Data management means actively planning how to manage data during and after a project. Thinking through different aspects of data management at an early stage can make the research process more efficient. It is also important for securing reliable and well documented data that can be preserved and made available for reuse.
Research data refers to information collected to be examined and considered as a basis for reasoning, discussion, or calculation (definition from Horizon 2020). Data management involves technical and organisational as well as sustainability aspects. Issues to work with before, during and after a project include for example:
- Collecting data
How will data be collected, what types of data and formats will be used? What ethical and legal aspects must be considered? Accountability and costs for managing data?
- Managing data during the project
How will data be stored, structured and versioned? Who should have access to data? How will data be continuously documented and described to be understandable both during the project and in the future?
- Archiving and making data available
How will access to data be secured after the end of the project? What data should be kept for long-term preservation? Will data be published?
The definition of research data also encompasses data generated by SLU’s Environmental Monitoring and Assessment programmes (EMA). The purpose of these data is to form a basis for decision making within government, industry and other organisations in their work on sustainability. Since several years EMA is working systematically with quality in data management according to the framework Kvalitetsguide för SLU:s miljödatahantering.
Data management plans
It is a good idea to write a data management plan (DMP) as part of preparing for a new project, and to make this plan a living document as an aid during the course of the project. A data management plan is a means to think through procedures for the organisation and management of research material to make research more efficient and to make data as FAIR as possible (see FAIR data below). The following documents can guide you when developing a data management plan:
- SLU data management and preservation plan – general
- SLU data management plan – Environmental Monitoring and Analysis (Swedish)
- Swedish National Data Service – checklist for data management plans
Some research funders, e.g. Formas and Horizon 2020, require a data management plan that accounts for how collected data will be managed, documented and made available. Similar requirements are expected to become more common in the near future. The Swedish Research Council has decided to require data management plans for all new research projects, starting with the 2019 call.
- Formas – template for data management plans
- Horizon 2020 - policy and template for data management plans
The FAIR principles for data management is a framework for facilitating access to and reuse of research results. Creating and managing data in a way that makes them Findable, Accessible, Interoperable and Reusable (FAIR) will enhance their value for the wider research community and the public, and will increase research reproducibility.
These principles have been embraced by a broad community of international and national stakeholders. The Swedish Research Council has developed criteria and guidance for applying the FAIR principles. The European Commission has put forward an action plan, Turning FAIR into reality, in order to accelerate research and ensure transparency, reproducibility and societal utility of research data.
The FAIR Guiding Principles for scientific data management and stewardship - the original paper by Wilkinson et.al. 2016
How FAIR are your data? - checklist for FAIR data