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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
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 \u2013 both individually and combined \u2013 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 \u2013 such as instrumentation centres, data repositories, source code repositories, biobanks and collections \u2013 are becoming increasingly important in the life sciences.<\/p>\n\n\n\n
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.<\/strong><\/p>\n\n\n
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Characteristics of life science research and associated requirements for quality-promoting measures<\/p>\n","protected":false},"featured_media":0,"menu_order":0,"template":"","kommentar_cats":[266],"kommentar_tags":[224,222,180,238,197,284,201],"class_list":["post-6236","kommentare","type-kommentare","status-publish","hentry","kommentar_cats-life-sciences","kommentar_tags-early-career-researchers","kommentar_tags-model-systems","kommentar_tags-professional-ethics","kommentar_tags-quality-assurance","kommentar_tags-research-data","kommentar_tags-research-infrastructure","kommentar_tags-research-software"],"acf":[],"yoast_head":"\n