopt existing standards, encodings and ontologies where possible ○Use structured workflows and record every processing step ○Capture provenance so results are interpretable and repeatable ●Links (from RDMkit): ○Documentation and metadata → How to align formats and terminologies ○Data provenance → How to track processing steps ○Tool assembly → How tools fit together in workflows ○Machine actionability → How to support automation Area 7: Access models and data sharing ●What: Deciding how data will be shared or made available under which conditions ●Why: Clear access models support transparency, enable collaboration and ensure lawful and ethical data use ●How: ○Define who can access which data and under what conditions ○Select appropriate access routes (open, restricted, closed) ○Ensure that metadata remain findable even when data cannot be open ●Links (from RDMkit): ○Data publication → How to make outputs discoverable ○Data sensitivity → How access restrictions are applied ○Licensing → What terms facilitate sharing and reuse Key messages to take home •Plan before you start: A DMP helps you make informed decisions for health and sensitive data •Build on what already exists: Reusing data, templates and standards saves effort and improves quality •Be consistent across teams and solutions: Shared procedures, metadata and workflows make integration possible •Use trusted ELIXIR resources: RDMkit and the Data Stewardship Wizard guide you step by step Thank you for your attention Please feel welcome to contact me via [email protected] in case of questions or if you would like to explore any of these resources further