Data management plan
Prior to research data collection/generation and processing you have create data management plan (DMP). DMP is a tool which will enable researchers to closely consider all main aspects of research data management, thus guiding them to the best result possible. DMP will help researchers to plan all activities regarding collection of data (including ethical and technical provisions), processing data and publishing it.
Main aspects
To develop a Data Management Plan (DMP) you have to follow all the instructions given by the funding agency or RSU (in cases when RSU template has been provided). DMP shall include all necessary information to make data FAIR, as well as consider resources and security of the data. Link and instructions RSU DMP template in ARGOS will be provided as soon as RSU template has been published in ARGOS. You can also see different examples and useful guides in the 'Online Tools and Useful Links' tab below.
The questions below provide a starting point to think about and formulate a DMP.
- General
State the purpose of the data collection/generation, as well as explain the relation to the objectives of the project. Give a summary of the origin of the data. At the same time you are expected to provide information on types, formats and size of data generated/collected. It is important to specify, if you will use already existing data set (re-using data). Outline the data utility: to whom will it be useful!
Questions
- What is the purpose of the research?
- What is the data? How and in what format will the data be collected? Is it numerical data, image data, text sequences, or modelling data?
- How much data will be generated for this research?
- Are you using data that someone else produced? If so, where is it from?
- What data will be shared, when, and how?
- Findability
Explain how your data will be findable. For that you have to outline the discoverability of data (metadata provision), use of standard identification mechanisms (persistent and unique identifiers). You have to specify, if you plant to use any naming conventions for keywords. Will it be possible to clearly see versions of the data set? Also very important to specify standards used for metadata creation. If there are no standards in your discipline, describe what type of metadata will be created and how.
Questions
- Are you using metadata that is standard to your field? How will the metadata be managed and stored?
- What directory and file naming convention will be used?
- Will this research be published in a journal that requires the underlying data to accompany articles?
- Accessibility
Explain how accessible will your data be. In order to do that you have to specify which data in data sets will be made openly available, as well as give explanation in cases, if some data is kept closed. To make data more accessible, you also have to describe how the data will be made available by specifying methods and software tools needed to access the data, where the data and associated metadata, documentation and code are deposited. Significant part is to explain how access will be provided in case there are any restrictions to the data.
Questions
- Is a discipline-specific repository available?
- What documentation will you be creating in order to make the data understandable by other researchers?
- What tools or software are required to read or view the data?
- Will there be any embargoes on the data?
- Are software or tools needed to use the data? Will these be archived?
- Interoperability
Assess the potential of interoperability of your data, i.e. allowing data exchange and re-use between researchers, institutions, organisations, countries, etc. Specify what standards, vocabularies and taxonomies will you follow to facilitate interoperability. Specify whether you will be using standard vocabulary for all data types present in your data set to allow inter-disciplinary interoperability.
Questions
- What file formats will be used? Do these formats conform to an open standard and/or are they proprietary?
- How will you prepare data for preservation or data sharing? Will the data need to be anonymized or converted to more stable file formats?
- Are you using a file format that is standard to your field? If not, how will you document the alternative you are using?
- Reusability
Specify how the data will be licenced to permit the widest reuse possible. When the data will be made available for re-use? If applicable, specify why and for what period a data embargo is needed? Specify whether the data produced and/or used in the project is useable by third parties, in particular after the end of the project. Specify the length of time for which the data will remain re-usable. If the re-use of some data is restricted, explain why. Describe data quality assurance processes.
Questions
- How long will the data be collected and how often will it change?
- Are there any patent- or technology-licensing-related restrictions on data sharing associated with this grant?
- Will you permit re-use or the creation of new tools, services, data sets, or products? Will commercial use be allowed?
- How will you be archiving the data? Will you be storing it in an archive or repository for long-term access? If not, how will you preserve access to the data?
- How long should the data be retained? 3-5 years, 10 years, or forever?
- Resources and Security
Outline the resources available to create, publish and store your data. Therefore, estimate the costs for making your data FAIR. Describe how you intend to cover these costs. Clearly identify responsibilities for data management in your project. Describe costs and potential value of longterm preservation. Address data recovery as well as secure storage and transfer of sensitive data. Describe any ethical or legal issues related to data you plan to publish. You especially have to consider how you prevent access to personal data or that you protect sensitive information. You also have to describe technical methods to process and access personal data or sensitive information. It is also significant to review national legislation, as well as to contact ethics committee.
Questions
- Who is responsible for managing the data? Who will ensure that the data management plan is carried out?
- Who has the right to manage this data? Is it the responsibility of the PI, student, lab, or funding agency?
- What are your local storage and backup procedures? Will this data require secure storage?
- Does sharing the data raise privacy, ethical, or confidentiality concerns? Do you have a plan to protect or anonymize data, if needed?
- Who holds intellectual property rights for the data and other information created by the project? Will any copyrighted or licensed material be used? Do you have permission to use/disseminate this material?
- Online tools and useful links
Argos DMP tool (created within OpenAIRE project)
DS-Wizard DMP tool
DMP tool by California Digital Library
DMP tool by Digital Curation Centre
DMP assistant by Digital Research Alliance Canada
Research data management glossary
Practical Guide to International Alignment of Research Data Management (Science Europe)
Data management plan elements with practical examples
