@misc{NickersonAtalagdeBonoetal.2016,
  author    = {Nickerson, David and Atalag, Koray and de Bono, Bernard and Geiger, Joerg and Goble, Carole and Hollmann, Susanne and Lonien, Joachim and Mueller, Wolfgang and Regierer, Babette and Stanford, Natalie J. and Golebiewski, Martin and Hunter, Peter},
  title     = {The Human Physiome: how standards, software and innovative service infrastructures are providing the building blocks to make it achievable},
  series = {Interface focus},
  volume    = {6},
  journal   = {Interface focus},
  publisher = {Royal Society},
  address   = {London},
  issn      = {2042-8898},
  doi       = {10.1098/rsfs.2015.0103},
  pages     = {57 -- 61},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}
@article{HollmannFrohmeEndrullatetal.2020,
  author    = {Hollmann, Susanne and Frohme, Marcus and Endrullat, Christoph and Kremer, Andreas and D'Elia, Domenica and Regierer, Babette and Nechyporenko, Alina},
  title     = {Ten simple rules on how to write a standard operating procedure},
  series = {PLOS Computational Biology},
  volume    = {16},
  journal   = {PLOS Computational Biology},
  number    = {9},
  publisher = {PLOS},
  address   = {San Francisco},
  pages     = {10},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@misc{HollmannFrohmeEndrullatetal.2020,
  author    = {Hollmann, Susanne and Frohme, Marcus and Endrullat, Christoph and Kremer, Andreas and D'Elia, Domenica and Regierer, Babette and Nechyporenko, Alina},
  title     = {Ten simple rules on how to write a standard operating procedure},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {9},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-52587},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-525877},
  pages     = {12},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}