The following comment refers to this/these guideline(s)
Cross-phase quality assurance
Researchers carry out each step of the research process lege artis. When research findings are made publicly available (in the narrower sense of publication, but also in a broader sense through other communication channels), the quality assurance mechanisms used are always explained. This applies especially when new methods are developed.
Continuous quality assurance during the research process includes, in particular, compliance with subject-specific standards and established methods, processes such as equipment calibration, the collection, processing and analysis of research data, the selection and use of research software, software development and programming, and the keeping of laboratory notebooks.
If researchers have made their findings publicly available and subsequently become aware of inconsistencies or errors in them, they make the necessary corrections. If the inconsistencies or errors constitute grounds for retracting a publication, the researchers will promptly request the publisher, infrastructure provider, etc. to correct or retract the publication and make a corresponding announcement. The same applies if researchers are made aware of such inconsistencies or errors by third parties.
The origin of the data, organisms, materials and software used in the research process is disclosed and the reuse of data is clearly indicated; original sources are cited. The nature and the scope of research data generated during the research process are described. Research data are handled in accordance with the requirements of the relevant subject area. The source code of publicly available software must be persistent, citable and documented. Depending on the particular subject area, it is an essential part of quality assurance that results or findings can be replicated or confirmed by other researchers (for example with the aid of a detailed description of materials and methods).
Quality assurance in the engineering sciences
In the engineering sciences, the complexity of a problem can sometimes make it difficult to arrive at a clear interpretation of the results. Results may deviate due to a variety of factors in the experimental set-up. An example of this is structured benchmarking, the method of so-called inter-laboratory tests in which identical samples are tested using different procedures.
The comment belongs to the following categories:
GL7 (Engineering/engineering sciences)