The authors gratefully acknowledge those people who have contributed their knowledge and time to the development of GWSDAT.
The authors wish to express their gratitude to Adrian Bowman, Claire Miller, Craig Alexander, Craig Wilkie, Ludger Evers and Daniel Molinari from the department of Statistics, University of Glasgow, for their invaluable contributions to the development of the spatiotemporal algorithm.
Thanks also to Ewan Mercer from the University of Glasgow for his assistance in the development of the GWSDAT user interface.
We acknowledge and thank the R project for Statistical Computing and all its contributors without which this project would not have been possible.
A big thank you to Shell's worldwide environmental consultants for assistance in evaluating and testing the earlier versions of GWSDAT.
Thanks also to the Shell Year in Industry students who spent a great deal of time testing GWSDAT and making suggestions for improvements.
Thanks to Andrew Kirkman and team from BP Remediation Solutions for contributing new functionality and enhancements in version 3.3.
Thanks to Fraser Spalding who as a summer student at the University of Glasgow, almost singlehandedly, delivered the GWSDAT Tutorials YouTube Channel.
Credit and acknowledgement to Jan Limbeck from Shell for providing the portable Windows R version optimized for linear algebra.
We thank both current and former colleagues including Matthew Lahvis, Jonathan Smith, George Devaull, Dan Walsh, Curtis Stanley, Marco Giannitrapani and Philip Jonathan for their support, vision and advocacy of GWSDAT.