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The Common Core Ontologies (CCO) comprise twelve ontologies that are designed to represent and integrate taxonomies of generic classes and relations across all domains of interest. CCO is a mid-level extension of Basic Formal Ontology (BFO), an upper-level ontology framework widely used to structure and integrate ontologies in the biomedical domain (Arp, et al., 2015). BFO aims to represent the most generic categories of entity and the most generic types of relations that hold between them, by defining a small number of classes and relations. CCO then extends from BFO in the sense that every class in CCO is asserted to be a subclass of some class in BFO, and that CCO adopts the generic relations defined in BFO (e.g., has_part) (Smith and Grenon, 2004). Accordingly, CCO classes and relations are heavily constrained by the BFO framework, from which it inherits much of its basic semantic relationships.

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)

Parsers and algorithms for computational chemistry logfiles.

Scalable toolkit for analyzing single-cell gene expression data, including preprocessing, visualization, clustering, and trajectory inference.

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)

Deep probabilistic framework for single-cell and spatial omics analysis, integrating scVI, scANVI, totalVI and other VAE-based models for batch correction, cell annotation, multi-omics integration, and RNA velocity (scverse/NumFOCUS, Nature Methods 2018/2024)

ChemML is a machine learning and informatics program suite for the analysis, mining, and modeling of chemical and materials data. (based on Tensorflow)

Python astronomy tools

Official Jupyter extension with `%%ai` magic commands and sidebar chat assistant, connecting multiple model providers and local inference

Python library from KIT for training deep learning models on large-sample hydrology, introducing LSTM-based rainfall-runoff modeling that learns universal, regional, and local hydrological behaviors from hundreds of catchments; widely used for streamflow forecasting, flood prediction, and drought research, supporting the CAMELS and Caravan benchmark datasets (581+ stars, BSD-3-Clause, actively maintained)

SSSOM is a Simple Standard for Sharing Ontological Mappings, providing - a TSV-based representation for ontology term mappings - a comprehensive set of standard metadata elements to describe mappings and - a standard translation between the TSV and the Web Ontology Language (OWL). Most metadata elements, such as "sssom:mapping_justification" are defined in the sssom namespace.

Deep learning software to decode EEG, ECG or MEG signals, providing standardized neural network models, preprocessing pipelines, and evaluation workflows for brain-computer interfaces and cognitive neuroscience research (1.2K+ stars, BSD 3-Clause, actively maintained)

From https://anndata.readthedocs.io/en/latest/ "Python package for handling annotated data matrices in memory and on disk, positioned between pandas and xarray."

A library containing basis sets for use in quantum chemistry calculations. In addition, this library has functionality for manipulation of basis set data.

Fast, interactive, multi-dimensional image viewer for Python, foundational platform for scientific imaging AI with a rich plugin ecosystem integrating deep learning segmentation, object tracking, and microscopy analysis workflows (2.6K+ stars)

Parallel computing with task scheduling.

MEG and EEG.

Machine learning and statistical learning for neuroimaging in Python, providing easy-to-use tools for fMRI and MRI analysis including decoding, connectivity estimation, and parcellation with seamless scikit-learn integration (INRIA Parietal team, 1.4K+ stars)

Manipulation and analysis of geometric objects.

A package for working with nuclear magnetic resonance (NMR) data including functions for reading common binary file formats and processing NMR data.

Calculate mass, elemental composition, and mass distribution spectrum of a molecule given by its chemical formula, relative element weights, or sequence.

Probabilistic framework for inferring cell fate decisions and trajectory dynamics from multi-view single-cell data using Markov chains and machine learning, integrating RNA velocity, pseudotime, and metabolic labeling to predict differentiation paths and terminal states (scverse/Theis Lab, 449+ stars, BSD 3-Clause)

Documentation Rectangle is an open-source Python package for single-cell-informed cell-type deconvolution of bulk and spatial transcriptomic data. Rectangle presents a novel approach to second-generation deconvolution, characterized by hierarchical signature building for fine-grained cell-type deconvolution, estimation and correction of unknown cellular content, and efficient handling of large-scale single-cell data during signature matrix computation. Rectangle was developed to overcome the current challenges in cell-type deconvolution, providing a robust and accurate methodology while ensuring a low computational profile.

Generalist deep learning algorithm for cell and nucleus segmentation across diverse image types, with human-in-the-loop training (2.0) and one-click image restoration (3.0), 70K+ training objects (Nature Methods 2021/2022/2025)

A tool and library for creating quantum chemistry input files.

Makes alchemical free energy calculations easier by leveraging the full power and flexibility of the PyData stack.

DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

Deep learning-based variant caller

Deep learning-based object detection and segmentation for star-convex shapes, widely adopted for cell and nucleus segmentation in fluorescence and electron microscopy via a compact neural network architecture with non-maximum suppression and shape-based post-processing (Nature Methods 2020, 1.2K+ stars)

A module for solving and visualizing the Schrödinger equation.

Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data

Molecular descriptor calculator based on [RDKit](http://www.rdkit.org/).

Open Drug Discovery Toolkit, a modular and comprehensive toolkit for use in cheminformatics, molecular modeling etc.

Spike detection and clustering-based spike sorting.