[upstream-sync] README.md from ai-boost/awesome-ai-for-science@e1893484 [catalogue]

2026-09-04 11:32:47 +00:00
1 changed files with 6 additions and 3 deletions
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 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/06f0c449/README.md
-upstream_sha: 06f0c449
-imported_at: 2026-08-27
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/e1893484/README.md
+upstream_sha: e1893484
+imported_at: 2026-09-04
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
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 - [Jupyter AI (JupyterLab Extension)](https://github.com/jupyterlab/jupyter-ai) - Official Jupyter extension with `%%ai` magic commands and sidebar chat assistant, connecting multiple model providers and local inference
 - [Notebook Intelligence (NBI)](https://github.com/notebook-intelligence/notebook-intelligence) - AI coding assistant for JupyterLab with agent mode, supporting arbitrary LLM providers (2025+)
 - [Google Colab AI Features](https://colab.research.google.com/) - Integrated AI assistance for data science and research notebooks
+- [OpenAI4S](https://github.com/PKU-YuanGroup/OpenAI4S) - Open-source hybrid scientific research agent and workbench replicating Claude Science, combining JSON tool orchestration with persistent Python/R Code-as-Action kernels, 604 bundled science skills, MCP connectors, sandboxed local execution, and multi-provider LLM support for end-to-end scientific workflows (PKU–YuanKong Intelligence, 377+ stars, MIT License, 2026)
 - [OpenBioMed](https://github.com/PharMolix/OpenBioMed) - Open-source biomedical AI platform integrating multimodal foundation models (BioMedGPT, PharmolixFM, LangCell) with agentic workflows and 45+ Claude Code skills for drug discovery, protein engineering, and single-cell omics analysis (PharMolix & Tsinghua AIR, 1K+ stars, 2023-2026)
 - [AutoR](https://github.com/AutoX-AI-Labs/AutoR) - Human-centered research OS with terminal-first harness and local browser Studio, turning research work into reproducible artifact-backed runs through a 9-stage workflow with human approval gates, resume/rollback controls, and venue-aware manuscript packaging (1K+ stars, 2026)
 - [ScholarAIO](https://github.com/ZimoLiao/scholaraio) - Agent-agnostic research infrastructure providing AI agents with a structured scientific workspace for deep PDF parsing, hybrid semantic/keyword literature search, citation-graph analysis, topic discovery, and academic writing workflows; natively integrates with Claude Code, Codex, Cursor, Cline, and AgentSkills.io (530+ stars, MIT License, 2026)
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 - [snntorch](https://github.com/jeshraghian/snntorch) - Deep learning with spiking neural networks in Python, providing gradient-based training of SNNs via PyTorch autodifferentiation for brain-inspired computing and neuromorphic research, with online learning capabilities and extensive tutorials (1.9K+ stars, actively maintained)
 - [nilearn](https://github.com/nilearn/nilearn) - Machine learning and statistical learning for neuroimaging in Python, providing easy-to-use tools for fMRI and MRI analysis including decoding, connectivity estimation, and parcellation with seamless scikit-learn integration (INRIA Parietal team, 1.4K+ stars)
 - [BrainIAC (Nature Neuroscience 2026)](https://github.com/AIM-KannLab/BrainIAC) - Self-supervised vision foundation model for generalized structural brain MRI analysis, pretrained on ~49,000 scans from diverse datasets and generalizing across brain age prediction, dementia/MCI classification, IDH mutation detection, glioma survival prediction, time-to-stroke estimation, MR sequence classification, and brain tumor segmentation; outperforms task-specific models especially with limited training data (Mass General Brigham & Harvard Medical School, 129+ stars)
+- [Zapbench (Google Research, 2025)](https://github.com/google-research/zapbench) - The Zebrafish Activity Prediction Benchmark for forecasting cellular-resolution neural activity throughout an entire vertebrate brain, combining light-sheet microscopy calcium-imaging data, forecasting tasks, and evaluation tools to advance whole-brain neural dynamics modeling (77+ stars, Apache 2.0)

 #### Computational Pathology & Digital Pathology
 - [UNI (Nature Medicine 2024)](https://github.com/mahmoodlab/UNI) - General-purpose pathology foundation model pretrained on 100K+ diagnostic whole-slide images across 20 major tissue types, achieving state-of-the-art transfer learning across 30+ clinical tasks and serving as a universal feature extractor for digital pathology (Mahmood Lab, 722+ stars)
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 ### Domain-Specific Models
 - [ESM](https://github.com/facebookresearch/esm) - Protein language models
 - [BioNeMo Framework](https://github.com/NVIDIA/bionemo-framework) - NVIDIA's open-source platform for building and adapting biological AI models at scale, bundling ESM-2, Geneformer, MolMIM and DNA embedding models with recipes for single-GPU to multi-node training (2025)
+- [BioNeMo Recipes (NVIDIA)](https://github.com/NVIDIA-BioNeMo/bionemo-recipes) - TransformerEngine-accelerated checkpoints and training recipes for scaling biological foundation models (ESM-2, AMPLIFY, Geneformer, CodonFM) from single-GPU prototyping to multi-node FSDP training with FP8/MXFP8/NVFP4 precision, compatible with PyTorch, HF Accelerate, and PyTorch Lightning, plus sparse-autoencoder interpretability tools for biological foundation models (851+ stars, 2026)
 - [IBM FM4M](https://github.com/IBM/materials) - IBM's open foundation model family for materials and chemistry, covering SMILES, SELFIES, molecular graphs, 3D atom positions, and electron density grids, with a unified toolkit for representation learning and downstream prediction/generation (Apache 2.0, 2024-2025)
 - [ChemGPT](https://huggingface.co/ncfrey/ChemGPT-1.2B) - Chemistry-focused language model
 - [BioGPT](https://github.com/microsoft/BioGPT) - Biomedical text generation