Benchmarking open problems in single-cell analysis.
Single-cell analysis is hard. We define the field's hardest computational challenges as formal, reproducible benchmarks, and rank the methods that solve them.
Four traits drive innovation in challenges.
From ImageNet to the Netflix Prize, formalized challenges have repeatedly accelerated entire fields. We bring that rigor to single-cell biology.
Clear definitions
Every task is mathematically well-defined, with a fixed input/output contract methods must satisfy.
Standardized datasets
Public, ready-to-use gold-standard datasets curated and version-controlled by the community.
Quantitative metrics
Success is measured by transparent, peer-reviewed metrics, never vibes.
Continuous leaderboards
State-of-the-art methods are ranked and re-run automatically as the field evolves.
The leaderboard, reinvented.
Our funky-heatmap encodes every method, dataset and metric in one legible figure. Size and intensity read at a glance; filters reshape it live.
Standing on the shoulders of shared benchmarks.
Learning from machine learning
ImageNet, the WMT translation task and the Netflix Prize each collapsed years of progress into a shared benchmark. We borrow that playbook.
Formalized challenges
DeepMind's CASP breakthrough, the DREAM Challenges and RxRx show what structured competition does for the biological sciences.
Cross-disciplinary innovation
Single-cell methods increasingly borrow from computer vision and NLP. We make that exchange a two-way street.
A community-owned platform for benchmarking single-cell analysis.
Open Problems is hosted on GitHub with benchmarks run on cloud infrastructure supported by the Chan Zuckerberg Initiative. Every task, dataset, metric and method is shaped by community input, and anyone can contribute a new method or propose a new problem.
Sustained by organizations backing open science.
Funders and infrastructure partners power the compute, engineering and scientific leadership behind every benchmark. Meet our sponsors
