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Author: Carole Goble6

Abstract (Expand)

Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR-findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.

Authors: N. J. Stanford, M. Scharm, P. D. Dobson, M. Golebiewski, M. Hucka, V. B. Kothamachu, D. Nickerson, S. Owen, J. Pahle, U. Wittig, D. Waltemath, C. Goble, P. Mendes, J. Snoep

Date Published: 12th Oct 2019

Publication Type: Journal

Abstract (Expand)

The FAIRDOMHub is a repository for publishing FAIR (Findable, Accessible, Interoperable and Reusable) Data, Operating procedures and Models (https://fairdomhub.org/) for the Systems Biology community. It is a web-accessible repository for storing and sharing systems biology research assets. It enables researchers to organize, share and publish data, models and protocols, interlink them in the context of the systems biology investigations that produced them, and to interrogate them via API interfaces. By using the FAIRDOMHub, researchers can achieve more effective exchange with geographically distributed collaborators during projects, ensure results are sustained and preserved and generate reproducible publications that adhere to the FAIR guiding principles of data stewardship.

Authors: K. Wolstencroft, O. Krebs, J. L. Snoep, N. J. Stanford, F. Bacall, M. Golebiewski, R. Kuzyakiv, Q. Nguyen, S. Owen, S. Soiland-Reyes, J. Straszewski, D. D. van Niekerk, A. R. Williams, L. Malmstrom, B. Rinn, W. Muller, C. Goble

Date Published: 4th Jan 2017

Publication Type: Journal

Abstract (Expand)

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.

Authors: D. Nickerson, K. Atalag, B. de Bono, J. Geiger, C. Goble, S. Hollmann, J. Lonien, W. Muller, B. Regierer, N. J. Stanford, M. Golebiewski, P. Hunter

Date Published: 7th Apr 2016

Publication Type: Not specified

Abstract (Expand)

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.

Authors: M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J. W. Boiten, L. B. da Silva Santos, P. E. Bourne, J. Bouwman, A. J. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C. T. Evelo, R. Finkers, A. Gonzalez-Beltran, A. J. Gray, P. Groth, C. Goble, J. S. Grethe, J. Heringa, P. A. 't Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S. J. Lusher, M. E. Martone, A. Mons, A. L. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S. A. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M. A. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao, B. Mons

Date Published: 15th Mar 2016

Publication Type: Journal

Abstract (Expand)

The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, and attended by a total of 96 people. This edition pioneered a first day devoted to modeling approaches in biology, which attracted a broad audience of scientists thanks to a panel of renowned speakers. During subsequent days, discussions were held on many subjects including the introduction of new features in the various COMBINE standards, new software tools that use the standards, and outreach efforts. Significant emphasis went into work on extensions of the SBML format, and also into community-building. This year’s edition once again demonstrated that the COMBINE community is thriving, and still manages to help coordinate activities between different standards in computational systems biology.

Authors: Dagmar Waltemath, Frank T. Bergmann, Claudine Chaouiya, Tobias Czauderna, Padraig Gleeson, , Martin Golebiewski, Michael Hucka, Nick Juty, , Nicolas Le Novère, Huaiyu Mi, Ion I. Moraru, Chris J. Myers, David Nickerson, Brett G. Olivier, Nicolas Rodriguez, Falk Schreiber, Lucian Smith, Fengkai Zhang, Eric Bonnet

Date Published: 15th Mar 2014

Publication Type: Journal

Abstract (Expand)

RightField is a Java application that provides a mechanism for embedding ontology annotation support for scientific data in Microsoft Excel or Open Office spreadsheets. The result is semantic annotation by stealth, with an annotation process that is less error-prone, more efficient, and more consistent with community standards. By automatically generating RDF statements for each cell a rich, Linked Data querying environment allows scientists to search their data and other Linked Data resources interchangeably, and caters for queries across heterogeneous spreadsheets. RightField has been developed for Systems Biologists but has since adopted more widely. It is open source (BSD license) and freely available from http://www.rightfield.org.uk

Authors: Katy Wolstencroft, Stuart Owen, Matthew Horridge, Wolfgang Mueller, Finn Bacall, Jacky Snoep, Franco du Preez, Quyen Nguyen, Olga Krebs, Carole Goble

Date Published: 2012

Publication Type: Journal

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