Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 3 (L1V3)
The creation of computational simulation experiments to inform modern biological research poses challenges to reproduce, annotate, archive, and share such experiments. Efforts such as SBML or CellML standardize the formal representation of computational models in various areas of biology. The Simula...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
19.03.2018
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| In: |
Journal of integrative bioinformatics
Year: 2018, Jahrgang: 15, Heft: 1 |
| ISSN: | 1613-4516 |
| DOI: | 10.1515/jib-2017-0086 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1515/jib-2017-0086 Verlag, Volltext: https://www.degruyterbrill.com/view/j/jib.2018.15.issue-1/jib-2017-0086/jib-2017-0086.xml 
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| Verfasserangaben: | Frank T. Bergmann, Jonathan Cooper, Matthias König, Ion Moraru, David Nickerson, Novère Nicolas Le, Brett G. Olivier, Sven Sahle, Lucian Smith, Dagmar Waltemath |
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| 520 | |a The creation of computational simulation experiments to inform modern biological research poses challenges to reproduce, annotate, archive, and share such experiments. Efforts such as SBML or CellML standardize the formal representation of computational models in various areas of biology. The Simulation Experiment Description Markup Language (SED-ML) describes what procedures the models are subjected to, and the details of those procedures. These standards, together with further COMBINE standards, describe models sufficiently well for the reproduction of simulation studies among users and software tools. The Simulation Experiment Description Markup Language (SED-ML) is an XML-based format that encodes, for a given simulation experiment, (i) which models to use; (ii) which modifications to apply to models before simulation; (iii) which simulation procedures to run on each model; (iv) how to post-process the data; and (v) how these results should be plotted and reported. SED-ML Level 1 Version 1 (L1V1) implemented support for the encoding of basic time course simulations. SED-ML L1V2 added support for more complex types of simulations, specifically repeated tasks and chained simulation procedures. SED-ML L1V3 extends L1V2 by means to describe which datasets and subsets thereof to use within a simulation experiment. | ||
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