FAQs

The FAIR principles are intended to ensure a sustainable research data management. Data and associated metadata are prepared, managed and stored in such a way that the data sets can be reused for further research.
The acronym FAIR represents for the four principles:

• Findable
• Accessible
• Interoperable
• Re-usable

Information on implementing the FAIR principles in your research can be found at forschungsdaten.info, on the website of the „GO FAIR“-Initiative or in the OpenAIRE guide for researchers "How to make your data FAIR", including a checklist for evaluating your own data with regard to the FAIR principles. In a blog post on FAIR the TIB Hannover take a closer look at the tasks of researchers and repositories in implementing the FAIR Data Principles for research data.

The CARE Principles for Indigenous Data Governance were developed by the Global Indigenous Data Alliance (GIDA) as a supplement to the FAIR principles. While the FAIR principles focus mainly on the technical aspects of sustainable research data management, the CARE principles are focusing on the content of the data. The CARE principles aim to sensitize researchers to the handling of data of indigenous origin and to support them in safeguarding the rights and interests of indigenous peoples during their research.
The principles of CARE are:

Further information on the CARE principles and their application can be found on the website of the Global Indigenous Data Alliance (GIDA)

A persistent identifier is a unique reference to digital objects, people or organizations, consisting of numeric or alphanumeric characters that ensures that they can be reliably and persistently located, identified, and cited. If the name or location of a resource changes, the unique code of the identifier remains unchanged. The use of PIDs increases the visibility and findability of research data and research activities.
Commonly used identifiers include:

The long-term availability of research data refers to the ability to keep data accessible and usable over an extended period of time. In contrast to pure data storage, which is limited to the physical storage of data, long-term availability also includes aspects such as the management, documentation, backup and maintenance of data. Long-term availability is often referred to for periods of more than 10 years. Further information on this topic can be found on the LZV Bayern website.

Long-term availability is important to ensure that research data remains accessible and usable in the future. This means that the data can also be used by other researchers and scientists, leading to increased scientific transparency and efficiency. In addition, long-term availability can contribute to the improvement of the quality and trustworthiness of research results, since the data can be verified and reproduced.

There are two components that are important to ensure the long-term use of data: From a technical perspective, it is important to ensure that the data can be interpreted correctly. This means that the data can be read correctly from the storage medium and that the file formats used can still be read in the future. From a content perspective, it is also necessary to ensure that the content of the data can be understood. It must be documented exactly which data is available and how it was collected. For both components, it is therefore important to archive the associated information as carefully as the data itself.

Insufficient long-term availability of research data can lead to the loss or non-reproducibility of important research results. This can lead to a loss of knowledge and compromise the quality and trustworthiness of research results. It can also lead to a waste of resources and time if the data has to be collected and processed again.

The National Research Data Infrastructure Association Germany (NFDI) was founded by the Federal Government and all 16 states on October 12, 2020. The aim of the Association is to establish and develop a comprehensive and sustainable research data management system and to ensure that valuable data sets are re-usable in the long-term.
The NFDI involves 26 consortia that represent the various scientific disciplines and, as a joint initiative of all NFDI consortia, Base4NFDI. Detailed information on the NFDI and all participating consortia as well as Base4NFDI can be found on the NFDI website.

A research data repository is an infrastructure for storing digital research data. Purpose of the research data repository is to enable long-term storage of the research data in a structured way by using metadata, so that it can be found, accessed and re-used as long as possible.

Uploading data to a repository does not necessarily mean that all interested parties get unrestricted access to the data. Depending on the respective repository, graduated access categories may be available. In addition to free access (with or without registration), some repositories also offer publishing research data with a delay (embargo period) or granting access to the metadata only, without sharing the data. Publishing the metadata allows others to find the dataset, but the research data itself is not accessible. Access to the data is then granted after consent to a use license (e.g. general use license for scientific use) or even only after consent by the data provider for the stated intended use.

FAIR Data does not necessarily mean open data. It is not always possible to publish research data open access. Ethical or legal reasons may restrict or even prevent the public sharing of research data. Provided there is clarity and transparency regarding the conditions for access and re-use of research data, access restrictions are in line with the FAIR principles. A detailed description can be found in the report of the GO FAIR initiative "What is the difference between “FAIR data” and “Open data” if there is one?“