TY - INPR A1 - Hollmann, Susanne A1 - Kremer, Andreas A1 - Baebler, Špela A1 - Trefois, Christophe A1 - Gruden, Kristina A1 - Rudnicki, Witold R. A1 - Tong, Weida A1 - Gruca, Aleksandra A1 - Bongcam-Rudloff, Erik A1 - Evelo, Chris T. A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Šafránek, David A1 - Regierer, Babette A1 - D'Elia, Domenica T1 - The need for standardisation in life science research - an approach to excellence and trust. [version 1; peer review: 3 approved] T2 - F1000Research N2 - 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. KW - Open Data KW - Open Access KW - Open Science KW - FAIR Principles KW - standardisation KW - education KW - quality management Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-13891 SN - 2046-1402 VL - 9 ER - TY - JOUR A1 - Hollmann, Susanne A1 - Frohme, Marcus A1 - Endrullat, Christoph A1 - Kremer, Andreas A1 - D'Elia, Domenica A1 - Regierer, Babette A1 - Nechyporenko, Alina T1 - Ten simple rules on how to write a standard operating procedure JF - PLoS Computational Biology N2 - 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. Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-13598 SN - 1553-7358 VL - 16 IS - 9 SP - e1008095 ER - TY - CHAP A1 - Hollmann, Susanne A1 - Regierer, Babette A1 - D'Elia, Domenica A1 - Frohme, Marcus A1 - Gruden, Kristina A1 - Pfeil, Juliane A1 - Baebler, Spela A1 - Sezerman, Ugur A1 - Evelo, Chris T. A1 - Erhart, Friederike A1 - Huppertz, Berthold A1 - Bongcam-Rudloff, Erik A1 - Trefois, Christophe A1 - Gruca, Aleksandra A1 - Duca, Deborah A1 - Colotti, Gianni A1 - Merino-Martinez, Roxana A1 - Ouzounis, Christos A1 - Hunewald, Oliver A1 - He, Feng A1 - Kremer, Andreas ED - Kalajdziski, Slobodan ED - Ackovska, Nevena T1 - Standardisation in life-science research - Making the case for harmonization to improve communication and sharing of data amongst researchers N2 - Modern, high-throughput methods for the analysis of genetic information, gene and metabolic products and their interactions offer new opportunities to gain comprehensive information on life processes. The data and knowledge generated open diverse application possibilities with enormous innovation potential. To unlock that potential skills in generating but also properly annotating the data for further data integration and analysis are needed. The data need to be made computer readable and interoperable to allow integration with existing knowledge leading to actionable biological insights. To achieve this, we need common standards and standard operating procedures as well as workflows that allow the combination of data across standards. Currently, there is a lack of experts who understand the principles and possess knowledge of the principles and relevant tools. This is a major barrier hindering the implementation of FAIR (findable, accessible, interoperable and reusable) data principles and the actual reusability of data. This is mainly due to insufficient and unequal education of the scientists and other stakeholders involved in producing and handling big data in life science that is inherently varied and complex in nature, and large in volume. Due to the interdisciplinary nature of life science research, education within this field faces numerous hurdles including institutional barriers, lack of local availability of all required expertise, as well as lack of appropriate teaching material and appropriate adaptation of curricula. KW - FAIR data KW - standardization KW - interoperability KW - standard operating procedures (SOPs) KW - quality management (QM) KW - quality control (QC) KW - education Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-20118 ER -