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

Dataset DOI: 10.5258/SOTON/D2872

ReadMe Author: Anthony James Willis Lindley, University of Southampton- orcid.org/0009-0005-8447-969X	


This dataset supports the thesis entitled "Design, fabrication and characterisation of a low-cost, acoustically focussed imaging flow cytometer for automated analysis of phytoplankton"
AWARDED BY: Univeristy of Southampton
DATE OF AWARD: [2023]



DESCRIPTION OF THE DATA
This dataset contains 5 subsets in .zip archives, each with their own README file. A summary of each of the subsets is as follows:

DESCRIPTION OF FILES:
phytocap.zip - Files necessary to control the imaging flow cytometer described in the above PHD thesis when run on an NVIDA Jetson Xavier NX. Extracted directory contains a README describing the dataset and an archive (phytocap-main.zip) containing python code. Attached with the code is a README file explaining the code functionality with usage examples.

KLM.zip - The results of a KLM Model (using the technique from Krimholtzet al. 1970) investigation into the performance of the acoustically focussed flow cell presented in the above thesis. Parameters used for the model are supplied in an excel spreadsheet and results in a .csv file. Different temperatures and salinities of the fluid in the model were used to probe the potential response (in terms of focussing performance as determined by resonant frequency drift and changing acoustic energy density) of a real flow cell to changing thermal and salinity properties of the fluid. 

CNN.zip - Convolutional Neural Network model (in pyTorch state dictionary format) from the thesis named above. The model is trained on 150 imaging flow cytometry images (from dataset contained in CNN_Trainingdata.zip, described below) containing cells of Rhodomonas salina, and outputs a 2D probability density map representing its confidence in the presence of a phytoplankton cell in each pixel. 

CNN_Trainingdata.zip - 150 images of an acoustically focussed imaging flow cytometer flow cell, containing labelled (bounding boxes) cells of Rhodomonas salina sp. phytoplankton in flow, in COCO128 format (train, validation and annotation folders), used to train neural networks in the PhD thesis named above. Extracted directory contains a README describing the dataset and an archive (CNN_training_data.zip) which contains the image and annotation files.

COMSOL.zip - COMSOL FEA model file, results and jupyter notebook for analysis, from a parameter sweep using a model of variously sized particles representing latex beads in flow (in water) within an acoustically focussed flow cell, in 2D. 




Licence:
[CC BY]

Related projects/Funders:
This work was supported by the Natural Environmental Research Council [grant number NE/N012070/1]





Date that the file was created: November, 2023

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