Implementations of fine-grained automated data provenance to support transparent environmental modelling. (August 2019)

• Record Type: Journal Article
• Authors: Spiekermann, Raphael; Jolly, Ben; Herzig, Alexander; Burleigh, Tom; Medyckyj-Scott, David
• Is Part Of: Environmental modelling & software. Volume 118(2019)
• Page Start: 134
• Page End: 145
• Publication Date: 2019-08
• Subjects: Fine-grained data provenance -- Provenance visualization -- Transparent science -- Environmental modelling -- LUMASS -- Pyluc; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.04.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Demand is increasing for greater transparency of the science underpinning decision-making processes in land resource management. To illustrate how the application of fine-grained data provenance can increase the credibility and transparency of scientific methods and outputs, we implement provenance tracking for two different modelling frameworks, pyluc and LUMASS, and present results from example models. Pyluc is a python-based framework for generating spatial land use classification data with automatically-generated technical documentation. LUMASS is a spatial modelling and optimisation framework within which New Zealand's sediment budget model SedNetNZ is implemented. In both cases, detailed provenance tracking resulted in a complexity of information which necessitated the development of an interactive data provenance visualization tool to help science producers and users explore, verify, and understand model outputs. We argue that best data management and sharing practice should include fine-grained data provenance to meet demands for the quality and integrity of science-based data and information. Highlights: We automate fine-grained provenance tracking for land resource modelling. Data provenance is integrated using a python-based tool and LUMASS modelling environment. Interactive visualization tools are developed for exploring provenance information.