SciDash: Equipping Neuroscientists with a Scientific Dashboard for Model Validation

About Client

Our client, a progressive professor at the University of Arizona specializing in neuroscience, sought a tailored platform to support his research. Focused on testing and validating multifaceted scientific models, he needed a solution to manage his extensive libraries, which are the key to advancing his work in neuroscience.

Recognizing the depth and specificity of his needs, it was clear that a generic solution wouldn’t suffice. The platform had to accommodate the unique demands of neuroscience research, enabling seamless integration of various datasets and tools while ensuring precise testing capabilities. This required not only technical expertise but also an understanding of the scientific standards involved.

Jellyfish.tech accepted the challenge, collaborating closely with the professor to develop a bespoke digital solution. By leveraging our experience in building specialized platforms, we aimed to create a user-friendly system that would optimize his research processes, allowing him to focus on neuroscience research.

The Challenge

Professionals often face parallel challenges in the rapidly evolving domains of software development and neuroscience. The cornerstone of their work lies in decoding the complexities of intricate projects, identifying every component, and ensuring that each element perfectly aligns to generate the desired input/output behavior. 

However, a significant hurdle emerges when there’s a lack of a unifying platform to validate these components, particularly in scientific modeling against tangible experimental data.

Dev Process

Feature-Rich Output Table

We aimed to design an output table that would not only allow the testing of different scientific models but would also facilitate the seamless visualization of the results. We believed that a transparent, intuitive representation of data would be critical in aiding neuroscientists in their endeavors.

Model Validation Dashboard

The dashboard we developed became the nexus for validating scientific models against experimental evidence. The robust backend leveraged the Python framework SciUnit and tapped into the versatile NeuronUnit library, which was explicitly designed for neuron testing. This ensured a meticulous and accurate validation process.

Comprehensive Features

Our dashboard has features to enhance the user experience. It houses a model dashboard that lists model names, source URLs, classes, instances, and tags. Additionally, it incorporates facilities to run scientific tests, view suite scores, and schedule tests, thus offering an all-encompassing solution for the user’s needs.

Design Process

We Started With Code

Our goal was to build a compelling product story that removed users from the idea of paying rent or managing a property and instead, transported them to a place where these tasks were actually exciting. How do we make paying rent rewarding? How do we create an experience where finding a tenant rewarding or closing on a broker commission is rewarding beyond the financial gain but on an emotional level?

We Started With Code

Our goal was to build a compelling product story that removed users from the idea of paying rent or managing a property and instead, transported them to a place where these tasks were actually exciting. How do we make paying rent rewarding? How do we create an experience where finding a tenant rewarding or closing on a broker commission is rewarding beyond the financial gain but on an emotional level?

We Started With Code

Our goal was to build a compelling product story that removed users from the idea of paying rent or managing a property and instead transported them to a place where these tasks were actually exciting. How do we make paying rent rewarding? How do we create an experience where finding a tenant rewarding or closing on a broker commission is rewarding beyond the financial gain but on an emotional level?

Outcome

SciDash isn’t merely a platform; it’s a vision. It epitomizes the drive to simplify and amplify the validation of computational scientific models against experimental data. SciDash has become a handy tool for university professors, particularly in neuroscience.

Through our collaboration, this non-profit project has grown to become a resource, offering vivid visualization and reliable unit testing of scientific models. Jellyfish.tech development made the platform not only streamline the validation process but also provide a user-friendly experience tailored for academic researchers.

In today’s rapidly advancing tech landscape, bridging the gap between software development and scientific research is more essential than ever. By combining our software expertise with the client’s scientific vision, we’ve created a solution that seamlessly integrates these fields, unlocking new potential for scientific exploration and discovery.