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      The need for standardisation in life science research - an approach to excellence and trust.

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          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.

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            1,500 scientists lift the lid on reproducibility.

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              The Economics of Reproducibility in Preclinical Research

              Low reproducibility rates within life science research undermine cumulative knowledge production and contribute to both delays and costs of therapeutic drug development. An analysis of past studies indicates that the cumulative (total) prevalence of irreproducible preclinical research exceeds 50%, resulting in approximately US$28,000,000,000 (US$28B)/year spent on preclinical research that is not reproducible—in the United States alone. We outline a framework for solutions and a plan for long-term improvements in reproducibility rates that will help to accelerate the discovery of life-saving therapies and cures.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Funding AcquisitionRole: Project AdministrationRole: SupervisionRole: Writing – Original Draft Preparation
                Role: Writing – Review & Editing
                Role: Writing – Review & Editing
                Role: Writing – Review & Editing
                Role: Writing – Review & Editing
                Role: Writing – Review & Editing
                Role: Writing – Review & Editing
                Role: Writing – Review & Editing
                Role: Project AdministrationRole: Writing – Review & Editing
                Role: Writing – Review & Editing
                Role: Writing – Review & Editing
                Role: Writing – Review & Editing
                Role: Writing – Review & Editing
                Role: ConceptualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000 Research Limited (London, UK )
                2046-1402
                4 December 2020
                2020
                : 9
                : 1398
                Affiliations
                [1 ]Faculty of Science, University of Potsdam, Potsdam, Brandenburg, 14476, Germany
                [2 ]SB Science Management UG (Haftungsbeschränkt), Berlin, Berlin, 12163, Germany
                [3 ]Information Technology for Translational Medicine S.A. ITTM S.A., Esch-sur-Alzette, Esch, 4354, Luxembourg
                [4 ]Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, Slovenia
                [5 ]Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, 4367, Luxembourg
                [6 ]Institute of Computer Science, University of Białystok, Białystok, 15-328, Poland
                [7 ]Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, Jefferson, USA
                [8 ]Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, 44-100, Poland
                [9 ]Department of Animal Breeding and Genetics, Bioinformatics section, University of Agricultural Sciences, Uppsala, 750 07, Sweden
                [10 ]Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
                [11 ]Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6229 ER, The Netherlands
                [12 ]Department of Systems Engineering, Kharkiv National University of Radio Electronics, Kharkiv Oblast, 61000, Ukraine
                [13 ]Division Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Brandenburg, 15745, Germany
                [14 ]Masaryk University, Brno, 601 77, Czech Republic
                [15 ]Leibniz Institute of Vegetable and Ornamental Crops (IGZ), Großbeeren, Brandenburg, 14979, Germany
                [16 ]Institute for Biomedical Technologies, National Research Council, Italy, Bari, 70126, Italy
                [1 ]Valore Qualità, Pavia, Italy
                [2 ]Vita-Salute San Raffaele University, Milan, Italy
                [1 ]SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
                [2 ]University of Fribourg, Fribourg, Switzerland
                [1 ]Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
                Author notes

                No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Author information
                https://orcid.org/0000-0001-9032-2035
                https://orcid.org/0000-0003-4776-7164
                https://orcid.org/0000-0002-8991-6810
                https://orcid.org/0000-0002-5301-3142
                https://orcid.org/0000-0002-5263-4553
                Article
                10.12688/f1000research.27500.1
                7863991
                ac044ba8-ba31-4f63-a621-59165802a27c
                Copyright: © 2020 Hollmann S et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 November 2020
                Funding
                Funded by: Horizon 2020
                Award ID: CA15110
                This publication is based upon work from COST Action CHARME (CA15110) supported by COST (European Cooperation in Science and Technology) European Commission under the Grant Agreement CA15110. www.cost.eu.
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Opinion Article
                Articles

                open data,open access,open science,fair principles,standardisation,education,quality management

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