Data Management Plan

Data Management Plan (hereinafter referred to as "DMP") specifies what data and how it will be created during the research and contains information about its availability and possibilities of use. Therefore, the DMP needs to be updated regularly to reflect what was actually happening with the data.

Why create a DMP?

  • Anticipating potential problems
  • Reducing the risk of duplication of work, data loss, and security breaches
  • Ensuring that data is accurate, complete, and reliable
  • Ensuring the continuity and consistency of long-term projects
  • Saving time and energy (e.g., when retrieving data, recovery)
  • Help with data sharing
  • Fulfilling the conditions of finance providers

Nowhere is it strictly defined in which form the DMP should be processed. Each institution or financial support provider can prioritize different data management process areas. Providers of financial support within the framework of grant projects may have specific requirements that should be respected as part of creating the DMP.

The content of the DMP reflects the type of data collected and the nature of the research. Different research areas may require different request priorities, such as areas related to sensitive data, ethics, and processing of personal data, working with large data volumes, the method of data storage, and subsequent data backup.

For your reference, there are examples of DMPs in the links below.

DMPonline (plans available)

Digital Curation Centre (sorted by providers)

LIBER (catalog managed by the LIBER association)

 

Main aspects of a DMP

1. Administrative data

Basic information that will provide context to the given DMP — basic information about the research (e.g., project name, name, contact details, and ID of the principal investigator, funder, and project partners), a short description of the research to which the data relate (abstract), regulations, measures or directives that govern data retention.

2. Data collection

Define what data will be created and how — type and volume of data, use of already existing data, use of standards and methodologies, data format, software used, data quality control, naming and structure of data/files, versioning.

3. Documentation and metadata

Provide information needed to read and interpret the data in the future — attachment with metadata and documentation, metadata standards, information on the methodology used, persistent identifiers used, e.g., DOI, data quality control.

4. Ethical and legal issues

Consider ethical and legal issues — consent for data retention and sharing, sensitive data, anonymization/pseudonymization of data, data licenses, existing embargoes, and data sharing.

5. Data storage and backup

Consider where the data will be stored and how it will be backed up, including access to it and how to ensure security — sufficient storage space (data repository), cost of storage space, the volume of data, assigning responsibility for data backup and recovery, the potential risk to data security including its solutions, secure access of co-researchers to data.

6. Long-term protection

Determine which data is suitable for long-term preservation and how best to preserve it — existing contractual/legal conditions for data retention, data selection for long-term retention, and time and financial costs of preparing data for long-term retention and sharing.

7. Data Sharing

Consider what data you will share and how, as well as how the users of your data will be able to credit you — with whom and under what conditions, how the data will be shared, when the data will be available, restrictions, data sharing conditions, how potential users will learn about the data.

8. Liability and Resources

Determine roles and responsibilities for all research data activities and consider potential costs associated with data creation and storage — who will be responsible for managing the DMP, how accountability will be divided among project investigators, additional equipment or expertise, use of software and hardware, other financial and human resources for data collection, processing, and storage.

How to create a Data Management Plan?

DMP can be implemented in free form, such as handwritten text or electronic in one of the text editors. However, it is more suitable to use freely available tools created just for this purpose, which in an adequate way and especially in the form of appropriately asked questions, lead scientists to answers to their questions.

These tools are, for example, Data Stewardship Wizard and DMPonline.

Online tools for creating a DMP

 

Online tools, mostly free, can be used by researchers in collaboration with their colleagues and simultaneously create or update the emerging plan. These online tools can guide users through each step of the plan entry process. The form of the plan can be adapted in these online tools to the requirements of individual projects or according to institutional specifics.

Online DMP creation tool. The service is integrated into the Open AIRE platform.

Offers the possibility of creating a DMP using templates prepared for the requirements of specific finance providers. It allows collaboration with colleagues and public sharing of the designed plan. The British Digital Curation Center (hereinafter referred to as "DCC") is behind the creation of this tool. If the user is not preparing a DMP for a specific finance provider, s/he can choose a general DCC template. In addition to the pre-set template, it also contains help to aid the user in filling in individual sections. The created plan can then be downloaded in various formats (e.g., csv, html, docx, pdf or json).

A freely available web tool developed within the ELIXIR research infrastructure. The tool contains guiding questions that help intuitively complete the DMP.

The author of the emerging plan does not fill out a specific template. Instead, it displays individual sections of managed research data, and thanks to the form of knowledge models, this tool displays only appropriate questions based on previous answers. The answers will gradually lead the author of the plan to topics related to data management.

The advantage of this tool is the availability of FAIR metrics (when entering relevant questions), thereby enabling data to be processed in a way that is consistent with these principles.

Like DMPonline, DS-Wizard provides help and supports collaboration and sharing of team projects. The final DMP product can be exported in several available templates (Machine-Actionable DMP, Horizon 2020, Horizon Europe, Science Europe) and formats (e.g., pdf, docx, html, LaTeX, and json).

Researcher Access: Researchers allows you to create DMPs for free after registration. Example procedure: Log in to DSW: Projects: Create: Custom – selection Knowledge model, Tags: e.g., Horizon 2020.

You can try the tool in the DEMO version as well.

Presentation from the Data Stewardship Wizard workshop (November 19, 2021, available in Czech only) HERE. Instructions for working with DSW.

The Open Science Framework is an open-source web platform for project management. It enables the management of the entire project cycle from planning to research implementation, reporting results, and archiving. It helps teams collaborate in one centralized location and provides file hosting, version control, persistent URLs, and DOI registration.

Data repositories

A trusted data repository: 1) provides persistent identifiers, 2) contains metadata, 3) has licenses for data use, 4) preserves data for the long term.

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