Nov 16, 2023
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Service categories
Service Lines
Web Development
Domain focus
Education
Other
Programming language
JavaScript
Python
Frameworks
Django
React.js
Challenge
Challenge 1.
Too much time is spent on manual model verification. The way to assess the scientific model quality automatically according to certain characteristics is required.
Challenge 2.
An area to work with models for testing, adding their parameters, and tags, and creating instances is required.
Challenge 3.
The process of competitive scientific model testing against a suite of unit tests should be automated.
Challenge 4.
A user should have the ability to create, configure, and save a custom set of tests for a particular model.
Challenge 5.
A user-friendly work area to launch tests is needed.
Challenge 1.
Too much time is spent on manual model verification. The way to assess the scientific model quality automatically according to certain characteristics is required.
Challenge 2.
An area to work with models for testing, adding their parameters, and tags, and creating instances is required.
Challenge 3.
The process of competitive scientific model testing against a suite of unit tests should be automated.
Challenge 4.
A user should have the ability to create, configure, and save a custom set of tests for a particular model.
Challenge 5.
A user-friendly work area to launch tests is needed.
Solution
The test Scores table is responsible for the validation of scientific models against experimental data. It helps understand how accurate the model is using the Python framework SciUnit and the extensible NeuronUnit library for neuron testing. The dashboard fetches the test repository data from GitHub, making it easy to execute unit tests locally or in the cloud.
The model dashboard has been developed from scratch with the following model features: name; source URL; class; instance; and tags.
Suite Scores is the dashboard built to incorporate and visualize the set of tests for each model.
The test Scores table is responsible for the validation of scientific models against experimental data. It helps understand how accurate the model is using the Python framework SciUnit and the extensible NeuronUnit library for neuron testing. The dashboard fetches the test repository data from GitHub, making it easy to execute unit tests locally or in the cloud.
The model dashboard has been developed from scratch with the following model features: name; source URL; class; instance; and tags.
Suite Scores is the dashboard built to incorporate and visualize the set of tests for each model.
Results
The portal is actually an output table that has been created with a possibility to test different scientific models oneself and visualize the results.
Customer came to us with the specifications that our full-stack developer brought to life.
“SciDash aims to make validation of computational scientific models against experimental data easy, transparent, and continuously integrated into the model development process.”
The non-profit project is of practical use for university professors who work on reproducible execution and visualization of data-driven unit tests for assessing model quality. It is a 21st-century vision of the scientific method.
The portal is actually an output table that has been created with a possibility to test different scientific models oneself and visualize the results.
Customer came to us with the specifications that our full-stack developer brought to life.
“SciDash aims to make validation of computational scientific models against experimental data easy, transparent, and continuously integrated into the model development process.”
The non-profit project is of practical use for university professors who work on reproducible execution and visualization of data-driven unit tests for assessing model quality. It is a 21st-century vision of the scientific method.