Data management concerns the practices and decisions related to the collection, processing, analysis, sharing, publication, preservation, and reuse of digital and physical data in a research project.
The purpose of data management is to support reliable and transparent research and to structure the handling of data in a way that ensures compliance with guidelines and data policies.
Data management is often carried out in accordance with the FAIR principles, which increase the opportunities for research data to be found, deciphered, and reused.
AU Library supports data management at Aarhus University. We offer support and guidance to researchers and students regarding the management, planning, and sharing of research data throughout the research process.
A Data Management Plan (DMP) is a tool that can support the systematic handling of data in research projects. Ideally, the plan should be initiated at the start of the project and used to describe how you will collect, process, analyse, share, publish, preserve, and reuse data. The plan should be updated continuously to provide an accurate reflection of the data management practices you have chosen.
A DMP is also a useful tool for aligning expectations between researchers, research assistants and other collaborators. Furthermore, it helps raise awareness of important topics you need to address, such as copyright, data protection (GDPR), research ethics approvals, FAIR principles, and more.
When developing a data management plan, you can use or be inspired by existing templates - such as those provided by DeiC DMP - depending on what best suits your project.
DeiC DMP
As a researcher, you can use DeiC DMP to create and complete your data management plan. Here, you can find templates from the Digital Curation Centre (DCC), European Research Council (ERC), Horizon2020, and Horizon Europe. AU Library, in collaboration with Aarhus University, offers support for DeiC DMP.
If you need assistance or wish to use a different DMP template, please contact your liaison librarian.
The FAIR principles are part of a broader Open Science agenda. FAIR stands for Findable, Accessible, Interoperable, and Reusable, and represents a set of principles aimed at making data as open as possible for both humans and machines.
How to make your data FAIR?
To make your data as FAIR as possible, you can:
- Publish your data or metadata in a repository or another online searchable resource, so others can find them. There are many open repositories available to you. Often, your choice of repository will depend on your field of study, but there are also several interdisciplinary options.
Read more about publishing data. - Use unique and persistent identifiers, also known as PIDs. A PID is a key that can be used to identify a dataset, a research article, or a researcher. For example, you can use a DOI (Digital Object Identifier) for your datasets and articles, and ORCID as your researcher ID.
- Use recognized and open formats for your data.
Read more in the CESSDA Data Management Expert Guide. - Specify access conditions and licenses that define the terms and conditions for the use of data. On Choosealicense, you can get an overview of different licenses. One example is the Creative Commons CC BY license, which indicates that data must be cited when reused.
- Ensure that your data is accompanied by metadata that describe the dataset in detail so that other researchers can understand and reuse it.
Read more in the CESSDA Data Management Expert Guide.
If you wish to publish your data, it is a good idea to follow the FAIR principles and assign the data the appropriate license, which informs others what they can or cannot do with your data.
Read more about licenses in the section about FAIR principles.
If you work with personal data, it is important that you follow the applicable regulations in this area.
Read more about data protection on Aarhus University's website.
There are many repositories to choose from when you want to publish, archive, or store your data, both subject-specific and general. The list below is a selection.
Find more data repositories on Re3data, both general and subject specific repositories.
Selected General Repositories:
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Pure - Aarhus University's registration system for research publications, projects, data, activities, and more.
Data can be shared in various ways and found in different places.
You can find datasets in, for example:
- General data repositories
- Subject-specific data repositories
- Bibliographic databases that include datasets
- Registers and databases outside academia
In addition to searching directly in various databases, you can also find datasets through, for example:
- Scientific articles
- Search engines and AI
When searching for data, you should expect a less structured and systematic approach compared to searching for scientific articles.
AU Library can help you find data for your research project.
In this guide, you can read about how to search for data.
Do you need a presentation or course?
AU Library offers tailored presentations/courses on data management for research environments. For example, we can come to a department or staff meeting and talk about:
- Data management plans
- Searching for data for research projects
- Storing and sharing data when your research project is completed
We also offer targeted courses for your students on good data practices.
We can take into account the type of data your students need to collect or the specific assignment they are working on.
Additionally, AU Library regularly holds general courses on good data practices for students.
AU Library Arts and AU LIbrary BSS offer courses and workshops in programs and tools that can support and motivate students, researchers and teachers in working with data.
These include tools such as R, Python, Whisper, Voyant, VOSViewer, Eikon, Orbis, and more.
Keep an eye on the calendar
