Index

FAIR principles

FAIR principles are requirements that ensure sustainable and re-usable research data. The acronym FAIR stands for Findable, Accessible, Interoperable and Re-Usable. A number of research funding providers (including the EU, the DFG and the SNF) believe the FAIR principles are an important requirement for sustainable research and therefore expect them to be complied with. Using persistent identifiers and detailed metadata is considered particularly important for the findability of data. Using standards for interfaces, metadata and data supports accessibility and interoperability of data. Extensive content-related metadata and documentation and clear rights of reuse make it easier to reuse data. Data do not have to be classed as open in order to meet FAIR principles, but their metadata ought to be freely accessible. By complying with these guidelines, “machine-actionability” is to be ensured. This means that a computer-aided system can find, access and reuse the digital objects with minimal human input.

Further information

Persistent Identifiers (PI)

Persistent identifiers (PI) are long-lasting references to digital resources. A PI is a unique name for digital objects of any kind (essays, data, software, etc., especially data records in research data management). This name, usually a longer sequence of digits and / or alphanumeric characters, is linked to the web URL of the digital resource. If the URL for the resource changes, only the address to which the PI refers has to be changed, whilst the PI itself can stay the same. This guarantees, for example, that a resource cited using the PI can still be found even if its physical storage place has changed. Examples of persistent identifiers are digital object identifiers (DOI), uniform resource names (URN) and handles.

Using a specific example, this video clearly explains what persistent identifiers are.

 

Data publication

In order to ensure transparent research and traceability of results, research data should be published wherever possible. In order to comply with FAIR principles, the corresponding metadata must be recorded. A repository is required for the publication of the research data.

FAUWissKICloud

FAUWissKICloud
The purpose of FAUWissKICloud is to host and maintain the FAU WissKI instances. The CDI manages the software and RRZE is responsible for maintaining the hardware. WissKIs are maintained and updated using the WissKI Distillery.

Ontology

An ontology is a system of terms that attempts to relate and thus define all concepts of a subject area as far as possible. Relationships include: “superordinate term – subordinate term”, “whole – component” or also “means the same as” (synonym). The terms are not simply named by words, but more precisely and unambiguously by URIs.

eLabFTW

eLabFTW is open source software used as an electronic lab notebook (ELN), data management platform and laboratory inventory management system.

The data can be exported to various formats that make it easier to import to another system with JSON and CSV files.

File format

The file format defines the structure of the data contained in the file. This allows applications to interpret the contents of a file.

Many filenames contain an extension separated from the filename by a period. This declares the file format.

Under the following link you will find details and a list of which file formats are suitable for long-term storage.

Open source

“Open source” means that the source code is publicly available and the license permits modification and reproduction.
In addition, there are no restrictions on using the source code in other products or services.

Further details are available from the Open Source Initiative.

Cold research data

Cold data are data records that have been finalized and will not be modified. These are usually data that are stored in repositories together with the descriptive metadata (e.g. for publication or archiving). Only cold research data can get a DOI.

Warm research data

Warm data are rarely changed. It is also acceptable if access to warm data takes longer, for instance when copying files (“copy’n’tea”). Warm data are usually already suitable for sharing in the research group or with external researchers.