Reproducible Research
Showing 36 of 82 projects
An open dataset and toolkit for training static PE malware machine learning models, featuring extracted features from millions of Windows executable files.
renv creates isolated, portable, and reproducible project environments for R by managing private package libraries and lockfiles.
A teaching platform providing interactive Jupyter Notebooks for learning computer-aided drug design (CADD) using open-source tools.
A step-by-step tutorial and template for setting up a reproducible Python development environment using VSCode and Docker.
A comprehensive guide to building, documenting, testing, and distributing R packages.
A bioinformatics-native AI agent skill library for reproducible, local-first genomic analysis, built on OpenClaw.
Generate reproducible HTML5 slide decks from R Markdown for data-driven presentations.
An R package that creates reproducible examples from R code for sharing on GitHub, Stack Overflow, Slack, and other platforms.
IPython-based environment for reproducible machine learning research with unified wrappers for multiple ML libraries.
Capture, analyze, and transform messy Jupyter notebooks into production data pipelines with just two lines of code.
A curated guide to essential R packages organized by their role in the data science workflow.
A decentralized data management system built on Git and git-annex for versioning and distributing large datasets.
A Python interface for interactive web-based visualization of multidimensional images, point sets, and geometry in Jupyter notebooks.
An R package for two-way R Markdown to Microsoft Word conversion, enabling reversible documents that retain edits and tracked changes.
A minimal example book demonstrating how to create books and documents using R Markdown and the bookdown package.
A gallery of 298 TikZ drawings for teaching statistics and creating scientific illustrations.
A curated repository of university courses, workshops, and online materials for learning and teaching R programming and data science.
A pytest plugin that validates Jupyter notebooks by executing cells and comparing outputs to ensure consistency.
F# kernel for Jupyter notebooks, enabling interactive data science and exploration with F#.
A light-touch approach to designing, building, and describing visualizations using literate programming in Markdown.
Source code and documentation for the Quarto documentation website, built with Quarto itself.
R package containing datasets and code examples for the book 'Statistical Analysis of Network Data with R, 2nd Edition'.
Official GitHub Actions for installing, rendering, and publishing Quarto documents and projects.
An R package for installing and updating software on Windows, with special focus on R itself and development tools.
A PyTorch framework for deep learning on point clouds, providing a modular and reproducible foundation for 3D vision tasks.
Manage Jupyter notebooks as plain Python code with embedded Markdown for better development workflows and version control.
Automates citation collection for scientific software, methods, and datasets used in Python analyses.
R bindings to the libgit2 library, providing programmatic access to Git repositories from R.
An R package to interact with NCBI's Entrez system, enabling programmatic search and retrieval of biological data.
Archived R package for accessing open data from various government and scientific sources.
An R package to programmatically retrieve chemical information from various web databases and APIs.
An R package that installs packages from MRAN snapshots to ensure reproducible environments by locking package versions to a specific date.
A tool for creating one-button reproducible workflows with Jupyter Notebook and Scons.
A Julia package for reproducible data setup, automating dataset downloads and management for scientific computing.
An R interface to the Quarto CLI for rendering documents, previewing, and managing projects directly from the R console.
Open-source implementation of the winning solution for the 2018 Data Science Bowl Kaggle competition using PyTorch and U-Net.
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