Provenance with the PROV standard

DATA71011 Understanding Data and their Environment

Stian Soiland-Reyes

This work is licensed under a
Creative Commons Attribution 4.0 International License.

https://doi.org/10.5281/zenodo.13986378

Intended Learning Outcomes

1. Understanding why and when to use provenance standards
2. Ability to distinguish PROV concepts
3. Knowledge of considerations for modelling choices 
4. Ability to write a machine-readable metadata language
5. Skill of modelling processes in a formal language (in lab, assessment)

Motivation

Why using a standard for

machine-readable provenance?

Motivation for W3C PROV Standards

PROV standards enhance data interoperability, transparency, and reproducibility across various domains.

Consuming PROV Documents in Python

Utilize libraries like prov and rdflib to parse and manipulate PROV data programmatically.

Combining PROV Traces

Integrate multiple PROV documents to create comprehensive provenance records for complex datasets.

Benefits of Using PROV

Improves data trustworthiness, enhances collaboration, and supports reproducible research practices.