Plan data usage

Good data management is a central part of good research practice. Therefore, it is important to identify at an early stage what data is needed to answer the research question and to establish a data management plan. Research principals and research infrastructures often offer support in this work.

Identify what data is needed to answer the research question

In the initial stage of the research data cycle, the research question is central and guides subsequent data management. The specific questions that the research aims to answer will determine which data needs to be ordered. A clearly formulated research question makes it easier for the data holder to guide you in what data is needed for the specific project and with the subsequent process of ordering data from relevant data holders and ensuring compliance with rules and guidelines. Selecting the appropriate data from the outset saves both time and resources.

Use a data management plan for support

A data management plan provides support in planning and ensuring that data management is carried out in accordance with applicable legislation and follows the guidelines and procedures for information management, archiving and disposal applicable to the research location. A data management plan that clearly shows how information security and personal data protection are to be handled can facilitate the disclosure of data, as data holders often require this information when ordering data. The data management plan is part of the transition to open science. The plan could clarify whether data is to be shared and under what terms and conditions, which promotes the reuse of data in new research.

Ensure, already at the planning stage, that there is expertise and resources for good and secure data management. It should be clear who is responsible for the data management plan and how responsibility for data management is distributed within the project. Feel free to use the tools and templates offered by research principals or research infrastructures. Creating a data management plan can also facilitate the project's assessment of requirements regarding storage, support, time and cost.

The data management plan can be developed and revised throughout the project's life cycle, but it is not intended to replace or serve as a detailed logbook of each individual element in data management.

FAIR data management

Planning and good data management are of great importance in order to enable the reuse of research data. FAIR data management means that research data should be structured and documented to ensure it is:

• searchable (Findable)
• accessible (Accessible)
• interoperable (Interoperable)
• reusable (Reusable).

These principles should be taken into account in the planning of the project's data management, in the data management plan and during the duration of the project.

Take advantage of support from research principals and infrastructures

Research principals often offer support throughout the research data cycle. The scope and focus of the support, as well as how it is organised, varies between different research principals. It is usually divided into different stages of the research process, and support and information are typically available on:

• planning data management
• collecting, documenting and storing research data
• analysing and sharing data
• publishing or making data accessible according to the principle of “as open as possible, as closed as necessary”, and archiving data at the end of the project.

Information is available on university websites and is often provided by local units that work with research data management, known as Data Access Units (DAUs). These are grouped together in a national network run by the Swedish National Data Service (SND), which also includes certain government agencies.

SND also offers support in finding and managing research data. The web portal researchdata.se contains, among other things:

• a checklist for data management plans
• descriptions of data standards and metadata management
• information about file formats, data storage and data sharing
• some legal guidance on the handling of personal data in research
• various services for finding and documenting data.

Research infrastructures, such as NAISS at Linköping University, MONA at Statistics Sweden, and the Swedish National Data Service (SND), also offer various types of support to researchers depending on the data generated. The Swedish Research Council's website lists the research infrastructures that the authority funds, divided into different subject areas, including digital infrastructure.

Check for requirements from research funders or research principals regarding data management

At the start of a project, it is advisable to verify whether the research funder or research principal has any specific requirements that may affect the planning of data management. Several research funders, including the Swedish Research Council, require the development of a data management plan for funded projects. The research principal is responsible for ensuring this, and in most cases a plan is required to be in place when the project begins.

The Swedish Research Council, other research funders and several Swedish universities recommend, for example, that research data be managed according to the FAIR principles. Within the framework of Horizon Europe, managing research data in accordance with FAIR principles is mandatory. Several research funding bodies that require a data management plan to be drawn up for funded projects allow the inclusion of project-related costs for FAIR data management in the funding application.

Practical support and guidance for data management

Support functions at universities, research infrastructures, domain-specific and international initiatives and organisations often have templates for data management plans. These may be free or offered as fee-based services. Some research infrastructures have developed their own support material for data management plans. The material may differ in terms of format and terminology, for example, but is often based on Science Europe’s basic requirements and guidance.

The Swedish Research Council’s support and guidance

The Swedish Research Council recommends that research projects use the template developed in collaboration with the Association of Swedish Higher Education Institutions (SUHF). The template is based on Science Europe’s baseline requirements. The Swedish Research Council also offers a digital service for data management plans, via Sunet, which affiliated higher education institutions can use for a fee.

SciLifeLab:s tools

SciLifeLab provides support for data management in life sciences, covering the entire research life cycle. They recommend using tools provided by the researcher’s home institution, which is usually DMPOnline, or the Data Stewardship Wizard offered by SciLifeLab.

Clinical Studies Sweden’s templates and support documents

Clinical Studies Sweden provides quality-assured templates for data management in clinical studies, including support for data management plans, study databases and data collection in REDCap.