Data management plan

Data management planning is the preparation of a timely strategy that helps to avoid potential risks and unnecessary costs in the management of research data and to promote the value and impact of research data after the project is completed. A practical tool for this purpose is a data management plan (DMP) - a document that covers data management-related activities throughout the project. At Rīga Stradiņš University, a data management plan must be developed within one month of project approval.

The DMP should cover a wide range of data management aspects, including:

• general information about the project;
• a description of the datasets used and/or generated;
• a description of metadata, ontologies and data documentation;
• choosing storage solutions, data security and retention strategies during and after the project;
• the way, time and place of data sharing and publication;
• the costs and resources required for data management;
• ethical and legal issues, such as privacy, intellectual property and licenses.

Developing a DPP provides a number of advantages that make the research process more transparent, efficient and sustainable:

• compliance with the requirements of research organizations and funders;
• clear planning of resources and equipment within the budget;
• precisely defined roles and responsibilities in the project team;
• early identification of risks and determination of solutions.

When developing a DPP, it is important to follow some basic principles that help ensure its quality:

• guidelines, policies and tools set by the funder;
• applicable standards and good practices for the equipment and infrastructure used;
• resources and consultations provided by the institution's support departments (IT, libraries, data curators, legal, etc.);
• national and international guidelines, tools and resources.

A data management plan is a living document that can be updated during the development of the project in accordance with infrastructure changes, software updates or new collaborations.

Rīga Stradiņš University recommends that its researchers use the Argos online platform for developing a data management plan.

Key aspects of a research data management plan

When developing a DPP, it is necessary to follow both the research institution and the funder, as well as generally accepted guidelines for the information to be included in the plan - it must be in accordance with the FAIR principles, as well as subordinate to available resources and ensuring data protection. The DPP must include and describe all stages related to the data life cycle - from data acquisition and processing to their sharing and long-term preservation.

General information

• What is the purpose of the research? State the brief purpose of the data collection and generation - why is the data being collected and how does it relate to the project objectives.
• 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 study? State the main characteristics of the data (type, format, volume) and the methods of collection and generation.
• Are you using data prepared by someone else? Where did you get it? It is important to clarify whether you will use an existing data set; in this case, you need to provide information about its sources and requirements for reuse (access conditions, license). Describe the use of the data - who will it be useful for.
• What data will be shared, when and how? Who will it be useful for? Describe the intended target audience of the data, the time and type of sharing, for example, a repository. Specify whether all data, part of the data or only metadata will be available, as well as indicate any access restrictions.

Findability

• Do you use metadata that meets industry standards? How will the metadata be managed and stored? Explain how the data and associated metadata will be made discoverable and identifiable, for example, by using appropriate metadata standards and persistent identifiers. (If there are no industry standards, describe what type of metadata will be created and how.)
• What approach will you use to create folders and files? Indicate whether and what file and folder naming conventions will be used, as well as the use of version control.
• Will this research be published in a journal that requires attribution of the data on which the research was based? If necessary, specify how these requirements will be met.

Accessibility

• Is a repository relevant to the field available? Indicate the chosen repository or alternative storage location and its access conditions.
• What documentation will you develop to make the data understandable to other researchers? Describe the planned documentation (metadata, code book, ReadMe, etc.)
• What tools or software are needed to view, read or use the data? Will it be archived? List the minimum software requirements for viewing the data and, if necessary, indicate the availability options (licenses, open source tools).
• Will there be any restrictions on the availability of the data? Specify the restrictions and their justification (confidentiality, commercial interests, security). In case of limited availability, explain how access will be provided.

Interoperability

• What file formats will be used? Are these formats open standards and commercially available? Will you use file formats that meet industry standards? If not, how will they be documented? When answering these questions, consider the availability and long-term usability of the data.
• How will you prepare the data for preservation and sharing? Describe how you will ensure data reliability and reuse, for example by using controlled vocabularies, taxonomies, etc. industry standards.

Reusability

• How long will the data be collected and how often will it change? Indicate the planned data collection and retention period, as well as the frequency of version updates.
• Are there any project-related patent or technology licensing restrictions on data sharing? Describe any legal restrictions and their impact on data availability, justifying their necessity.
• Will you allow reuse or creation of new tools, services, datasets or products? Will commercial use be allowed? Indicate how the data will be licensed to ensure the widest possible reuse. Specify whether it will be available to third parties, especially after the project is completed.
• How will you archive the data? Will you store it in an archive or repository for long-term access? If not, how will you maintain access to the data? Provide information on the chosen long-term storage location and its accessibility, as well as the need and duration of an embargo period, if applicable.
• How long will the data need to be retained? 3-5 years, 10 years or forever? Specify and justify the data retention period.

Resources and Security

• What budget and resource considerations should be taken into account when implementing the project? Indicate the costs associated with data management - data storage, backup solutions, software and personnel costs. Specify how these costs will be covered. Also note the resources/aspects of data management that are provided institutionally.
• Who is responsible for data management? Who will ensure the implementation of the data management plan? Who has the authority to manage this data? Is it the responsibility of the project manager, the student, the laboratory or the funding institution? Who will have access to this data? Clearly indicate the distribution of roles in the project, including the person responsible for adhering to the plan, as well as researchers and others who have access to the research data and the procedure for granting them.
• What are the local storage and backup procedures? Will this data require secure storage? Where do you plan to store the data? How do you plan to share the data? Describe the provisions for data recovery, as well as the secure storage and transfer of sensitive data.
• Does sharing the data raise privacy, ethical or confidentiality concerns? Is there a plan for data protection or anonymization, if necessary? Clarify the ethical or legal aspects of the data to be published, as well as methods for protecting sensitive data. It is important to review regulatory enactments and communicate with the ethics committee (and, if necessary, the data protection officer and IT department) to provide this information and to ensure the secure management of sensitive data.
• Who has the intellectual property rights to the data and other information generated by the project? Will copyrighted or licensed materials be used? Is permission to use or distribute this material? Clarify the distribution of ownership and the terms of use to avoid any confusion about the future use of the data and materials.

Useful Resources

Form No. ZD-15 “Data Management Plan - Institutional Template”

RSU Research Data Management Procedures

DS-Wizard Data Management Plan Development Tool

Science Europe Practical Guide to the International Alignment of Research Data Management