In the life sciences, quality assurance in a research project begins with thorough research into the state of knowledge and a clear definition of where there are gaps in knowledge. Researchers are expected to use contemporary analytical methods for literature research and data analysis such as the use of subject-specific databases, which can be very helpful in addressing complex questions. Research will frequently include a reuse of existing data sets, too. The potential of existing data sets should always be considered when assessing the validity of a new research question. Systematic reviews are much more time-consuming, but they are important additions to the quality assurance of very complex scientific approaches.<\/p>\n\n\n\n
In research within largely unknown contexts, it may initially make sense to proceed experimentally using a manageable volume of samples so as to generate scientific hypotheses. Findings are then often tested for possible generalisation and further validated using a range of different methodological approaches. This is where the relevance of insights already gained often emerges, enabling potential new correlations to be identified. If the research aims to achieve a further transfer of knowledge \u2013 in particular to a clinical application at a later stage \u2013 selected findings can be verified by confirmatory studies. These different approaches to research also differ in terms of the quality assurance measures they require, so this has to be reflected accordingly in research project design. For example, statistical planning is all the more important, the more the approach is based on previously established knowledge which is to be tested by means of confirmative trials.<\/p>\n\n\n\n
In the life sciences, model systems are frequently used to establish research questions. The choice of a suitable model system poses a particular challenge since each model is subject to its own restrictions and limitations. For this reason, the strategy for choosing the model system must always be plausible and carefully assessed. In those areas of the life sciences where findings can be applied in practice, the choice of model should also reflect the transferability and compatibility of the results. The origin and characteristics of the data, organisms, materials or software used must be known and described in verifiable terms. Here it is advisable to use certified infrastructures or those with verifiable quality assurance functions as a source. When using animal models, the sex of the organism or other relevant parameters such as age may influence the results; this should be taken into account when planning and implementing projects. It is imperative that these issues are thought through and justified when planning a project. In ecosystem analyses, the spatial or temporal variability of the processes, structures and pools under investigation should be recorded if possible.<\/p>\n\n\n\n
In particular, the diversity and speed of method development in the life sciences require an awareness of the limits of one\u2019s own capabilities and a willingness to cooperate. Due to the rapid availability of methods through commercial \u201csets\u201d, competence acquisition might superficially appear simple in connection with standard methods. As a rule, however, in-depth critical examination of the methodology is still required if the necessary quality standards are to be met.<\/p>\n\n\n\n
When it comes to managing the resulting research data, appropriate precautions have to be taken early on in the planning phase of the project so as to ensure that the documentation is verifiable for other researchers and that key research data can be reused \u2013 at least after completion of the project, if not before. As a minimum requirement, projects can be expected to address the reuse potential of datasets, demonstrate an awareness of subject-specific metadata standards and suitable repositories, and make use of advisory and support services. The costs involved should be considered during planning, and data protection concerns must also be taken into account.<\/p>\n\n\n\n
As in other fields of academic research, investigation in the life sciences sometimes focuses on events or generates findings that cannot be reproduced due to their singularity or contingency. However, there is still a fundamental requirement for the findings to be robust and applicable. This prerequisite is often ensured by indirectly confirming results based on different models. The targeted repetition (replication studies) of particularly key research findings has become established as a valuable additional contribution to quality assurance. Valuable insights into previously unknown aspects can also be gained from unsuccessful replications. Another helpful method for increasing the robustness and applicability of results is the pre-registration of studies or investigative approaches.<\/p>\n\n\n
In the life sciences, quality assurance in a research project begins with thorough research into the state of knowledge and a clear definition of where there are gaps in knowledge. Researchers are expected to use contemporary analytical methods for literature research and data analysis such as the use of subject-specific databases, which can be very […]<\/p>\n","protected":false},"featured_media":0,"menu_order":0,"template":"","kommentar_cats":[266],"kommentar_tags":[252,195,222,238,242,197,202],"class_list":["post-5940","kommentare","type-kommentare","status-publish","hentry","kommentar_cats-life-sciences","kommentar_tags-diversity","kommentar_tags-fair-principles","kommentar_tags-model-systems","kommentar_tags-quality-assurance","kommentar_tags-replication-reproduction","kommentar_tags-research-data","kommentar_tags-state-of-research"],"acf":[],"yoast_head":"\n