Comment on:

The following comment refers to this/these guideline(s)

Guideline 5

Dimensions of performance and assessment criteria

To assess the performance of researchers, a multidimensional approach is called for; in addition to academic and scientific achievements, other aspects may be taken into consideration. Performance is assessed primarily on the basis of qualitative measures, while quantitative indicators may be incorporated into the overall assessment only with appropriate differentiation and reflection. Where provided voluntarily, individual circumstances stated in curricula vitae – as well as the categories specified in the German General Equal Treatment Act (Allgemeines Gleichbehandlungsgesetz) – are taken into account when forming a judgement.


High-quality research is oriented towards criteria specific to individual disciplines. In addition to the generation of and critical reflection on findings, other aspects of performance are taken into consideration in the evaluation process. Examples include involvement in teaching, academic self-governance, public relations, and knowledge and technology transfer; contributions to the general good of society may also be recognised. An individual’s approach to research, such as an openness to new findings and a willingness to take risks, is also considered. Appropriate allowance is made for periods of absence due to personal, family or health reasons or for prolonged training or qualification phases resulting from such periods, and for alternative career paths or similar circumstances.

Guideline 7

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).

Guideline 10

Legal and ethical frameworks, usage rights

Researchers adopt a responsible approach to the constitutionally guaranteed freedom of research. They comply with rights and obligations, particularly those arising from legal requirements and contracts with third parties, and where necessary seek approvals and ethics statements and present these when required. With regard to research projects, the potential consequences of the research should be evaluated in detail and the ethical aspects should be assessed. The legal framework of a research project includes documented agreements on usage rights relating to data and results generated by the project.


Researchers maintain a continual awareness of the risks associated with the misuse of research results. Their responsibility is not limited to compliance with legal requirements but also includes an obligation to use their knowledge, experience and skills such that risks can be recognised, assessed and evaluated. They pay particular attention to the aspects associated with security-relevant research (dual use). HEIs and non-HEI research institutions are responsible for ensuring that their members’ and employees’ actions comply with regulations and promote this through suitable organisational structures. They develop binding ethical guidance and policies and define procedures to assess ethical issues relating to research projects.

Where possible and practicable, researchers conclude documented agreements on usage rights at the earliest possible point in a research project. Documented agreements are especially useful when multiple academic and/or non-academic institutions are involved in a research project or when it is likely that a researcher will move to a different institution and continue using the data he or she generated for his or her own research purposes. In particular, the researcher who collected the data is entitled to use them. During a research project, those entitled to use the data decide whether third parties should have access to them (subject to data protection regulations).

Guideline 12


Researchers document all information relevant to the production of a research result as clearly as is required by and is appropriate for the relevant subject area to allow the result to be reviewed and assessed. In general, this also includes documenting individual results that do not support the research hypothesis. The selection of results must be avoided. Where subject-specific recommendations exist for review and assessment, researchers create documentation in accordance with these guidelines. If the documentation does not satisfy these requirements, the constraints and the reasons for them are clearly explained. Documentation and research results must not be manipulated; they are protected as effectively as possible against manipulation.


An important basis for enabling replication is to make available the information necessary to understand the research (including the research data used or generated, the methodological, evaluation and analytical steps taken, and, if relevant, the development of the hypothesis), to ensure that citations are clear, and, as far as possible, to enable third parties to access this information. Where research software is being developed, the source code is documented.

Guideline 13

Providing public access to research results

As a rule, researchers make all results available as part of scientific/academic discourse. In specific cases, however, there may be reasons not to make results publicly available (in the narrower sense of publication, but also in a broader sense through other communication channels); this decision must not depend on third parties. Researchers decide autonomously – with due regard for the conventions of the relevant subject area – whether, how and where to disseminate their results. If it has been decided to make results available in the public domain, researchers describe them clearly and in full. Where possible and reasonable, this includes making the research data, materials and information on which the results are based, as well as the methods and software used, available and fully explaining the work processes. Software programmed by researchers themselves is made publicly available along with the source code. Researchers provide full and correct information about their own preliminary work and that of others.


In the interest of transparency and to enable research to be referred to and reused by others, whenever possible researchers make the research data and principal materials on which a publication is based available in recognised archives and repositories in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable). Restrictions may apply to public availability in the case of patent applications. If self-developed research software is to be made available to third parties, an appropriate licence is provided.

In line with the principle of “quality over quantity”, researchers avoid splitting research into inappropriately small publications. They limit the repetition of content from publications of which they were (co-)authors to that which is necessary to enable the reader to understand the context. They cite results previously made publicly available unless, in exceptional cases, this is deemed unnecessary by the general conventions of the discipline.

Guideline 17


Researchers back up research data and results made publicly available, as well as the central materials on which they are based and the research software used, by adequate means according to the standards of the relevant subject area, and retain them for an appropriate period of time. Where justifiable reasons exist for not archiving particular data, researchers explain these reasons. HEIs and non-HEI research institutions ensure that the infrastructure necessary to enable archiving is in place.


When scientific and academic findings are made publicly available, the research data (generally raw data) on which they are based are generally archived in an accessible and identifiable manner for a period of ten years at the institution where the data were produced or in cross-location repositories. This practice may differ depending on the subject area. In justified cases, shorter archiving periods may be appropriate; the reasons for this are described clearly and comprehensibly. The archiving period begins on the date when the results are made publicly available.

Handling research data in the humanities and social sciences

In the humanities and social sciences, all documents, materials, images, audio and video recordings, texts, measurement and evaluation data that are generated or processed can be considered research data in the broadest sense of the word. They form an integral part of the research results and are important both from the point of view of verifiability and very often with regard to reuse in further research, too (e.g. in source editions or longitudinal social science studies).

Very frequently, data generated by research in the humanities and social sciences either cannot be replicated, or else its recovery is barely feasible from a practical point of view. Examples of the first instance include surveys of political attitudes at specific points in time or excavations in a certain archaeological context, while the second instance might include lengthy text editions or documentation of museum objects which would be virtually impossible to carry out or finance more than once.

In order to ensure quality assurance across all phases, effective and reliable data management is crucial in all research projects in the humanities and social sciences where research data (as defined above) is generated or processed on a significant scale. Given the increasing importance of larger volumes of data, data management itself has a growing impact on the quality of research results. For this reason, good research practice in the humanities and social sciences should not just involve devoting the necessary attention to data management, but also recognise the contributions made by researchers in this regard as a relevant performance criterion. Individual academic achievement is not only reflected in publications: it is increasingly linked to the processing of research data as well as the initial or further development of research software.

Even though data in general is becoming increasingly important in research, its role can vary greatly due to the enormous diversity of methods and project constellations within the humanities and social sciences. While certain projects explicitly aim to obtain and process large amounts of data, in other cases only certain sections or phases of the research involve data analysis. Although the effort involved can vary considerably, research data management always involves systematic preparation and organisation of the entire data handling procedure – from collection, processing and documentation through to storage, archiving and provision for reuse. The analytical steps carried out using various (software) tools are therefore an integral part of research data management.

Ideally, research projects can be based on standards and best practices that are recommended by learned societies or other relevant organisations or institutions. In recent years, a number of learned societies and DFG review boards have formulated guidelines and recommendations on the handling of research data, also addressing the specific requirements of various subject areas and research approaches. These can be found on the DFG website (see link below).

Data have to be reliably saved (or, in the case of analogue materials, put into safe storage) and – depending on context and re-usability – archived on a long-term basis. Ideally, in addition to securing the data, external access should also be made possible for verification purposes and for reuse, unless there are particular reasons to the contrary. If reuse is to be enabled, access must not be limited to merely viewing the data but should allow further processing according to current requirements. However, there are certain fundamental preliminary considerations to be made when it comes to archiving and reuse. Decisions have to be made about what is of “archival value”, what effort and/or cost can realistically be incurred and, lastly, what legal provisions have to be observed.

In some cases, the archiving and provision of the processed data is obligatory (e.g. heritage conservation) or is part of the goal of a project (e.g. source editions). In such cases, decisions still have to be made regarding the state (versioning) in which data is to be recorded and to what extent all data generated during the research process is to be included. In many other instances, when deciding whether and under what conditions data is to be made available for scientific reuse, it will be important to take into account the level of demand within the research community as well as assess the cost/benefit ratio of data preparation and documentation for dissemination purposes.

In the case of long-term social science projects, for example, it would be reasonable to expect a high level of demand for the data. In addition, the type of data or content will often determine whether the data is suitable for disclosure. This is often problematic in the case of sensitive data from qualitative interviews or video material for reasons of data privacy: such data is more difficult to anonymise or when anonymised, essential information may be lost.

Where data is released for reuse, the question of licensing arises. There are a range of different usage licences that regulate the extent to which data may be used by third parties, whether data may only be viewed or also altered, for example, and for what purposes it may be used. An overview of various usage licences and the rights involved is provided by the Consortium of European Social Science Data Archives, for example (see link below).

Finally, permission for reuse can also be granted for the investigation of a specific question only, for example, and not for any kind of analysis. Here, data producers can also set embargo periods for the reuse of data: the reason might be to ensure publication of the results obtained from the project and completion of any qualification work, for example.

As a matter of principle, researchers would be expected to answer the following questions individually for any given project and handle the research data accordingly: Do archiving and provision obligations apply? Does the research community have an interest in the data? Is the data suitable for publication, and if so, to what extent, in what form and at what point in time? In social science research projects in particular, it is good research practice to observe data protection requirements when carrying out studies on or with individuals from the start (see the current German Data Forum guide – the link is provided below).

The comment belongs to the following categories:

GL5 (Humanities and social sciences) , GL7 (Humanities and social sciences) , GL10 (Humanities and social sciences) , GL12 (Humanities and social sciences) , GL13 (Humanities and social sciences) , GL17 (Humanities and social sciences)