[upstream-sync] README.md from ai-boost/awesome-ai-for-science@5c0bca43 [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
2026-08-26 10:55:13 +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
25 changed files with 3838 additions and 387 deletions
@@ -2,9 +2,9 @@
 title: "Awesome Genomic Skills [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/GoekeLab/awesome-genomic-skills/blob/f88d9494/README.md
-upstream_sha: f88d9494
-imported_at: 2026-06-26
+upstream_source: https://github.com/GoekeLab/awesome-genomic-skills/blob/8cf42e1d/README.md
+upstream_sha: 8cf42e1d
+imported_at: 2026-08-08
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -50,7 +50,7 @@ Skill libraries and tool collections specifically targeting genomics, bioinforma
  - **Description:** AWS sample bundle for the Kiro IDE: 24 MCP servers wrapping 100+ databases/tools across genomics, proteomics, structural biology, and clinical/pharma (NCBI, Ensembl, ClinVar, gnomAD, UniProt, STRING, PDB, AlphaFold, ChEMBL, Open Targets, etc.), plus 10 domain skills and 16 workflows, with cross-database search and AWS HealthOmics pipeline execution. MIT-0; the MCP servers use standard MCP and are portable, though the skills and workflows are built for Kiro. [Blog post](https://aws.amazon.com/blogs/publicsector/accelerating-life-sciences-research-with-kiro-a-unified-ai-interface-to-100-open-source-databases/).
  - **Developers:** AWS (AWS Samples).
 - [ClawBio](https://github.com/ClawBio/ClawBio)
-  - **Description:** The first bioinformatics-native AI agent skill library; provides reproducible, local-first skills for genomics tasks (variant calling, RNA-seq, population genetics) that work with Claude Code, Copilot, Codex, and other agents.
+  - **Description:** Bioinformatics-native AI agent skill library; 95 reproducible, local-first skills for genomics tasks (variant calling, RNA-seq, population genetics) that work with Claude Code, Copilot, Codex, and other agents. Since v0.6.1 the whole library is also callable as an MCP server (`uvx --from 'clawbio[mcp]' clawbio mcp`) from Cursor, Claude Desktop, VS Code, or Zed (see the MCP section below). Skills are actively [benchmarked](https://clawbio.ai/benchmarks.html).
  - **Developers:** Independent open-source project built on [OpenClaw](https://openclaw.ai).
 - [SciAgent-skills](https://github.com/jaechang-hits/SciAgent-Skills)
  - **Description:** 197 open-source skills for Claude Code, Cursor, Codex, and Windsurf, covering genomics-bioinformatics, proteomics-protein engineering, structural biology, drug discovery, systems biology, biostatistics, and scientific writing; achieves 92% accuracy on BixBench-Verified-50 (+26.7 pts over Claude Code baseline). The hosted OmicsHorizon web platform runs these skills in-browser.
@@ -83,6 +83,9 @@ Model Context Protocol (MCP) servers that give AI agents direct access to bioinf
 - [gget-mcp](https://github.com/longevity-genie/gget-mcp) -  MCP server wrapping the Pachter Lab [gget](https://github.com/pachterlab/gget) bioinformatics toolkit. Exposes 13 tools covering gene search and metadata (Ensembl), sequence retrieval, BLAST/BLAT/MUSCLE alignment, expression data (ARCHS4), functional enrichment (Enrichr), protein structure (PDB, AlphaFold), cancer mutations (COSMIC), and single-cell queries (CellxGene).
 - [Seqera MCP](https://docs.seqera.io/platform-cloud/seqera-mcp/overview) - Hosted MCP server from Seqera Labs (the developers of Nextflow) exposing the Seqera Platform (workflow launch/management), Wave (container provisioning), nf-core modules, and SRA/ENA/GEO retrieval. 
 - [knowledgebase-mcp](https://github.com/biocontext-ai/knowledgebase-mcp) - BioContextAI Knowledgebase MCP server, included in the BioContextAI registry (below), one of the most comprehensive single MCP packages (wraps STRINGDb, Open Targets, Reactome, UniProt, HPA, KEGG, AlphaFold, Ensembl, ClinicalTrials.gov, bioRxiv, etc.)
+- [ClawBio MCP](https://github.com/ClawBio/ClawBio) - MCP mode of the [ClawBio](#bioinformatics-and-genomics-agent-skills) skill library (0.6.1): exposes all 95 genomics skills as callable tools over local stdio, letting different agents (Cursor, Zed, etc.) run analyses (variant calling, RNA-seq, population genetics). 
+- [roda-mcp](https://github.com/awslabs/mcp/tree/main/src/roda-mcp-server) - AWS Labs MCP server for discovering and exploring datasets in the Registry of Open Data on AWS (RODA), covering 1,100+ public datasets across life sciences, climate, geospatial, and satellite imagery. Search/filter by keyword, organization, or license; inspect dataset details; and browse or sample public S3 bucket contents directly (no AWS account required) without downloading full files. Not genomics-specific, but useful for locating and previewing life-sciences datasets (e.g. SG-NEx) hosted on AWS.
+- [plant-genomics-mcp](https://github.com/musharna/plant-genomics-mcp) - Plant-genomics MCP server exposing 50 tools across 23 backends, keyed on TAIR-style loci with organism resolution across 12 crop and model species. Covers plant-specific resources that general bioinformatics servers do not (Ensembl Plants, Phytozome, Gramene, Planteome PO/TO, PlantCyc/PMN, AraGWAS, 1001 Genomes, ThaleMine, BAR, JASPAR) alongside the usual UniProt/KEGG/STRING/AlphaFold/PDBe/InterPro/Europe PMC, plus cross-source synthesis tools that compose several backends into a single gene report.

 Existing registries and lists of MCP servers: 
 - [BioContextAI Registry](https://github.com/biocontext-ai/registry/) - Community-curated catalogue of biomedical MCP servers, with submission criteria requiring biomedical focus, free academic access, OSI-approved open-source licenses, and MCP specification compliance. Ships a [cookiecutter template](https://github.com/biocontext-ai/mcp-server-cookiecutter) for new servers and follows Schema.org ontologies for metadata. [A community hub for agentic biomedical systems](https://www.nature.com/articles/s41587-025-02900-9)
@@ -2,9 +2,9 @@
 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/be91bc08/README.md
-upstream_sha: be91bc08
-imported_at: 2026-07-25
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/5c0bca43/README.md
+upstream_sha: 5c0bca43
+imported_at: 2026-08-26
 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)
@@ -216,7 +217,14 @@ validated: false
 - [OpenScience (Synthetic Sciences)](https://github.com/synthetic-sciences/openscience) - Open-source AI workbench for scientific research that automates the full research loop — literature review, hypothesis generation, code writing, experiment execution, database querying, and report writing — with 290+ skills, specialized research agents, and a browser-based workspace (1453+ stars, Apache 2.0, 2026)
 - [Claude Scholar](https://github.com/Galaxy-Dawn/claude-scholar) - Semi-automated research assistant for academic research and software development, supporting Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and publication (Galaxy-Dawn, 4.5K+ stars, MIT License, 2026)
 - [K-Dense BYOK](https://github.com/K-Dense-AI/k-dense-byok) - Free, open-source desktop AI research assistant that runs locally and turns natural-language requests into real data analysis, literature search, figure generation, and manuscript review; ships with 149 scientific skills, 326 workflow templates, and 229 databases across genomics, proteomics, drug discovery, and materials science, plus a living lab notebook, 60+ scientific file previews, and LaTeX editing (K-Dense-AI, 908+ stars, MIT License, 2026)
+- [Science Superpowers (K-Dense-AI)](https://github.com/K-Dense-AI/science-superpowers) - Composable computational-science methodology skills for AI research agents emphasizing pre-registration, reproducible workspaces, and red-team review to guard against p-hacking and HARKing; zero third-party dependencies and runs with any agent harness plus a POSIX shell (281+ stars, MIT License, 2026)
+- [Wisp Science](https://github.com/xuzhougeng/wisp-science) - Open-source, local-first desktop AI research workbench for scientific computing with Python/R, MCP bioinformatics tools, SSH/WSL/GPU runtimes, and OpenAI/Anthropic models (857+ stars, 2026)
 - [Academic Research Skills (ARS)](https://github.com/Imbad0202/academic-research-skills) - Comprehensive Claude Code skill suite covering the full academic pipeline from deep research and paper writing to multi-perspective peer review, revision, and finalization; features multi-agent teams, PRISMA systematic review, style calibration, claim-level citation audits, integrity gates, and human-in-the-loop safeguards (38K+ stars, CC BY-NC 4.0, 2026)
+- [Qinyan Academic Skills](https://github.com/LeonChaoX/qinyan-academic-skills) - Curated, multilingual library of 182 installable AI agent skills for end-to-end academic research spanning literature discovery, scientific writing, grant development, bioinformatics, drug discovery, clinical research, machine learning, and data analysis (779+ stars, MIT License, 2026)
+- [SkillOpt (Microsoft, 2026)](https://github.com/microsoft/SkillOpt) - Text-space optimizer that treats agent skill documents as trainable parameters for frozen LLMs, using scored rollouts and held-out validation gates to iteratively improve reusable natural-language skills; includes SkillOpt-Sleep for nightly self-evolution and improves accuracy across Claude Code, Codex, Copilot, and direct-chat harnesses, making it a meta-tool for evolving scientific agent skill workflows (15.5K+ stars, MIT License, PyPI)
+- [Open Science (AIPOCH)](https://github.com/aipoch/open-science) - Open-source, local-first, model-agnostic AI research workbench for reproducible scientific discovery; runs Python/R notebooks, searches the web, calls scientific data connectors, and produces inspectable reports, tables, and figures in a self-hosted desktop workspace (1.5K+ stars, Apache 2.0, 2026)
+- [OmicsClaw](https://github.com/TianGzlab/OmicsClaw) - Local-first, conversational AI research partner for multi-omics analysis with CLI, desktop app, and 95+ reproducible skills; keeps raw data local while routing natural-language requests to Python/R/CLI tools with persistent memory, autonomous analysis paths, and multi-method consensus workflows (TianGzlab, 155+ stars, Apache 2.0, 2026)
+- [MedgeClaw](https://github.com/xjtulyc/MedgeClaw) - Open-source AI research assistant for biomedicine — chat to run RNA-seq, drug discovery, clinical analysis, and more; built on OpenClaw and Claude Code with 140 K-Dense scientific skills, real-time dashboard, and RStudio/JupyterLab integration (xjtulyc, 669+ stars, 2026)

 ### Literature Management Plugins
 - [llm-for-zotero](https://github.com/yilewang/llm-for-zotero) - Research agent system deeply integrated with Zotero supporting Agent Mode, skills, multi-model backends (OpenAI-compatible, Claude Code, WebChat, Codex), and MinerU PDF parsing for literature Q&A, summarization, figure inspection, and source comparison (1.3K+ stars, 2026)
@@ -229,6 +237,7 @@ 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)

 ---
@@ -240,6 +249,7 @@ validated: false
 - [GraphGen](https://github.com/open-sciencelab/GraphGen) - Knowledge graph-guided synthetic data generation for LLM fine-tuning, achieving strong performance on scientific QA (GPQA-Diamond) and math reasoning (AIME)
 - [KoPA](https://github.com/zjukg/KoPA) - Structure-aware prefix adaptation for integrating LLMs with knowledge graphs (ACM MM 2024)
 - [Scholarly KGQA](https://arxiv.org/abs/2311.09841) - LLM-powered question answering over scholarly knowledge graphs (ArXiv paper)
+- [SciAtlas](https://github.com/zjunlp/SciAtlas) - Large-scale knowledge graph and pip-installable client for literature-grounded automated scientific research, connecting papers, authors, institutions, venues, keywords, citations, and a four-level research taxonomy across medicine, social sciences, engineering, computer science, materials science, and more (ZJU NLP, arXiv 2026, 136+ stars, MIT License)

 ### Knowledge Graph Resources
 - [Awesome-LLM-KG](https://github.com/RManLuo/Awesome-LLM-KG) - Comprehensive collection of papers on unifying LLMs and knowledge graphs
@@ -254,10 +264,13 @@ validated: false
 - [SkyDiscover](https://github.com/skydiscover-ai/skydiscover) - Modular framework for AI-driven scientific and algorithmic discovery, providing a unified interface for implementing, running, and fairly comparing discovery algorithms across 200+ optimization tasks; introduces AdaEvolve and EvoX adaptive/evolutionary algorithms and natively supports OpenEvolve, GEPA, and Harbor-format benchmarks (skydiscover-ai, 568+ stars, Apache 2.0, 2026)
 - [EvoMaster (SJTU SAI, arXiv 2026)](https://github.com/sjtu-sai-agents/EvoMaster) - Foundational auto-research agent framework for agentic science at scale, providing modular agent construction, run-level self-evolution, and multiple SciMaster domain agents (ML-Master, X-Master, Browse-Master); outperforms general-purpose agents across authoritative benchmarks including the OpenAI Frontier Science Benchmark (206+ stars, Apache 2.0, 2026)
 - [Virtual Lab (Stanford Zou Group, Nature 2025)](https://github.com/zou-group/virtual-lab) - AI-human collaborative research platform where a human researcher works with a team of LLM agents via team and individual meetings to perform scientific research; demonstrated by designing new SARS-CoV-2 nanobodies with wet-lab validation
+- [AI Co-Scientist (Google DeepMind, Nature Medicine 2026)](https://deepmind.google/blog/co-scientist-a-multi-agent-ai-partner-to-accelerate-research/) - Multi-agent AI research partner that generates, reviews, ranks, and evolves research hypotheses alongside scientists, with experimental validation in biomedicine and other domains (2026)
+- [Hyra (Tencent Hunyuan, 2026)](https://hy.tencent.com/research/hyra) - Hunyuan Research Agent for autonomous open-ended discovery across AI4Science, mathematics, and engineering, releasing reproducible solution artifacts for autocorrelation constants, Erdős problems, PARP1 docking, qubit routing, and record-breaking packing problems ([Hyra-results](https://github.com/Tencent-Hunyuan/Hyra-results), 112+ stars, Apache 2.0)
 - [The AI Scientist (SakanaAI)](https://github.com/SakanaAI/AI-Scientist) - First fully autonomous open-ended scientific discovery system with official implementation: hypothesis→experiment→writing→review simulation (13.8K+ stars, 2024)
 - [The AI Scientist v2 (SakanaAI)](https://github.com/SakanaAI/AI-Scientist-v2) - Official implementation of the second-generation fully autonomous scientific discovery system, extending the original with agentic tree search and reduced template dependency to achieve workshop-level accepted papers (6.7K+ stars, 2025)
 - [The AI Scientist v1 (2024)](https://arxiv.org/abs/2408.06292) - First fully autonomous research system: hypothesis→experiment→writing→review simulation
 - [The AI Scientist v2 (2025)](https://arxiv.org/abs/2504.08066) - Enhanced with Agentic Tree Search, reduced template dependency, first workshop-level accepted paper
+- [FAROS (OpenNSWM-Lab)](https://github.com/OpenNSWM-Lab/FAROS) - Foundation AutoResearch Operating System: blueprint-driven runtime for orchestrating AI research workflows from idea generation and experiments to paper writing and peer review (OpenNSWM-Lab, 2.4K+ stars, 2026)
 - [DeepScientist](https://github.com/ResearAI/DeepScientist) - First system progressively surpassing human SOTA on frontier AI tasks (183.7%, 1.9%, 7.9% improvements), month-long autonomous discovery with 20,000+ GPU hours
 - [ASI-Arch (GAIR-NLP, arXiv 2025)](https://github.com/GAIR-NLP/ASI-Arch) - Autonomous multi-agent research loop for model architecture discovery that ran 1,773 experiments over 20,000 GPU hours and produced 106 state-of-the-art linear-attention architectures, surpassing human-designed baselines including Mamba2 and DeltaNet (1.1K+ stars, Apache 2.0)
 - [Kosmos](https://github.com/jimmc414/Kosmos) - Extended autonomy AI scientist with 200 parallel agent rollouts, 42K lines of code execution, 1.5K papers analyzed per run, achieving 79.4% accuracy and 7 scientific discoveries (Edison Scientific)
@@ -291,14 +304,17 @@ 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
 - [AIRS-Bench (Meta, 2026)](https://github.com/facebookresearch/airs-bench) - Benchmark quantifying end-to-end autonomous AI research abilities of LLM agents across 20 tasks from SOTA machine learning papers spanning NLP, code, math, biochemical modelling, and time series forecasting, with normalized score metrics against human SOTA and HuggingFace dataset
 - [PaperBench (OpenAI, 2025)](https://github.com/openai/preparedness/tree/main/project/paperbench) - Benchmark evaluating AI agents' ability to replicate 20 ICML 2024 Spotlight/Oral papers from scratch, with 8,316 gradable tasks and author-co-developed rubrics
+- [PaperGuru (AutoTrustAI, 2026)](https://github.com/AutoTrustAI/PaperGuru-Benchmark) - Lifecycle-Aware Memory (LAM) primitive and benchmark for long-horizon research agents, achieving 65.95% mean reproduction on PaperBench and 94.66% on SurveyBench through Capital Chunk Memory (CCM) with versioned content, structural multi-hop relevance, and provenance-grounded composition; 10 peer-reviewed acceptances at FSE/ICML/TOSEM/AEI/ICoGB (1.3K+ stars)
 - [MLE-Bench (OpenAI, 2024)](https://github.com/openai/mle-bench) - Benchmark evaluating AI agents on 75 curated Kaggle-style ML engineering competitions with reproducible Docker-based grading harness, human baselines, and end-to-end task lifecycle, used as a primary benchmark for autonomous ML research agents (e.g., InternAgent #1 at 36.44%)
 - [ScienceBoard (ICLR 2026)](https://github.com/OS-Copilot/ScienceBoard) - Evaluating multimodal autonomous agents in realistic scientific workflows across real scientific software environments (KAlgebra, Celestia, Grass GIS, Lean 4, etc.) with VM-based evaluation infrastructure and agent trajectories
 - [BuildArena](https://github.com/AI4Science-WestlakeU/BuildArena) - First physics-aligned interactive benchmark for LLM agents in engineering construction, designing rockets/cars/bridges in physics simulator with 3D spatial geometry library
@@ -315,13 +331,18 @@ 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)
@@ -380,6 +401,7 @@ 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)

@@ -624,6 +646,7 @@ 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)
@@ -643,6 +666,7 @@ 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)
@@ -685,6 +709,7 @@ 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)
@@ -711,6 +736,7 @@ 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

@@ -722,10 +748,12 @@ 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)
@@ -770,6 +798,7 @@ 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
@@ -779,6 +808,7 @@ 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)

 #### Ecological Modeling
 - [BioSimulators](https://github.com/biosimulators/Biosimulators) - Biological simulation tools
@@ -794,6 +824,26 @@ validated: false
 - [AgentSociety](https://github.com/tsinghua-fib-lab/AgentSociety) - Modern LLM-native agent simulation platform for social science research and experimental design, providing a flexible framework for creating and managing intelligent agents in simulated environments (Tsinghua FIB Lab, 984+ stars, 2025)
 - [Auto-Empirical-Research-Skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills) - Curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines, enabling reproducible social science research with AI agents (Stanford REAP & CoPaper.AI, 3K+ stars, 2026)
 - [EDSL](https://github.com/expectedparrot/edsl) - Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs (460+ stars, 2024)
+- [GABRIEL (OpenAI, 2026)](https://github.com/openai/GABRIEL) - Generalized Attribute Based Ratings Information Extraction Library; official OpenAI toolkit that turns messy qualitative corpora into analysis-ready datasets for social scientists and data scientists, measuring quantitative attributes in text, images, or audio using the GPT API. See the [official blog post](https://openai.com/index/scaling-social-science-research/) and [NBER working paper](http://www.nber.org/papers/w34834) (413+ stars, Apache 2.0)
+
+---
+
+## 🏗 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)

 ---

@@ -880,6 +930,7 @@ validated: false

 ### Specialized Frameworks
 - [MDAnalysis](https://github.com/MDAnalysis/mdanalysis) - Molecular dynamics analysis
+- [Nano World Model](https://github.com/simchowitzlabpublic/nano-world-model) - Minimalist, batteries-included repository for training video world models with diffusion-forcing, supporting long-horizon rollouts, 3D point-cloud generation, and model-predictive control with pretrained checkpoints (Simchowitz Lab, 700+ stars, MIT License, 2026)
 - [e3nn](https://github.com/e3nn/e3nn) - Euclidean neural networks for arbitrary point transformations enabling E(3)-equivariant deep learning, foundational library for building geometry-aware neural networks in molecular dynamics, materials science, and physics
 - [MDtrajNet](https://arxiv.org/abs/2505.16301) - Neural network foundation model that directly generates MD trajectories bypassing force calculations, accelerating simulations by up to 100× with equivariant Transformer architecture (2025)
 - [ASE](https://wiki.fysik.dtu.dk/ase/) - Atomic Simulation Environment for materials modeling
@@ -959,12 +1010,14 @@ This project builds upon and complements several excellent resources:
 - [awesome-ai4s](https://github.com/hyperai/awesome-ai4s) - 200+ AI for Science papers with Chinese interpretations
 - [Awesome AI Scientist Papers](https://github.com/openags/Awesome-AI-Scientist-Papers) - Autonomous AI scientist research
 - [Awesome Scientific Machine Learning](https://github.com/MartinuzziFrancesco/awesome-scientific-machine-learning) - Physics-informed ML and SciML
+- [Awesome Scientific Skills](https://github.com/InternScience/Awesome-Scientific-Skills) - Curated collection of agent skills for scientific research (InternScience, 493+ stars, 2026)
 - [Awesome Agents for Science](https://github.com/OSU-NLP-Group/awesome-agents4science) - LLM agents across scientific domains
 - [Awesome LLM Agents Scientific Discovery](https://github.com/zhoujieli/Awesome-LLM-Agents-Scientific-Discovery) - Biomedical AI agents
 - [Awesome Foundation Models for Weather and Climate](https://github.com/shengchaochen82/Awesome-Foundation-Models-for-Weather-and-Climate) - Comprehensive survey of foundation models for weather and climate data understanding

 ### 📊 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/12d87583/README.md
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+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)
@@ -265,6 +266,8 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [Tabula Sapiens](https://tabula-sapiens-portal.ds.czbiohub.org/) — Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors.
 - [TAPE (Tasks Assessing Protein Embeddings)](https://github.com/songlab-cal/tape) — Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations.
 - [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) — Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients.
+- [TCGA virtual spatial transcriptomics atlas](https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas) — DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).
+- [HEST Xenium virtual spatial transcriptomics](https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics) — DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).
 - [Therapeutics Data Commons (TDC)](https://tdcommons.ai/) — Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.
 - [Tox21](https://tripod.nih.gov/tox21/challenge/) — 12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction.
 - [UK Biobank](https://www.ukbiobank.ac.uk/) — Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics and disease studies.
@@ -320,6 +323,7 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [sciPENN](https://github.com/jlakkis/sciPENN) — RNN-based method for simultaneous protein expression prediction, uncertainty estimation, and cell-type label transfer from CITE-seq and scRNA-seq data.
 - [MOGONET](https://github.com/txWang/MOGONET) — Multi-omics graph convolutional network framework for patient classification and biomarker identification.
 - [AutoZyme](https://github.com/ElliotXie/autozyme) — Autonomous agentic framework that speeds up bioinformatics software (e.g. Scanpy, Seurat) on CPUs while preserving the original results.
+- [SeqBench](https://seqbench.com/) — Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction digest), CRISPR guide RNA design, and sequence analysis, with a public REST API, OpenAPI 3.1 spec, and MCP server.

 ---

@@ -337,10 +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

@@ -412,6 +421,10 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [Phikon](https://huggingface.co/owkin/phikon) — ViT-based pathology foundation model pretrained with iBOT self-supervision on TCGA whole-slide images.
 - [Nicheformer](https://github.com/theislab/nicheformer) — Foundation model for single-cell and spatial omics using a transformer architecture with positional embeddings to encode spatial cell information.
 - [scGPT-spatial](https://github.com/bowang-lab/scGPT-spatial) — Extension of scGPT for spatial transcriptomics with continual pretraining and a mixture-of-experts decoder for spatial gene expression analysis.
+- [DeepSpot](https://github.com/ratschlab/DeepSpot) — Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.
+- [DeepSpot2Cell](https://github.com/ratschlab/DeepSpot2Cell) — Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).
+- [DeepSpot-M](https://github.com/ratschlab/DeepSpotM) — Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.
+- [AESTETIK](https://github.com/ratschlab/aestetik) — Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.

 ##### Multi-Omics Foundation Models

@@ -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/12d87583/data/resources.yml
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+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
@@ -248,6 +248,17 @@ resources:
    organism: []
    api: false

+  - id: hest_xenium_virtual_spatial_transcriptomics
+    name: "HEST Xenium virtual spatial transcriptomics"
+    type: benchmark
+    url: https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics
+    description: "DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1)."
+    tags: [benchmarks-and-datasets]
+    tasks: []
+    modalities: []
+    organism: []
+    api: false
+
  - id: jump_cell_painting_datasets
    name: "JUMP Cell Painting Datasets"
    type: benchmark
@@ -435,6 +446,17 @@ resources:
    organism: []
    api: false

+  - id: tcga_virtual_spatial_transcriptomics_atlas
+    name: "TCGA virtual spatial transcriptomics atlas"
+    type: benchmark
+    url: https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas
+    description: "DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1)."
+    tags: [benchmarks-and-datasets]
+    tasks: []
+    modalities: []
+    organism: []
+    api: false
+
  - id: the_cancer_genome_atlas_tcga
    name: "The Cancer Genome Atlas (TCGA)"
    type: benchmark
@@ -1491,6 +1513,17 @@ resources:
    organism: []
    api: false

+  - id: aestetik
+    name: "AESTETIK"
+    type: model
+    url: https://github.com/ratschlab/aestetik
+    description: "Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge."
+    tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
+    tasks: [Foundation Model]
+    modalities: [Single Cell, Spatial Transcriptomics]
+    organism: []
+    api: false
+
  - id: ai4chem_chemllm_7b_chat
    name: "AI4Chem/ChemLLM-7B-Chat"
    type: model
@@ -1645,6 +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
@@ -1694,8 +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
@@ -1722,6 +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
@@ -1749,8 +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
@@ -1810,6 +1865,39 @@ resources:
    organism: []
    api: false

+  - id: deepspot
+    name: "DeepSpot"
+    type: model
+    url: https://github.com/ratschlab/DeepSpot
+    description: "Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution."
+    tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
+    tasks: [Foundation Model]
+    modalities: [Single Cell, Spatial Transcriptomics]
+    organism: []
+    api: false
+
+  - id: deepspot_m
+    name: "DeepSpot-M"
+    type: model
+    url: https://github.com/ratschlab/DeepSpotM
+    description: "Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology."
+    tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
+    tasks: [Foundation Model]
+    modalities: [Single Cell, Spatial Transcriptomics]
+    organism: []
+    api: false
+
+  - id: deepspot2cell
+    name: "DeepSpot2Cell"
+    type: model
+    url: https://github.com/ratschlab/DeepSpot2Cell
+    description: "Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics)."
+    tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
+    tasks: [Foundation Model]
+    modalities: [Single Cell, Spatial Transcriptomics]
+    organism: []
+    api: false
+
  - id: dgdrp
    name: "DGDRP"
    type: model
@@ -2409,8 +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
@@ -3097,6 +3185,17 @@ resources:
    organism: []
    api: false

+  - id: seqbench
+    name: "SeqBench"
+    type: toolkit
+    url: https://seqbench.com/
+    description: "Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction digest), CRISPR guide RNA design, and sequence analysis, with a public REST API, OpenAPI 3.1 spec, and MCP server."
+    tags: [preprocessing-tools]
+    tasks: [Preprocessing]
+    modalities: []
+    organism: []
+    api: false
+
  - id: seurat
    name: "Seurat"
    type: toolkit
@@ -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	6f0cac09f0	[upstream-sync] README.md from ai-boost/awesome-ai-for-science@5c0bca43 [catalogue]	2026-08-26 10:55:13 +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
promptadmin	4478db9429	Merge pull request '[Upstream sync] inoue0426/awesome-computational-biology (github) — 0 added, 4 modified' (#53 ) from upstream-sync/awesome-computational-biology-20260717-478be8-wkjf into main Reviewed-on: #53	2026-08-08 15:42:14 +00:00
promptadmin	ea596262a3	Merge pull request '[Upstream sync] ai-boost/awesome-ai-for-science (github) — 0 added, 1 modified' (#63 ) from upstream-sync/awesome-ai-for-science-20260808-34bc16-dqxe into main Reviewed-on: #63	2026-08-08 15:34:08 +00:00
promptadmin	6dd9ab61fc	Merge pull request '[Upstream sync] GoekeLab/awesome-genomic-skills (github) — 0 added, 1 modified' (#64 ) from upstream-sync/awesome-genomic-skills-20260808-8cf42e-kipo into main Reviewed-on: #64	2026-08-08 15:33:28 +00:00
promptadmin	3d3e964173	Merge pull request '[Upstream sync] zhoujieli/Awesome-LLM-Agents-Scientific-Discovery (github) — 0 added, 1 modified' (#65 ) from upstream-sync/awesome-llm-agents-scientific-discovery-20260808-bb3be5-vxhe into main Reviewed-on: #65	2026-08-08 15:32:08 +00:00
promptadmin	2a32b20ab6	[upstream-sync] README.md from GoekeLab/awesome-genomic-skills@8cf42e1d [catalogue]	2026-08-08 15:30:17 +00:00
promptadmin	259566ec9a	[upstream-sync] README.md from ai-boost/awesome-ai-for-science@34bc16f8 [catalogue]	2026-08-08 15:29:51 +00:00
promptadmin	1601315615	[upstream-sync] docs/data/resources.json from inoue0426/awesome-computational-biology@478be843 [catalogue]	2026-07-17 21:14:17 +00:00
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promptadmin	86a74a726b	[upstream-sync] data/resources.json from inoue0426/awesome-computational-biology@478be843 [catalogue]	2026-07-17 21:13:57 +00:00
promptadmin	783d416ecb	[upstream-sync] README.md from inoue0426/awesome-computational-biology@478be843 [catalogue]	2026-07-17 21:13:48 +00:00