READ ME File For Dataset for Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi 

Dataset DOI: 10.5258/SOTON/D0758

ReadMe Author: James A Grant-Jacob, University of Southampton ORCID ID: 0000-0002-4270-4247	

This dataset supports the publication:
Grant-Jacob, J., Xie, Y., MacKay, B. S., Praeger, M., McDonnell, M. D. T., Heath, D. J., ... Mills, B. (2019). Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi. Environmental Research Communications, 1(1)
https://doi.org/10.1088/2515-7620/ab14c9

This dataset contains:

Figure_1.bmp is an image of the training setup.
Figure_2.bmp is an image of the schematic of the training neural network.
Figure_3a.txt is the data for the prediction of solids concentration versus actual solids concentration including the error for silicon dioxide particles.
Figure_3b.txt is the data for the prediction of solids concentration versus actual solids concentration including the error for melamine resin particles.
Figure_4.txt is the data for the prediction of salinity versus actual salinity including the error.


Date of data collection: ADD IN COLLECTION DATES

Information about geographic location of data collection: University of Southampton, U.K.

Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

Related projects:
Ben Mills - Beam-shaping for laser-based additive and subtractive manufacturing technology EPSRC EP/N03368X/1
A New Mechanism Behind the Health Effects of Air Pollution? The Academy of Medical Sciences SBF003/1063


Date that the file was created: May, 2019