Computational Reproducibility Validation in Cloud Big Data Pipelines Through Provenance and Machine Learning

This deposit contains all artefacts required to reproduce the experimental results reported in Paper 3 of the PhD thesis *Computational Reproducibility and Provenance in Cloud-Based Climate Data Applications*. The work proposes and validates a three-layer computational reproducibility scoring framework that uses provenance metadata and machine learning to assess whether a scientific workflow can be reliably reproduced across different cloud platforms.