[upstream-sync] README.md from ai-boost/awesome-ai-for-science@d57c2d50 [catalogue]

Merge pull request '[Upstream sync] ai-boost/awesome-ai-for-science (github) — 0 added, 1 modified' (#83 ) from upstream-sync/awesome-ai-for-science-20260821-ec6f36-ihmj into main
Reviewed-on: #83
2026-09-01 05:15:18 +00:00 · 2026-08-21 14:37:05 +00:00 · 2026-08-21 10:38:52 +00:00 · 2026-08-09 18:32:11 +00:00 · 2026-08-09 17:02:16 +00:00 · 2026-08-09 15:54:26 +00:00
24 changed files with 3514 additions and 421 deletions
@@ -2,9 +2,9 @@
 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/22846384/README.md
-upstream_sha: 22846384
-imported_at: 2026-08-08
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/d57c2d50/README.md
+upstream_sha: d57c2d50
+imported_at: 2026-09-01
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -63,6 +63,7 @@ validated: false
  - [🌍 Earth & Climate Science](#-earth--climate-science)
  - [🌾 Agriculture & Ecology](#-agriculture--ecology)
  - [🧠 Social Sciences](#-social-sciences)
+  - [🏗 Engineering & Built Environment](#-engineering--built-environment)
 - [🤖 Foundation Models for Science](#-foundation-models-for-science)
 - [📈 Datasets & Benchmarks](#-datasets--benchmarks)
 - [💻 Computing Frameworks](#-computing-frameworks)
@@ -206,6 +207,7 @@ validated: false
 - [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)
@@ -236,6 +238,7 @@ validated: false
 - [Obsidian Smart Connections](https://github.com/brianpetro/obsidian-smart-connections) - AI-powered note linking and research graph navigation
 - [Research Rabbit](https://www.researchrabbit.ai/) - AI-powered literature discovery and research network mapping
 - [SciWrite](https://github.com/labarba/sciwrite) - Agent skill for AI-assisted scientific manuscript writing review distilled from Stanford's *Writing in the Sciences* course, performing five sequential editorial audit passes on clarity, voice, structure, consistency, and integrity (2026)
+- [PaperSpine](https://github.com/WUBING2023/PaperSpine) - Motivation-driven academic writing system for Claude Code, Codex, OpenClaw, and Hermes CLI that learns from strong papers, builds evidence-aware central-argument blueprints, and rewrites manuscripts with revision matrices and LaTeX-safe audits (4.9K+ stars, MIT License, 2026)
 - [Claude Prism](https://github.com/delibae/claude-prism) - Offline-first scientific writing workspace powered by Claude, integrating LaTeX, Python, and 100+ scientific skills with local execution, Zotero integration, and privacy-focused design (2026)

 ---
@@ -302,9 +305,11 @@ validated: false
 - [Science-Star (USTC AI4Science, 2025)](https://github.com/ustc-ai4science/Science-Star) - Open-source platform for building, extending, and experimenting with scientific agents, providing modular agent construction tools and standardized evaluation pipelines for accelerating autonomous scientific discovery research (748+ stars, MIT License)
 - [SR-Scientist (ICLR 2026)](https://github.com/GAIR-NLP/SR-Scientist) - Scientific equation discovery with agentic AI, elevating LLMs from equation proposers to autonomous scientists that write code, analyze data, implement equations, and optimize based on experimental feedback; outperforms baselines by 6-35% across four science disciplines with robustness to noise and out-of-domain generalization (GAIR-NLP / SJTU, 49+ stars, Apache 2.0)
 - [ARA (Agent-Native Research Artifact)](https://github.com/ARA-Labs/Agent-Native-Research-Artifact) - Research ecosystem for rigorous and trustworthy AI scientists — a protocol and skill bundle that makes autonomous research verifiable, crystallized, and observable through structured, machine-executable research artifacts and five agent skills for research management, compilation, verification, visualization, and publication (ARA-Labs, 447+ stars, MIT License, 2026)
+- [XScientist](https://github.com/smileformylove/XScientist) - Local-first autonomous research system implementing a Git-like research protocol for long-running scientific discovery; explores competing explanations, executes experiments inside an isolation boundary, self-criticizes results, and exports the entire path as typed Agent-Native Research Artifacts (ARA) with exploration DAGs, claim-to-evidence anchors, content hashes, and re-execution hooks (126+ stars, Apache 2.0, arXiv 2026)
 - [Scholar Loop](https://github.com/renee-jia/scholar-loop) - Autonomous multi-agent AI scientist that mirrors a PhD workflow: literature review → grounded hypothesis → real ML experiments → self-critique → write-up; features a deterministic harness with frozen-metric scoring, edit allowlists, and a verified registry to make reward-hacking and hallucination impossible, plus 108 unit tests runnable without API keys or GPUs (461+ stars, MIT License, 2026)
 - [ResearchStudio (Microsoft)](https://github.com/microsoft/ResearchStudio) - AI co-author covering the entire research lifecycle — from an under-specified research direction to a published paper; includes ResearchStudio-Idea for evidence-grounded research ideation and ResearchStudio-Reel for turning finished papers into posters, narrated videos, blogs, and interactive reels; runs as skills on Claude Code and Codex (1.2K+ stars, MIT License, 2026)
 - [Principia](https://github.com/pzqpzq/Principia) - Principle-first scientific idea discovery framework that extracts reusable principles from public literature and private research materials, composes them into traceable Idea Cards with prior-art comparisons, and exports validation-ready research packs; emphasizes inspectable scientific objects, risk disclosure, and falsification paths (ICML 2026, 411+ stars, MIT License)
+- [Imbue Catalyst](https://github.com/imbue-ai/catalyst) - Semi-autonomous AI scientist for scientific theory discovery and verifiable goal solving, using adversarial review-refinement loops and evolution-inspired candidate populations; integrates with Claude Code, Gemini CLI, Antigravity, and Codex harnesses (Imbue, 31+ stars, AGPL-3.0, 2026)

 ### Evaluation & Benchmarking
 - [ScienceAgentBench (ICLR 2025)](https://github.com/OSU-NLP-Group/ScienceAgentBench) - 102 executable tasks from 44 peer-reviewed papers across 4 disciplines with containerized evaluation
@@ -328,14 +333,17 @@ validated: false
 ### Domain-Specific Research Agents
 - [Aletheia](https://arxiv.org/abs/2602.10177) - Google DeepMind's autonomous mathematics research agent powered by Gemini Deep Think, autonomously solving 4 open problems from 700 Erdős conjectures and generating complete research papers without human intervention (February 2026)
 - [AlphaProof Nexus (Google DeepMind, arXiv 2026)](https://github.com/google-deepmind/alphaproof-nexus-results) - LLM-driven formal proof search system that pairs large language models with Lean verification to solve open mathematics problems; autonomously resolved 9 of 353 Erdős problems and 44 of 492 OEIS conjectures, with proofs and natural-language prose released for combinatorics, optimization, graph theory, algebraic geometry, and quantum optics collaborations (282+ stars, Apache 2.0)
+- [Ten Proofs (OpenAI, 2026)](https://github.com/openai/ten-proofs) - Lean 4 formalizations of ten major advances in mathematics and theoretical computer science, including improved sphere-packing bounds, non-sofic groups, a counterexample to Connes's rigidity conjecture, and quantum parallel repetition; released with the OpenAI paper and reasoning walkthroughs (57+ stars, Apache 2.0)
 - [AlphaGeometry](https://github.com/google-deepmind/alphageometry) - DeepMind's Olympiad-level geometry theorem prover combining neural language model with symbolic deduction engine, AlphaGeometry2 solves 84% of IMO geometry problems (42/50) at gold-medalist level (Nature 2024)
 - [Goedel-Prover-V2](https://github.com/Goedel-LM/Goedel-Prover-V2) - Strongest open-source automated theorem prover in Lean 4, 8B model matches DeepSeek-Prover-V2-671B at 84.6% MiniF2F, 32B model achieves 90.4% with self-correction, using scaffolded data synthesis and verifier-guided proof refinement (Princeton, 2025)
 - [DeepSeek-Prover-V2](https://github.com/deepseek-ai/DeepSeek-Prover-V2) - DeepSeek's open-source large language model for formal theorem proving in Lean 4, integrating informal and formal mathematical reasoning through recursive subgoal decomposition and reinforcement learning powered by DeepSeek-V3, with open weights and ProverBench evaluation (2025)
 - [LeanDojo](https://github.com/lean-dojo/LeanDojo) - Open-source toolkit and benchmark for learning-based theorem proving in Lean, providing programmatic Lean interaction, a 98K+ theorem dataset extracted from 217 Lean projects, and ReProver—the first retrieval-augmented LLM-based theorem prover for Lean—with reproducible training pipelines underpinning much subsequent Lean prover research (Caltech & NVIDIA, NeurIPS 2023 Outstanding Paper, Datasets & Benchmarks)
 - [Lean Copilot](https://github.com/lean-dojo/LeanCopilot) - LLMs as copilots for theorem proving in Lean 4, exposing native tactics (`suggest_tactics`, `search_proof`, `select_premises`) that embed language model inference and premise retrieval directly inside the Lean proof environment, supporting local CTranslate2/CUDA inference as well as remote model APIs for interactive and automated proof search (Caltech & NVIDIA, NeurIPS 2024, 1.2K+ stars)
 - [MathCode](https://github.com/math-ai-org/mathcode) - Terminal AI coding assistant with a built-in math formalization engine that converts plain-language math problems into Lean 4 theorems and attempts formal proofs; bundles a local Lean toolchain and WebUI for interactive mathematical reasoning (math-ai-org, 582+ stars, 2026)
+- [TorchLean (lean-dojo, 2026)](https://github.com/lean-dojo/TorchLean) - First unified Lean 4 framework for neural-network specification, execution, and verification; tensor shapes are part of the types, models are executable Lean programs, and the same definitions can be used by training code, graph transformations, certificate checkers, and proofs with CPU/CUDA backends (123+ stars, MIT License)
 - [Get Physics Done (PSI)](https://github.com/psi-oss/get-physics-done) - First open-source agentic AI physicist turning research questions into structured workflows with rigorous verification and multi-step analytical work for long-horizon physics projects; integrates with Claude Code, Codex, Gemini CLI, and OpenCode (804+ stars, Apache 2.0, 2026)
 - [Foam-Agent (NeurIPS 2025)](https://github.com/csml-rpi/Foam-Agent) - End-to-end composable multi-agent framework for automating OpenFOAM-based CFD simulations from natural language prompts, managing meshing, case setup, execution, error correction, and post-processing; achieves 100% success rate on 110 FoamBench tasks with Claude Opus 4.6 through Architect-Input Writer-Runner-Reviewer agent collaboration with RAG-enhanced generation and MCP tool integration (RPI CSML, 242+ stars, MIT License)
+- [AI CFD Scientist (RPI CSML, arXiv 2026)](https://github.com/csml-rpi/AI-CFD-Scientist) - Open-ended AI scientist for computational fluid dynamics that spans literature-grounded ideation, OpenFOAM execution via Foam-Agent, vision-language physics verification of rendered flow fields, source-code modification for new physical models, and figure-grounded LaTeX manuscript writing within a single inspectable workflow (43+ stars, Python)
 - [Zephyrus (ICLR 2026)](https://github.com/Rose-STL-Lab/Zephyrus) - First agentic framework for weather science, pairing an LLM with ZephyrusWorld (a code-execution environment exposing WeatherBench 2 data, geolocation, forecasting, simulation, and climatology tools) and ZephyrusBench (2,230 Q&A pairs across 49 weather-science tasks); outperforms text-only baselines by up to 44.2 percentage points (UC San Diego Rose-STL-Lab, 99+ stars, MIT License, 2026)
 - [BioDiscoveryAgent](https://github.com/snap-stanford/BioDiscoveryAgent) - AI agent for biological discovery and research automation
 - [Biomni](https://github.com/snap-stanford/Biomni) - General-purpose biomedical AI agent integrating LLM reasoning with retrieval-augmented planning and code-based execution to autonomously execute diverse biomedical research tasks and generate testable hypotheses (Stanford SNAP, bioRxiv 2025)
@@ -394,6 +402,7 @@ validated: false
 - [Fourier Neural Operator](https://github.com/neuraloperator/neuraloperator) - Learning operators in Fourier space
 - [Poseidon](https://github.com/camlab-ethz/poseidon) - Efficient foundation models for PDEs with pretrained transformer-based neural operators and downstream task fine-tuning pipelines, HuggingFace integration for models and datasets (ETH Zurich CAMLab, arXiv 2024)
 - [GAOT (NeurIPS 2025)](https://github.com/camlab-ethz/GAOT) - Geometry Aware Operator Transformer serving as an efficient and accurate neural surrogate for PDEs on arbitrary domains, combining geometric priors with transformer architectures for scientific computing (ETH Zurich CAMLab, 92+ stars)
+- [TensorMesh (ETH Zurich CAMLab, arXiv 2026)](https://github.com/camlab-ethz/TensorMesh) - Fast, differentiable, JIT-free finite element library for PyTorch enabling GPU-native PDE solving with native autograd, tensorized assembly, and sparse linear algebra; part of the TensorGalerkin framework (218+ stars, Apache 2.0)
 - [PhiFlow](https://github.com/tum-pbs/PhiFlow) - Differentiable PDE solving framework for machine learning with built-in fluid simulation, supporting PyTorch/JAX/TensorFlow backends and enabling neural network training within physical simulations (TUM, MIT License)
 - [exponax](https://github.com/Ceyron/exponax) - Efficient differentiable n-dimensional PDE solvers built on JAX and Equinox, shipping 46+ built-in equations with Fourier spectral methods, exponential time differencing, and full auto-differentiation for physics-based deep learning workflows (MIT, 200+ stars, 2024)

@@ -638,6 +647,7 @@ validated: false
 - [SpikeInterface](https://github.com/SpikeInterface/spikeinterface) - Unified Python framework for extracellular electrophysiology, standardizing interfaces to 10+ ML-based spike sorting algorithms including Kilosort for reproducible neural spike sorting workflows (792+ stars, actively maintained)
 - [CaImAn (Flatiron Institute)](https://github.com/flatironinstitute/CaImAn) - Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization, using machine learning for automated neuron detection and activity inference in two-photon and one-photon calcium imaging data (723+ stars, actively maintained)
 - [TRIBE v2](https://github.com/facebookresearch/tribev2) - Meta FAIR's foundation model of vision, audition, and language for in-silico neuroscience, predicting fMRI brain responses to naturalistic multimodal stimuli (video, audio, text) through unified Transformer architecture mapped to the cortical surface (2026)
+- [Brain2Qwerty (Meta FAIR, Nature Neuroscience 2026)](https://github.com/facebookresearch/brain2qwerty) - Non-invasive decoding of typed sentences from MEG and EEG brain recordings using a convolutional encoder, transformer, and character-level language model; official code for the Nature Neuroscience paper and Meta blog post on brain-AI communication (Meta FAIR, 894+ stars, CC BY-NC 4.0, 2026)
 - [braindecode](https://github.com/braindecode/braindecode) - Deep learning software to decode EEG, ECG or MEG signals, providing standardized neural network models, preprocessing pipelines, and evaluation workflows for brain-computer interfaces and cognitive neuroscience research (1.2K+ stars, BSD 3-Clause, actively maintained)
 - [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)
@@ -657,6 +667,7 @@ validated: false
 - [PathChat (Nature Medicine 2024)](https://github.com/MahmoodLab/PathChat) - Multimodal generative AI assistant for computational pathology enabling interactive visual-language conversations over histopathology images for diagnostic reasoning, case discussion, and education, built on a Mistral-7B backbone with domain-specific fine-tuning (Mahmood Lab, Harvard Medical School, 1.2K+ stars)
 - [SlideChat (CVPR 2025)](https://github.com/uni-medical/SlideChat) - First large vision-language assistant for gigapixel whole-slide pathology image understanding, released with the SlideInstruction dataset and SlideBench benchmark (uni-medical, Apache 2.0, 2025)
 - [HEST (NeurIPS 2024)](https://github.com/mahmoodlab/HEST) - Dataset and benchmarking framework integrating histology and spatial transcriptomics, enabling multimodal analysis of whole-slide images with matched spatial gene expression for advancing computational pathology and tissue microenvironment research (Mahmood Lab, Harvard Medical School, 411+ stars)
+- [spmind (ICML 2026)](https://github.com/tomtommyyuan/spmind) - Autonomous AI agent for end-to-end spatial proteomics analysis, featuring SP-Bench for agentic multiplexed-imaging workflows (tomtommyyuan, 140+ stars, 2026)

 #### Medical AI & Clinical Applications
 - [Cellpose](https://github.com/MouseLand/cellpose) - Generalist deep learning algorithm for cell and nucleus segmentation across diverse image types, with human-in-the-loop training (2.0) and one-click image restoration (3.0), 70K+ training objects (Nature Methods 2021/2022/2025)
@@ -699,6 +710,7 @@ validated: false
 #### Materials Discovery
 - [GNoME](https://github.com/google-deepmind/materials_discovery) - DeepMind's graph neural network for materials exploration, discovering 2.2M new crystal structures (380K most stable) equivalent to 800 years of traditional research, with 520K+ materials dataset open-sourced (Nature 2023)
 - [FAIRChem (OMat24)](https://github.com/FAIR-Chem/fairchem) - Meta's comprehensive ML ecosystem for materials/chemistry with 118M+ DFT calculations, EquiformerV2 models achieving top Matbench Discovery performance
+- [Skala 1.1 (Microsoft Research, 2026)](https://github.com/microsoft/skala) - Neural network-based exchange-correlation functional for density functional theory (DFT) that surpasses state-of-the-art hybrid functionals in accuracy for main-group thermochemistry, kinetics, and non-covalent interactions at semi-local DFT cost; includes PySCF/GPU4PySCF/ASE bindings and C++/Fortran integrations (248+ stars, MIT License)
 - [All-atom Diffusion Transformers (ADiT)](https://github.com/facebookresearch/all-atom-diffusion-transformer) - Unified latent diffusion transformer that jointly generates periodic crystals and non-periodic molecules, scaling to 500M parameters with SOTA results on QM9, MP20, and GEOM-DRUGS (Meta FAIR, ICML 2025, 310+ stars)
 - [JARVIS](https://github.com/usnistgov/jarvis) - NIST's open-source platform for data-driven atomistic materials design, integrating DFT datasets (JARVIS-DFT), machine learning property prediction (JARVIS-ML), and a comprehensive leaderboard for benchmarking materials AI methods across the periodic table (384+ stars)
 - [NVIDIA ALCHEMI Toolkit](https://github.com/NVIDIA/nvalchemi-toolkit) - Developer toolkit for accelerating training and inference for AI in chemistry and material science, providing optimized GPU-accelerated workflows for molecular and materials machine learning (NVIDIA, 2026)
@@ -725,6 +737,7 @@ validated: false

 #### Lab Automation & Robotics
 - [PyLabRobot](https://github.com/PyLabRobot/pylabrobot) - Interactive and hardware-agnostic SDK for laboratory automation, enabling programmatic control of liquid handlers, plate readers, and other lab instruments across multiple vendors; foundational infrastructure for self-driving laboratories and AI-driven experimental execution (447+ stars)
+- [RoboChem-Flex](https://github.com/Noel-Research-Group/Robochem_Flex) - Low-cost, modular self-driving laboratory platform democratizing autonomous chemical experimentation with open control software, device CAD/PCB files, and example optimization campaigns (Noël Research Group, University of Amsterdam, Apache 2.0, 2026)

 ### 🌌 Physics & Astronomy

@@ -736,10 +749,12 @@ validated: false
 - [Neural ODEs](https://github.com/rtqichen/torchdiffeq) - Differential equations with neural networks
 - [Physics-Informed Neural Networks](https://github.com/maziarraissi/PINNs) - Physics-constrained ML
 - [EquiformerV2](https://github.com/atomicarchitects/equiformer_v2) - Improved equivariant Transformer for 3D atomic graphs (ICLR2024)
+- [EquiformerV3](https://github.com/atomicarchitects/equiformer_v3) - Scaling efficient, expressive, and general SE(3)-equivariant graph attention transformers for atomic systems and machine-learned interatomic potentials (MIT License, 2026)
 - [Equiformer](https://github.com/atomicarchitects/equiformer) - Equivariant graph attention Transformer (ICLR2023)
 - [TORAX](https://github.com/google-deepmind/torax) - Differentiable tokamak core transport simulator for fusion energy research, coupling PDE solvers with JAX auto-differentiation and neural-network surrogates for fast forward modelling, pulse-design, and trajectory optimization (Google DeepMind, Apache 2.0)
 - [DiffPhysDrone (Nature Machine Intelligence 2025)](https://github.com/HenryHuYu/DiffPhysDrone) - First real quadrotor robot trained end-to-end with differentiable physics for vision-based agile flight, bridging simulation-based learning and real-world deployment with physics-informed neural network controllers (558+ stars)
 - [Walrus (arXiv 2025)](https://github.com/PolymathicAI/walrus) - Cross-domain foundation model for continuum dynamics trained on 19 physical scenarios spanning 63 variables, featuring adaptive compute via stride modulation and patch jittering for long-run stability (Polymathic AI, 293+ stars, MIT License)
+- [GeoPT (ICML 2026)](https://github.com/Physics-Scaling/GeoPT) - Unified pre-trained model for general physics simulation via lifted geometric pre-training, augmenting static geometry with synthetic dynamics to enable dynamics-aware self-supervision without physics labels; improves industrial-fidelity benchmarks spanning fluid mechanics and solid mechanics while reducing labeled data requirements by 20–60% (Physics-Scaling, 224+ stars)

 #### Astronomy & Astrophysics
 - [AstroCLIP](https://github.com/PolymathicAI/AstroCLIP) - Cross-modal self-supervised foundation model for galaxies by Polymathic AI, jointly embedding multi-band galaxy imaging and optical spectra into a shared latent space to enable zero/few-shot redshift estimation, galaxy property prediction, morphology classification, and cross-modal similarity search (MNRAS Letters 2024)
@@ -784,6 +799,7 @@ validated: false
 - [SkySensePlusPlus](https://github.com/kang-wu/SkySensePlusPlus) - Semantic-enhanced multi-modal remote sensing foundation model for Earth observation (Nature Machine Intelligence 2025), enabling universal interpretation across diverse satellite imagery modalities with open-source weights and benchmarks
 - [TESSERA (CVPR 2026)](https://github.com/ucam-eo/tessera) - University of Cambridge's foundation model for time-series satellite imagery, enabling efficient extraction of temporal patterns from Earth observation for land classification, canopy height prediction, and other remote sensing tasks
 - [TerraMind (IBM & ESA, 2025)](https://github.com/IBM/terramind) - First any-to-any generative foundation model for Earth Observation, enabling unified multimodal understanding and generation across diverse satellite sensors and geospatial tasks through a single architecture (258+ stars)
+- [GeoAgent (opengeos, 2026)](https://github.com/opengeos/GeoAgent) - Shared multimodal AI agent layer for geospatial Python packages (leafmap, geoai, geemap, STAC, NASA Earthdata) and QGIS, exposing geospatial tools to LLMs with structured metadata, confirmation hooks, and support for OpenAI, Anthropic, Google Gemini, Ollama, and more; includes the OpenGeoAgent QGIS plugin (456+ stars, MIT License)
 - [Awesome Remote Sensing Foundation Models](https://github.com/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models) - Curated collection of papers, datasets, benchmarks, code, and pre-trained weights for Remote Sensing Foundation Models (RSFMs), tracking the rapidly evolving landscape of vision, vision-language, generative, and agent-based geospatial AI (1.9K+ stars, 2024-2026)

 ### 🌾 Agriculture & Ecology
@@ -793,6 +809,8 @@ validated: false
 - [AgML](https://github.com/Project-AgML/AgML) - Agricultural machine learning platform
 - [FarmVibes.AI](https://github.com/microsoft/farmvibes-ai) - Multi-modal geospatial ML platform for agriculture and sustainability, fusing satellite imagery (RGB, SAR, multispectral), drone imagery, weather data, and sensor data for crop identification, carbon footprint estimation, and microclimate prediction (Microsoft Research, MIT License)
 - [PlantCV](https://github.com/danforthcenter/plantcv) - Open-source image analysis toolkit for high-throughput plant phenotyping, extracting morphological, color, and texture traits from RGB, hyperspectral, and thermal imagery with modular Python workflows for crop improvement, stress detection, and plant biology research (Donald Danforth Plant Science Center, 795+ stars, MPL-2.0)
+- [Virdis](https://github.com/Thanas-R/Virdis) - Satellite-powered agricultural and land analytics platform combining Sentinel-2 imagery, Google Earth Engine processing, real-time weather data, soil science databases, and AI-driven crop planning into a unified web dashboard (145+ stars, AGPL-3.0, 2026)
+- [Agribound](https://github.com/montimaj/agribound) - AI-powered field boundary delineation toolkit combining satellite foundation models, embeddings, and global training data for accurate agricultural parcel/field boundary mapping, with Google Earth Engine integration and PyPI distribution (84+ stars, Apache 2.0, 2026)

 #### Ecological Modeling
 - [BioSimulators](https://github.com/biosimulators/Biosimulators) - Biological simulation tools
@@ -812,6 +830,25 @@ validated: false

 ---

+## 🏗 Engineering & Built Environment
+
+### Mechanical, Aerospace & Industrial Engineering
+- [Noether (Emmi AI)](https://github.com/Emmi-AI/noether) - Open software framework for Engineering AI built on transformer building blocks, enabling teams to build, train, and operate industrial simulation models across engineering verticals; includes ready-to-use recipes for CFD (AB-UPT on DrivAerML), external aerodynamics, and heat transfer (234+ stars, ENPL non-commercial license, 2026)
+
+### Structural & Civil Engineering
+- [StructureClaw](https://github.com/structureclaw/structureclaw) - AI-assisted structural engineering workspace for AEC workflows: natural language to structural model, analysis, code-check, and report (171+ stars, MIT License, 2026)
+
+### Construction & Built Environment Management
+- [OpenConstructionERP](https://github.com/datadrivenconstruction/OpenConstructionERP) - Open-source construction ERP with AI-powered cost matching, BOQ generation, and PDF/CAD/BIM takeoff; 42 regional catalogues, 21 languages, 71 modules (DataDrivenConstruction, 717+ stars, AGPL-3.0, 2026)
+
+### Architectural Design & BIM
+- [Aedifex](https://github.com/TangSY/aedifex) - Open-source 3D architectural editor with an AI design assistant; build floor plans with walls, doors, windows, and furniture using natural language, with real-time WebGPU-powered previews (TangSY, 59+ stars, MIT License, 2026)
+
+### Electrical & Electronics Engineering
+- [kicad-happy](https://github.com/aklofas/kicad-happy) - AI coding agent skills for KiCad electronics design that turn Claude Code, Codex, Gemini CLI, and other coding agents into full electronics design assistants; parses schematics and PCB layouts, builds power trees, audits connectors/ESD protection, validates passive networks, runs SPICE simulation, sources components from major distributors, and prepares boards for fabrication (aklofas, 974+ stars, MIT License, 2026)
+
+---
+
 ## 🤖 Foundation Models for Science

 ### General Science Models
@@ -982,6 +1019,7 @@ This project builds upon and complements several excellent resources:

 ### 📊 Paper & Research Collections
 - [Scientific LLM Papers](https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models) - 260+ scientific language models
+- [Awesome Scientific LLM Benchmarks](https://github.com/subinium/Awesome-Scientific-LLM-Benchmarks) - Curated, accuracy-first collection of benchmarks for evaluating LLMs on scientific reasoning and discovery across mathematics, physics, chemistry, materials science, biology, and agentic science (subinium, 29+ stars, MIT License, 2026)
 - [LLM4SR Repository](https://github.com/du-nlp-lab/LLM4SR) - LLM for scientific research survey materials
 - [PINNs Paper Collection](https://github.com/idrl-lab/PINNpapers) - Physics-informed neural networks research
 - [SciML Papers](https://sciml.ai/papers/) - Scientific computing and machine learning papers
@@ -0,0 +1,61 @@
+---
+title: "Ai4Bio Schema Check"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/ai4bio-schema-check.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+name: AI4Bio Schema Check
+
+on:
+  pull_request:
+    paths:
+      - data/resources.yml
+      - data/enrichment.yml
+      - 'data/enrichment.*.yml'
+      - data/vocabulary.yml
+      - docs/data/resource.schema.json
+      - scripts/enrichment_fragments.py
+      - scripts/validate_resources.py
+      - scripts/build_resources_v2.py
+  push:
+    branches: [main]
+    paths:
+      - data/resources.yml
+      - data/enrichment.yml
+      - 'data/enrichment.*.yml'
+      - data/vocabulary.yml
+      - docs/data/resource.schema.json
+      - scripts/enrichment_fragments.py
+      - scripts/validate_resources.py
+      - scripts/build_resources_v2.py
+
+permissions:
+  contents: read
+
+jobs:
+  validate:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v3
+      - name: Validate schema and enrichment
+        run: uv run --with pyyaml python scripts/validate_resources.py
+      - name: Build enriched artifacts
+        run: uv run --with pyyaml python scripts/build_resources_v2.py
+      - name: Verify enriched artifacts are committed
+        run: |
+          if git diff --quiet; then
+            echo "AI4Bio artifacts are in sync."
+            exit 0
+          fi
+          echo "Generated AI4Bio artifacts are out of date. Run:"
+          echo "  uv run --with pyyaml python scripts/build_resources_v2.py"
+          git status --short
+          exit 1
@@ -0,0 +1,43 @@
+---
+title: "Landscape Ui Check"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/landscape-ui-check.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+name: Landscape UI Check
+
+on:
+  pull_request:
+    paths:
+      - docs/landscape.html
+      - docs/landscape.css
+      - docs/landscape.js
+      - .github/workflows/landscape-ui-check.yml
+  push:
+    branches: [main]
+    paths:
+      - docs/landscape.html
+      - docs/landscape.css
+      - docs/landscape.js
+      - .github/workflows/landscape-ui-check.yml
+
+permissions:
+  contents: read
+
+jobs:
+  ui-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-node@v4
+        with:
+          node-version: 'lts/*'
+      - name: Check landscape JavaScript syntax
+        run: node --check docs/landscape.js
@@ -2,9 +2,9 @@
 title: "Sync Resources"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.github/workflows/sync_resources.yml
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/sync_resources.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -20,6 +20,8 @@ on:
    paths:
      - README.md
      - data/resources.yml
+      - data/enrichment.yml
+      - 'data/enrichment.*.yml'
      - scripts/*.py
      - scripts/**/*.py

@@ -55,7 +57,10 @@ jobs:

      - name: Sync From README
        if: contains(steps.changes.outputs.changed, 'README.md')
-        run: uv run python scripts/sync_resources_from_readme.py
+        run: uv run --with pyyaml python scripts/sync_resources_from_readme.py
+
+      - name: Validate Resource Schema
+        run: uv run --with pyyaml python scripts/validate_resources.py

      - name: Build Artifacts
        run: uv run --with pyyaml python scripts/build_resources.py
@@ -2,9 +2,9 @@
 title: "Awesome Computational Biology [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/478be843/README.md
-upstream_sha: 478be843
-imported_at: 2026-07-17
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/README.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -70,6 +70,7 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
  - [Machine Learning Tasks and Models](#machine-learning-tasks-and-models)
    - [Drug Discovery](#drug-discovery)
      - [Drug Response Prediction](#drug-response-prediction)
+      - [Drug Perturbation](#drug-perturbation)
      - [Drug Repurposing](#drug-repurposing)
      - [Drug Target Interaction](#drug-target-interaction)
      - [Compound-Protein Interaction](#compound-protein-interaction)
@@ -340,10 +341,15 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [RECOVER](https://github.com/RECOVERcoalition/Recover) — Machine learning framework for predicting synergistic drug combination responses across cell lines.
 - [TGSA](https://github.com/violet-sto/TGSA) — Tumor gene set and attention-based model leveraging biological pathway knowledge for drug response prediction.
 - [HiDRA](https://github.com/bsml320/HiDRA) — Hierarchical network model incorporating gene and pathway-level information for cancer drug response prediction.
- [PRNet](https://github.com/Perturbation-Response-Prediction/PRnet) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.
+- [DRUML](https://github.com/CutillasLab/DRUMLR) — Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs from proteomics and RNA-seq data.
+
+#### Drug Perturbation
+
+- [CellOT](https://github.com/bunnech/cellot) — Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations.
+- [CMonge](https://github.com/AI4SCR/conditional-monge-gap) — Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and doses.
 - [chemCPA](https://github.com/theislab/chemCPA) — Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations.
 - [cycleCDR](https://github.com/hliulab/cycleCDR) — Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations.
- [DRUML](https://github.com/CutillasLab/DRUMLR) — Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs from proteomics and RNA-seq data.
+- [PRNet](https://github.com/Perturbation-Response-Prediction/PRnet) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.

 #### Drug Repurposing

@@ -2,9 +2,9 @@
 title: "Cspell"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/cspell.json
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/cspell.json
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -157,7 +157,21 @@ validated: false
    "Pacc",
    "multiomics",
    "Pathomic",
-    "PLIP"
+    "PLIP",
+    "Omni",
+    "Bento",
+    "FFPE",
+    "Xenium",
+    "Zyme",
+    "Neur",
+    "Imageomics",
+    "AESTETIK",
+    "CellOT",
+    "CMonge",
+    "bowang",
+    "ctheodoris",
+    "OpenAI",
+    "GPT"
  ],
  "ignorePaths": [
    "node_modules/**"
@@ -0,0 +1,56 @@
+---
+title: "Provenance-backed single-cell and biomedical benchmark enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.benchmark-v2.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed single-cell and biomedical benchmark enrichment batch.
+resources:
+  scmulan:
+    entities: [cell, gene]
+    methods: [language-model, transformer]
+    modalities: [epigenomics, multi-omics, proteomics, single-cell-rna-seq, transcriptomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    github: https://github.com/SuperBianC/scMulan
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/SuperBianC/scMulan
+
+  proteingym:
+    entities: [protein]
+    modalities: [protein-sequence]
+    tasks: [regression]
+    github: https://github.com/OATML-Markslab/ProteinGym
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/OATML-Markslab/ProteinGym
+
+  lincs_l1000:
+    entities: [cell, compound, gene]
+    modalities: [transcriptomics]
+    tasks: [perturbation-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data
+
+  prism:
+    entities: [cell, drug]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://depmap.org/portal/prism/
+
+  pharmgkb:
+    entities: [drug, gene, phenotype, variant]
+    modalities: [clinical, genomics]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.pharmgkb.org/
@@ -0,0 +1,91 @@
+---
+title: "Provenance-backed database and API enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.database-api-v1.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed database and API enrichment batch.
+resources:
+  chembl_web_services:
+    entities: [molecule, protein]
+    modalities: [chemical-structure]
+    documentation: https://www.ebi.ac.uk/chembl/api/data/docs
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.ebi.ac.uk/chembl/api/data/docs
+
+  clinicaltrials_gov_api:
+    entities: [disease, drug]
+    modalities: [clinical]
+    documentation: https://clinicaltrials.gov/data-api/api
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://clinicaltrials.gov/data-api/api
+
+  ensembl_rest_api:
+    entities: [gene, genome, transcript, variant]
+    modalities: [genomics]
+    documentation: https://rest.ensembl.org/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://rest.ensembl.org/
+
+  kegg_rest_api:
+    entities: [compound, gene, pathway]
+    documentation: https://www.kegg.jp/kegg/rest/keggapi.html
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.kegg.jp/kegg/rest/keggapi.html
+
+  ncbi_e_utilities:
+    entities: [gene, genome, protein, transcript, variant]
+    modalities: [genomics, transcriptomics]
+    documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.ncbi.nlm.nih.gov/books/NBK25501/
+
+  open_targets_platform_api:
+    entities: [disease, drug, gene, variant]
+    modalities: [genomics, knowledge-graph]
+    documentation: https://platform.opentargets.org/api
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://platform.opentargets.org/api
+
+  pubmed_e_utilities_esearch_efetch:
+    documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.ncbi.nlm.nih.gov/books/NBK25501/
+
+  uniprot_rest_api:
+    entities: [protein]
+    modalities: [protein-sequence, proteomics]
+    documentation: https://www.uniprot.org/help/api
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.uniprot.org/help/api
+
+  drugbank:
+    entities: [disease, drug, protein]
+    modalities: [chemical-structure]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://go.drugbank.com/
+
+  string:
+    entities: [protein]
+    modalities: [knowledge-graph, proteomics]
+    documentation: https://string-db.org/help/api/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://string-db.org/
+      - https://string-db.org/help/api/
@@ -0,0 +1,73 @@
+---
+title: "Provenance-backed foundation model enrichment batch 2."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.foundation-models-v2.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed foundation model enrichment batch 2.
+resources:
+  nicheformer:
+    entities: [cell, gene, tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, spatial-transcriptomics, transcriptomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    year: 2024
+    github: https://github.com/theislab/nicheformer
+    paper: https://doi.org/10.1101/2024.04.15.589472
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/theislab/nicheformer
+      - https://doi.org/10.1101/2024.04.15.589472
+
+  genept:
+    entities: [cell, gene]
+    methods: [language-model]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [batch-correction, classification, representation-learning]
+    year: 2023
+    github: https://github.com/yiqunchen/GenePT
+    paper: https://www.biorxiv.org/content/10.1101/2023.10.16.562533v2
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/yiqunchen/GenePT
+      - https://www.biorxiv.org/content/10.1101/2023.10.16.562533v2
+
+  scgpt_spatial:
+    entities: [cell, gene, tissue]
+    methods: [generative-model, self-supervised-learning, transformer]
+    modalities: [multi-omics, single-cell-rna-seq, spatial-transcriptomics]
+    tasks: [foundation-model-pretraining, imputation, representation-learning]
+    year: 2025
+    github: https://github.com/bowang-lab/scGPT-spatial
+    paper: https://www.biorxiv.org/content/10.1101/2025.02.05.636714v1
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/bowang-lab/scGPT-spatial
+      - https://www.biorxiv.org/content/10.1101/2025.02.05.636714v1
+
+  scprint:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks:
+      - batch-correction
+      - cell-type-annotation
+      - foundation-model-pretraining
+      - gene-regulatory-network-inference
+      - imputation
+      - representation-learning
+    year: 2025
+    github: https://github.com/cantinilab/scPRINT
+    documentation: https://www.jkobject.com/scPRINT/
+    paper: https://www.nature.com/articles/s41467-025-58699-1
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/cantinilab/scPRINT
+      - https://www.nature.com/articles/s41467-025-58699-1
@@ -0,0 +1,62 @@
+---
+title: "Provenance-backed molecular model and benchmark enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.molecular-v2.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed molecular model and benchmark enrichment batch.
+resources:
+  chemberta_2:
+    entities: [molecule]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [chemical-structure]
+    tasks: [representation-learning]
+    github: https://github.com/seyonechithrananda/bert-loves-chemistry
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/seyonechithrananda/bert-loves-chemistry
+
+  molformer:
+    entities: [molecule]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [chemical-structure]
+    tasks: [representation-learning]
+    github: https://github.com/IBM/molformer
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/IBM/molformer
+
+  grover:
+    entities: [molecule]
+    methods: [graph-neural-network, self-supervised-learning, transformer]
+    modalities: [chemical-structure]
+    tasks: [representation-learning]
+    github: https://github.com/tencent-ailab/grover
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/tencent-ailab/grover
+
+  moleculenet:
+    entities: [molecule]
+    modalities: [chemical-structure]
+    tasks: [classification, regression]
+    github: https://github.com/deepchem/moleculenet
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/deepchem/moleculenet
+
+  guacamol:
+    entities: [molecule]
+    modalities: [chemical-structure]
+    tasks: [molecular-generation]
+    github: https://github.com/BenevolentAI/guacamol
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/BenevolentAI/guacamol
@@ -0,0 +1,92 @@
+---
+title: "Provenance-backed drug-response and pharmacogenomics enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.pharmacogenomics-v1.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed drug-response and pharmacogenomics enrichment batch.
+resources:
+  beat_aml:
+    entities: [cell, disease, drug, gene]
+    modalities: [genomics]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://biodev.github.io/BeatAML2/
+
+  cancer_therapeutics_response_portal_ctrp:
+    entities: [cell, drug]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://portals.broadinstitute.org/ctrp/
+
+  bindingdb_curated_sets:
+    entities: [molecule, protein]
+    modalities: [chemical-structure]
+    tasks: [drug-target-interaction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.bindingdb.org/
+
+  bace:
+    entities: [molecule, protein]
+    modalities: [chemical-structure]
+    tasks: [classification, regression]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.kaggle.com/datasets/gokturkkoch/bace
+
+  clintox:
+    entities: [drug]
+    modalities: [clinical]
+    tasks: [classification]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://tdcommons.ai/single_pred_tasks/tox/#clintox
+
+  sider_side_effect_resource:
+    entities: [drug, phenotype]
+    modalities: [clinical]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - http://sideeffects.embl.de/
+
+  pk_db:
+    entities: [drug]
+    modalities: [clinical]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://pk-db.com/
+
+  scperturb:
+    entities: [cell, drug, gene]
+    modalities: [single-cell-rna-seq]
+    tasks: [perturbation-prediction]
+    github: https://github.com/sanderlab/scPerturb
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/sanderlab/scPerturb
+
+  genomics_of_drug_sensitivity_in_cancer_gdsc:
+    entities: [cell, drug, gene]
+    modalities: [genomics]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.cancerrxgene.org/
+
+  cellminer_cross_database_cellminercdb:
+    entities: [cell, drug, gene]
+    modalities: [genomics]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://discover.nci.nih.gov/cellminercdb/
@@ -0,0 +1,62 @@
+---
+title: "Provenance-backed protein and drug-discovery enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.protein-drug-v1.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed protein and drug-discovery enrichment batch.
+resources:
+  esmfold:
+    entities: [protein]
+    methods: [language-model, transformer]
+    modalities: [molecular-structure, protein-sequence]
+    tasks: [representation-learning, structure-prediction]
+    year: 2023
+    github: https://github.com/facebookresearch/esm
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/facebookresearch/esm
+
+  proteinmpnn:
+    entities: [protein]
+    methods: [graph-neural-network, message-passing-neural-network]
+    modalities: [molecular-structure, protein-sequence]
+    tasks: [protein-sequence-design]
+    year: 2022
+    github: https://github.com/dauparas/ProteinMPNN
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/dauparas/ProteinMPNN
+
+  diffdock:
+    entities: [molecule, protein]
+    methods: [diffusion, geometric-deep-learning]
+    modalities: [molecular-structure]
+    tasks: [docking]
+    year: 2023
+    github: https://github.com/gcorso/DiffDock
+    paper: https://openreview.net/forum?id=kKF8_K-mBbS
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/gcorso/DiffDock
+      - https://openreview.net/forum?id=kKF8_K-mBbS
+
+  uni_mol:
+    entities: [molecule, protein]
+    methods: [self-supervised-learning, transformer]
+    modalities: [chemical-structure, molecular-structure]
+    tasks: [docking, representation-learning]
+    year: 2023
+    github: https://github.com/deepmodeling/Uni-Mol
+    paper: https://openreview.net/forum?id=6K2RM6wVqKu
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/deepmodeling/Uni-Mol
+      - https://openreview.net/forum?id=6K2RM6wVqKu
@@ -0,0 +1,64 @@
+---
+title: "Provenance-backed protein model enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.protein-v2.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed protein model enrichment batch.
+resources:
+  esm3:
+    entities: [protein]
+    methods: [generative-model, language-model, transformer]
+    modalities: [molecular-structure, protein-sequence]
+    tasks: [protein-sequence-design, representation-learning]
+    github: https://github.com/evolutionaryscale/esm
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/evolutionaryscale/esm
+
+  evolutionary_scale_modeling_esm:
+    entities: [protein]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [protein-sequence]
+    tasks: [representation-learning]
+    github: https://github.com/facebookresearch/esm
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/facebookresearch/esm
+
+  prottrans:
+    entities: [protein]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [protein-sequence]
+    tasks: [representation-learning]
+    github: https://github.com/agemagician/ProtTrans
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/agemagician/ProtTrans
+
+  progen2:
+    entities: [protein]
+    methods: [generative-model, language-model, transformer]
+    modalities: [protein-sequence]
+    tasks: [protein-sequence-design, representation-learning]
+    github: https://github.com/salesforce/progen
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/salesforce/progen
+
+  alphafold3:
+    entities: [molecule, protein, protein-complex]
+    methods: [diffusion]
+    modalities: [molecular-structure, protein-sequence]
+    tasks: [structure-prediction]
+    github: https://github.com/google-deepmind/alphafold3
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/google-deepmind/alphafold3
@@ -0,0 +1,110 @@
+---
+title: "Provenance-backed spatial transcriptomics and imaging enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.spatial-imaging-v1.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed spatial transcriptomics and imaging enrichment batch.
+resources:
+  aestetik:
+    entities: [cell, gene, tissue]
+    methods: [autoencoder]
+    modalities: [histopathology, spatial-transcriptomics]
+    tasks: [representation-learning]
+    github: https://github.com/ratschlab/aestetik
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/ratschlab/aestetik
+
+  conch:
+    entities: [tissue]
+    methods: [contrastive-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [foundation-model-pretraining, representation-learning]
+    github: https://github.com/mahmoodlab/CONCH
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/mahmoodlab/CONCH
+
+  deepspot:
+    entities: [gene, tissue]
+    modalities: [histopathology, spatial-transcriptomics]
+    tasks: [regression]
+    github: https://github.com/ratschlab/DeepSpot
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/ratschlab/DeepSpot
+
+  deepspot_m:
+    entities: [gene, tissue]
+    modalities: [histopathology, spatial-transcriptomics, transcriptomics]
+    tasks: [foundation-model-pretraining, regression]
+    github: https://github.com/ratschlab/DeepSpotM
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/ratschlab/DeepSpotM
+
+  deepspot2cell:
+    entities: [cell, gene, tissue]
+    modalities: [histopathology, spatial-transcriptomics]
+    tasks: [regression]
+    github: https://github.com/ratschlab/DeepSpot2Cell
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/ratschlab/DeepSpot2Cell
+
+  gigapath:
+    entities: [tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [foundation-model-pretraining, representation-learning]
+    github: https://github.com/prov-gigapath/prov-gigapath
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/prov-gigapath/prov-gigapath
+
+  phikon:
+    entities: [tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [foundation-model-pretraining, representation-learning]
+    documentation: https://huggingface.co/owkin/phikon
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://huggingface.co/owkin/phikon
+
+  plip:
+    entities: [tissue]
+    methods: [contrastive-learning]
+    modalities: [histopathology, imaging]
+    tasks: [classification, representation-learning]
+    github: https://github.com/PathologyFoundation/plip
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/PathologyFoundation/plip
+
+  uni:
+    entities: [tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [foundation-model-pretraining, representation-learning]
+    github: https://github.com/mahmoodlab/UNI
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/mahmoodlab/UNI
+
+  hest_xenium_virtual_spatial_transcriptomics:
+    entities: [cell, gene, tissue]
+    modalities: [histopathology, spatial-transcriptomics, transcriptomics]
+    tasks: [regression]
+    documentation: https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics
@@ -0,0 +1,128 @@
+---
+title: "AI4Bio landscape enrichment overlay"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# AI4Bio landscape enrichment overlay
+#
+# README.md remains the canonical source for resource membership and basic fields.
+# Add richer, independently curated metadata here, keyed by the stable resource id.
+# scripts/build_resources.py merges these fields into generated JSON/CSV artifacts.
+#
+# Enrichment values should be source-verifiable. Controlled vocabulary fields are
+# validated against data/vocabulary.yml.
+
+resources:
+  scgpt:
+    entities: [cell, gene]
+    methods: [generative-model, self-supervised-learning, transformer]
+    modalities: [multi-omics, single-cell-rna-seq, transcriptomics]
+    tasks:
+      - cell-type-annotation
+      - foundation-model-pretraining
+      - gene-regulatory-network-inference
+      - perturbation-prediction
+      - representation-learning
+    year: 2024
+    github: https://github.com/bowang-lab/scGPT
+    documentation: https://scgpt.readthedocs.io/en/latest/
+    paper: https://www.nature.com/articles/s41592-024-02201-0
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/bowang-lab/scGPT
+      - https://www.nature.com/articles/s41592-024-02201-0
+
+  geneformer:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks:
+      - classification
+      - foundation-model-pretraining
+      - perturbation-prediction
+      - representation-learning
+    year: 2023
+    documentation: https://geneformer.readthedocs.io/
+    paper: https://www.nature.com/articles/s41586-023-06139-9
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://huggingface.co/ctheodoris/Geneformer
+      - https://www.nature.com/articles/s41586-023-06139-9
+
+  scfoundation:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks:
+      - cell-type-annotation
+      - drug-response-prediction
+      - foundation-model-pretraining
+      - perturbation-prediction
+      - representation-learning
+    year: 2024
+    github: https://github.com/biomap-research/scFoundation
+    paper: https://www.nature.com/articles/s41592-024-02305-7
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/biomap-research/scFoundation
+      - https://www.nature.com/articles/s41592-024-02305-7
+
+  genecompass:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    year: 2024
+    github: https://github.com/xCompass-AI/GeneCompass
+    paper: https://www.nature.com/articles/s41422-024-01034-y
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/xCompass-AI/GeneCompass
+      - https://www.nature.com/articles/s41422-024-01034-y
+
+  uce:
+    entities: [cell]
+    methods: [self-supervised-learning]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    year: 2026
+    github: https://github.com/snap-stanford/UCE
+    paper: https://www.nature.com/articles/s41586-026-10689-z
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/snap-stanford/UCE
+      - https://www.nature.com/articles/s41586-026-10689-z
+
+  cellplm:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    year: 2023
+    github: https://github.com/OmicsML/CellPLM
+    paper: https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/OmicsML/CellPLM
+      - https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1
+
+  scbert:
+    entities: [cell, gene]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [cell-type-annotation, classification, foundation-model-pretraining]
+    year: 2022
+    github: https://github.com/TencentAILabHealthcare/scBERT
+    paper: https://www.nature.com/articles/s42256-022-00534-z
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/TencentAILabHealthcare/scBERT
+      - https://www.nature.com/articles/s42256-022-00534-z
@@ -2,9 +2,9 @@
 title: "Awesome Computational Biology - machine-readable resource list"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/478be843/data/resources.yml
-upstream_sha: 478be843
-imported_at: 2026-07-17
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/resources.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -1678,6 +1678,17 @@ resources:
    organism: []
    api: false

+  - id: cellot
+    name: "CellOT"
+    type: model
+    url: https://github.com/bunnech/cellot
+    description: "Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations."
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
+    modalities: [Small Molecule]
+    organism: []
+    api: false
+
  - id: cellplm
    name: "CellPLM"
    type: model
@@ -1727,8 +1738,8 @@ resources:
    type: model
    url: https://github.com/theislab/chemCPA
    description: "Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations."
-    tags: [drug-discovery, drug-response-prediction]
-    tasks: [Drug Discovery, Drug Response Prediction]
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
    modalities: [Small Molecule]
    organism: []
    api: false
@@ -1755,6 +1766,17 @@ resources:
    organism: []
    api: false

+  - id: cmonge
+    name: "CMonge"
+    type: model
+    url: https://github.com/AI4SCR/conditional-monge-gap
+    description: "Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and doses."
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
+    modalities: [Small Molecule]
+    organism: []
+    api: false
+
  - id: concerto
    name: "Concerto"
    type: model
@@ -1782,8 +1804,8 @@ resources:
    type: model
    url: https://github.com/hliulab/cycleCDR
    description: "Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations."
-    tags: [drug-discovery, drug-response-prediction]
-    tasks: [Drug Discovery, Drug Response Prediction]
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
    modalities: [Small Molecule]
    organism: []
    api: false
@@ -2475,8 +2497,8 @@ resources:
    type: model
    url: https://github.com/Perturbation-Response-Prediction/PRnet
    description: "Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery."
-    tags: [drug-discovery, drug-response-prediction]
-    tasks: [Drug Discovery, Drug Response Prediction]
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
    modalities: [Small Molecule]
    organism: []
    api: false
@@ -0,0 +1,105 @@
+---
+title: "Canonical vocabulary for new AI4Bio enrichment metadata."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/vocabulary.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Canonical vocabulary for new AI4Bio enrichment metadata.
+#
+# These values are enforced only for fields explicitly added through
+# data/enrichment.yml. README-derived legacy values remain backward compatible.
+# Canonical terms use lowercase kebab-case.
+
+version: 1
+
+controlled_fields:
+  entities:
+    - cell
+    - compound
+    - disease
+    - drug
+    - gene
+    - genome
+    - molecule
+    - organism
+    - pathway
+    - phenotype
+    - protein
+    - protein-complex
+    - regulatory-element
+    - tissue
+    - transcript
+    - variant
+
+  methods:
+    - autoencoder
+    - contrastive-learning
+    - convolutional-neural-network
+    - diffusion
+    - generative-model
+    - geometric-deep-learning
+    - graph-neural-network
+    - knowledge-graph
+    - language-model
+    - message-passing-neural-network
+    - multi-agent-system
+    - optimal-transport
+    - recurrent-neural-network
+    - reinforcement-learning
+    - retrieval-augmented-generation
+    - self-supervised-learning
+    - state-space-model
+    - supervised-learning
+    - transformer
+    - unsupervised-learning
+    - variational-autoencoder
+
+  modalities:
+    - cell-painting
+    - chemical-structure
+    - clinical
+    - dna-sequence
+    - electronic-health-record
+    - epigenomics
+    - genomics
+    - histopathology
+    - imaging
+    - knowledge-graph
+    - metabolomics
+    - molecular-structure
+    - multi-omics
+    - protein-sequence
+    - proteomics
+    - rna-sequence
+    - single-cell-rna-seq
+    - spatial-transcriptomics
+    - transcriptomics
+
+  tasks:
+    - batch-correction
+    - cell-type-annotation
+    - classification
+    - dimensionality-reduction
+    - docking
+    - drug-response-prediction
+    - drug-target-interaction
+    - foundation-model-pretraining
+    - gene-regulatory-network-inference
+    - imputation
+    - link-prediction
+    - molecular-generation
+    - perturbation-prediction
+    - protein-function-prediction
+    - protein-sequence-design
+    - regression
+    - representation-learning
+    - structure-prediction
+    - trajectory-inference
+    - virtual-screening
@@ -0,0 +1,73 @@
+---
+title: "AI4Bio Landscape Database"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/AI4BIO_LANDSCAPE.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# AI4Bio Landscape Database
+
+The landscape view treats the existing computational biology registry as a multidimensional database rather than a single hierarchical list.
+
+## Design goals
+
+- Keep the current curated resource records and generation pipeline intact.
+- Expose orthogonal facets so one resource can be explored by resource type, biological/ML task, data modality, organism, and domain tag.
+- Make the landscape useful without introducing a server or build-time dependency.
+- Keep the data model extensible for richer AI4Bio metadata over time.
+
+## Current facet model
+
+The landscape UI derives the following dimensions from `docs/data/resources.json`:
+
+| Dimension | Source field | Example values |
+|---|---|---|
+| Resource type | `type` | `database`, `benchmark`, `model`, `toolkit`, `api` |
+| Task | `tasks` | `drug-response-prediction`, `cell-type-annotation`, `molecular-generation` |
+| Modality | `modalities` | `transcriptomics`, `spatial-transcriptomics`, `protein-sequence` |
+| Organism | `organism` | `human`, `mouse`, `multi-species` |
+| Domain/tag | `tags` | `drug-discovery`, `single-cell`, `foundation-model` |
+
+These are deliberately treated as separate axes. A model can therefore be, for example, a `model` that performs `perturbation-prediction` on `single-cell-rna-seq` data for `human` and carry tags such as `drug-discovery` and `foundation-model`.
+
+## Recommended schema evolution
+
+The current schema is compatible with a richer landscape database. New fields should be added incrementally and only when they can be curated consistently.
+
+Suggested fields:
+
+| Field | Type | Purpose |
+|---|---|---|
+| `entities` | array of strings | Biological entities such as `gene`, `protein`, `compound`, `cell`, `disease` |
+| `methods` | array of strings | Method families such as `transformer`, `gnn`, `diffusion`, `optimal-transport` |
+| `organizations` | array of strings | Primary organizations responsible for the resource |
+| `year` | integer | Initial public release/publication year |
+| `github` | string | Source repository when distinct from the canonical landing page |
+| `documentation` | string | Documentation URL |
+| `maintenance_status` | string | Curated status such as `active`, `maintenance`, `archived`, `unknown` |
+| `last_checked` | string | Date the metadata/link was last manually or automatically checked |
+
+Avoid adding dynamic popularity metrics such as GitHub stars directly to canonical records unless a reproducible refresh pipeline is introduced. Such values become stale quickly and should be stored as generated metadata rather than curated facts.
+
+## Canonical-source policy
+
+At present, `README.md` is the canonical curated list, with generated YAML/JSON/CSV artifacts. The landscape page intentionally consumes `docs/data/resources.json` without changing that policy.
+
+A future migration may make `data/resources.yml` the canonical source once all README-only categorization semantics can be represented explicitly in structured fields. That migration should be a separate change because it changes contribution workflow and source-of-truth semantics.
+
+## Landscape page
+
+Open `docs/landscape.html` through GitHub Pages. It provides:
+
+- full-text search across names, descriptions, tasks, modalities, organisms, and tags;
+- filters for type, task, modality, organism, and tag;
+- summary counts for resources and major dimensions;
+- frequency bars recalculated for the current filtered result set;
+- direct resource and paper links;
+- client-side rendering with no additional dependencies.
@@ -0,0 +1,72 @@
+---
+title: "Foundation Model Enrichment"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/FOUNDATION_MODEL_ENRICHMENT.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Foundation Model Enrichment
+
+This document tracks the first curated metadata-enrichment pass for AI4Bio foundation models.
+
+## Scope
+
+The initial pass focuses on representative single-cell and transcriptomics foundation models already present in the resource registry, beginning with:
+
+- scGPT
+- Geneformer
+
+The scope may be expanded incrementally once the curation rules below are validated in practice.
+
+## Curation rules
+
+Metadata must be supported by at least one primary or official source:
+
+- official project repository or model card;
+- official documentation;
+- primary peer-reviewed publication or preprint.
+
+Unknown or ambiguous metadata is omitted rather than inferred.
+
+For each resource, curate fields where evidence is available:
+
+- `entities`
+- `methods`
+- `organizations`
+- `year`
+- `github`
+- `documentation`
+- `maintenance_status`
+- `last_checked`
+- `metadata_sources`
+
+`maintenance_status` should only be marked `active` when there is direct evidence of ongoing maintenance, such as a recent official release or repository activity. Otherwise use `unknown` or omit the field.
+
+## Initial evidence targets
+
+### scGPT
+
+Primary evidence should include the official `bowang-lab/scGPT` repository and the Nature Methods publication.
+
+### Geneformer
+
+Primary evidence should include the official `ctheodoris/Geneformer` model repository/model card and the primary Nature publication.
+
+## Completion criteria
+
+A resource is considered enriched when:
+
+1. all added metadata is supported by `metadata_sources`;
+2. no unsupported organization, method, year, or maintenance claim is introduced;
+3. generated JSON/CSV artifacts are regenerated and committed;
+4. schema validation and resource-consistency CI checks pass.
+
+## Provenance
+
+This enrichment pass is being prepared with assistance from OpenAI GPT-5.6 Sol. Final metadata is intended to remain source-verifiable and reviewable through the recorded provenance URLs.
@@ -0,0 +1,21 @@
+---
+title: "AI4Bio data files"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/README.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# AI4Bio data files
+
+- `resources.json`: generated merged resource registry consumed by GitHub Pages.
+- `resource.schema.json`: JSON Schema 2020-12 contract for one resource object.
+- `SCHEMA.md`: original schema notes.
+- `SCHEMA_V2.md`: richer AI4Bio landscape schema and enrichment workflow.
+
+The enriched build path is `scripts/build_resources_v2.py`, which combines `data/resources.yml` with `data/enrichment.yml` and runs `scripts/validate_resources.py` before writing artifacts.
@@ -0,0 +1,132 @@
+---
+title: "AI4Bio Resource Schema v2"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/SCHEMA_V2.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# AI4Bio Resource Schema v2
+
+This document defines the richer landscape metadata layered on top of the curated Awesome Computational Biology list.
+
+## Source model
+
+The repository intentionally separates **membership/basic metadata** from **landscape enrichment**:
+
+1. `README.md` is the canonical curated resource list.
+2. `scripts/sync_resources_from_readme.py` derives `data/resources.yml` from README headings and bullets.
+3. `data/enrichment.yml` stores richer metadata keyed by stable resource `id`.
+4. `data/vocabulary.yml` defines canonical terms for controlled enrichment dimensions.
+5. `scripts/build_resources.py` merges base records and enrichment, validates them, and writes `data/resources.json`, `data/resources.csv`, and `docs/data/resources.json`.
+
+This separation prevents hand-curated AI4Bio metadata from being erased by README synchronization.
+
+## Core identity fields
+
+These fields are required and may not be overridden by `data/enrichment.yml`:
+
+| Field | Type | Meaning |
+|---|---|---|
+| `id` | string | Stable lowercase `snake_case` identifier |
+| `name` | string | Official display name |
+| `type` | enum | `api`, `benchmark`, `database`, `model`, `resource`, or `toolkit` |
+| `url` | URL | Canonical landing page |
+| `description` | string | Short factual description |
+
+## Landscape dimensions
+
+| Field | Type | Meaning |
+|---|---|---|
+| `tasks` | string[] | Biological or ML tasks performed |
+| `modalities` | string[] | Input/output data modalities |
+| `organism` | string[] | Covered organisms or species groups |
+| `entities` | string[] | Biological entities: gene, protein, compound, cell, disease, etc. |
+| `methods` | string[] | Method families: transformer, GNN, diffusion, optimal transport, etc. |
+| `tags` | string[] | Broad domain and curation labels |
+| `organizations` | string[] | Organizations maintaining or primarily responsible for the resource |
+
+These dimensions are deliberately orthogonal. Do not encode a task as a modality or a biological entity as a resource type.
+
+## Controlled vocabulary
+
+New values added through `data/enrichment.yml` for `entities`, `methods`, `modalities`, and `tasks` must use canonical terms from `data/vocabulary.yml`.
+
+Canonical terms use lowercase kebab-case, for example:
+
+```yaml
+entities: [cell, gene]
+methods: [transformer, self-supervised-learning]
+modalities: [single-cell-rna-seq, transcriptomics]
+tasks: [foundation-model-pretraining, cell-type-annotation]
+```
+
+This rule is intentionally applied only to enrichment metadata. Existing README-derived values remain valid for backward compatibility and can be migrated separately without blocking routine resource updates.
+
+When a required concept is missing, add a reusable canonical term to `data/vocabulary.yml` instead of inventing a one-off spelling in an enrichment record. `tags`, `organism`, and `organizations` remain free-form because their vocabularies are broader or context dependent.
+
+## Provenance and lifecycle fields
+
+| Field | Type | Meaning |
+|---|---|---|
+| `year` | integer | Initial public release or primary publication year |
+| `github` | URL | Source repository when available |
+| `documentation` | URL | Documentation landing page |
+| `paper` | URL | Primary publication or preprint |
+| `license` | string | SPDX identifier preferred |
+| `api` | boolean | Programmatic API availability |
+| `access` | enum | `open`, `registration`, `restricted`, `commercial`, `unknown` |
+| `maintenance_status` | enum | `active`, `maintenance`, `archived`, `unknown` |
+| `updated` | date | Last-known upstream update date |
+| `last_checked` | date | Date this repository verified the metadata |
+| `metadata_sources` | URL[] | Sources supporting enriched metadata |
+
+`last_checked` is a curation timestamp, not an upstream release date. `updated` should only be populated when an upstream update date is known.
+
+## Enrichment rules
+
+`data/enrichment.yml` is a mapping keyed by resource id:
+
+```yaml
+resources:
+  example_resource:
+    entities: [gene, disease]
+    methods: [transformer]
+    organizations: [Example Lab]
+    year: 2025
+    github: https://github.com/example/project
+    documentation: https://example.org/docs
+    maintenance_status: active
+    access: open
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://example.org/about
+```
+
+Enrichment cannot override `id`, `name`, `type`, `url`, or `description`. A referenced id must already exist in `data/resources.yml`.
+
+## Validation contract
+
+`python scripts/validate_resources.py` checks:
+
+- required fields and field types;
+- stable id format and id uniqueness;
+- allowed enum values;
+- HTTP(S) URL shape;
+- ISO `YYYY-MM-DD` dates;
+- list uniqueness and non-empty values;
+- enrichment references and forbidden identity overrides;
+- controlled enrichment terms against `data/vocabulary.yml`;
+- vocabulary uniqueness and lowercase kebab-case normalization;
+- unknown field names.
+
+The machine-readable resource counterpart is `docs/data/resource.schema.json` (JSON Schema 2020-12). Controlled vocabulary enforcement is performed at the enrichment layer because legacy README-derived values intentionally remain backward compatible.
+
+## Curation guidance
+
+Prefer verified metadata over exhaustive metadata. Unknown fields should be omitted rather than guessed. For facts likely to change, include `last_checked` and at least one `metadata_sources` URL. Dynamic popularity metrics such as GitHub stars should remain generated telemetry rather than canonical curated fields.
@@ -0,0 +1,53 @@
+---
+title: "Resource.Schema"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/resource.schema.json
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "$id": "https://inoue0426.github.io/awesome-computational-biology/data/resource.schema.json",
+  "title": "AI4Bio Resource",
+  "type": "object",
+  "required": ["id", "name", "type", "url", "description"],
+  "additionalProperties": false,
+  "properties": {
+    "id": {"type": "string", "pattern": "^[a-z0-9]+(?:_[a-z0-9]+)*$"},
+    "name": {"type": "string", "minLength": 1},
+    "type": {"enum": ["api", "benchmark", "database", "model", "resource", "toolkit"]},
+    "url": {"type": "string", "format": "uri", "pattern": "^https?://"},
+    "description": {"type": "string", "minLength": 1},
+    "tags": {"$ref": "#/$defs/stringArray"},
+    "tasks": {"$ref": "#/$defs/stringArray"},
+    "modalities": {"$ref": "#/$defs/stringArray"},
+    "organism": {"$ref": "#/$defs/stringArray"},
+    "entities": {"$ref": "#/$defs/stringArray"},
+    "methods": {"$ref": "#/$defs/stringArray"},
+    "organizations": {"$ref": "#/$defs/stringArray"},
+    "metadata_sources": {"type": "array", "items": {"type": "string", "format": "uri", "pattern": "^https?://"}, "uniqueItems": true},
+    "license": {"type": "string"},
+    "api": {"type": "boolean"},
+    "paper": {"type": "string", "format": "uri", "pattern": "^https?://"},
+    "github": {"type": "string", "format": "uri", "pattern": "^https://github\\.com/"},
+    "documentation": {"type": "string", "format": "uri", "pattern": "^https?://"},
+    "year": {"type": "integer", "minimum": 1900, "maximum": 2100},
+    "maintenance_status": {"enum": ["active", "maintenance", "archived", "unknown"]},
+    "access": {"enum": ["open", "registration", "restricted", "commercial", "unknown"]},
+    "updated": {"type": "string", "format": "date"},
+    "last_checked": {"type": "string", "format": "date"}
+  },
+  "$defs": {
+    "stringArray": {
+      "type": "array",
+      "items": {"type": "string", "minLength": 1},
+      "uniqueItems": true
+    }
+  }
+}
Author	SHA1	Message	Date
promptadmin	969c13a5c3	[upstream-sync] README.md from ai-boost/awesome-ai-for-science@d57c2d50 [catalogue]	2026-09-01 05:15:18 +00:00
promptadmin	0a25b5b792	Merge pull request '[Upstream sync] ai-boost/awesome-ai-for-science (github) — 0 added, 1 modified' (#83 ) from upstream-sync/awesome-ai-for-science-20260821-ec6f36-ihmj into main Reviewed-on: #83	2026-08-21 14:37:05 +00:00
promptadmin	27303f36c1	[upstream-sync] README.md from ai-boost/awesome-ai-for-science@ec6f3650 [catalogue]	2026-08-21 10:38:52 +00:00
promptadmin	ea7701920e	Merge pull request '[Upstream sync] inoue0426/awesome-computational-biology (github) — 17 added, 6 modified' (#67 ) from upstream-sync/awesome-computational-biology-20260808-7a064b-pniq into main Reviewed-on: #67	2026-08-09 18:32:11 +00:00
promptadmin	fcd5fbc473	Merge pull request '[Upstream sync] ai-boost/awesome-ai-for-science (github) — 0 added, 1 modified' (#68 ) from upstream-sync/awesome-ai-for-science-20260809-024f42-tzfc into main Reviewed-on: #68	2026-08-09 17:02:16 +00:00
promptadmin	1fdd6c59ed	[upstream-sync] README.md from ai-boost/awesome-ai-for-science@024f42bf [catalogue]	2026-08-09 15:54:26 +00:00
promptadmin	53c2e77288	[upstream-sync] docs/data/resources.json from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:51:26 +00:00
promptadmin	718179d35a	[upstream-sync] data/resources.yml from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:51:24 +00:00
promptadmin	bbb4a28582	[upstream-sync] data/resources.json from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:51:22 +00:00
promptadmin	89ee6aff51	[upstream-sync] cspell.json from inoue0426/awesome-computational-biology@7a064bf0 [unknown]	2026-08-08 21:51:19 +00:00
promptadmin	9d277f9d01	[upstream-sync] README.md from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:51:17 +00:00
promptadmin	0c421f2ac1	[upstream-sync] .github/workflows/sync_resources.yml from inoue0426/awesome-computational-biology@7a064bf0 [unknown]	2026-08-08 21:51:15 +00:00
promptadmin	93be653ade	[upstream-sync] docs/data/resource.schema.json from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:51:12 +00:00
promptadmin	4aba0445d9	[upstream-sync] docs/data/SCHEMA_V2.md from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:51:11 +00:00
promptadmin	751894b8ec	[upstream-sync] docs/data/README.md from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:51:09 +00:00
promptadmin	3331a1ace5	[upstream-sync] docs/FOUNDATION_MODEL_ENRICHMENT.md from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:51:07 +00:00
promptadmin	c4cc7c574e	[upstream-sync] docs/AI4BIO_LANDSCAPE.md from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:51:05 +00:00
promptadmin	396d32d996	[upstream-sync] data/vocabulary.yml from inoue0426/awesome-computational-biology@7a064bf0 [unknown]	2026-08-08 21:51:03 +00:00
promptadmin	6752ebfbee	[upstream-sync] data/enrichment.yml from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:51:01 +00:00
promptadmin	25ed42073b	[upstream-sync] data/enrichment.spatial-imaging-v1.yml from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:50:59 +00:00
promptadmin	dc89023147	[upstream-sync] data/enrichment.protein-v2.yml from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:50:57 +00:00
promptadmin	32c34ff1eb	[upstream-sync] data/enrichment.protein-drug-v1.yml from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:50:55 +00:00
promptadmin	cea9e1b812	[upstream-sync] data/enrichment.pharmacogenomics-v1.yml from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:50:53 +00:00
promptadmin	4cf5928724	[upstream-sync] data/enrichment.molecular-v2.yml from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:50:51 +00:00
promptadmin	bbde223edb	[upstream-sync] data/enrichment.foundation-models-v2.yml from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:50:48 +00:00
promptadmin	72507e799e	[upstream-sync] data/enrichment.database-api-v1.yml from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:50:46 +00:00
promptadmin	9d3222ddc4	[upstream-sync] data/enrichment.benchmark-v2.yml from inoue0426/awesome-computational-biology@7a064bf0 [catalogue]	2026-08-08 21:50:44 +00:00
promptadmin	c15032eed1	[upstream-sync] .github/workflows/landscape-ui-check.yml from inoue0426/awesome-computational-biology@7a064bf0 [unknown]	2026-08-08 21:50:42 +00:00
promptadmin	dffd9a2f99	[upstream-sync] .github/workflows/ai4bio-schema-check.yml from inoue0426/awesome-computational-biology@7a064bf0 [unknown]	2026-08-08 21:50:40 +00:00