Find open-source science resources

A benchmark for ML-guided high-throughput materials discovery.

The DCAT-AP conversion to a LinkML Schema is the intended point of truth for the DCAT-AP+ schema, but could be used alternatively as a LinkML representation of DCAT-AP for other Projects. It is a port of DCAT-AP to the LinkML world that is as faithful to the original as possible. This Persistent Identifier does not only provide the SHACL Shape, but could also be used as described [here](https://github.com/perma-id/w3id.org/tree/cecbc2e5f40d928f05ed5306d24fc60db0e7bb21/nfdi-de/dcat-ap-plus). DCAT-AP+ is a [LinkML](https://linkml.io/)-based extension of the [DCAT Application Profile 3.0](https://semiceu.github.io/DCAT-AP/releases/3.0.0/) that adds a provenance layer for describing how a dataset was generated and what it is about, using the [Starting Point Terms of PROV-O](https://www.w3.org/TR/prov-o/#description-starting-point-terms), the [QUDT ontology](https://www.qudt.org/), and [Dublin Core Terms](http://purl.org/dc/terms/).

A modeling paradigm-specific registry of prefixes and their URL expansions

The System Package Data Exchange™ (SPDX®) specification is an open standard designed to represent systems containing software components as Software Bill of Materials (SBOMs). Additionally, SPDX supports AI, data, and security references, making it suitable for a wide range of risk management use cases. This _spdx3_ prefix is for SPDX 3.x versions. For earlier versions, use _spdx.term_.

BRANCHSNV reports strict clade-exclusive nucleotide markers separately from single-nucleotide substitutions reconstructed on a selected edge of a rooted phylogenetic tree, while retaining ambiguity across equally parsimonious ancestral-state reconstructions.

E(3)-equivariant neural network interatomic potentials achieving DFT accuracy with up to 1000× less training data than invariant models, foundational architecture behind MACE and Allegro (Harvard, MIT, Nature Communications 2022)

MCP server, CLI, and agent skills for searching and downloading academic papers from multiple open sources (arXiv, PubMed, bioRxiv, Semantic Scholar, OpenAlex, CORE, Europe PMC, etc.) with unified, deduplicated, LLM-friendly retrieval and an OA-first download fallback chain (OpenAGS, 1.9K+ stars, MIT License, 2025)

Python toolkit for fine-tuning geospatial foundation models

Graph deep learning library for materials science powering the M3GNet universal interatomic potential across periodic-table elements, with property prediction, structure relaxation, and crystal generation workflows built on PyTorch and DGL (576+ stars, BSD-3-Clause, actively maintained)

Byte-level BPE tokenizer for Chargaff, our DNA prediction model. No training, no merges: 1 token per UTF-8 byte.

Pretrained time series foundation model for long-horizon forecasting across diverse scientific domains including climate variables, biomedical signals, and physical observations; decoder-only Transformer architecture with strong zero-shot generalization (19.8K+ stars, Apache 2.0, 2024-2025)

Local-first, open-source healthcare AI toolkit for clinical NLP and PHI/PII de-identification across 12 languages, running entirely on-device with 1,000+ specialized medical models; provides Python SDK, REST API, Docker deployment, and native Swift apps via OpenMedKit with Apple MLX/CoreML acceleration, supporting HIPAA-aware de-identification with 247 PII checkpoints (3K+ stars, Apache 2.0, arXiv 2508.01630)

For a convenient overview and download list, visit our model page for this model.

Probabilistic programming

Graph neural network library for PyTorch enabling molecular modeling, materials discovery, protein interaction networks, and scientific knowledge graph learning (23.7k+ stars)

The ESRFET ontology describes beamline experimental techniques at ESRF to enhance the FAIRness of experimental metadata. It supports semantic interoperability and reasoning over relationships between techniques. (from homepage)

nnU-Net is a self-configuring method for deep learning-based biomedical image segmentation, developed by the Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at the German Cancer Research Center (DKFZ). It is designed to automatically adapt to a given dataset, analyzing the provided training cases to configure a matching U-Net-based segmentation pipeline without requiring expertise from the user. The tool provides pretrained models for Pancreas and Pancreas tumor segmentation, Colon cancer primaries segmentation, Abdominal organ segmentation, Liver and liver tumor segmentation, Kidney and kidney tumor segmentation, Brain Tumor segmentation and Hippocampus (MR data) segmentation

An extension of Schema.org to annotate metadata on software projects

💻 GitHub | 📘 E-SMILES 2.0 Spec | 📄 Report | 🚀 Demo

Lightweight Markdown-only skills for autonomous ML research with cross-model review loops, idea discovery, and experiment automation; no framework lock-in, works with Claude Code, Codex, OpenClaw, or any LLM agent (12.8K+ stars, MIT License, 2026)

AI coding assistant for JupyterLab with agent mode, supporting arbitrary LLM providers (2025+)

The information resource registry is a listing of data sources present in the NCATS Data Translator system. Each information resource has an identifier, a short description, and a URL to more information about that resource.

Beyond text-to-slides generation with PPTEval multi-dimensional evaluation (EMNLP 2025)

PanAbyss is a tool for exploring and visualizing pangenome graphs. It allows users to search for and display regions of a pangenome using coordinates on a reference individual or based on annotations. It also enables searching for regions associated with a selected set of individuals (for example, those linked to a phenotype), computing proximity trees, and retrieving sequences from a given region.

Python Library for Automating Molecular Simulation: input preparation, job execution, file management, output processing and building data workflows.

Machine learning toolkit for many-body quantum systems, implementing neural quantum states, variational Monte Carlo, and tensor network algorithms to solve ground-state and dynamical problems in condensed matter physics and quantum chemistry (EPFL & collaborators, Nature Physics 2019/2022+, 670+ stars)

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)

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)

Foundation model for tabular data that predicts on unseen real-world tables in a single forward pass, achieving accurate small-data classification and regression without task-specific training; widely applicable to scientific datasets with limited samples (7.4K+ stars, 2022-2026)

iona-denoise-50m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 50m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

iona-denoise-400m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 400m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

iona-denoise-200m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 200m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

iona-denoise-100m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 100m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

Three genomic foundation models, packaged together for local inference on Apple silicon.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

EMMO is a multidisciplinary effort to develop a standard representational framework (the ontology) for applied sciences. It is based on physics, analytical philosophy and information and communication technologies. It has been instigated by materials science to provide a framework for knowledge capture that is consistent with scientific principles and methodologies. (from GitHub)

The primary goal of this ontology is to standardize the representation of molecular simulation data, processes, and methodologies across disparate simulation platforms, engines (e.g., GROMACS, AMBER, NAMD), and analysis tools, while ensuring these terms are interoperable with existing life sciences ontologies

PyTorch toolkit for deep neural networks in atomistic simulations, implementing SchNet, DimeNet++, PaiNN, and GemNet for molecular dynamics and quantum chemistry (900+ stars)

Analysis of molecular dynamics trajectories.

Agent-agnostic research infrastructure providing AI agents with a structured scientific workspace for deep PDF parsing, hybrid semantic/keyword literature search, citation-graph analysis, topic discovery, and academic writing workflows; natively integrates with Claude Code, Codex, Cursor, Cline, and AgentSkills.io (530+ stars, MIT License, 2026)

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)

Robust deep learning-based segmentation of >100 anatomical structures in CT and MR images, built on nnU-Net and widely adopted in clinical radiology and surgical planning workflows (2.6K+ stars)

OEO is a domain reference ontology for energy system modeling.

Parsers and algorithms for computational chemistry logfiles.

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)