scholarly journals Extracting reproducible simulation studies from model repositories using the CombineArchive Toolkit

2015 ◽  
Author(s):  
Martin Scharm ◽  
Dagmar Waltemath
Keyword(s):  
Computational Biology ◽  
Simulation Study ◽  
Reproducible Research ◽  
Virtual Experiment ◽  
Graph Database ◽  
Simulation Studies ◽  
Valuable Resource ◽  
Open Model ◽  
Research Results ◽  
Biomodels Database

The COMBINE archive is a digital container format for files related to a virtual experiment in computational biology. It eases the management of numerous files related to a simulation study, fosters collaboration, and ultimately enables the exchange of reproducible research results. The CombineArchive Toolkit is a software for creating, exploring, modifying, and sharing COMBINE archives. Open model repositories such as BioModels Database are a valuable resource of models and associated simulation descriptions. However, so far no tool exists to export COMBINE archives for a given simulation study from such databases. Here we demonstrate how the CombineArchiveToolkit can be used to extract reproducible simulation studies from model repositories. We use the example of Masymos, a graph database with a sophisticated link concept to connect model-related files on the storage layer.

scholarly journals Extracting reproducible simulation studies from model repositories using the CombineArchive Toolkit

2015 ◽  
Author(s):  
Martin Scharm ◽  
Dagmar Waltemath
Keyword(s):  
Computational Biology ◽  
Simulation Study ◽  
Reproducible Research ◽  
Virtual Experiment ◽  
Graph Database ◽  
Simulation Studies ◽  
Valuable Resource ◽  
Open Model ◽  
Research Results ◽  
Biomodels Database

The COMBINE archive is a digital container format for files related to a virtual experiment in computational biology. It eases the management of numerous files related to a simulation study, fosters collaboration, and ultimately enables the exchange of reproducible research results. The CombineArchive Toolkit is a software for creating, exploring, modifying, and sharing COMBINE archives. Open model repositories such as BioModels Database are a valuable resource of models and associated simulation descriptions. However, so far no tool exists to export COMBINE archives for a given simulation study from such databases. Here we demonstrate how the CombineArchiveToolkit can be used to extract reproducible simulation studies from model repositories. We use the example of Masymos, a graph database with a sophisticated link concept to connect model-related files on the storage layer.

scholarly journals A fully featured COMBINE archive of a simulation study on syncytial mitotic cycles in Drosophila embryos

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2421 ◽  
Author(s):  
Martin Scharm ◽  
Dagmar Waltemath
Keyword(s):  
Computational Biology ◽  
Simulation Study ◽  
Scientific Community ◽  
Original Work ◽  
Original Publication ◽  
Valuable Resource ◽  
Data Files ◽  
Drosophila Embryos ◽  
Archived Data

COMBINE archives are standardised containers for data files related to a simulation study in computational biology. This manuscript describes a fully featured archive of a previously published simulation study, including (i) the original publication, (ii) the model, (iii) the analyses, and (iv) metadata describing the files and their origin. With the archived data at hand, it is possible to reproduce the results of the original work. The archive can be used for both, educational and research purposes. Anyone may reuse, extend and update the archive to make it a valuable resource for the scientific community.

scholarly journals The CombineArchive Toolkit - facilitating the transfer of research results

2014 ◽  
Author(s):  
Martin Scharm ◽  
Florian Wendland ◽  
Martin Peters ◽  
Markus Wolfien ◽  
Tom Theile ◽  
...  
Keyword(s):  
Computational Biology ◽  
In Silico ◽  
Reproducible Research ◽  
Research Results ◽  
Manual Handling

Sharing in silico experiments is essential for the advance of research in computational biology. Consequently, the COMBINE archive was designed as a digital container format. It eases the management of numerous files, fosters collaboration, and ultimately enables the exchange of reproducible research results. However, manual handling of COMBINE archives is tedious and error prone. We therefore developed the CombineArchive Toolkit. It supports scientists in promoting and publishing their work by means of creating, exploring, modifying, and sharing archives.

scholarly journals The CombineArchive Toolkit - facilitating the transfer of research results

2014 ◽  
Author(s):  
Martin Scharm ◽  
Florian Wendland ◽  
Martin Peters ◽  
Markus Wolfien ◽  
Tom Theile ◽  
...  
Keyword(s):  
Computational Biology ◽  
In Silico ◽  
Reproducible Research ◽  
Research Results ◽  
Manual Handling

Sharing in silico experiments is essential for the advance of research in computational biology. Consequently, the COMBINE archive was designed as a digital container format. It eases the management of numerous files, fosters collaboration, and ultimately enables the exchange of reproducible research results. However, manual handling of COMBINE archives is tedious and error prone. We therefore developed the CombineArchive Toolkit. It supports scientists in promoting and publishing their work by means of creating, exploring, modifying, and sharing archives.

scholarly journals COMODI: An ontology to characterise differences in versions of computational models in biology

2016 ◽  
Author(s):  
Martin Scharm ◽  
Dagmar Waltemath ◽  
Pedro Mendes ◽  
Olaf Wolkenhauer
Keyword(s):  
Computational Biology ◽  
Simulation Study ◽  
Computational Models ◽  
Open Model ◽  
Semantic Level ◽  
Difference Detection ◽  
Model Repository ◽  
Error Corrections ◽  
Systems Models ◽  
Over Time

Motivation: Open model repositories provide ready-to-reuse computational models of biological systems. Models within those repositories evolve over time, leading to many alternative and subsequent versions. Taken together, the underlying changes reflect a model’s provenance and thus can give valuable insights into the studied biology. Currently, however, changes cannot be semantically interpreted. To improve this situation, we developed an ontology of terms describing changes in computational biology models. The ontology can be used by scientists and within software to characterise model updates at the level of single changes. When studying or reusing a model, these annotations help with determining the relevance of a change in a given context. Methods: We manually studied changes in selected models from BioModels and the Physiome Model Repository. Using the BiVeS tool for difference detection, we then performed an automatic analysis of changes in all models published in these repositories. The resulting set of concepts led us to define candidate terms for the ontology. In a final step, we aggregated and classified these terms and built the first version of the ontology. Results: We present COMODI, an ontology needed because COmputational MOdels DIffer. It empowers users and software to describe changes in a model on the semantic level. COMODI also enables software to implement user-specific filter options for the display of model changes. Finally, COMODI is the next step towards predicting how a change in a model influences the simulation study. Conclusion: COMODI, coupled with our algorithm for difference detection, ensures the transparency of a model’s evolution and it enhances the traceability of updates and error corrections. Availability: COMODI is encoded in OWL. It is openly available at http://comodi.sems.uni-rostock.de/.

scholarly journals COMODI: An ontology to characterise differences in versions of computational models in biology

2016 ◽  
Author(s):  
Martin Scharm ◽  
Dagmar Waltemath ◽  
Pedro Mendes ◽  
Olaf Wolkenhauer
Keyword(s):  
Computational Biology ◽  
Simulation Study ◽  
Computational Models ◽  
Open Model ◽  
Semantic Level ◽  
Difference Detection ◽  
Model Repository ◽  
Error Corrections ◽  
Systems Models ◽  
Over Time

Motivation: Open model repositories provide ready-to-reuse computational models of biological systems. Models within those repositories evolve over time, leading to many alternative and subsequent versions. Taken together, the underlying changes reflect a model’s provenance and thus can give valuable insights into the studied biology. Currently, however, changes cannot be semantically interpreted. To improve this situation, we developed an ontology of terms describing changes in computational biology models. The ontology can be used by scientists and within software to characterise model updates at the level of single changes. When studying or reusing a model, these annotations help with determining the relevance of a change in a given context. Methods: We manually studied changes in selected models from BioModels and the Physiome Model Repository. Using the BiVeS tool for difference detection, we then performed an automatic analysis of changes in all models published in these repositories. The resulting set of concepts led us to define candidate terms for the ontology. In a final step, we aggregated and classified these terms and built the first version of the ontology. Results: We present COMODI, an ontology needed because COmputational MOdels DIffer. It empowers users and software to describe changes in a model on the semantic level. COMODI also enables software to implement user-specific filter options for the display of model changes. Finally, COMODI is the next step towards predicting how a change in a model influences the simulation study. Conclusion: COMODI, coupled with our algorithm for difference detection, ensures the transparency of a model’s evolution and it enhances the traceability of updates and error corrections. Availability: COMODI is encoded in OWL. It is openly available at http://comodi.sems.uni-rostock.de/.

scholarly journals Simulation Studies as a Tool to Understand Bayes Factors

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592097262
Author(s):  
Don van Ravenzwaaij ◽  
Alexander Etz
Keyword(s):  
Simulation Study ◽  
Prior Distribution ◽  
Bayes Factor ◽  
R Package ◽  
Bayes Factors ◽  
Simulation Studies ◽  
Social Scientists ◽  
Simple Simulation ◽  
Set Up

When social scientists wish to learn about an empirical phenomenon, they perform an experiment. When they wish to learn about a complex numerical phenomenon, they can perform a simulation study. The goal of this Tutorial is twofold. First, it introduces how to set up a simulation study using the relatively simple example of simulating from the prior. Second, it demonstrates how simulation can be used to learn about the Jeffreys-Zellner-Siow (JZS) Bayes factor, a currently popular implementation of the Bayes factor employed in the BayesFactor R package and freeware program JASP. Many technical expositions on Bayes factors exist, but these may be somewhat inaccessible to researchers who are not specialized in statistics. In a step-by-step approach, this Tutorial shows how a simple simulation script can be used to approximate the calculation of the Bayes factor. We explain how a researcher can write such a sampler to approximate Bayes factors in a few lines of code, what the logic is behind the Savage-Dickey method used to visualize Bayes factors, and what the practical differences are for different choices of the prior distribution used to calculate Bayes factors.

scholarly journals VARX and GSTARX Models for Forecasting Currency Inflow and Outflow with Multiple Calendar Variations Effect

MATEMATIKA ◽  
2018 ◽  
Vol 34 (3) ◽  
pp. 57-72 ◽  
Author(s):  
Suhartono Suhartono ◽  
Muhammad Munawir Gazali ◽  
Dedy Dwi Prastyo
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Simulation Study ◽  
Forecast Accuracy ◽  
Forecasting Model ◽  
Mean Square ◽  
Simulation Studies ◽  
Exogenous Variables ◽  
Vector Autoregressive ◽  
Weight Model

VARX and GSTARX models are an extension of Vector Autoregressive (VAR) and Generalized Space-Time Autoregressive (GSTAR) models. These models include exogenous variable to increase the forecast accuracy. The objective of this research is to develop and compare the forecast accuracy of VARX and GSTARX models in predicting currency inflow and outflow in Bali, West Nusa Tenggara, and East Nusa Tenggara that contain multiple calendar variations effects. The exogenous variables that are used in this research are holidays in those three locations, i.e. EidFitr, Galungan, and Nyepi. The proposed VARX and GSTARX models are evaluated through simulation studies on the data that contain trend, seasonality, and multiple calendar variations representing the occurrence of EidFitr, Galungan, and Nyepi. The criteria for selecting the best forecasting model is Root Mean Square Error (RMSE). The results of a simulation study show that VARX and GSTARX models provide similar forecast accuracy. Furthermore, the results of currency inflow and outflow data in Bali,West Nusa Tenggara, and East Nusa Tenggara show that the best model for forecasting inflow and outflow in these three locations are VARX and GSTARX (with uniform weight) model, respectively. Both models show that currency inflow and outflow in Bali, West Nusa Tenggara, and East Nusa Tenggara have a relationship in space and time, and contain trends, seasonality and multiple calendar variations.

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