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 - D'Elia, Domenica A1 - Truu, Jaak A1 - Lahti, Leo A1 - Berland, Magali A1 - Papoutsoglou, Georgios A1 - Ceci, Michelangelo A1 - Zomer, Aldert A1 - Lopes, Marta B. A1 - Ibrahimi, Eliana A1 - Gruca, Aleksandra A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Klammsteiner, Thomas A1 - Carrillo de Santa Pau, Enrique A1 - Marcos-Zambrano, Laura Judith A1 - Hron, Karel A1 - Pio, Gianvito A1 - Simeon, Andrea A1 - Suharoschi, Ramona A1 - Moreno-Indias, Isabel A1 - Temko, Andriy A1 - Nedyalkova, Miroslava A1 - Apostol, Elena-Simona A1 - Truică, Ciprian-Octavian A1 - Shigdel, Rajesh A1 - Telalović, Jasminka Hasić A1 - Bongcam-Rudloff, Erik A1 - Przymus, Piotr A1 - Jordamović, Naida Babić A1 - Falquet, Laurent A1 - Tarazona, Sonia A1 - Sampri, Alexia A1 - Isola, Gaetano A1 - Pérez-Serrano, David A1 - Trajkovik, Vladimir A1 - Klucar, Lubos A1 - Loncar-Turukalo, Tatjana A1 - Havulinna, Aki S. A1 - Jansen, Christian A1 - Bertelsen, Randi J. A1 - Claesson, Marcus Joakim T1 - Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action JF - Frontiers in Microbiology N2 - The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices. KW - microbiome KW - machine learning KW - artificial intelligence KW - standard KW - best practice Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18004 SN - 1664-302X VL - 14 PB - Frontiers ER -