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Dr Hugh Dickinson

Profile summary

Externally funded projects

Preservation and Visualization of African Indigenous Knowledge for Resilient Food Systems
RoleStart dateEnd dateFunding source
Lead01 Oct 202029 Oct 2021STFC Science & Technology Facilities Council;Brunel University London

Our study aims at exploring the potential of the STFC’s data science and citizen science expertise to develop an interactive, digital, open infrastructure that pulls together African indigenousagriculture and food systems data from multiple sources, making it accessible and reusable for policy, research and practice in modern food production efforts.African Indigenous Knowledge (AIK) refers to tacit knowledge held in different languages, cultures and skills that drive food production, preservation and consumption for more than 80% of citizens in Africa. The documentation and dissemination of AIK remain a big challenge confronting librarians and other information professionals in Africa, and there is a risk of losing AIK. There is also a clear disconnect between the AIK and scientific knowledge and modern efforts for sustainable food production. Combining citizens’ participation and STFC’s expertise, this project will identify and pilot documentation of the first interactive AIK platform along with standards for nicethically collecting, preserving and sharing the knowledge.

Publications

Transfer learning for galaxy feature detection: Finding giant star-forming clumps in low-redshift galaxies using Faster Region-based Convolutional Neural Network (2024)
Popp, Jürgen J.; Dickinson, Hugh; Serjeant, Stephen; Walmsley, Mike; Adams, Dominic; Fortson, Lucy; Mantha, Kameswara; Mehta, Vihang; Dawson, James M; Kruk, Sandor and Simmons, Brooke
RAS Techniques and Instruments, 3(1) (pp. 174-197)


Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys (2023-12)
Walmsley, Mike; Géron, Tobias; Kruk, Sandor; Scaife, Anna M M; Lintott, Chris; Masters, Karen L; Dawson, James M; Dickinson, Hugh; Fortson, Lucy; Garland, Izzy L; Mantha, Kameswara; O’Ryan, David; Popp, Jurgen; Simmons, Brooke; Baeten, Elisabeth M and Macmillan, Christine
Monthly Notices of the Royal Astronomical Society, 526(3) (pp. 4768-4786)


High-throughput design of cultured tissue moulds using a biophysical model: optimising cell alignment (2023-10-30)
Hague, James P; Andrews, Allison E and Dickinson, Hugh
Physical Biology, 20, Article 66006(6)


Rapid prediction of lab-grown tissue properties using deep learning (2023-10-19)
Andrews, Allison; Dickinson, Hugh and Hague, James Peter
Physical biology, 20, Article 66005


Using cGANs for Anomaly Detection: Identifying Astronomical Anomalies in JWST Imaging (2023-10-19)
Pearce-Casey, Ruby; Dickinson, Hugh; Serjeant, Stephen and Bromley, Jane M.
Research Notes of the AAS, 7, Article 217(10)


An Ice Age JWST inventory of dense molecular cloud ices (2023)
McClure, M. K.; Rocha, W. R. M.; Pontoppidan, K. M.; Crouzet, N.; Chu, L. E. U.; Dartois, E.; Lamberts, T.; Noble, J. A.; Pendleton, Y. J.; Perotti, G.; Qasim, D.; Rachid, M. G.; Smith, Z. L.; Sun, Fengwu; Beck, Tracy L.; Boogert, A. C. A.; Brown, W. A.; Caselli, P.; Charnley, S. B.; Cuppen, Herma M.; Dickinson, H.; Drozdovskaya, M. N.; Egami, E.; Erkal, J.; Fraser, H.; Garrod, R. T.; Harsono, D.; Ioppolo, S.; Jiménez-Serra, I.; Jin, M.; Jørgensen, J. K.; Kristensen, L. E.; Lis, D. C.; McCoustra, M. R. S.; McGuire, Brett A.; Melnick, G. J.; Öberg, Karin I.; Palumbo, M. E.; Shimonishi, T.; Sturm, J. A.; van Dishoeck, E. F. and Linnartz, H.
Nature Astronomy, 7 (pp. 431-443)


The LOFAR Two-Metre Sky Survey (2023)
Hardcastle, M. J.; Horton, M. A.; Williams, W. L.; Duncan, K. J.; Alegre, L.; Barkus, B.; Croston, J. H.; Dickinson, H.; Osinga, E.; Röttgering, H. J. A.; Sabater, J.; Shimwell, T. W.; Smith, D. J. B.; Best, P. N.; Botteon, A.; Brüggen, M.; Drabent, A.; de Gasperin, F.; Gürkan, G.; Hajduk, M.; Hale, C. L.; Hoeft, M.; Jamrozy, M.; Kunert-Bajraszewska, M.; Kondapally, R.; Magliocchetti, M.; Mahatma, V. H.; Mostert, R. I. J.; O’Sullivan, S. P.; Pajdosz-Śmierciak, U.; Petley, J.; Pierce, J. C. S.; Prandoni, I.; Schwarz, D. J.; Shulewski, A.; Siewert, T. M.; Stott, J. P.; Tang, H.; Vaccari, M.; Zheng, X.; Bailey, T.; Desbled, S.; Goyal, A.; Gonano, V.; Hanset, M.; Kurtz, W.; Lim, S. M.; Mielle, L.; Molloy, C. S.; Roth, R.; Terentev, I. A. and Torres, M.
Astronomy & Astrophysics, 678, Article A151


Galaxy Zoo: Clump Scout - Design and first application of a two-dimensional aggregation tool for citizen science (2022-12)
Dickinson, Hugh; Adams, Dominic; Mehta, Vihang; Scarlata, Claudia; Fortson, Lucy; Serjeant, Stephen; Krawczyk, Coleman; Kruk, Sandor; Lintott, Chris; Mantha, Kameswara; Simmons, Brooke D and Walmsley, Mike
Monthly Notices of the Royal Astronomical Society, 517(4) (pp. 5882-5911)


Practical galaxy morphology tools from deep supervised representation learning (2022-06)
Walmsley, Mike; Scaife, Anna M M; Lintott, Chris; Lochner, Michelle; Etsebeth, Verlon; Géron, Tobias; Dickinson, Hugh; Fortson, Lucy; Kruk, Sandor; Masters, Karen L; Mantha, Kameswara Bharadwaj and Simmons, Brooke D
Monthly Notices of the Royal Astronomical Society, 513(2) (pp. 1581-1599)


Galaxy Zoo: Clump Scout: Surveying the Local Universe for Giant Star-forming Clumps (2022-05)
Adams, Dominic; Mehta, Vihang; Dickinson, Hugh; Scarlata, Claudia; Fortson, Lucy; Kruk, Sandor; Simmons, Brooke and Lintott, Chris
The Astrophysical Journal, 931(1)


A SuperWASP Light Curve Displaying a Single Long-duration Transit: A Jupiter Size Exoplanet in a Very Distant Orbit? (2022)
Norton, Andrew; Dickinson, Hugh; McMaster, Adam; Middleton, Matthew and West, Richard
Research Notes of the American Astronomical Society, 6, Article 84(4)


Identification of Single Spectral Lines in Large Spectroscopic Surveys Using UMLAUT: an Unsupervised Machine-learning Algorithm Based on Unbiased Topology (2021-12)
Baronchelli, I.; Scarlata, C. M.; Rodríguez-Muñoz, L.; Bonato, M.; Morselli, L.; Vaccari, M.; Carraro, R.; Barrufet, L.; Henry, A.; Mehta, V.; Rodighiero, G.; Baruffolo, A.; Bagley, M.; Battisti, A.; Colbert, J.; Dai, Y. S.; De Pascale, M.; Dickinson, H.; Malkan, M.; Mancini, C.; Rafelski, M. and Teplitz, H. I.
The Astrophysical Journal Supplement Series, 257, Article 67(2)


Predicting the self-lensing population in optical surveys (2021-10-01)
Wiktorowicz, Grzegorz; Middleton, Matthew; Khan, Norman; Ingram, Adam; Gandhi, Poshak and Dickinson, Hugh
Monthly Notices of the Royal Astronomical Society, 507(1) (pp. 374-384)


Super-resolving Herschel imaging: a proof of concept using Deep Neural Networks (2021-10)
Lauritsen, Lynge; Dickinson, Hugh; Bromley, Jane; Serjeant, Stephen; Lim, Chen-Fatt; Gao, Zhen-Kai and Wang, Wei-Hao
Monthly Notices of the Royal Astronomical Society, 507(1) (pp. 1546-1556)


VeSPA: The SuperWASP Variable Star Photometry Archive (2021-10)
Mcmaster, Adam; Norton, Andrew J.; Dickinson, Hugh J.; Thiemann, Heidi B. and Kolb, Ulrich C.
Research Notes of the AAS, 5(10) (p 228)


An Old Stellar Population or Diffuse Nebular Continuum Emission Discovered in Green Pea Galaxies (2021-05-10)
Clarke, Leonardo; Scarlata, Claudia; Mehta, Vihang; Keel, William C.; Cardamone, Carolin; Hayes, Matthew; Adams, Nico; Dickinson, Hugh; Fortson, Lucy; Kruk, Sandor; Lintott, Chris and Simmons, Brooke
The Astrophysical Journal Letters, 912, Article L22(2)


Investigating Clumpy Galaxies in the Sloan Digital Sky Survey Stripe 82 Using the Galaxy Zoo (2021-05)
Mehta, Vihang; Scarlata, Claudia; Fortson, Lucy; Dickinson, Hugh; Adams, Dominic; Chevallard, Jacopo; Charlot, Stéphane; Beck, Melanie; Kruk, Sandor and Simmons, Brooke
The Astrophysical Journal, 912, Article 49(1)


SuperWASP Variable Stars: Classifying Light Curves Using Citizen Science (2021-03)
Thiemann, Heidi B.; Norton, Andrew J.; Dickinson, Hugh J.; McMaster, Adam and Kolb, Ulrich C.
Monthly Notices of the Royal Astronomical Society, 502(1) (pp. 1299-1311)


High‐throughput rational design of artificial tissue moulds using a biophysical in‐silico model and machine learning (2023-06)
Hague, James; Andrews, Allison and Dickinson, Hugh
In : TERMIS-EU 2023 (28-31 Mar 2023, Manchester, UK)


Identifying muon rings in VERITAS data using convolutional neural networks trained on images classified with Muon Hunters 2 (2021)
Flanagan, K.; Quinn, J. (for the VERITAS Collaboration]; Wright, D.; Dickinson, H.; Wilcox, P. D.; Laraia, M. and Serjeant, S.
In : 37th International Cosmic Ray Conference (ICRC) (12-23 Jul 2021, [Online - Berlin, Germany])


VeSPA: The SuperWASP Variable Star Photometry Archive (2021-06)
McMaster, Adam; Norton, Andrew; Dickinson, Hugh; Thiemann, Adelheid and Kolb, Ulrich
Postgraduate Research Poster Competition, The Open University