Automatic FAIR Provenance Collection and Visualization for Time Series

Abstract

Provenance provides data lineage and history of different transformations applied to a dataset. A complete trace of data provenance can enable the reanalysis, reproducibility, and reusability of features, which are essential for validating results and extending them in many projects. Open time series datasets are readily accessible and discoverable, but their full reproducibility and reusability require clear metadata provenance. This paper introduces an assessment of provenance variables using an algorithm for collecting FAIR (Findable, Accessible, Interoperable, Reusable) characteristics in open time series and generating an associated provenance graph. We have evaluated the FAIRness of provenance traces by automatically mapping their properties to a provenance data model graph for a case study employing open time series from weather stations. Our approach arguably enables researchers to analyse time series datasets with similar characteristics, prompting new research questions, insights, and investigations. As a result, this approach has the potential to promote reusability and reproducibility, which are critical factors in scientific research.