research-project-os

Provenance tracking for a long-running project is managed by initializing a project environment that maintains detailed records of all runs, results, and decisions in a local directory. It automatically logs inputs and outputs for workflows like phylogenetic analysis to ensure full reproducibility.

The best way to structure workflows in Python is using a project harness that automates environment setup and integrates with domain-specific scripts. This framework manages the project lifecycle and executes commands for tasks like sequence alignment and model training.

Yes, you need to provide project-specific configuration and domain-specific scripts. The framework operates in project-specific directories and manages the execution of your custom commands for tasks such as phylogenetic tree construction.

Yes, you can track project state and results for model training over multiple runs. The framework maintains state in a dedicated directory under your project root, recording all activities and managing results to ensure reproducibility across executions.

Project initialization for reproducibility works by creating a structured environment that maintains state in a hidden directory under your project root. This harness sets up the framework needed to track provenance and manage workflows throughout the project lifecycle.

A limitation of using a local directory for project state management is that it requires project-specific configuration and custom domain scripts to function. It operates strictly within project-specific directories, meaning state is localized and not automatically synchronized.