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Define and benchmark challenges in single-cell data science via open standards and continuous leaderboards to promote method development and guide users in method selection. This organization hosts the code, tasks, datasets, and docs behind the Open Problems platform.
More detailed documentation below.
Tech stack highlights:
Explore current leaderboards Check out live tasks and results on the Benchmarks page.
Run a benchmark locally Read the platform Documentation for install requirements and common commands. Components are containerized; workflows run on laptop, HPC, or cloud.
Add your method to a task Follow the docs ("Create component → Add a method") and open a PR in the corresponding task_* repo. See repo READMEs for task‑specific APIs.
Propose or start a new task Start from task_template and the docs ("Create a new task"). Open an issue to coordinate scope and maintainership.
Join the community
If you use Open Problems, please cite:
Luecken, M.D., Gigante, S., Burkhardt, D.B. et al. Defining and benchmarking open problems in single‑cell analysis. Nature Biotechnology (2025). https://doi.org/10.1038/s41587-025-02694-w
To reference specific tasks or datasets, please cite the corresponding task or dataset publications mentioned in the task descriptions and dataset pages. For example, to reference our Open Problems multimodal BMMC datasets, please cite Luecken et al., NeurIPS 2021.
Also see earlier NeurIPS challenge reports and proceedings referenced on the Events page.
Open Problems is free open source software and distributed under the MIT License. However, Open Problems tasks may include references to data or code distributed by a third party under a different license. If any question about license arises, please consult the specific repository in which a particular asset is hosted. If further clarification is needed, please open a GitHub Issue referencing the asset if any clarification is needed.
Open Problems is supported by a growing community and sponsors including the Chan Zuckerberg Initiative, Data Intuitive, Helmholtz Munich, Saturn Cloud, and Seqera. See the website for the latest list.
Predicting the profiles of one modality (e.g. protein abundance) from another (e.g. mRNA expression).
This repo is a template to create a new task that has the correct files and structure needed to start a new task.
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