The DSBU provides tailored templates and guidance ensuring that FBM UNIL-CHUV researchers have access to customized life-science and biomedical models suited to their needs. Our service has set-up templates and questionnaires facilitating the creation of SNFS-compliant DMPs by aligning with research cycle phases and integrating responses into the SNFS template. Additionally, the DSBU provides a DMP review service before submission to the funding agency, ensuring compliance and quality.
Data Management Plan Description
A DMP is a crucial document in research projects that outlines how data will be managed throughout its entire life cycle. The aim of a DMP is to provide a structured approach to ensure that data is effectively collected, processed, stored, shared, and preserved in a way that promotes data quality, accessibility, and long-term usability. By creating and following a well-structured Data Management Plan, researchers can enhance the quality of their research, facilitate collaboration, comply with funding agency requirements, and ensure the long-term value and accessibility of their data.
Key components of a Data Management Plan
Data Description: A detailed description of the data to be collected or generated, including its format, structure, and potential volume.
Data Collection: Information about how the data will be collected, including methodologies, instruments, and tools.
Data Documentation: Plans for documenting the data, such as metadata standards, data dictionaries, and annotations, to ensure that others can understand and use the data.
Data Organization and Storage: Details about how the data will be organized, named, and stored during the project. This may involve considerations of file formats, folder structures, and storage locations.
Data Sharing and Access: Plans for making the data accessible to others, which might involve repositories, embargo periods, access controls, and licensing arrangements.
Data Preservation and Archiving: Strategies for preserving the data beyond the project’s completion, including considerations of data formats, storage options, and potential repositories or archives.
Data Security and Ethics: Measures to ensure data security and ethical handling, such as anonymization, encryption, and compliance with relevant regulations or standards.
Roles and Responsibilities: Clearly defined roles and responsibilities for individuals involved in data management, including researchers, collaborators, and data stewards.
Budget and Resources: Allocation of resources, both financial and human, needed for effective data management throughout the project.
Data Disposal: Plans for the secure disposal or retention of data, taking into account legal and ethical considerations.
Data Management Training: Details about any training that will be provided to researchers to ensure they understand and follow proper data management practices.
DMP Creation with the DSW Tool as Key Resources
We help you develop, implement, and maintain a robust Data Management Plan (DMP) that underpins every stage of your research.
- Comprehensive DMP Guidance We demystify the DMP process by walking you through each section—from data description and metadata standards, to storage, backup, and long-term archiving. Our goal is to make your plan coherent, funder-ready, and grounded in best practices.
- FAIR Principles at the Core Every DMP we help you craft is explicitly aligned with FAIR (Findable, Accessible, Interoperable, Reusable) principles. We show you how to choose appropriate identifiers, metadata schemas, and open formats so that your data can be discovered and reused by others.
- Alignment with SNF & Horizon Europe Requirements Whether you’re applying to the Swiss National Science Foundation (SNF) or Horizon Europe, we ensure your DMP meets all specific criteria—mapping each requirement onto your workflow and documenting evidence of compliance.
- DSW-Powered Efficiency Using the Data Stewardship Wizard (DSW-ELIXIR), we streamline DMP creation:
- Pre-built question sets aligned with SNF and Horizon Europe templates
- Discipline-specific life-science and clinical models for targeted guidance
- Real-time collaborative editing so your whole team can contribute
- Iterative Review and Updates A DMP isn’t “set and forget.” We support you in regularly reviewing and updating your plan as your project evolves—capturing protocol changes, new data types, or shifts in access policies.
- Training, Templates, and Examples Our workshops and one-on-one sessions include hands-on exercises using real-world templates and case studies. We also provide annotated example DMPs, highlighting what makes them strong and funder-compliant.
- Metrics and Quality Assessment Within DSW, each DMP section includes clear evaluation criteria and scoring metrics. These help you self-audit your plan’s completeness and readiness for peer review.
- Integration with Our FAIRShare Explorer @DSBU tool Once your DMP defines “how” you’ll share data, our FAIRShare Explorer @DSBU Database advises you on “where” and “in what formats,” drawing on the FairShare Explorer’s recommendations for repositories, metadata standards, and file formats.
By combining expert consultancy, targeted training, and the power of the DSW tool, we ensure your Data Management Plan is not only a compliance document but a strategic asset—maximizing the transparency, reproducibility, and impact of your research.
Practical courses about these aspects are provided by our service – link.
Contact the DSBU team for tailored support: link