Find open-source science resources

Automated and rigorous experiments using AI agents for scientific discovery

GPU-accelerated differentiable physics simulation engine built on NVIDIA Warp, supporting rigid/soft body, cloth, and gradient-based optimization for scientific ML, initiated by Disney Research, DeepMind, and NVIDIA (Linux Foundation, Apache 2.0, 2025)

Incremental knowledge graph construction using LLMs with entity extraction and Neo4j visualization

ECMWF's unified framework and command-line tool to run AI-based weather forecasting models (GraphCast, Aurora, Pangu, NeuralGCM, FourCastNet) with operational ECMWF data infrastructure, enabling standardized inference and benchmarking across state-of-the-art meteorological AI systems (ECMWF, 576+ stars)

Open-source framework for building physics-ML models at scale (renamed from Modulus, 2025)

Transformer encoder-decoder for de novo peptide sequencing from tandem mass spectrometry, translating MS/MS spectra directly to peptide sequences without reference databases, enabling identification of novel peptides for immunopeptidomics, antibody repertoires, and metaproteomes (Noble Lab UW, Nature Communications 2024)

General multimodal protein design framework enabling DNA-encoding of chemistry for programmable enzyme design and diverse protein generation through diffusion-based generative modeling (190+ stars, Apache 2.0, 2026)

Cross-platform library for differentiable programming of quantum computers with automatic differentiation, enabling hybrid quantum-classical machine learning for quantum chemistry, quantum physics, and NISQ algorithm research (Xanadu, 3k+ stars)

Diffusion model for scalable protein structure design with multi-motif scaffolding capabilities, achieving state-of-the-art designability, diversity, and novelty through SE(3)-equivariant attention and massive data augmentation (AlQuraishi Lab, 2024)

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)

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)

Pythonic Access to the Ensembl database.

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

Modular toolchain for an extensible and customizable ETL pipeline that extracts, transforms, and loads clinical data and medical imaging metadata, applying dataset-specific mappings to generate outputs compatible with the EUCAIM Common Data Model (CDM). Its design aims to minimize manual data preparation efforts and facilitate customization and integration with other components, such as data quality assurance tools. Containerized, currently supports input datasets in CSV, JSON, XLSX.

Automatically detects duplicate and near-duplicate DICOM image series in large medical imaging datasets. Uses a tiered pipeline combining DICOM metadata analysis, SHA-based pixel hashing, and image similarity metrics (SSIM, cosine, MAD) to identify exact copies, re-exported series, and near-identical acquisitions. All findings are reported for human expert review — no files are modified or deleted automatically. For scenarios requiring strict, image-level deduplication based on pixel content, fully agnostic to metadata changes, consider using [https://bio.tools/image_duplicate_check_tool]

This module provides a command line tool to validate DICOM SEG files against predefined requirements specified in an Excel file. It contains components for finding relevant DICOM files, loading and parsing validation requests and applying validation rules. The main validation process checks each DICOM file for compliance with the Type 1, 1C, 2, 2C and 3 attributes specified in the requirements file. A detailed report is generated highlighting issues such as missing, invalid or conditionally required attributes, including file paths and affected DICOM tags. The tool is designed to ensure data integrity and compliance with DICOM standards.