Amytis is a visual, node‑based platform that integrates data, analysis, and results into a single project graph, allowing researchers to attach datasets to functional nodes for tasks such as plotting, dimensionality reduction, clustering, regression, classification, and ODE modeling without extensive coding. The system maintains explicit links between inputs, processing steps, and outputs, enabling reproducible, shareable workflows and easy collaboration across teams.
Funding
Funding not disclosed
Founders
Product
Problem
Researchers often rely on multiple disconnected tools and scripts to manage data, perform analyses, and generate visualizations, which leads to loss of context, reproducibility challenges, and steep learning curves for advanced methods.
Solution
Amytis provides a visual, node‑based workspace that integrates data, analysis, and results within a single project graph. Users attach datasets to functional nodes that execute tasks such as plotting, dimensionality reduction, clustering, regression, classification, and dynamic system modeling without extensive coding. The platform maintains explicit links between inputs, processing steps, and outputs, enabling easy revisiting, sharing, and documentation of workflows. Drag‑and‑drop Python scripts are automatically converted into call graphs, allowing custom code to blend seamlessly with visual nodes. By offering both a free organisational mode and a Pro mode for running analyses, Amytis lets researchers adopt advanced computational methods early in their projects while preserving a clear, reproducible workflow.
Target Audience
Primary users are academic researchers, graduate students, and laboratory teams who need an integrated, low‑code environment to manage data, perform statistical and machine‑learning analyses, and document reproducible workflows.
Features
- Visual project graph that connects data, analysis nodes, and results for end‑to‑end traceability
- Functional nodes for common analyses: line plots, histograms, PCA, PLS‑DA, clustering, linear/logistic regression, classification, regression, and ODE modeling
- Drag‑and‑drop Python script integration with automatic call‑graph generation
- Modular, reusable workflow components that can be shared across team members
- Built‑in utilities such as timer nodes for experiment tracking and flexible node organization
- Presentation mode for communicating workflow structure and outcomes without code
- Free version for project organization and Pro version for full analysis capabilities