Find open-source science resources

The Bioregistry is integrative meta-registry of biological databases, ontologies, and nomenclatures that is backed by an open database.

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)

Multi-LLM consensus framework for automated cell type annotation in single-cell transcriptomics, integrating predictions from 10+ large language models with iterative discussion and uncertainty quantification to reduce single-model biases, achieving up to 95% accuracy without reference datasets; available as CRAN R package and PyPI Python package with Scanpy/Seurat integration (2025)

Toolbox for larger-than-memory scientific computing and visualization, providing efficient out-of-core data structures using files or shared memory, for dense and sparse vectors, matrices, and arrays, with applications to nonuniformly sampled signals and images.

An ultrafast protein aligner for `blastp` and `blastx` like searches.

Probabilistic programming

Ensemble of automated machine learning protocols that can be run sequentially through a single command line. The program works for regression and classification problems.

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_.

A large RDF store built from American governmental data. This semantic space has a mixture of direct terms and subspaces.

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)

Julia differential equations suite

Deep learning-based multi-animal pose tracking and behavior classification, enabling automated quantification of social interactions and collective behavior across species (Nature Methods 2022, 2.2K+ stars)

Biomedical Model Context Protocol (MCP) server unifying literature search across PubMed/Europe PMC, entity pivoting across genes/variants/drugs/diseases/pathways/proteins, local study analytics, and Claude Code/Codex integration for agentic biomedical research (531+ stars, MIT License, 2025-2026)

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

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)

Vitro is a full stack framework for building semantic web applications. It is not domain specific.

This package implements a variety of methods for batch correction in single-cell RNA sequencing (scRNA-seq) data. It incorporates quantitative metrics (e.g. Wasserstein distance, Adjusted Rand Index) to evaluate their performance. Furthermore, the package assists users in identifying and applying the optimal method for specific datasets.

Google DeepMind's unified DNA sequence foundation model predicting molecular consequences of genetic variants from single-base resolution up to 1 megabase context, jointly outputting thousands of regulatory tracks (RNA expression, splicing, chromatin accessibility, TF binding, contact maps) for human and mouse genomes via a Python client and non-commercial API (2025)

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, 1.1K+ stars, 2026)

A novel framework to correct for batch effects prior to any downstream analysis in microbiome data based on Projection to Latent Structures Discriminant Analysis. The main method is named “PLSDA-batch”. It first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data whilst preserving biological variation of interest. PLSDA-batch is highly suitable for microbiome data as it is non-parametric, multivariate and allows for ordination and data visualisation. Combined with centered log-ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far. Two other variants are proposed for 1/ unbalanced batch x treatment designs that are commonly encountered in studies with small sample sizes, and for 2/ selection of discriminative variables amongst treatment groups to avoid overfitting in classification problems. These two variants have widened the scope of applicability of PLSDA-batch to different data settings.

Graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations, enabling large-scale atomistic modeling with machine learning potentials (MDIL-SNU, MIT License)

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)

Utility functions for working with CONCH data, listing remote files. One function assigns HoverNet nuclei to ProvGigaPath tiles with a scale factor to align coordinates. Provides internal utility functions for 'imageFeatureTCGA' and most functions are not meant for end users.

The package imports data from HoverNet, and ProvGigaPath pipelines. Pipeline output data are hosted in a self-owned online repository. Package functionality conveniently incorporates pipeline data into existing MultiAssayExperiment instances from curatedTCGAData.

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

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 scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users.

Automated academic illustration generation for AI scientists, converting research papers into publication-ready figures using VLMs and diffusion models with iterative refinement (PKU & Google Research, 6.2K+ stars, 2026)

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)

GlycoDash is an R Shiny dashboard for processing glycomics data obtained from LaCyTools, SweetSuite and Skyline.

Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided.

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)

R Package for interactive visualization and browsing NGS data. It contains a browser for both transcript and genomic coordinate view. In addition a QC and general metaplots are included, among others differential translation plots and gene expression plots. The package is still under development.

Turn any AI agent into a life science expert with NVIDIA BioNeMo skills, enabling agentic workflows for drug discovery, protein engineering, and biomolecular design (329+ stars, Apache 2.0 / CC-BY-4.0, 2026)

Production-grade ETL for transforming complex documents into structured formats, with open-source API

R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more.

RBPBench is a multi-function tool to evaluate CLIP-seq and other related genomic region data using a comprehensive collection of known RNA-binding protein (RBP) binding motifs. RBPBench can be used for a variety of purposes, from RBP motif search (database or user-supplied RBP motifs) in genomic regions, over motif enrichment and co-occurrence analysis, in-depth comparisons over multiple datasets via sequence and genomic annotation statistics, to benchmarking CLIP-seq peak caller methods as well as comparisons across cell types and CLIP-seq protocols. RBPBench supports both sequence and structure motifs, as well as regular expressions (sequence and structure patterns). Moreover, users can easily provide their own motif collections.

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)

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.

Machine learning in Julia

Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics.

Python toolkit for fine-tuning geospatial foundation models

The Basic Register of Thesauri, Ontologies & Classifications (BARTOC) is a database of Knowledge Organization Systems and KOS related registries. The main goal of BARTOC is to list as many Knowledge Organization Systems as possible at one place in order to achieve greater visibility, highlight their features, make them searchable and comparable, and foster knowledge sharing. BARTOC includes any kind of KOS from any subject area, in any language, any publication format, and any form of accessibility. BARTOC’s search interface is available in 20 European languages and provides two search options: Basic Search by keywords, and Advanced Search by taxonomy terms. A circle of editors has gathered around BARTOC from all across Europe and BARTOC has been approved by the International Society for Knowledge Organization (ISKO).

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

A powerful, high-performance bioinformatics framework for discovering, evaluating, and verifying microRNA (miRNA) interactions across DNA, RNA, and protein target sequences. The mirnaprotpred package provides two core modules: SeqFinder: A discovery engine to find all potential miRNA interactions across a genome or target sequence. Validator: A targeted verification engine to test specific, user-provided miRNAs against a target sequence. Both modules are powered by a shared, rigorous biological engine that evaluates exact seed matching, wobble pairing, AU-rich context, and RNAduplex thermodynamic stability.

A cross-platform and ultrafast toolkit for FASTA/Q file manipulation in Golang.

Parsers and algorithms for computational chemistry logfiles.

DeepTaxa is a hybrid CNN-BERT deep learning framework for multi-rank taxonomic classification of 16S rRNA gene sequences. It predicts all seven Linnaean ranks from domain to species in a single forward pass and provides pre-trained checkpoints for full-length 16S and V3-V4 amplicons.

15TB collection of 16 large-scale numerical simulation datasets spanning fluid dynamics, MHD, astrophysics, biological systems, and acoustic scattering, with unified PyTorch dataloaders and benchmarks for training foundation models on physical sciences (Polymathic AI, NeurIPS 2024)