Understandably, research data management may be perceived as a troublesome task, but it offers many advantages. For example, good data management can:
Remember that you must always comply with AU's "Regulations for storing and managing research data".
Management at AU has also adopted 3 strategic goals in relation to researchers.
All reseachers at AU must:
Get an overview of how to process data in the different phases of your research here.
The structure of the following follows this version of the data lifecycle.
Before you start a research project, you should consider the type of data you will be working with. The data covered by AU’s Instructions for research data storage and management are listed below:
Personal data incl. pseudonymous data
Other types of restricted data
Types of unrestricted data
It is becoming more common to publish and share data sets. Therefore, find out whether data sets already exist that you can reuse or integrate into your research.
Some sources of possible data are listed under the Publication section further down this page.
Many use Zenodo, so you can also go directly there.
To search for suitable repositories, we recommend Re3data. Re3data.org covers both general and subject-specific repositories.
When you draw up a DMP, you comply with the principles of openness and transparency by documenting where data come from.
By describing who has created data and metadata, as well as when and under what conditions the data have been generated, a DMP helps to understand data and make research results reproducible.
A DMP describes the workflows data undergo in a large or small research project.
In general, it is a good idea to consider how you expect to collect, store and archive/publish data. Sometimes a funder will require that a DMP is attached to your application, and often funders have their own template. Therefore, find out whether the foundation to which you want to apply for funding has a requirement for DMP and its own template you can use.
A good DMP is a living document that is updated throughout the research process. A good DMP must be able to answer the following:
DMP Online is a tool that guides you through preparation of a DMP. In collaboration with AU Library, AU offers support in DMP Online.
There are approved tools and/or integrated environments for a number of situations. Assistance will often be available from many sources. These include:
AU IT can also help if, as a researcher, you have a special need in relation to a smaller IT solution or service. Please note that in general AU IT will establish all new IT solutions and services in the cloud.
Read more about AU IT's server services or contact your local IT support for help with cloud solutions.
At the start of the project, it is recommended that you decide what tool you want to use to document your collection and analysis. Several of the above have chosen one or more tools. The following can also be used:
NOTE! THE LIST IS REGULARLY UPDATED
At AU, the following data collection tools have been approved for sensitive personal data:
When you collect data, it is important that you store the data in storage solutions approved for the type af data you are collecting. Find information about data classification and see where you can store different types of data.
You should use the university’s primary storage solution to store your research data. List of AU's data-storage solutions
In some cases, there may be a need for a special storage solution; either because you have a very large amount of data or if there are additional security measures that need to be met, for example if you are working with sensitive personal data.
If you have a special need AU IT can help you find a suitable storage solution. Please contact your local IT support team.
There are, of course, situations where data cannot be readily stored on network drives etc. For example when you collect data in the field, or while calculations are in progress. In such cases, you should make sure that the data is backed up as soon as possible. In the meantime, AU recommends that you use alternative encrypted back-up options, such as an encrypted USB flash drive. Read more about encryption.
You should use the university’s primary storage solution to store your research data. List of AU's data-storage solutions.
There are various solutions if you need to share your data with partners:
Teams and SharePoint
Many programs can be used to analyse data.
Consider which program(s) you want to use to process/analyse data. You can choose to use the cloud solutions with computing and storage available from AU IT. You can activate and install different programs here. You can also use one of the other platforms available. Some of these are pure calculation and storage capacity, while others offer support for choice of methodology.
For a number of situations there are integral milieus at Aarhus University and often assistance is also offered. These include:
DeiC coordinates the use of the national supercomputers available to Danish researchers. The supercomputers are run and developed by the universities, which make computing time available to researchers irrespective of their institutional affiliation.
As part of the national initiative, the eight universities will set up a local front office to provide support for HPC users at the universities. The national HPC centres and the back office in DeiC will collaborate with the local front offices to get users off to a good start with the HPC resources.
At AU, the DeiC front office is Sara Marie Westh, who you can contact via email@example.com.
AU has a special role in connection with 2 of the HPC-centres and offers back office support for those.
Type 1: DeiC Interactive
Center for Humanities Computing Aarhus (CHCAA) - firstname.lastname@example.org
Type 2: DeiC Throughput
When you have finished processing and analysing data, the research results must be shared, for example, in research records or books.
You can also share data in data journals or data repositories, so that others can reuse your data. In this way, you ensure that your data is used, quoted and licensed correctly.
In order to do this, you must ensure that the data is anonymous and that data is stored securely with a so-called persistent identifier (PID, DOI,...)
Research data is of great value to you, but also to other researchers. You can make it easier for others to refer to or use your research data by giving them a persistent identifier and by sharing your data with a clear license.
A PID (persistent identifier) is a unique key used to identify a given document, dataset or a person permanently.
Often, some descriptive metadata is linked to a PID. PIDs make it possible for others to find your data and refer them to them.
See: FAIR principles make PID data etc. findable and accessible (F and A in the FAIR principles).
Examples of persistent identifiers:
The conditions for accessing and reusing data must also be stated. This can be through using data licenses to show whether your data may be used by others, and how it may be reused (R in the FAIR principles).
In order for personal data to be considered anonymous, it must not be possible to identify individual persons on the basis of the data alone or in combination with other information.
Until anything else is decided, we refer to the Finnish National Digital Preservation Service's (DPS) list of recommended formats: File Formats (digitalpreservation.fi). An exception is made for EPUB and an addition of FITS is made. Further corrections to this list may occur.
Repositories can help make your data findable (the F in the FAIR principles).
They can guide you a long way towards making your research compatible with the FAIR principles. If you publish your data in an open repository, e.g. Dataverse or Zenodo, they will help you to:
At AU, we don’t have an institutional repository, but there are many open repositories you can use. The choice will often depend on the subject area, but there are also several cross-disciplinary repositories.
Want to find out more?
Want to find out more?
LOAR - The Royal Library Open Access Repository
Want to find out more?
Want to find out more?
Want to find out more?
The major journal publishers usually work with selected repositories. Here are some of the publishers' recommendations:
Licensing agreements with OA-publishing: https://pro.kb.dk/en/licensing
An author of a work may transfer all or part of his/her copyright through a license. There are different licences for different purposes.
Choosealicense.com has a list of the various licenses.
In accordance with the Policy for research integrity, freedom of research and responsible conduct of research at Aarhus University, research data must be stored for a period of at least five years, unless another statutory storage period applies with regard to the research project, and the data requires longer storage.
Note that relevant legislation may be adjusted in collaboration agreements.
Research data may only be destroyed, anonymised and/or archived after they have been submitted in their entirety to the Danish National Archives or after one of the following conditions have been met:
In accordance with the Ministerial Order on the reporting of research data to the National Archives (please note the link is in Danish), Digital research data must be reported to the Danish National Archives when the research project concludes. In the case of data collections created by monitoring or continual collection of data, the data must be reported when data collection begins.
Here, digital research data are defined as data created in connection with research through the use of scientific method, along with associated documentation that explains the nature and content of the data, how the data has been obtained, and for what overall purpose.
All research data must be reported excepting the following types of data:
Use this form to report your research data (virk.dk) (please note the link is in Danish).
AU has four different types of storage of data in final form. There is thus a solution for all data worth storing, regardless of the data’s form or how they are to be read.
The national strategy for research data management, which is based on the FAIR principles, advises that data be shared with other researchers as far as possible – that data be made FAIR.
If your data comply with the FAIR principles, others will be able to find and reuse them. If others are to know how they may use your data, it is important that you have linked a license to the data, describing conditions.
On www.howtoFAIR.dk, you can learn more about how you can make your research data more FAIR in practice.