@unpublished{HollmannKremerBaebleretal.2020, author = {Hollmann, Susanne and Kremer, Andreas and Baebler, Špela and Trefois, Christophe and Gruden, Kristina and Rudnicki,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} } @inproceedings{HollmannRegiererD'Eliaetal.2018, author = {Hollmann, Susanne and Regierer, Babette and D'Elia, Domenica and Frohme, Marcus and Gruden, Kristina and Pfeil, Juliane and Baebler, Spela and Sezerman, Ugur and Evelo, Chris T. and Erhart, Friederike and Huppertz, Berthold and Bongcam-Rudloff, Erik and Trefois, Christophe and Gruca, Aleksandra and Duca, Deborah and Colotti, Gianni and Merino-Martinez, Roxana and Ouzounis, Christos and Hunewald, Oliver and He, Feng and Kremer, Andreas}, title = {Standardisation in life-science research - Making the case for harmonization to improve communication and sharing of data amongst researchers}, editor = {Kalajdziski, Slobodan and Ackovska, Nevena}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-20118}, pages = {11}, year = {2018}, abstract = {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.}, language = {en} }