Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.

Joint Crab paper git repository

A visualization tool for analyzing the reproducibility of Jupyter Notebooks

Automated Quantum Mechanical Environments (AQME) offers transparent and reproducible workflows available for Jupyter Notebooks and command lines, including: 1) RDKit- and CREST-based conformer generation, 2) QM input file creation, 3) post-processing of QM output files, 4) generation of xTB, DFT and RDKit descriptors. https://aqme.readthedocs.io

This repository demonstrates the "Ten Simple Rules for Reproducible Research in Jupyter Notebook" rules for a simple machine learning problem

Reproduce Jupyter Notebooks inside Docker Containers.

"scorecal - Empirical score calibration under the microscope" presentation given at Credit Scoring & Credit Control XVI, Edinburgh, UK, 2019-08-30

🔬 R package: Analysis of Large Affymetrix Microarray Data Sets

Interactive ML Toolset

Reproducible Python notebooks: Using satellite imagery to analyse dynamics of urban neighbourhood.

Generate Powerpoint presentations from Jupyter Notebook results

This repository accompanies the bachelor's thesis: "Evaluation of Reproducibility Platforms – A Comparative Study on Usability, Performance and Application Areas"

An example project that uses a Jupyter notebook as a pipeline.

A reproducibility study using the Colomoto notebook

Reproducibilidad en notebooks de google - Python y R

FAIR Jupyter: a knowledge graph for semantic sharing and granular exploration of a computational notebook reproducibility dataset.

Kaggle notebooks organized by competition with metrics, validation, and takeaways — learning in public.

Run any Python script in a clean, disposable virtual environment — automatically.

Opinionated notebooks for EDA, baselines, anomaly detection, time series and evaluation — statistics first, reproducible.

FinBERT fine-tuning with quantum optimization (QAOA/VQE) for financial sentiment; notebooks + environment for reproducible runs.