Find open-source science resources

Plain-text, git-tracked electronic lab notebook (ELN) for reproducible bioinformatics — threads your R & Python figures into living lab notes with full provenance. Built for single-cell / CyTOF / flow cytometry; works with Obsidian, Quarto & Jupyter.

PyTorch domain library for geospatial deep learning providing standardized datasets, samplers, transforms, and pre-trained models for remote sensing, land cover mapping, and environmental monitoring (Microsoft, 4K+ stars)

Utilities for working with CSV/Tab-delimited files.

The R package decemedip is a novel computational paradigm developed for inferring the relative abundances of cell types and tissues measure by methylated DNA immunoprecipitation sequencing (MeDIP-Seq). This paradigm allows using reference data from other technologies such as microarray or WGBS.

Python package for segmenting geospatial data with the Segment Anything Model (SAM), enabling zero-shot object segmentation in satellite and aerial imagery for remote sensing and Earth observation (MIT, 4k+ stars)

Scalable genomic analysis.

MCP server enabling spatial transcriptomics analysis via natural language, integrating 60+ methods including SpaGCN, Cell2location, LIANA+, CellRank for Visium, Xenium, MERFISH platforms

Microsoft AI for Good Lab's open-source biodiversity research hub providing AI models, edge devices, and tools for wildlife monitoring and conservation, including MegaDetector (camera trap animal detection), SPARROW (species recognition), PytorchWildlife (conservation AI toolkit), and bioacoustics analysis pipelines (1K+ stars)

Provides functionality for processing and statistical analysis of multiplexed assays of variant effect (MAVE) and similar data. The package contains functions covering the full workflow from raw FASTQ files to publication-ready visualizations. A broad range of library designs can be processed with a single, unified interface.

Bring the power and flexibility of AnnData to the R ecosystem, allowing you to effortlessly manipulate and analyse your single-cell data. This package lets you work with backed h5ad and zarr files, directly access various slots (e.g. X, obs, var), or convert the data into SingleCellExperiment and Seurat objects.

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)

The HGVS Nomenclature is an internationally-recognized standard for the description of DNA, RNA and protein sequence variants. It is used to convey variants in clinical reports and to share variants in publications and databases. The HGVS Nomenclature is administered by the [HGVS Variant Nomenclature Committee (HVNC)](https://hgvs-nomenclature.org/stable/hvnc/) under the auspices of the [Human Genome Organization (HUGO)](https://hugo-int.org/).

Open source PEM (Proton Exchange Membrane) fuel cell simulation tool.

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)

Unified interface for local, global, gradient-based and derivative-free optimization (800+ stars)

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)

bettr provides a set of interactive visualization methods to explore the results of a benchmarking study, where typically more than a single performance measures are computed. The user can weight the performance measures according to their preferences. Performance measures can also be grouped and aggregated according to additional annotations.

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)

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 Go library and command line utility for engineering organisms.

Module for single-cell data extraction given a segmentation mask and multi-channel image.

A toolkit for visualizations in materials informatics.

The main purpose of HTSlib is to provide access to genomic information files, both alignment data (SAM, BAM, and CRAM formats) and variant data (VCF and BCF formats). The library also provides interfaces to access and index genome reference data in FASTA format and tab-delimited files with genomic coordinates. It is utilized and incorporated into both SAMtools and BCFtools.

Provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package.

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

Spatial transcriptomic technologies have helped to resolve the connection between gene expression and the 2D orientation of tissues relative to each other. However, the limited single-cell resolution makes it difficult to highlight the most important molecular interactions in these tissues. SpaceMarkers, R/Bioconductor software, can help to find molecular interactions, by identifying genes associated with latent space interactions in spatial transcriptomics.

Microsoft's generative model for sampling protein equilibrium conformations 100,000× faster than MD simulations, predicting domain motions, local unfolding and cryptic binding pockets on a single GPU (Science 2025)

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)

PseudoScope is an automated, locally-executable computational pipeline designed specifically for comprehensive Pseudomonas aeruginosa genomic surveillance. It integrates seven essential analysis modules into a single, cohesive workflow: FASTA QC (assembly quality metrics), MLST (Oxford scheme), PAST serotyping (O-antigen typing), AMRFinderPlus (antimicrobial resistance gene detection), ABRicate (multi-database screening for resistance, virulence, plasmids, biocides), Ultimate Reporter (gene-centric integration with interactive HTML), and Visualisation Dashboard (publication-ready interactive plots including PCA, networks, boxplots). PseudoScope runs entirely locally (or on HPC clusters), protects data privacy, and produces beautiful interactive reports in minutes.

Kleboscope is an automated, locally‑executable computational pipeline designed specifically for comprehensive Klebsiella pneumoniae genomic surveillance. It addresses the growing threat of multidrug‑resistant and hypervirulent K. pneumoniae by integrating eight essential analysis modules into a single, cohesive workflow. Kleboscope offers two complementary report views: Gene‑centric – each gene is shown with all genomes that contain it, together with its frequency, enabling rapid cross‑genome pattern discovery; and Sample‑centric – each isolate gets its own interactive box with typing badges (MLST, K‑locus, O‑locus, hypervirulence), per‑database tables (AMR, Virulence, BACMET, Plasmids), and full mutation details – perfect for clinical reports and patient‑level investigations.

Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way.

A two-step desktop GUI application for RNA-seq differential gene expression (DEG) analysis. Step 1 reads raw GDC/TCGA STAR gene-count files together with a GDC sample sheet, matches samples to a Tumor/Normal design, and runs PyDESeq2 to produce normalized counts and DEG statistics. Step 2 generates publication-style volcano plots, MA plots, summary bar charts, and expression heatmaps (with optional gene labeling) from the results, exportable as PNG, PDF, SVG, or TIFF. Requires no coding from the user.

Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed.

SpaceTrooper performs Quality Control analysis using data driven GLM models of Image-Based spatial data, providing exploration plots, QC metrics computation, outlier detection. It implements a GLM strategy for the detection of low quality cells in imaging-based spatial data (Transcriptomics and Proteomics). It additionally implements several plots for the visualization of imaging based polygons through the ggplot2 package.

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)

Another cross-platform, efficient, practical and pretty CSV/TSV toolkit.

Web-based platform for discovering professional contacts, organizations, and business email addresses using advanced search and filtering capabilities.

StaphScope is an automated, locally-executable computational pipeline designed specifically for comprehensive Staphylococcus aureus genomic surveillance. It addresses the critical bottleneck in MRSA research by integrating seven essential genotyping methods into a single, cohesive workflow.

Deep learning atomistic model across elements, temperatures, and pressures

Benchmark evaluating AI agents for end-to-end automated research from re-discovery to new-discovery, with 40 real-science tasks across 10 disciplines, curated datasets from published papers, and expert-curated multimodal rubrics (170+ stars, MIT License)

A genomic surveillance framework for Staphylococcus aureus

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)

This package provides functions used in Seqtometry (Kousnetsov et al. 2024), a method for analyzing single cell (scRNA-seq or scATAC-seq) data via signature (gene set) enrichment scores. The Seqtometry scores may be useful for annotating or characterizing cells, either in a flow cytometry like workflow (where scores are standalone features used for progressive partitoning as described in the Seqtometry publication) or in a cluster-based workflow (as features of clusters). The exported impute function (a port of Python's MAGIC-impute, van Dijk et al. 2018), may also be useful for single cell analysis on its own.

Agent skills (SKILL.md + deterministic tools) for the AI4S workflow — topic exploration, literature survey, runnable experiments, publication-grade papers, and integrity audit, with every citation and number traceable to its source (by ai4s-research, maintainers of this list; MIT, 2026)

Java-based browser. Fast, efficient, scalable visualization tool for genomics data and annotations. Handles a large variety of formats.

Pathogensurveillance is a population genomics pipeline for pathogen identification, variant detection, and biosurveillance. The pipeline accepts paths to raw reads for one or more organisms and creates reports in the form of an interactive HTML document. Significant features include the ability to analyze unidentified eukaryotic and prokaryotic samples, creation of reports for multiple user-defined groupings of samples, automated discovery and downloading of reference assemblies from NCBI RefSeq, and rapid initial identification based on k-mer sketches followed by a more robust multi gene phylogeny and SNP-based phylogeny.

PyTorch-native atomistic simulation engine for the machine-learned interatomic potential (MLIP) era, enabling batched molecular dynamics and structural relaxation with automatic GPU memory management; supports MACE, Fairchem, SevenNet, ORB, MatterSim and other popular MLIPs with up to 100x speedup over ASE (Radical AI, AI for Science 2026, 468+ stars, MIT License)

AcinetoScope is an automated, comprehensive bioinformatics pipeline designed specifically for the genomic analysis of Acinetobacter baumannii, a WHO Critical Priority pathogen responsible for devastating hospital-acquired infections. It integrates seven analysis types (MLST, ABRicate, AMRFinder, Kaptive 3, APT, PlasmidFinder, and mutation detection) into a single automated workflow — from FASTA to actionable insights. The pipeline offers both gene-centric and sample-centric reporting, dynamic grouping by typing, and is optimised for HPC, cloud, and container environments.

Scientific machine learning benchmarks & differential equation solvers

Rust implementations of algorithms and data structures useful for bioinformatics.