widely adopted as a tool for doing science<\/a>\u00a0for about twenty years now.<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” _builder_version=”4.16″ custom_padding=”||0px|” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”FESTIM” url=”https:\/\/festim.readthedocs.io\/en\/latest\/” admin_label=”FESTIM” _builder_version=”4.25.0″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
FESTIM, a leading open-source hydrogen transport simulation tool, relies on FEniCS. Globally adopted, it serves diverse sectors like nuclear design and hydrogen aviation advancements.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Open OnDemand” url=”https:\/\/openondemand.org\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2024\/06\/Open-OnDemand-300×300-1.png” admin_label=”Open On Demand” _builder_version=”4.25.2″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
Developed by the Ohio Supercomputer Center (OSC) and funded by the National Science Foundation, Open OnDemand is an open-source portal that enables web-based access to HPC services. Clients manage files and jobs, create and share apps, run GUI applications and connect via SSH, all from any device with a web browser.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Visual Python” url=”https:\/\/visualpython.ai\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2024\/07\/Visual-Python-300×300-1.png” admin_label=”Visual Python” _builder_version=”4.25.2″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
Visual Python is a GUI-based Python code generator, developed on the Jupyter Lab, Jupyter Notebook and Google Colab as an extension. Visual Python is an open source project started for students who struggle with coding during Python classes for data science.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”GP Jax” url=”https:\/\/jaxgaussianprocesses.com\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2024\/09\/GP-Jax-300×300-1.png” admin_label=”GP Jax” _builder_version=”4.27.0″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
GPJax is a Python library that enables Bayesian inference with Gaussian processes using JAX on both CPUs and GPUs. The abstractions provided by GPJax are designed to be tightly coupled with the underlying math, providing a framework that is intuitive to researchers and practitioners alike. Support for classification, regression, and decision making\/Bayesian optimization are available thanks to work from numerous contributors.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” _builder_version=”4.16″ custom_padding=”||0px|” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”toqito” url=”https:\/\/toqito.readthedocs.io\/en\/latest\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2024\/09\/toqito-logo-300×300-1.png” admin_label=”toqito” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
The toqito package is an open-source library for studying various objects in quantum information, namely, states, channels, and measurements. toqito provides numerical tools to study problems about entanglement theory, nonlocal games, and other aspects of quantum information often associated with computer science.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Folium” url=”https:\/\/python-visualization.github.io\/folium” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2024\/09\/Folium-Logo-300×300-1.png” admin_label=”Folium” _builder_version=”4.27.0″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in a Leaflet map via Folium.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Snakemake” url=”https:\/\/snakemake.github.io” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2024\/10\/Snakemake-300×300-1.png” admin_label=”Snakemake” _builder_version=”4.27.2″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
The Snakemake workflow management system is a framework for reproducible and scalable data analyses. Workflows are described via a human readable, Python based language.<\/p>\n
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They can be seamlessly scaled to server, cluster, grid and cloud environments, without the need to modify the workflow definition. Snakemake workflows can entail a description of required software, which will be automatically deployed to any execution environment. Finally, Snakemake can automatically generate server-free, graphical, interactive reports that connect the results of a data analyisis with the code, parameters, and software used for each step, ensuring data provenance and transparency. With on average more than 11 new citations per week in 2023, and almost 950000 downloads on anaconda.org, Snakemake is extremely popular.<\/p>\n
[\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”SunPeek” url=”https:\/\/docs.sunpeek.org” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/01\/Sun-Peak-300×300-1.png” admin_label=”SunPeak” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
SunPeek is a python package and web application for assesing the operational performance of Large Solar-thermal Arrays. It implements the ISO 24194 Performance Check standard, and is intended to assist researchers and plant operators in understanding the long term performance of these systems.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” _builder_version=”4.16″ custom_padding=”||0px|” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Bambi” url=”https:\/\/bambinos.github.io\/bambi\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/04\/Bambi-logo-RGB-300×300-1.png” content_last_edited=”off|desktop” admin_label=”Bambi” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}” content__hover_enabled=”off|desktop”]<\/p>\n
Bambi is a high-level interface to build, fit, and explore Bayesian statistical models.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Open2C” url=”https:\/\/open2c.github.io\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/04\/Open2C-logo-300×300-1.png” admin_label=”Open2C” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
Open2C is a community that develops and maintains open-source tools for 3D chromosome biology and genomic data science, primarily in Python.
<\/span><\/p>\n[\/et_pb_blurb][et_pb_toggle title=”Read More” _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
We are particularly interested in 3D genomics, and most of the tools are focused on the analysis of data obtained using Hi-C and related high-throughput technologies. We like our tools to be easy to use, flexible, and scalable in order to facilitate active development of novel analytical approaches and to make use of the latest and largest datasets.<\/span><\/p>\n[\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Poly” url=”https:\/\/pkg.go.dev\/github.com\/bebop\/poly” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/04\/Poly-logo-300×300-1.png” admin_label=”Poly” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
The aim of Poly is to provide a comprehensive software framework for engineering biology. Poly already ships a suite of parsers, optimizers, and various tools to engineer DNA and other biological sequences which we want to further expand to support more complex engineering workflows<\/span><\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”PRQL” url=”https:\/\/prql-lang.org\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/04\/PRQL-logo-300×300-1.png” admin_label=”PRQL” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
PRQL is a modern language for transforming data \u2014 a simple, powerful, pipelined SQL replacement<\/span><\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” _builder_version=”4.16″ custom_padding=”||0px|” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”PUDL” url=”https:\/\/catalystcoop-pudl.readthedocs.io\/en\/nightly\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/04\/PUDL-logo-300×300-1.png” content_last_edited=”off|desktop” admin_label=”PUDL” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}” content__hover_enabled=”off|desktop”]<\/p>\n
PUDL is a data processing pipeline created by Catalyst Cooperative that cleans, integrates, and standardizes some of the most widely used public energy datasets in the US.<\/span><\/p>\n[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
The data serve researchers, activists, journalists, and policy makers that might not have the technical expertise to access it in its raw form, the time to clean and prepare the data for bulk analysis, or the means to purchase it from existing commercial providers.<\/span><\/p>\n[\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”PyData Sparse” url=”https:\/\/sparse.pydata.org\/en\/stable\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/04\/PyData-Sparse-logo-300×300-1.png” admin_label=”PyData Sparse” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
N-dimensional sparse arrays for the PyData Ecosystem<\/span><\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”SPHinXsys” url=”https:\/\/www.sphinxsys.org\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/04\/SPHinXsys-logo-300×300-1.png” admin_label=”SPHinXsys” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
SPHinXsys provides C++ APIs for physical accurate simulation and aims to model coupled industrial dynamic systems including fluid, solid, multi-body dynamics and beyond.<\/span><\/p>\n[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
The multi-physics library is based a unique, unified computational framework by which strong couplings have been achieved for all involved physics.<\/span><\/p>\n[\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”SageMath” url=”https:\/\/www.sagemath.org\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/04\/SageMath-logo-300×300-1.png” admin_label=”SageMath” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
SageMath is a comprehensive mathematical software system, developed since 2005.<\/span><\/p>\n[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
Its scope ranges from general untyped symbolic computation to research-level computational tools in numerous areas of mathematics. Sage makes use of hundreds of third-party, separately maintained packages written either in Python\/Cython or in other languages (C, C++, Common Lisp). The Sage library consists of about 3000 first-party Python and Cython modules.<\/span><\/p>\n[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” _builder_version=”4.16″ custom_padding=”||0px|” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”BayesFlow” url=”https:\/\/bayesflow.org\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/05\/BayesFlow-logo-300×300-1.png” content_last_edited=”off|desktop” admin_label=”BayesFlow” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}” content__hover_enabled=”off|desktop”]<\/p>\n
BayesFlow implements amortized Bayesian workflows with deep learning. This means users first train a neural network on simulated data. Then they obtain posterior inference on any real data almost instantly.<\/span><\/p>\n[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
The Python library is based on Keras3 which allows users to choose between a PyTorch, TensorFlow, or JAX backend. BayesFlow follows a modular software architecture that is built for machine learning scientists and applied domain users alike.<\/span><\/p>\n[\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”HiGHS” url=”https:\/\/highs.dev\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/05\/HiGHS-logo-300×300-1.png” admin_label=”HiGHS” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
HiGHS offers open-source high-performance linear optimization software. In the industry-standard independent benchmarks, it is seen to be the best such software in the world. It can be used through many language and application interfaces, including NumFOCUS projects SciPy and JuMP.<\/span><\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”RxInfer” url=”https:\/\/rxinfer.com\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/05\/RxInfer-logo-300×300-1.png” admin_label=”RxInfer” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
RxInfer is a Julia package for fast and scalable Bayesian inference in probabilistic models. This toolbox is very suited for fast online inference in freely definable non-linear state-space models.<\/span><\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”sbi” url=”https:\/\/sbi-dev.github.io\/sbi\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/06\/sbi-logo-300×300-1.png” admin_label=”sbi” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}”]<\/p>\n
Many areas of science and engineering make extensive use of complex, stochastic, numerical simulations to describe the structure and dynamics of the processes being investigated. A key challenge in simulation-based science is constraining these simulation models\u2019 parameters with observational data.<\/span><\/p>\n[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
Bayesian inference provides a general and powerful framework to invert the simulators, i.e. describe the parameters which are consistent both with empirical data and prior knowledge. In the case of simulators, a key quantity required for statistical inference, the likelihood of observed data given parameters, is typically intractable, rendering conventional statistical approaches inapplicable. `sbi` implements machine-learning methods that address this problem.<\/span><\/p>\n[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” _builder_version=”4.16″ custom_padding=”||0px|” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Narwhals” url=”https:\/\/github.com\/narwhals-dev\/narwhals” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/06\/Narwhals-logo-300×300-1.png” content_last_edited=”off|desktop” admin_label=”Narwhals” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}” content__hover_enabled=”off|desktop”]<\/p>\n
Lightweight compatibility layer between dataframe libraries<\/span><\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”marimo” url=”https:\/\/marimo.io\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/06\/marimo-logo-300×300-1.png” content_last_edited=”off|desktop” admin_label=”marimo” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}” content__hover_enabled=”off|desktop”]<\/p>\n
marimo is a reactive Python notebook: run a cell or interact with a UI element, and marimo automatically runs dependent cells (or marks them as stale), keeping code and outputs consistent.<\/span><\/p>\n[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
marimo notebooks are stored as pure Python, executable as scripts, and deployable as apps. marimo is built from scratch, designed specifically for working with data.<\/span><\/p>\n[\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Albumentations” url=”https:\/\/albumentations.ai\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/06\/Albumentations-logo-300×300-1.png” content_last_edited=”off|desktop” admin_label=”Albumentations” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}” content__hover_enabled=”off|desktop”]<\/p>\n
Albumentations is a fast and flexible image augmentation library that supports over 70 different transforms for object detection, segmentation and image classification tasks.<\/span><\/p>\n[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
It accelerates deep learning development by providing an efficient, hardware-optimized toolkit for data augmentation that’s readily compatible with popular ML frameworks like PyTorch and TensorFlow.<\/span><\/p>\n[\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Materials Project Software Foundation” url=”https:\/\/next-gen.materialsproject.org” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/07\/Materials-Project-Software-Foundation-logo-300×300-1.png” content_last_edited=”off|desktop” admin_label=”Materials Project Software Foundation” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}” content__hover_enabled=”off|desktop”]<\/p>\n
The mission of the Materials Project Software Foundation (MPSF) is to provide community-driven, inclusive, coordinated, transparent, and accountable governance of select public-facing and open-source Materials Project software packages.<\/span><\/p>\n[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
These software packages constitute an ecosystem of complementary codes that together form the foundation of the Materials Project Database, while also enabling numerous capabilities in materials science, high-throughput computations, and analysis of molecular simulation results. MPSF supported codes are now used by a wide community of global researchers, as evidenced by nearly 2,000 GitHub stars and 1,000 forks. Our work ensures that these codes remain up-to-date, interoperable, and continue to serve the ever-evolving needs of this research community.<\/span><\/p>\n[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” _builder_version=”4.16″ custom_padding=”||0px|” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”UXarray” url=”https:\/\/uxarray.readthedocs.io\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/08\/UXarray-logo-300×300-1.png” content_last_edited=”off|desktop” admin_label=”UXarray” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}” content__hover_enabled=”off|desktop”]<\/p>\n
UXarray aims to address the geoscience community’s need for tools that enable standard data analysis techniques to operate directly on unstructured grid data.<\/span><\/p>\n[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
UXarray provides Xarray-styled functionality to better read in and use unstructured grid datasets that follow standard conventions, including UGRID, MPAS, ICON, SCRIP, ESMF, and Exodus grid formats. This effort is a result of the collaboration between Project Raijin (NSF NCAR and Pennsylvania State University) and the SEATS Project (Argonne National Laboratory, UC Davis, and Lawrence Livermore National Laboratory). The UXarray team welcomes community members to become part of this collaboration at any level of contribution.<\/span><\/p>\n[\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”BrainGlobe” url=”http:\/\/brainglobe.info\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/11\/BrainGlobe-300×300@4x.png” content_last_edited=”off|desktop” admin_label=”BrainGlobe” _builder_version=”4.27.4″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” global_colors_info=”{}” content__hover_enabled=”off|desktop”]<\/p>\n
The BrainGlobe Initiative exists to facilitate the development of interoperable Python-based tools for computational neuroanatomy.<\/p>\n
[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.5″ hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n
We have three main aims. These are to develop core tools to help others build software, to develop specialist software ourselves, and to build a community of users and developers.<\/p>\n
[\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb title=”Project Pythia” url=”https:\/\/projectpythia.org\/” image=”https:\/\/numfocus.org\/wp-content\/uploads\/2025\/12\/Project-Pythia.png” content_last_edited=”off|desktop” admin_label=”Project Pythia” _builder_version=”4.27.5″ header_level=”h3″ header_font=”|600|||||||” header_text_color=”#007d8a” text_orientation=”center” hover_enabled=”0″ global_colors_info=”{}” content__hover_enabled=”off|desktop” alt=”Project Pythia” title_text=”Project Pythia” sticky_enabled=”0″]<\/p>\n
Project Pythia is an education and training hub for the geoscientific Python community. We are also the education arm for the Pangeo initiative.<\/p>\n
[\/et_pb_blurb][et_pb_toggle title=”Read More” admin_label=”Toggle” _builder_version=”4.27.5″ hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]Project Pythia is a home for Python-centered learning resources that are open-source, community-owned, geoscience-focused, and high-quality. Our educational goals include helping geoscientists make sense of huge volumes of numerical scientific data using tools that facilitate open, reproducible science, and building an inclusive community of practice. Pythia works as an organization of several repositories, so one should check out the https:\/\/github.com\/ProjectPythia organization for comprehensive information but the main portal repository is https:\/\/github.com\/ProjectPythia\/projectpythia.github.io[\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ background_color=”#fafafa” custom_padding=”10px|20px|30px|20px” global_colors_info=”{}” custom_padding__hover=”|||”][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”on|on|on” admin_label=”Random Testimonial” module_id=”spirit-panel” _builder_version=”4.4.9″ background_color=”#ffffff” background_image=”https:\/\/www.numfocus.org\/wp-content\/uploads\/2017\/12\/spirit-panel-bg1.png” disabled=”on” global_module=”1773″ global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.16″ z_index_tablet=”500″ box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ background_color=”rgba(225,82,61,0.05)” custom_padding=”|||” custom_padding_tablet=”65px|35px|60px|35px” custom_padding_phone=”” custom_padding_last_edited=”on|desktop” global_colors_info=”{}” custom_padding__hover=”|||”][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ custom_padding=”|||” custom_padding_tablet=”65px|35px|60px|35px” custom_padding_phone=”65px|35px|60px|35px” custom_padding_last_edited=”on|desktop” background_color_gradient_direction_tablet=”180deg” background_color_gradient_direction_phone=”180deg” background_color_gradient_stops_tablet=”#2b87da 0%|#29c4a9 100%” background_color_gradient_stops_phone=”#2b87da 0%|#29c4a9 100%” background_color_gradient_start_position_tablet=”0%” background_color_gradient_start_position_phone=”0%” background_color_gradient_end_position_tablet=”100%” background_color_gradient_end_position_phone=”100%” z_index_tablet=”9″ z_index_phone=”9″ filter_hue_rotate_tablet=”0deg” filter_hue_rotate_phone=”0deg” filter_saturate_tablet=”100%” filter_saturate_phone=”100%” filter_brightness_tablet=”100%” filter_brightness_phone=”100%” filter_contrast_tablet=”100%” filter_contrast_phone=”100%” filter_invert_tablet=”0%” filter_invert_phone=”0%” filter_sepia_tablet=”0%” filter_sepia_phone=”0%” filter_opacity_tablet=”100%” filter_opacity_phone=”100%” filter_blur_tablet=”0px” filter_blur_phone=”0px” animation_duration_tablet=”1000ms” animation_duration_phone=”1000ms” animation_delay_tablet=”0ms” animation_delay_phone=”0ms” animation_intensity_slide_tablet=”50%” animation_intensity_slide_phone=”50%” animation_intensity_zoom_tablet=”50%” animation_intensity_zoom_phone=”50%” animation_intensity_flip_tablet=”50%” animation_intensity_flip_phone=”50%” animation_intensity_fold_tablet=”50%” animation_intensity_fold_phone=”50%” animation_intensity_roll_tablet=”50%” animation_intensity_roll_phone=”50%” animation_starting_opacity_tablet=”0%” animation_starting_opacity_phone=”0%” hover_transition_duration_tablet=”300ms” hover_transition_duration_phone=”300ms” hover_transition_delay_tablet=”0ms” hover_transition_delay_phone=”0ms” box_shadow_horizontal_tablet=”0px” box_shadow_horizontal_phone=”0px” box_shadow_vertical_tablet=”0px” box_shadow_vertical_phone=”0px” box_shadow_blur_tablet=”40px” box_shadow_blur_phone=”40px” box_shadow_spread_tablet=”0px” box_shadow_spread_phone=”0px” disabled=”%91object HTMLInputElement%93″ global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_blurb use_icon=”on” font_icon=”|||divi||400″ image_icon_width=”32px” _builder_version=”4.16″ custom_margin=”||0px” custom_margin_tablet=”” custom_margin_phone=”||0px” custom_margin_last_edited=”on|phone” custom_css_blurb_image=”margin-bottom: 0px;” icon_font_size=”32px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb use_icon=”on” font_icon=”|||divi||400″ image_icon_width=”32px” _builder_version=”4.16″ custom_margin=”||0px” custom_margin_tablet=”” custom_margin_phone=”||0px” custom_margin_last_edited=”on|phone” custom_css_blurb_image=”margin-bottom: 0px;” icon_font_size=”32px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_font_size=”20px” text_orientation=”center” custom_margin=”||10px” global_colors_info=”{}”]<\/p>\n
The NumFOCUS toolkit is indispensable to a data scientist.<\/p>\n
Daniel Kapitan<\/strong><\/p>\n\n[\/et_pb_text][et_pb_button button_url=”\/donate” button_text=”Support NumFOCUS” button_alignment=”center” module_class=”red-button” _builder_version=”4.16″ custom_button=”on” button_text_color=”#ffffff” button_bg_color=”#e1523d” button_border_color=”#e1523d” global_colors_info=”{}” button_text_color__hover_enabled=”on” button_text_color__hover=”#007d8a”][\/et_pb_button][et_pb_button button_url=”https:\/\/numfocus.org\/community\/people#people-disccommittee” button_text=”Meet the Committee” _builder_version=”4.16″ background_layout=”dark” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"
NumFOCUS Affiliated Projects benefit from their association with NumFOCUS through access to community, certain funding opportunities, and promotion of the project through our network. NumFOCUS Affiliated Projects are scientifically oriented, open, and kind. (What does that mean?) Affiliated Projects are not fiscally sponsored by NumFOCUS. Affiliated Projects enjoy a number of benefits. If your project […]<\/p>\n","protected":false},"author":5,"featured_media":1847,"parent":308,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"class_list":["post-1111","page","type-page","status-publish","has-post-thumbnail","hentry"],"yoast_head":"\n
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