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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)

AI-powered note linking and research graph navigation

ReviewAid is an open-source AI-assisted tool for full-text screening and data extraction in systematic reviews. It supports evidence synthesis workflows by using large language models to classify articles according to user-defined PICO criteria and extract structured information from full-text publications. ReviewAid is designed as a supplementary reviewer rather than a replacement for human judgement. It aims to reduce manual workload, improve consistency, and assist researchers during screening and data extraction while maintaining human oversight throughout the evidence synthesis process.

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

NOS-TLPlot is an open-source tool for visualizing Newcastle–Ottawa Scale (NOS) risk-of-bias assessments in systematic reviews. It converts NOS star ratings into publication-ready traffic-light plots and 12 specialized visualizations, enabling reviewers and readers to interpret study-level risk-of-bias results clearly and reproducibly.

RejuvenationKit is an open-source Python toolkit for reproducible auditing and analysis of longitudinal preclinical rejuvenation studies. It provides protocol-aware missingness checks, experimental-confounding diagnostics, attrition and analysis-readiness profiling, covariance-aware multichannel change detection, sequential response monitoring, randomized longitudinal inference, visualization, and integrity-tracked report bundles.

Simulation of large-scale brain models

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

This is a R package to compute the automorphisms between pairwise aligned DNA sequences represented as elements from a Genomic Abelian group. In a general scenario, from genomic regions till the whole genomes from a given population (from any species or close related species) can be algebraically represented as a direct sum of cyclic groups or more specifically Abelian p-groups. Basically, we propose the representation of multiple sequence alignments of length N bp as element of a finite Abelian group created by the direct sum of homocyclic Abelian group of prime-power order.

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.

Aggregate results from bioinformatics analyses across many samples into a single report.

Interaction Fingerprints for protein-ligand complexes and more.

Next-generation benchmark for data-driven global weather models with standardized evaluation framework and curated datasets for ML forecasting (Google Research, 2024)

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)

Python library for blazing-fast genomic interval operations and genomic file formats I/O on Polars DataFrames

Simulations of spiking neural networks.

This is an R/shiny package to perform functional enrichment analysis for microbiome data. This package was based on clusterProfiler. Moreover, MicrobiomeProfiler support KEGG enrichment analysis, COG enrichment analysis, Microbe-Disease association enrichment analysis, Metabo-Pathway analysis.

Medical large vision-language model unifying comprehension and generation via heterogeneous knowledge adaptation, enabling holistic medical image understanding, visual question answering, and clinical report generation across diverse modalities (ZJU4HealthCare, 1.6K+ stars)

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

A toolkit for visualizations in materials informatics.

Microsoft's foundation model for the Earth system supporting weather, air pollution, and ocean wave forecasting at multiple resolutions, trained on 1M+ hours of diverse atmospheric data (Nature 2025)

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.

SQUARNA is a tool for RNA secondary structure prediction. It can take a single RNA sequence or an alignment of sequences as input. SQUARNA handles pseudoknots and can predict alternative structures. SQUARNA allows structural restraints and chemical probing data as additional input and is available at https://github.com/febos/SQUARNA and https://larnal.imol.institute/.

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

Meta's comprehensive ML ecosystem for materials/chemistry with 118M+ DFT calculations, EquiformerV2 models achieving top Matbench Discovery performance

PyTorch-based embedding instance segmentation algorithm optimized for accurate, efficient, and portable cell and nucleus segmentation across fluorescence and brightfield microscopy images, achieving state-of-the-art speed and accuracy with lightweight model sizes suitable for edge deployment (224+ stars, Apache 2.0)

Toolbox for comparative genomics of MAGs

A library for processing, analyzing and modeling spectroscopic data.

Chemical Information from the Web.

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.

ClustIRR analyzes repertoires of B- and T-cell receptors. It starts by identifying communities of immune receptors with similar specificities, based on the sequences of their complementarity-determining regions (CDRs). Next, it employs a Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires, allowing the identification of expanding or contracting communities in response to e.g. infection or cancer treatment.

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.

Acausal modeling framework for automatically parallelized scientific machine learning (1.5k+ stars)

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.

Java framework for processing biological data.

A Python script that converts positional information from a SAM dataset into interval format with 0-based start and 1-based end. CIGAR string of SAM format is used to compute the end coordinate.

High-throughput extensible toolkit for processing FASTQ data. The goal of this package is to empower users to quickly build out small programmatic 'kernels' to define any FASTQ processing task they may need. Builds on Intel TBB’s flow graph to orchestrate concurrent I/O and data processing; throughput can be as fast as compression and disk speed allows. The package also ships with a suite of predefined kernels for common FASTQ tasks.

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.

Scientific Computing for Chemists with Python is a Jupyter book teaching basic python in chemistry skills, including relevant libraries, and applies them to solving chemical problems.

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.

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

An R interface to the HISAT2 spliced short-read aligner by Kim et al. (2015). The package contains wrapper functions to create a genome index and to perform the read alignment to the generated index.

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)

Package fills a helper package role for whole gDR suite. It helps to support good development practices by keeping style requirements and style tests for other packages. It also contains build helpers to make all package requirements met.