Introduction to data management

Research data is any information that has been generated or compiled as part of a research activity. In the context of the SLU research data guide, data from environmental monitoring and assessment can be regarded in the same way as data from research. Furthermore, research data and environmental monitoring and assessment data in the data management guide mainly refer to digital data.

• Read more: Data definitions

The purpose of good data management is to improve the value of the data and the research and facilitate more efficient use of resources. Or, in the words of Wilkinson et al (2016):

Good data management is not a goal in itself, but rather is the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse by the community after the data publication process.

Good research data management is also beneficiary for the researcher and the research project. Below some examples of the benefits of good research management are listed.

Increase research efficiency

Good research data management will enable you to organise files and data for easy access and analysis, both on an individual as well as project level. By organising files and data in a structured way future data retrieval will be optimised and risk of data loss minimised.

Improve research integrity

Accurate and complete research data is crucial for validating, evaluating, and reproducing research output.

Enhance research visibility

Making data available boosts the visibility of your findings and ideally also increases the specific number of citations. Research data – if correctly formatted, described, and annotated – will have significant ongoing value and can continue to have impact long after the completion of a research project.

Enable collaboration

By facilitating sharing and reuse of data for future research, you could be creating opportunities for collaboration with other researchers. Then again, sharing well-managed research data and enabling others to use it will help prevent duplication of effort.

Keep research data safe

You can reduce the risk of data loss by keeping research data safe. The use of robust and appropriate data storage facilities will help to reduce the loss of data through accidents or neglect.

Comply with current legislation and funder policies

Good RDM will help you comply with funder research data expectations and policies. Many funders and a growing number of journals as well as publishers now require you to share the data at the end of a project or at the time of publishing the corresponding research results. An increasing number of funders also require that a data management plan (DMP) is in place for each project.

Ensure data is FAIR

FAIR in relation to data means that data is Findable, Accessible, Interoperable, and Reusable. The FAIR Guiding Principles for scientific data management and stewardship are a set of guidelines intended to enhance the usability of data with the ultimate goal to optimise its reuse. To achieve this, data should be provided with sufficient documentation and metadata, preferably in a machine-readable format, so that it can be replicated and/or combined in different settings. On our FAIR page, we (i.e., the Data Management Support Unit) offer some practical advice on how to make data more FAIR.

Demonstrate responsible practice

By managing research data according to good practice and making it publicly available, you can show that the use of public resources to fund research is done responsibly. Good RDM improves the possibilities for validation of research results and strengthens research integrity.

In the SLU Data management guide you may find resources to help you plan your data management, write a data management plan, publish data, find already published data as well as much more. The guide follows the steps in a conceptual model of the data life cycle: planning, collecting, processing, analysing, preserving, sharing and re-using.

• Png image of the data life cycle

Planning data management

Good research data management requires planning, which ideally is done before the projects starts. To draw up a data management plan (DMP) using a template or online tools such as DMPonline (available without cost for all SLU employees) is a good starting point.

• SLU Data management guide: Plan data management
• SLU Data management guide: Data management plans, templates and tools

Collecting, organising, and storing data

Organising, structuring, and documenting data systematically from the very beginning of a project is fundamental to good data management. It saves both time and energy and can improve data quality. To keep research data safe, it is essential to take on adequate security and protection measures when storing the data.

• SLU Data management guide: Collect, organise, and store data

Processing and analysing data

Before data is analysed it may need to be properly and accurately prepared or processed. While processing and analysing the data, it is important to organise, structure, and document the data, as well as the workflow.

• SLU Data Management Guide: Processing and analysing data

Archiving and preserving data

In Sweden, all material produced as a result of research activities must be archived and preserved in order to ensure the right of access to public records, cultural heritage, and research needs. Archiving data includes more than just saving, backing up, and depositing it – it needs to be done in a way that meets the legal requirement. Furthermore, special consideration must be taken when preserving data containing personal or sensitive information.

• SLU Data Management Guide: Archiving and preserving data.
  
• The SLU unit for archives, information governance and records (Air) has useful information on information and archives management.
• Besides research data, research records may also be important to manage, see Manual for managing research material.

More information on the distinction between research data and other research material from the SLU unit for archives, information governance: Management and preservation.

Sharing and publishing data

With good data management, sharing data becomes quite simple. There are many ways to share data. Greater impact and visibility are achieved by publishing data in an open repository or in a data journal.

• SLU Data Management Guide: Share and publish data
• Publishing data via Swedish National Data Service (SND)

Discovering, reusing, and citing data

When using already existing data, it is important to make sure the data licence is suitable for your purposes and to respect the authors’ intellectual property, and to properly cite the data.

• SLU Data Management Guide: Discovering, reusing, and citing data