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Dataset: 5D imaging of freezing emulsions with solute effects
Version 2 2018-04-20, 06:39
Version 1 2018-04-19, 19:48
dataset
posted on 2018-04-20, 06:39 authored by Sylvain DevilleSylvain DevilleWelcome to this dataset !
This dataset and code were used and developed for the paper "5D imaging of freezing emulsions with solute effects", by Dmytro Dedovets, Cécile Monteux, and Sylvain Deville, published in Science, April 20, 2018.
The paper is here: http://science.sciencemag.org/content/360/6386/303
The Python code has been developed to analyse the confocal images obtained during the freezing of emulsions.
If you want to use this code and the data we provided, you should organise your files as follows. Create a main directory with 2 subdirectory:
- "code": put the three notebooks here. Create a folder inside called "functions". In this functions folder, you need to put the detect peak script which you can find here http://nbviewer.jupyter.org/github/demotu/BMC/blob/master/notebooks/DetectPeaks.ipynb
- "data": put the data here
The dataset consists in image stacks (8.7Gb total). For each interface velocity, we have two sequences: one with the fluorescent channel of the liquid (filename ends with "-solute"), and a second one with the fluorescent channel of the droplet. We analyse both sequences separately in the code.
We tested the following interface velocities: 1, 2, 3, 4, 5, 6, 8, and 10 microns/s, for a temperature gradient of 5°C/mm.
When you start the notebook, another directory ("Results") will be created, and all the .csv files and figures generated will be saved there.
We are not Python experts, but the code runs relatively fast. The whole analysis for one sequence runs in a few minutes on a 2016 MacBook Pro (3,3 GHz Intel Core i7).
We still work actively on the code to improve it, so any feedback and suggestions are warmly welcome. Please keep in touch with us if you want to discuss anything.
-Sylvain Deville
Laboratoire de Synthèse et Fonctionnalisation des Céramiques, Cavaillon, France
Email: sylvain.deville@saint-gobain.com, also on twitter @DevilleSy
This dataset and code were used and developed for the paper "5D imaging of freezing emulsions with solute effects", by Dmytro Dedovets, Cécile Monteux, and Sylvain Deville, published in Science, April 20, 2018.
The paper is here: http://science.sciencemag.org/content/360/6386/303
The Python code has been developed to analyse the confocal images obtained during the freezing of emulsions.
If you want to use this code and the data we provided, you should organise your files as follows. Create a main directory with 2 subdirectory:
- "code": put the three notebooks here. Create a folder inside called "functions". In this functions folder, you need to put the detect peak script which you can find here http://nbviewer.jupyter.org/github/demotu/BMC/blob/master/notebooks/DetectPeaks.ipynb
- "data": put the data here
The dataset consists in image stacks (8.7Gb total). For each interface velocity, we have two sequences: one with the fluorescent channel of the liquid (filename ends with "-solute"), and a second one with the fluorescent channel of the droplet. We analyse both sequences separately in the code.
We tested the following interface velocities: 1, 2, 3, 4, 5, 6, 8, and 10 microns/s, for a temperature gradient of 5°C/mm.
When you start the notebook, another directory ("Results") will be created, and all the .csv files and figures generated will be saved there.
We are not Python experts, but the code runs relatively fast. The whole analysis for one sequence runs in a few minutes on a 2016 MacBook Pro (3,3 GHz Intel Core i7).
We still work actively on the code to improve it, so any feedback and suggestions are warmly welcome. Please keep in touch with us if you want to discuss anything.
-Sylvain Deville
Laboratoire de Synthèse et Fonctionnalisation des Céramiques, Cavaillon, France
Email: sylvain.deville@saint-gobain.com, also on twitter @DevilleSy
