To provide support for decision making, computational modeling workflows in geoscientific simulation need to be efficient, reproducible, and transparent. Further, the community strives for new standards related to open and inclusive practices. Open and transparent workflows allow for detailed scrutiny of the techniques, assumptions, and interpretations of data, models, and their uncertainties. Such workflows also address issues of inclusion and diversity by providing all details of the path from data to results which can benefit all stakeholders, (more) independent of their background. Advancements in software engineering and data hosting provide sophisticated tools to improve transparency, reproducibility, and accessibility of all steps of the modeling chain.
For this session, we seek contributions of open-source tools and workflows striving for these goals. We aim to stimulate discussion based on lessons learned from challenges as well as success stories.
Software tools may include, but are not limited to:
• techniques to automate modeling workflow elements or increase efficiency, reproducibility, robustness of decision-support modeling elements.
• frameworks to build models from original data in flexible ways that may enable hypothesis testing in the form of changing discretization, process representation, and other modeling decisions.
• multi-model frameworks such as Bayesian-model selection/combination, as well as frameworks to accommodate model structural error.
• Methods for uncertainty analysis, data assimilation, and management optimization under uncertainty in the decision-support context.
• machine-learning approaches for decision support analyses.
For this session, we seek contributions of open-source tools and workflows striving for these goals. We aim to stimulate discussion based on lessons learned from challenges as well as success stories.
Software tools may include, but are not limited to:
• techniques to automate modeling workflow elements or increase efficiency, reproducibility, robustness of decision-support modeling elements.
• frameworks to build models from original data in flexible ways that may enable hypothesis testing in the form of changing discretization, process representation, and other modeling decisions.
• multi-model frameworks such as Bayesian-model selection/combination, as well as frameworks to accommodate model structural error.
• Methods for uncertainty analysis, data assimilation, and management optimization under uncertainty in the decision-support context.
• machine-learning approaches for decision support analyses.