studio

studio is a GUI based online workflow pipeline tools for processing animal MRI data.

studio helps researchers try multiple data analysis methods, visualize the results, and construct the data analysis pipelines easily and quickly on GUI without setting up local environment.

studio also supports reproducibility of scientific research, standardization of analysis protocols, and developments of novel analysis tools as plug-in.

Key Features

🔰 Easy-To-Create Workflow

• zero-knowledge of coding: studio allows you to create analysis pipelines easily on the GUI.

⚡ Visualizing analysis results

• quick visualization: studio supports you visualize the analysis results by plotly.

🚀 Managing Workflows

• recording and reproducing: studio records and reproduces the workflow pipelines easily.

Installation

Prepare configuration files

Prepare .env

• Copy .env.example to .env and replace SECRET_KEY in .env with your SECRET_KEY.
  • To create random SECRET_KEY:

    openssl rand -hex 32
    

Prepare firebase configs

This application uses firebase.

• Copy firebase_config.example.json to firebase_config.json and replace content of firebase_config.json with your Firebase config.
• Copy firebase_private.example.json to firebase_private.json and replace content of firebase_private.json with your Firebase private key.

Prepare firebase configs

Running Application

Docker required: Please install docker in advance.

• On Linux(Ubuntu) or macOS or Windows:

  • Run the file to set up and start the app:

    docker compose up
    

Open browser. http://localhost:8000

Using GUI

Workflow

• studio allows you to make your analysis pipelines by graph style using nodes and edges on GUI. Parameters for each analysis are easily changeable.

Visualize

• studio allows you to visualize the analysis results with one click by plotly. It supports a variety of plotting styles.

Record

• studio supports you in recording and reproducing workflow pipelines in an organized manner.

Contributors

Proposers

Sho Yagishita

Developers

Keita Matsumoto, Nobuo Kawada, Rei Hashimoto, Naoki Takada, Atsuo Matsueda

References

This product is based on OptiNiSt, proposed by OIST Neural Computation Unit