3 papers
cs.DC2026
Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data
Nicola Giuseppe Marchioro, Gabriele Padovani, Amal Gueroudji +5
Model Cards and Data Cards have demonstrated the value of structured, human-readable documentation for machine learning artifacts, capturing their context, parameters, limitations,…
cs.SE2026
yProv4DV: Reproducible Data Visualization Scripts Out of the Box
Gabriele Padovani, Sandro Fiore
While results visualization is a critical phase to the communication of new academic results, plots are frequently shared without the complete combination of code, input data, exec…
cs.LG2025
yProv4ML: Effortless Provenance Tracking for Machine Learning Systems
Gabriele Padovani, Valentine Anantharaj, Sandro Fiore
The rapid growth of interest in large language models (LLMs) reflects their potential for flexibility and generalization, and attracted the attention of a diverse range of research…