{"id":155,"date":"2026-03-06T09:49:07","date_gmt":"2026-03-06T09:49:07","guid":{"rendered":"http:\/\/openscience.bc.cas.cz\/?page_id=155"},"modified":"2026-09-07T11:38:50","modified_gmt":"2026-09-07T09:38:50","slug":"data-management-plan","status":"publish","type":"page","link":"https:\/\/openscience.bc.cas.cz\/en\/fair-data\/data-management-plan\/","title":{"rendered":"Data Management Plan"},"content":{"rendered":"
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Biology Centre CAS<\/span><\/mark><\/a><\/strong><\/strong><\/strong><\/strong><\/p>\n\n\n\n

Data Management Plan<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

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More...<\/a><\/div>\n\n\n\n
CAS FAIR Wizad<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/main>\n<\/div><\/main>\n\n\n\n
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Data Management Plan<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.).<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/main>\n\n\n\n

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Why does it matter?<\/strong><\/h2>\n\n\n\n
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Requirement of funding
providers<\/h2>\n\n\n\n

All grant organizations today require DMP or data management information. DMP increases the chances of project approval and compliance.<\/p>\n<\/div>\n\n\n\n

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Reduces the risk of data
loss and corruption<\/h2>\n\n\n\n

Planning backups, versioning, and storage reduces the likelihood of irreversible data loss. Lost data is like lost time...<\/p>\n<\/div>\n\n\n\n

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Ensures long-term data
usability<\/h2>\n\n\n\n

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.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Increases research impact<\/h2>\n\n\n\n

Publications linked to research data that can be easily found and reused tend to have higher citation rates and benefit the community.<\/p>\n<\/div>\n\n\n\n

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Legal and ethical protection<\/h2>\n\n\n\n

A data management plan will help you properly handle personal data, contractual restrictions, and intellectual property.<\/p>\n<\/div>\n\n\n\n

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Speeds up the writing of publications<\/h2>\n\n\n\n

Well-described data saves time when preparing articles, supplementary materials, and responding to reviewers.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/main>\n\n\n\n

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Download DMP guide<\/strong><\/a><\/div>\n<\/div>\n<\/div><\/main>\n\n\n\n
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Recommended parts of DMP<\/strong><\/h2>\n\n\n\n
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1. Project information<\/a><\/a><\/summary>\n

Project name, plan version, date, change history table, project registration number, project members and their roles, affiliations, and contacts.<\/p>\n<\/details>\n\n\n\n

2. Data Summary
<\/a><\/a><\/summary>\n

Use of existing data, data types and formats (prefer open formats over proprietary ones), purpose of data creation\/collection, expected data size, data origin.<\/p>\n<\/details>\n\n\n\n

3. FAIR Data
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Data and Metadata Findability<\/strong><\/em>
Persistent identifiers, metadata standards, metadata discoverability.<\/p>\n\n\n\n

Data Accessibility<\/strong><\/em>
Trusted repositories, data availability, metadata availability.<\/p>\n\n\n\n

Data Interoperability<\/strong><\/em>
Standards, formats, methodologies, ontologies or dictionaries.<\/p>\n\n\n\n

Data Reuse<\/strong><\/em>
Documentation, data licensing, data quality.<\/p>\n<\/details>\n\n\n\n

4. Other Research Outputs
<\/a><\/a><\/summary>\n

Software, workflows, protocols, methodology, utility models, etc.<\/p>\n<\/details>\n<\/div>\n\n\n\n

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5. Allocation of resources
<\/a><\/a><\/summary>\n

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.<\/p>\n<\/details>\n\n\n\n

6. Data security
<\/a><\/a><\/summary>\n

Data storage and backup during research, data protection, encryption, access control, long-term data retention.<\/p>\n<\/details>\n\n\n\n

7. Ethical and legal aspects
<\/a><\/a><\/summary>\n

Legal aspects, ethical aspects, informed consent, ethics committee, consortium agreement governing intellectual property, etc.<\/p>\n<\/details>\n\n\n\n

8. Other issues
<\/a><\/summary>\n

Data management policies.<\/p>\n<\/details>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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CAS FAIR Wizard - DMP nice & easy<\/strong><\/h2>\n\n\n\n
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1. What is FAIR Wizard good for?<\/summary>\n

For at least the initial preparation of a DMP, we recommend the CAS FAIR Wizard<\/span><\/a>This 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 \u010cR, 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.<\/p>\n<\/details>\n<\/div>\n\n\n\n

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2. What else it can do?<\/summary>\n