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bluecloud-plankton

Spatial interpolation of plankton data using a neural network

https://github.com/gher-uliege/bluecloud-plankton

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
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    Academic publication links
    Links to: zenodo.org
  • ○
    Committers with academic emails
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.8%) to scientific vocabulary

Keywords

data data-analysis data-visualization neural-network oceanography
Last synced: 6 months ago · JSON representation ·

Repository

Spatial interpolation of plankton data using a neural network

Basic Info
  • Host: GitHub
  • Owner: gher-uliege
  • License: gpl-2.0
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 3.69 MB
Statistics
  • Stars: 1
  • Watchers: 4
  • Forks: 0
  • Open Issues: 0
  • Releases: 2
Topics
data data-analysis data-visualization neural-network oceanography
Created over 3 years ago · Last pushed over 3 years ago
Metadata Files
Readme License Citation

README.md

Build Status DOI

Interpolation of zooplankton data using the inverse methods DIVAnd and neural networks

The aim of this notebook is to create a gridded data set of the Continuous Plankton Recorder within the Virtual Research Environment developed in BlueCloud.

Input data

Data from the continuous plankton recorder operated by the Marine Biological Association, UK is used as input data for this exercise.

Other sources of information are used to improve the interpolation ("co-variables"), namely:

  • Sea water temperature (SeaDataCloud)
  • Salinity (SeaDataCloud)
  • Distance from coast (NASA Goddard Space Flight Center)
  • Bathymetry (GEBCO)
  • Nitrate, Silicate and Phosphate (World Ocean Atlas 2018)

The URLs of all these resources are available in the notebook DIVAndNN_analysis.ipynb. All input data are downloaded and imported automatically when the notebook is executed.

Software requirements

The notebook needs Julia 1.5 or later (already preinstalled on BlueCloud). Additional Julia software packages are installed automatically if they are not present. The notebooks have been developed on Julia 1.6 and the versions of all software dependencies are saved in the file Manifest.toml. Per default, Julia inside Jupyter environment will use the same versions (more information on julia notebook and on instantiating a julia environment) in order to enhance reproducibility of results.

Running the notebook

This data product uses two jupyter notebooks:

  1. DIVAndNN_analysis.ipynb executing the following steps:
    • Install required Julia packages (if necessary)
    • Download the data
    • Preparation of co-variables
    • Split data into training and validation data set
    • Execute DIVAnd with the neural network analysis
    • Results in form of NetCDF files are stored in BlueCloud-data/results directly under your home directory. The last message of the notebook shows the precise location where the files are saved.

  1. DIVAndNN_plot_res.ipynb executing the following steps:
    • Visualization of the results
    • Figures (PNG files) are stored in BlueCloud-data/figures directly under your home directory. The last message of the notebook shows the precise location where the figures are saved.
    • The figures are also presented directly on your notebook.

You can only run the notebook DIVAndNN_plot_res.ipynb only after DIVAndNN_analysis.ipynb is finished. Your home directory is a temporary storage on BlueCloud. Consider copying the files to a permanent storage like your BlueCloud workspace.

Documentation on the jupyter lab user interface is available here.

These notebooks are developed by the GHER, University of Liege, Belgium and released under the terms of the GPL version 2 (or later, at your option).

Owner

  • Name: GHER
  • Login: gher-uliege
  • Kind: organization
  • Location: Sart Tilman, Liège, Belgium

The GHER is a research group of the University of Liège. It is focused on marine and environmental study and modelling.

Citation (CITATION.cff) 

cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Barth
    given-names: Alexander
    orcid: https://orcid.org/0000-0003-2952-5997
  - family-names: Troupin
    given-names: Charles
    orcid: https://orcid.org/0000-0002-0265-1021
title: BlueCloud-Plankton: v1.0.1
version: v1.0.1
date-released: 2022-09-06

GitHub Events

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Last Year

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 72
  • Total Committers: 4
  • Avg Commits per committer: 18.0
  • Development Distribution Score (DDS): 0.25
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Alexander Barth b****r@g****m 54
Troupin Charles c****n@u****e 12
Barth Alexander a****h@u****e 3
Charles Troupin c****n 3
Committer Domains (Top 20 + Academic)
uliege.be: 2

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
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  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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