Using a Knowledge Graph approach to link metadata from forestry models and datasets, to assist analysis and hypothesis generation

Kim Martin
Stellenbosch University

Monday 12 September 2022
15:00 - 16:00

Abstract

Forestry research has a rich history of computational models and datasets that cover a wide range of interacting scales. It was highlighted during a 2020 meeting of the Quantitative Wood Anatomy Network (Q-Net) – in a session titled ‘Modelling & QWA: From Cell To Ecosystem’ – that it is challenging for researchers of wood formation and ecophysiology to develop an integrated understanding of the models available (including how they could possibly be composed together). This project aims to assist researchers in the field of eucalypt wood formation and ecophysiology to explore datasets and computational models in a flexible and integrative way. The goal is to encompass models of phenomena at different scales; ranging from process-based models of the cellular determinants of wood formation, to empirical models of gross tree growth in different environmental contexts. The linked information should allow complex questions to be asked, including: how similar models differ; which datasets can be repurposed to test different model outputs; and identifying whether and how different models can be composed together. This will promote open scientific practices in this research area (through the use of common metadata standards and terms), and may serve as a valuable framework for collaborative knowledge capture and exploration.

Host: Alasdair Gray