Codabench

Codabench enables standardized, reproducible benchmarking of algorithms and software agents across datasets and tasks to support comparative evaluation of computational methods.

Key Features:

• Unified benchmarking environment: Enables comparison of algorithms or software agents against various datasets and tasks within a single evaluation framework.
• Controlled execution environment: Executes evaluations under identical software, hardware, data, and algorithmic settings to ensure consistent comparisons.
• Reusable templates: Provides reusable benchmark templates to standardize experiment configuration and promote reproducibility.
• Custom protocols and data formats: Supports custom protocols and arbitrary data formats for defining benchmarks and inputs.
• On-demand compute resources: Allocates on-demand compute resources for executing submitted algorithms or agents.

Scientific Applications:

• Graph machine learning: Benchmarks graph machine learning algorithms across datasets and tasks.
• Cancer heterogeneity analysis: Compares computational methods for cancer heterogeneity analysis.
• Clinical diagnosis: Evaluates methods relevant to clinical diagnosis.
• Reinforcement learning: Assesses reinforcement learning agents and algorithms.

Methodology:

Execute algorithms or software agents against defined datasets and tasks using reusable templates, custom protocols and data formats, and on-demand compute resources while enforcing identical software, hardware, data, and algorithmic settings for each run.