By simulating the interactions between phytoplankton motility and turbulent flow dynamics, we seek to explain the processes contributing to observed patchiness.
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Updated
Apr 3, 2024 - Python
By simulating the interactions between phytoplankton motility and turbulent flow dynamics, we seek to explain the processes contributing to observed patchiness.
Data, code and results from O'Brien D.A., Gal G., Thackeray S.J., Matsuzaki S.S & Clements C.F. 2022. Planktonic functional diversity changes in synchrony with lake ecosystem state.
A MATLAB toolbox for culture experiments to monitor cells & obtain/plot growth rate data
Image analysis part of ZooImage, with interface to ImageJ through RImageJ
The code and report of the 3MD4040 2022 challenge
Supplemental data and website for Lima-Mendez, G., et al. (2015). Determinants of community structure in the global plankton interactome Science, 348(6237).
This repository will have all the code for figures and videos constructed for the paper "Analysis and Simulation of a Novel Run-and-Tumble Model with Autochemotaxis", which will appear in the Journal of Mathematical Biology.
Testing new technics to improve Plankton taxonomic classification such as ResNets and Discriminative filters DFL-VGG16.
Data renalysis of Antell et al. (2021)
Global compilation of existing foraminifera census data in the Last Glacial Maximum (18-21 ka)
Automatic plankton image classification software
Code from my dissertation at the University of Delaware titled "Microscopic Interactions Lead to Macroscopic Dynamics: An Analysis of Various Flocking Mechanisms."
Supplemental data and website for Guidi, L., et al. (2016). Plankton networks driving carbon export in the oligotrophic ocean, Nature.
A C# program targeting .NET 4.6.1 that simulates plankton in a pond, with the emphasis put on fun over realism.
An implementation of the depth-resolved phytoplankton primary production model by Westberry et al. 2008.
Final project for my Master's Degree in Theoretical Physics
Planktos is an image recognition model tailored for classifying various species of phytoplankton. Built using cutting-edge machine learning algorithms, this tool enables researchers and environmentalists to automate and accelerate the identification process, enhancing studies in marine biology and ecology.
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