#Reproducible-research
Showing 60 of 62 repositories tagged #reproducible-research, ranked by stars
A Collection of Variational Autoencoders (VAE) in PyTorch.
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
A DSL for data-driven computational pipelines
FMA: A Dataset For Music Analysis
Collection of popular and reproducible image denoising works.
:cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Sionna: An Open-Source Library for Research on Communication Systems
An R-focused pipeline toolkit for reproducibility and high-performance computing
Function-oriented Make-like declarative workflows for R
Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
Experiments for understanding disentanglement in VAE latent representations
A research tool for the Iterated Prisoner's Dilemma
GPU-Jupyter: Your GPU-accelerated JupyterLab with a rich data science toolstack, TensorFlow and PyTorch for your reproducible deep learning experiments.
An elegant Python interface for visualization on the web platform to interactively generate insights into multidimensional images, point sets, and geometry.
๐ PILOT: A Pre-trained Model-Based Continual Learning Toolbox
Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.
LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.
Pweave is a scientific report generator and a literate programming tool for Python. It can capture the results and plots from data analysis and works well with numpy, scipy and matplotlib.
Open Science โ an open AI workbench for scientists. Open-source alternative to Claude Science: local-first, model-agnostic, reproducible AI research desktop (macOS & Windows), built on Tauri + MCP + agent skills.
Live code in Pandoc Markdown
CanvasXpress: A JavaScript Library for Data Analytics with Full Audit Trail Capabilities.
Ten Simple Rules for Writing and Sharing Computational Analyses in Jupyter Notebooks
A platform for end-to-end development of machine learning solutions in biomedical imaging
REANA: Reusable research data analysis platform
Lightweight, Python library for fast and reproducible experimentation :microscope:
Guide for Reproducible Research and Data Science in Jupyter Notebooks
Reference implementations of popular Binarized Neural Networks
Claude Code-powered end-to-end meta-analysis automation: AI-assisted literature review, screening, extraction, analysis, and manuscript generation for systematic reviews and clinical evidence synthesis
R package to interface with OpenML
Pure PyTorch implementation of Nvidia's hash grid encoding: https://nvlabs.github.io/instant-ngp/
Write reproducible code for getting and processing ChEMBL
Data version control for reproducible analysis pipelines in R with {targets}.
Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data
Paired-scientist AI assistant for reproducible research โ lightweight Go runtime, lifecycle hooks, manuscript integration, 12 baseline scientific skills. PicoClaw-compatible.
Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
OpenScope databook: a collaborative, versioned, data-centric collection of foundational analyses for reproducible systems neuroscience ๐๐ง ๐ฌ๐ฅ๏ธ๐
BRAPH 2.0 is a comprehensive software package for the analysis and visualization of brain connectivity data, offering flexible customization, rich visualization capabilities, and a platform for collaboration in neuroscience research.
Get started DVC project
A minimal example data analysis project with the targets R package
moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.
Example workflows for the drake R package
The user manual for the drake R package
Open solution to the Google AI Object Detection Challenge :maple_leaf:
Platform for integrating genomic analysis with Jupyter Notebooks.
:microscope: Empirical CLI
This tool presents a novel approach to bolstering network protocol verification by integrating the Shadow network simulator with the Ivy formal verification tool to check time properties. Furthermore, it extends Ivyโs capabilities with a dedicated time module, enabling the verification of complex quantitative-time properties.
A curated hub of autonomous-research skills & agents โ from idea to paper, on autopilot. | ่ชไธป็ง็ ๆ่ฝไธๆบ่ฝไฝ็ฒพ้ๅบ โโ ไป็ตๆๅฐ่ฎบๆ๏ผๅ จ็จ่ชๅจ้ฉพ้ฉถใ
unit-testing for a collection of jupyter notebooks using nbconvert
A reproducible ML study of Codeforces difficulty prediction, cold-start limits, temporal validation, and statement-structure features.
239 evaluated academic Claude/agent skills across 17 research domains (bioinformatics, data science, clinical, social-science methods, Turkish academia & more). Executable eval per skill, deterministic citation verifier, researchโwriteโreviewโpublish pipeline, and a skill-finder front door. Claude Code, Cursor, Codex, Gemini CLI & Copilot.
A setup script to generate VTK Python Wheels
An example of using make for a data analysis project
A quick start project for polyaxon
Immunology Informatics - Big Data Analysis in Immunology - Tutorials
Public website for the "Collaborative and Reproducible Data Science" course at UC Berkeley (Stat 159/259, Spring 2022 term).
Agent-native Stata bridge for empirical research โ run DiD/IV/RDD and publication-ready tables from Claude Code, Jupyter, or VS Code on one token-economy result schema, with StatsPAI cross-validation. | ้ขๅๅฎ่ฏ็ ็ฉถ็ๆบ่ฝไฝ Stata ๆกฅๆฅๅจโโๅจ Claude CodeใJupyterใVS Code ไธญ็จไธๅฅ็ token ็็ปๆๆ ผๅผ่ท DiD/IV/RDD ไธๅบ็็บง่กจๆ ผ๏ผๅนถๆฏๆ StatsPAI ่ทจๆ ไบคๅ้ช่ฏใ
Project layout for efficient AI for science
Reproduce Jupyter Notebooks inside Docker Containers.