Reproducible Research
Showing 34 of 34 projects
A collection of research code and datasets released by Google Research under open licenses.
Companion materials and IPython notebooks for the 'Python for Data Analysis' book, covering pandas, NumPy, and data science workflows.
A reactive Python notebook that's reproducible, git-friendly, and deployable as scripts or apps.
A metapackage for installing and documenting the Jupyter ecosystem of interactive computing tools.
A metapackage for installing and documenting the Jupyter ecosystem of interactive computing tools.
An extensible, next-generation web-based interface for interactive computing and data science, based on the Jupyter Notebook architecture.
A collection of implementations and illustrative code accompanying DeepMind's published research papers across AI and machine learning.
A collection of implementations and illustrative code accompanying DeepMind's published research papers across AI and scientific domains.
A research framework for fast prototyping of reinforcement learning algorithms, designed for easy experimentation and reproducibility.
A Python framework for creating reproducible, maintainable, and modular data engineering and data science pipelines.
A standardized, flexible project template for data science work using Cookiecutter to structure reproducible projects.
A collection of ready-to-run Docker images containing Jupyter applications and interactive computing tools.
A collection of ready-to-run Docker images containing Jupyter applications and interactive computing tools.
A cross-platform, language-agnostic binary package and environment manager for creating isolated software environments.
Convert Jupyter notebooks to and from plain text formats like Markdown, Python, Julia, or R scripts for better version control and editing.
A practical guide for researchers on how to properly structure and share data with statisticians to ensure efficient analysis.
A curated list of awesome R packages, frameworks, and software for data science and statistical computing.
A curated list of awesome R packages, frameworks, and software for data science and statistical computing.
A Python tool for parameterizing, executing, and analyzing Jupyter Notebooks at scale.
An open-source book teaching data science using R, covering data import, transformation, visualization, and modeling.
An R package for writing books, technical documents, and long-form reports with R Markdown.
A central hub for sharing, refining, and reusing code for analyzing the MIMIC family of critical care and hospital databases.
Docker image providing the Python environment used by Kaggle Notebooks for data science competitions.
An open source container platform designed for simplicity, speed, and security in HPC and shared computing environments.
A general-purpose literate programming engine for dynamic report generation in R, designed to give users full control over output.
A command-line tool for creating reproducible, container-based development environments for AI/ML workflows.
A curated list of Quarto talks, tools, examples, and articles for the open-source scientific publishing system.
A curated list of Quarto talks, tools, examples, and articles for the open-source scientific publishing system.
A toolkit and library for developing, evaluating, and reproducing reinforcement learning algorithms.
Create blogs and websites with R Markdown, integrating dynamic R code, graphics, and technical writing elements.
A meta-package for installing and loading core R packages for data science that share common design principles.
A native R kernel for Jupyter notebooks, enabling R programming within the Jupyter ecosystem.
A unified interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
A curated reading list and syllabus for a Stanford discussion class on applied data science topics.
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