Data Management Plan
A data management plan (DMP) is a key document that describes how data will be collected, processed, secured, stored and made accessible. Not only during the project, but also after it has ended.
Data Management Plan
You should prepare a DMP before the project begins. When planning your research, consider what data will be generated, who will manage it, where it will be stored, how it will be backed up, etc.
During the project, it is necessary to document the data well (description of methods, structure, versioning), back it up and ensure its secure storage, especially if you are working with sensitive data.
At the end of the project, you must decide what to do with the data: which data can be deleted, which data should be stored for the long term, and whether and how to make it accessible.
A data management plan is not a one-time formality. It should be updated throughout the project to reflect the actual work flow (format changes, new team members, decisions on data retention or deletion, etc.).
Why does it matter?
Requirement of funding
providers
All grant organizations today require DMP or data management information. DMP increases the chances of project approval and compliance.
Reduces the risk of data
loss and corruption
Planning backups, versioning, and storage reduces the likelihood of irreversible data loss. Lost data is like lost time...
Ensures long-term data
usability
DMP helps to choose appropriate formats
and data descriptions so that they are understandable and usable in the future,
even when software or team members change.
Increases research impact
Publications linked to research data that can be easily found and reused tend to have higher citation rates and benefit the community.
Legal and ethical protection
A data management plan will help you properly handle personal data, contractual restrictions, and intellectual property.
Speeds up the writing of publications
Well-described data saves time when preparing articles, supplementary materials, and responding to reviewers.
Recommended parts of DMP
1. Project information
Project name, plan version, date, change history table, project registration number, project members and their roles, affiliations, and contacts.
2. Data Summary
Use of existing data, data types and formats (prefer open formats over proprietary ones), purpose of data creation/collection, expected data size, data origin.
3. FAIR Data
Data and Metadata Findability
Persistent identifiers, metadata standards, metadata discoverability.
Data Accessibility
Trusted repositories, data availability, metadata availability.
Data Interoperability
Standards, formats, methodologies, ontologies or dictionaries.
Data Reuse
Documentation, data licensing, data quality.
5. Allocation of resources
The costs of data management and (long-term) preservation (including fees paid to repositories, staff time for a project data steward, etc.), as well as responsibilities related to data management.
6. Data security
Data storage and backup during research, data protection, encryption, access control, long-term data retention.
CAS FAIR Wizard - DMP nice & easy
1. What is FAIR Wizard good for?
For at least the initial preparation of a DMP, we recommend the CAS FAIR WizardThis tool facilitates the creation of a DMP and includes a template for the Horizon Europe programme. This template can also be used for other public funding providers (TA ČR, CSF, MEYS, etc.). The FAIR Wizard user answers questions about their research data, user is guided through the entire process, and at the end simply generates a finished DMP based on the template of the individual funding providers in DOCX or PDF format.
2. What else it can do?
- As you fill out the form, the tool automatically assesses the degree to which the FAIR principles and other metrics are met, providing accompanying explanatory comments. This guides the user through the creation of the DMP. Thanks to questions with predefined answer options, the user is spared the need to write an additional appendix to the project documentation.
- FAIR Wizard also allows you to share the DMP across the project team and add comments. When you tag a specific person from the group with whom you’re sharing the DMP, they’ll receive an email notification. Last but not least, you can mark questions that still need to be answered. The DMP can be versioned and exported into templates for each funding providers.
- In FAIR Wizard, you can return to the DMP and update it on an ongoing basis, e.g., for interim and final reports for funders. However, what is a strength of this tool at the beginning is somewhat weaker when it comes to updates. We therefore recommend exporting the DMP to MS Word for updates and editing it there.
3. How can I log in?
You don't need to go through a lengthy registration process. To log in to the tool, use your institutional Shibboleth credentials (the black button on the right on the login page ) These are the same as your VERSO login credentials, or the ones you normally use to log in to Nature, Elsevier, and Wiley journals. If you don’t know your credentials, contact the IT Department.
4. When should I start?
- The simple answer: Right at the start of the project or research! It helps the research team, who will know what data you’ll be collecting, how you’ll analyze it, how you’ll store and subsequently share it, whether there are any restrictions attached to the data, what legal or ethical questions need to be addressed, and much more. Second, some DMP funding providers require it at the very start of the project; the more lenient ones allow up to a year.
- If you leave preparing the DMP to the “last minute,” expect that no one else will be able to fully handle the DMP on behalf of the researchers. TAS staff do not have access to all the information necessary to create a DMP on behalf of the research team. If you leave it until the last 48 hours before the submission deadline (e.g., for interim reports for CSF projects), it may happen that no one will be able to help you with the preparation.