TY - JOUR A1 - Nickerson, David A1 - Atalag, Koray A1 - de Bono, Bernard A1 - Geiger, Joerg A1 - Goble, Carole A1 - Hollmann, Susanne A1 - Lonien, Joachim A1 - Mueller, Wolfgang A1 - Regierer, Babette A1 - Stanford, Natalie J. A1 - Golebiewski, Martin A1 - Hunter, Peter T1 - The Human Physiome: how standards, software and innovative service infrastructures are providing the building blocks to make it achievable JF - Interface focus N2 - Reconstructing and understanding the Human Physiome virtually is a complex mathematical problem, and a highly demanding computational challenge. Mathematical models spanning from the molecular level through to whole populations of individuals must be integrated, then personalized. This requires interoperability with multiple disparate and geographically separated data sources, and myriad computational software tools. Extracting and producing knowledge from such sources, even when the databases and software are readily available, is a challenging task. Despite the difficulties, researchers must frequently perform these tasks so that available knowledge can be continually integrated into the common framework required to realize the Human Physiome. Software and infrastructures that support the communities that generate these, together with their underlying standards to format, describe and interlink the corresponding data and computer models, are pivotal to the Human Physiome being realized. They provide the foundations for integrating, exchanging and re-using data and models efficiently, and correctly, while also supporting the dissemination of growing knowledge in these forms. In this paper, we explore the standards, software tooling, repositories and infrastructures that support this work, and detail what makes them vital to realizing the Human Physiome. KW - Human Physiome KW - standards KW - repositories KW - service infrastructure KW - reproducible science KW - managing big data Y1 - 2016 U6 - https://doi.org/10.1098/rsfs.2015.0103 SN - 2042-8898 SN - 2042-8901 VL - 6 SP - 57 EP - 61 PB - Royal Society CY - London 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 VL - 16 IS - 9 PB - PLOS CY - San Francisco ER - TY - GEN 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 T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1201 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-525877 SN - 1866-8372 IS - 9 ER -