Our webinar focuses on enhancing the uptake, utility and value of simulation models and data at the building-, suburban- and city-scale. In many cases, these models and datasets, developed in EPSRC-funded research projects, have limited practical use at present due to difficulties in both accessing and using complex models by academics and non-academics, data management challenges, and integrating outputs with existing stakeholder capabilities, systems and approaches.
The webinar is geared towards researchers with an interest in the built environment who are involved in the development, use, or management of projects using built environment models and tools, and who have an interest in enhancing model uptake and usability. It will highlight some of the issues, skills gaps and barriers which prevent research outputs from being more industry-ready. Two case studies will provide recommendations and learning points which could be used by research projects.
The webinar covered case studies on:
- The ARCADIA Impact Model, which supports climate-related risk assessment and appraisal of adaptation options across Greater London, and provides an example of the processes and benefits of making model code and outputs available online.
- The DM4T data management tool, MetaMaker, which was developed to add metadata to .csv files of building energy data, to allow users to query across data sources from different institutions.
More about MetaMaker
Dr Katie Jenkins, ARCC network / University of Oxford (with Briony Turner)
Enhancing the uptake of built environment and city simulation models
The aim of this project is to investigate and understand the current challenges and barriers, and ultimately provide a set of guiding principles to inform both stakeholders and researchers to help ensure data and models from research are suitable for informing policy-making and practice in the future.
Dr Julian Paget, University of Bath,
PI Data Management for TEDDINET using Semantic technologies (DM4T)
This project seeks to address the challenges of how to incorporate data management and legacy into much of (physical) science research. It aims to raise awareness with those who are directly responsible for data management, develop a framework to guide the process of making the choices for how to go about implementing data management, and demonstrate example tools that will enable researchers to bring together and re-analyse data from different projects more easily.