Index

European Science Cloud

The European Open Science Cloud (EOSC) is a multi-disciplinary information service where you can publish, search for data and find tools and services. This service is one of the flagship projects of the EU Framework Programme for Research and Innovation. The portal can be reached here.

Data literacy

Data literacy is a key skill in the 21st century and, in short, describes the ability of an individual to handle data. What knowledge, skills and attitudes are needed in society, the world of work and science today? Aspects of data literacy are described in detail in this document. Data literacy is fundamental to the entire research process in collecting, organizing, using, publishing and re-using data.

The following video illustrates the importance of data literacy as a key skill.

Repository

A repository is a managed location for storing digital objects. The visibility of the digital objects can be restricted.

For example:

  • the institutional repository of University Library, which enables FAU researchers to publish their dissertations and research papers free of charge.
  • The version management system GitLab, which is provided by the RRZE.
  • CERN offers a globally visible repository in Zenodo, for data sets < 50GB.

Details at forschungsdaten.info

Analog materials

Analog research materials include photographs, handwritten notes, books, audio cassettes, paintings or 3D objects, such as fossils or architectural models. In order to make them usable in a repository, the materials must first be digitized or at least the associated metadata must be provided in a digital form. Analog materials differ from “born digital” data, which exist in digital form from the beginning, for example digital photos, CAD drawings, measurement data or blogs.

Archiving

An archive is generally understood to be a collection of documents with the intention of preserving documents indefinitely. In the research data management context, an archive is a collection of data.

Research data policy

A research data policy contains basic guidelines for handling data in a larger organization, e.g. a university. In addition to general recommendations for action, a research data policy usually regulates the responsibilities and support structures on site. In some cases, the guidelines also include details on licensing and repositories for research data.

The current version of the FAU research data policy can be found at https://www.fau.info/fdm-policy.

CRIS

CRIS stands for Current Research Information System, the research information system at FAU. It stores information about research achievements, for example information about a publication, a research project, research data or inventions. Only metadata is stored, for example, the full text of a publication is not stored directly in CRIS, but it does indicate where this full text can be found (for example, by specifying the DOI).

In research information systems, the various data areas are linked: publications are not only assigned to persons, but also projects, projects in turn are assigned to specific research areas. For this purpose, internal data sources of the university are also used, which offers added value compared to classic list formats and data providers such as Scopus or Web of Science.

Data management plan

According to forschungsdaten.info, a data management plan (DMP) structures the handling of research data, or its “collection, saving, documentation, maintenance, processing, transfer, publication and storage, as well as the necessary resources, legal framework and responsible persons.” A data management plan (DMP) documents the entire data lifecycle.

Many third-party funding organizations (DFG, FWF, SNSF, Horizon Europe, Volkswagen Foundation) expect information on the handling of research data as part of a funding application for the allocation of funds from certain funding lines.

The DMP describes how to handle research data from the planning stage, to collection, to long-term archiving or, if applicable, planned deletion. At the very least, the data management plan answers the following questions:

  • What is collected?
  • Which bodies must be consulted before collecting data?
  • In what form and where will research data be stored in the various project phases?
  • Who can access the data and when will it be available?
  • Who is responsible for the individual steps?
  • Which legal requirements must be observed? The DMP is a useful and necessary part of the project application.
  • What exactly does this mean for research?

Why this approach is meaningful and sustainable is explained in this video.

Copyright

Literary, artistic and scientific works are protected by German copyright law.
In specific terms, this means that without a corresponding license, reuse is only possible in a restrictive manner.

We recommend licensing that is as open as possible, because this increases data reusability and boosts the reputation of researchers. If you have any questions, the CDI will be happy to advise you.

Read this article for further details.

Data organization

For many scientists, dealing with research data is the basis of their daily work. It therefore saves time and effort if this data is efficiently structured, documented and backed up from the outset.

Most of the data is initially stored in files. Files have different types or file formats that are sometimes identified by the file name extension, for example in the Windows operating system. Furthermore, files are stored in directories (folders). Naming files and directories systematically is very important. For example, the Stanford File Naming Handout.

Alternatively, data can also be stored in databases. Here, the effort is greater, because a database management system such as MySQL must first be set up. A database schema needs to be defined which provides a structure for storing data. Here, too, naming is of great importance. Databases support managed shared access to data much better than data stored in files. There are different types of databases: relational, hierarchical, graph-based, RDF triple stores and a few more.

The following animated video clearly summarizes the topic of data organization.