@unpublished{HollmannKremerBaebleretal.2020, author = {Hollmann, Susanne and Kremer, Andreas and Baebler, Špela and Trefois, Christophe and Gruden, Kristina and Rudnicki, Witold R. and Tong, Weida and Gruca, Aleksandra and Bongcam-Rudloff, Erik and Evelo, Chris T. and Nechyporenko, Alina and Frohme, Marcus and Šafr{\´a}nek, David and Regierer, Babette and D'Elia, Domenica}, title = {The need for standardisation in life science research - an approach to excellence and trust. [version 1; peer review: 3 approved]}, series = {F1000Research}, volume = {9}, journal = {F1000Research}, issn = {2046-1402}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-13891}, year = {2020}, abstract = {Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.}, language = {en} } @article{HollmannFrohmeEndrullatetal.2020, author = {Hollmann, Susanne and Frohme, Marcus and Endrullat, Christoph and Kremer, Andreas and D'Elia, Domenica and Regierer, Babette and Nechyporenko, Alina}, title = {Ten simple rules on how to write a standard operating procedure}, series = {PLoS Computational Biology}, volume = {16}, journal = {PLoS Computational Biology}, number = {9}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008095}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-13598}, pages = {e1008095}, year = {2020}, abstract = {Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.}, language = {en} }