Abstract

The assessment of surfaces for potential contamination by biological (e.g. anthrax pathogen Bacillus anthracis) and chemical (e.g. nerve agents such as VX) hazards is relevant for a range of military and civilian applications. To this end, Dstl and the Defence and Security Accelerator (DASA) are providing a dataset collected using a range of different sensor modalities that have measured various surfaces contaminated with surrogate bacteria, hazardous chemicals and relevant control materials. Both un-mixing and identification of the contaminant contribution from that of the underlying surface is non-trivial.

Participants were invited to explore how data science and machine learning techniques can be applied to recognise and discriminate between the various contaminants based on data from individual sensors or fusion of multiple data sources, and how models can be applied to characterise contamination on new surfaces without re-training.

Citation information

Data Study Group team. (2021, February 11). Data Study Group Final Report: Dstl – Anthrax and nerve agent detector. Zenodo. https://doi.org/10.5281/zenodo.4534219

Additional information

Ranjeet S. Bhamber, University of Bristol
Robert Chin, University of Melbourne and University of Birmingham
Laura Merritt, University of Reading
Melanie Vollmar, Diamond Light Source
Phillippa McCabe, Liverpool John Moores University
Kate Highnam, Imperial College London
Leila Yousefi, Brunel University London and UCL
Andrew W. Dowsey, University of Bristol