期刊
JOURNAL OF INTEGRATIVE BIOINFORMATICS
卷 18, 期 3, 页码 -出版社
WALTER DE GRUYTER GMBH
DOI: 10.1515/jib-2021-0021
关键词
computational modeling; reproducibility; simulation experiment
资金
- National Institute for Biomedical Imaging and Bioengineering award [P41GM109824]
- National Institute of General Medical Sciences [R01GM123032, R35GM119771]
- National Science Foundation [1933453]
- Federal Ministry of Education and Research (BMBF, Germany) [031L0104A, 031L0054]
- National Institute of Health [R35GM119770]
- German Research Foundation (DFG) [436883643]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1933453] Funding Source: National Science Foundation
SED-ML is a computer-readable format for sharing information about simulation experiments, supporting collaboration among modelers, experimentalists, and engineers. Version 4 of Level 1 significantly expands capabilities to cover additional types of models, model languages, parameter estimations, simulations, analyses, and visualizations.
Computational simulation experiments increasingly inform modern biological research, and bring with them the need to provide ways to annotate, archive, share and reproduce the experiments performed. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. The first versions of SED-ML focused on deterministic and stochastic simulations of models. Level 1 Version 4 of SED-ML substantially expands these capabilities to cover additional types of models, model languages, parameter estimations, simulations and analyses of models, and analyses and visualizations of simulation results. To facilitate consistent practices across the community, Level 1 Version 4 also more clearly describes the use of SED-ML constructs, and includes numerous concrete validation rules. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including eight languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, over 20 simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据