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Dataset supporting Doctoral Thesis "Design, fabrication and characterisation of a low-cost, acoustically focussed imaging flow cytometer for automated analysis of phytoplankton"

Dataset supporting Doctoral Thesis "Design, fabrication and characterisation of a low-cost, acoustically focussed imaging flow cytometer for automated analysis of phytoplankton"
Dataset supporting Doctoral Thesis "Design, fabrication and characterisation of a low-cost, acoustically focussed imaging flow cytometer for automated analysis of phytoplankton"
Dataset supporting Doctoral Thesis "Design, fabrication and characterisation of a low-cost, acoustically focussed imaging flow cytometer for automated analysis of phytoplankton". The dataset is in the form of model inputs/outputs and images used for training neural networks), models (in the form of a COMSOL finite element model and a trained convolutional neural network) and code (in the form of control code for the imaging flow cytometer) described in the above thesis, each contained in a .zip archive with a README file. This work was supported by the Natural Environmental Research Council [grant number NE/N012070/1
imaging flow cytometry, Finite element analysis, COMSOL, Convolutional neural network, computer vision, phytoplankton, Acoustic levitation
University of Southampton
Lindley, Anthony James Willis
327b3237-7539-46c3-a412-3af4e141c863
Glynne-Jones, Peter
6ca3fcbc-14db-4af9-83e2-cf7c8b91ef0d
McQuillan, Jonathan S.
697cdf72-f353-4779-b64a-45494f29772f
Hill, Martyn
0cda65c8-a70f-476f-b126-d2c4460a253e
Lindley, Anthony James Willis
327b3237-7539-46c3-a412-3af4e141c863
Glynne-Jones, Peter
6ca3fcbc-14db-4af9-83e2-cf7c8b91ef0d
McQuillan, Jonathan S.
697cdf72-f353-4779-b64a-45494f29772f
Hill, Martyn
0cda65c8-a70f-476f-b126-d2c4460a253e

Lindley, Anthony James Willis (2023) Dataset supporting Doctoral Thesis "Design, fabrication and characterisation of a low-cost, acoustically focussed imaging flow cytometer for automated analysis of phytoplankton". University of Southampton doi:10.5258/SOTON/D2872 [Dataset]

Record type: Dataset

Abstract

Dataset supporting Doctoral Thesis "Design, fabrication and characterisation of a low-cost, acoustically focussed imaging flow cytometer for automated analysis of phytoplankton". The dataset is in the form of model inputs/outputs and images used for training neural networks), models (in the form of a COMSOL finite element model and a trained convolutional neural network) and code (in the form of control code for the imaging flow cytometer) described in the above thesis, each contained in a .zip archive with a README file. This work was supported by the Natural Environmental Research Council [grant number NE/N012070/1

Archive
CNN.zip - Model
Available under License Creative Commons Attribution.
Download (210kB)
Archive
CNN_Trainingdata.zip - Dataset
Available under License Creative Commons Attribution.
Download (16MB)
Archive
KLM.zip - Dataset
Available under License Creative Commons Attribution.
Download (12kB)
Archive
phytocap.zip - Software
Available under License Creative Commons Attribution.
Download (450kB)
Archive
RMOD.zip - Model
Available under License Creative Commons Attribution.
Download (210kB)
Archive
COMSOL.zip - Dataset
Available under License Creative Commons Attribution.
Download (267MB)
Text
D2872_thesis_readme.txt - Dataset
Available under License Creative Commons Attribution.
Download (3kB)

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More information

Published date: 2023
Keywords: imaging flow cytometry, Finite element analysis, COMSOL, Convolutional neural network, computer vision, phytoplankton, Acoustic levitation

Identifiers

Local EPrints ID: 484881
URI: http://eprints.soton.ac.uk/id/eprint/484881
PURE UUID: b1e2a433-37f8-460d-90ae-1da246ca4405
ORCID for Anthony James Willis Lindley: ORCID iD orcid.org/0009-0005-8447-969X
ORCID for Peter Glynne-Jones: ORCID iD orcid.org/0000-0001-5684-3953
ORCID for Martyn Hill: ORCID iD orcid.org/0000-0001-6448-9448

Catalogue record

Date deposited: 23 Nov 2023 18:20
Last modified: 25 Nov 2023 03:10

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Contributors

Research team head: Peter Glynne-Jones ORCID iD
Research team head: Jonathan S. McQuillan
Research team head: Martyn Hill ORCID iD

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