The following comment refers to this/these guideline(s)
Scientific integrity forms the basis for trustworthy research. It is an example of academic voluntary commitment that encompasses a respectful attitude towards peers, research participants, animals, cultural assets, and the environment, and strengthens and promotes vital public trust in research. The constitutionally guaranteed freedom of research is inseparably linked to a corresponding responsibility. Taking this responsibility into full account and embedding it in individual conduct is an essential duty for every researcher and for the institutions where research is carried out. The research community itself ensures good practice through fair and honest attitudes and conduct as well as organisational and procedural regulations. In different roles, scientific and scholarly societies, research journals, publishers, research funding agencies, complainants, ombudspersons and the German Research Ombudsman also contribute to safeguarding good research practice; they harmonise their conduct in publicly or privately funded research with the principles of the Code.
Individuals who report a well-founded suspicion of misconduct fulfil a crucial function in the self-regulation of the research community. Scientific and academic societies promote good research practice by developing a shared understanding among their members and by defining binding ethical standards, which they establish within their specialist communities. Journal publishers take account of the requirements of high-quality research with a stringent peer-review process. The German Research Ombudsman, an independent body, and local ombudspersons are trustworthy points of contact that offer advice and conflict mediation on issues relating to good research practice and potential misconduct.
Funding organisations also play an important role in establishing and maintaining standards of good research practice. Through the design of their funding programmes, they create a framework that promotes research integrity. By ensuring that procedures are in place to deal with allegations of misconduct, they also help to combat dishonesty in research.
Within the scope of its responsibility, the DFG has prepared the following Guidelines for Safeguarding Good Research Practice. They represent the consensus among the member organisations of the DFG on the fundamental principles and standards of good practice and are upheld by these organisa- tions. These guidelines underline the importance of integrity in the everyday practice of research and provide researchers with a reliable reference with which to embed good research practice as an established and binding aspect of their work.
Preamble – Life Sciences
The life sciences encompass a very broad field, ranging from the study of single molecules and cellular structures through to the interactions between living organisms and their environment. Research questions typically attempt to explain life processes, gain an understanding of diseases and their treatment or investigate the functions of ecosystems. Due to the sheer thematic breadth involved, there are extensive interfaces with other research fields and many questions can only be answered by adopting a joint, interdisciplinary approach.
Research with and on living organisms, especially on human beings, entails a particular responsibility, and this is reflected in the numerous ethical and legal aspects that directly affect such work. Examples include addressing issues of security-relevant research, the prerequisites for research involving experiments on animals, regulations on the use of genetic resources in a global context and the requirements for conducting clinical trials. Moreover, the availability of new methods and research approaches often attracts considerable public interest, including critical debate regarding potential risks. For this reason, addressing ethical aspects and communicating the purpose and nature of research are integral elements of academic activity in the life sciences.
Research in this field is focused on the study of living systems that are dynamic, variable and complex. Ensuring the reproducibility of research results is therefore particularly challenging when it comes to methodology, design and documentation. Making use of defined model systems – both individually and combined – allows experimental approaches to be standardised and results compared; this also makes it possible to verify the generalisability of findings. At the same time, a high degree of standardisation is limiting where biological diversity, plasticity or genetic variability are the focus of attention, for example. When it comes to transferring research results to practical applications such as therapy or recommendations for land use, all findings must be clearly validated or specifically tested by means of confirmatory methods. In these cases, special requirements apply to statistical planning and measures to minimise unconscious bias. Many biological processes are so complex that interrelationships can only be fully understood by collating extensive data sets. For this reason, availability of and access to quality-assured data sets and the relevant methods for data integration are highly significant. In order to ensure findings are verifiable and reusable, the necessary support and service structures – such as instrumentation centres, data repositories, source code repositories, biobanks and collections – are becoming increasingly important in the life sciences.
To be able to meet the constantly changing and complex requirements of high-quality research in the life sciences, it is of crucial importance to address quality assurance issues not just during academic training but also at all other career stages. A culture in which it is possible to talk openly about mistakes or doubts is essential here. Researchers working in the life sciences have a particular responsibility to contribute to such a culture and to tackle these specific challenges.
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Preamble (Life sciences)
early career researchersmodel systemsprofessional ethicsquality assuranceresearch dataresearch infrastructureresearch software