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The template is divided into 5 sections with one or two general questions each. Additional questions, suggested answers, further information and useful links are given for inspiration in the corresponding guidelines. Not all questions are equally relevant for all areas of research.
For more information about the DMPonline tool, example DMPs and general information about data management, visit the website of the Digital Curation Center.
This template has been developed by the Office for Bibliometrics and Data Management at DTU.
Contact: Falco Hüser (falh@dtu.dk)
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Describe the data that will be collected.
Questions to consider:
- What type of data will be collected?
e.g. observational data, experimental data, simulation data, data products.
- How will the data be collected?
e.g. laboratory equipment, surveys, software.
- Which file formats are the data in?
e.g. open or proprietary formats.
- What are the estimated amounts of data?
in terms of GB or TB.
- How will the data be structured?
e.g. as data sets, naming conventions, ID numbers.
- How will the data be versioned?
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Describe any restrictions to the data.
Questions to consider:
- Are there any limitations on the use of existing data?
e.g. commercial databases or software, licenses.
- Are there any ethical or legal issues to be considered?
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Contact the legal advisors at DTU for additional help:
Susanne Schultz sus@dtu.dk and Ane Sandager anesa@dtu.dk
or the DTU Office for Bibliometrics and Data Management.
- Are there other external requirements?
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Describe the IT infrastructure to be used.
Questions to consider:
- Where are the raw data and results stored?
e.g. M- and O-drives, department server, lab notebooks.
- How are the data backed up?
e.g. AIT services, department IT.
- How is access control managed?
e.g. user groups.
- How are data shared within the project?
e.g. common file server.
- How is security for sensitive data guaranteed?
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Describe the metadata to be associated with the data.
Questions to consider:
- Are there metadata standards?
e.g. disciplinary standards.
- What metadata will be included?
e.g. title, timestamp, location, sample ID, creator, version, parameters.
- How will the metadata be generated?
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Describe the types of documentation that will accompany the data.
Questions to consider:
- How will data be documented?
e.g. electronic lab notebooks, accompanying ReadMe files, publications.
- How will the data be understandable for secondary users?
e.g. definitions of variables, vocabularies, units of measurement, any assumptions made.
- How will reproducibility of results be ensured?
e.g. description of the methodology, analytical and procedural information.
Data Sharing
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Research data is very valuable and of high interest for others in the scientific community. When research is funded by public money, its methods should be transparent and its outcomes should be made available for everyone. Sharing data will enable reuse and stimulate new research projects. Published data can - in the same way as regular articles - be acknowledged and cited and thereby increase the visibility of the scientists' work.
Describe which data will be shared.
Questions to consider:
- Which data will be shared?
e.g. describe value of the data for possible reuse.
- Which tools/software are needed to view/visualize/analyze the data?
e.g. freely available, open-source.
- Which data cannot be shared?
e.g. due to sensitivity, classification, commercial interests.
- Who will have access to the data?
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Describe how the data will be shared for possible reuse.
Questions to consider:
- When will data be shared?
e.g. along with a scientific publication, embargo periods.
- Where will data be shared?
e.g. in a public repository (see the Registry of Research Repositories for examples), data journal, Supplementary Material.
- How will the data be made discoverable?
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Describe how data will be archived beyond the scope of the research project.
Questions to consider:
- Which criteria will be used to select the data that should be archived for preservation and long-term access?
e.g. value for the scientific community or the public.
- Where will data be archived?
e.g. repository, National Archive.
- How will readability of the data be guaranteed?
e.g. long-lived file formats.
- Which data has to be destroyed?
e.g. due to contractual, legal or regulatory purposes.
- Who will be responsible for long-term preservation?
e.g. IT services.
- How long should the data be preserved?
e.g. 5 years, 10 years, permanently.
- How will long-term preservation be financed?
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