Science Score: 44.0%

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
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (14.4%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: DaniloCVieira
  • License: other
  • Language: R
  • Default Branch: main
  • Size: 3.97 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

iMESc: Interactive Machine Learning App for Environmental Sciences

iMESc is a state-of-the-art application designed to bring the power of machine learning to environmental science. Through its interactive interface, it facilitates a more intuitive and seamless experience for users to engage with complex data sets and analysis processes.

Installation & Usage

Option 1: Running iMESc in RStudio

Step 1: Install R and RStudio

If you haven't already, install R and RStudio by following the instructions on their respective websites.

Step 2: Open RStudio

Once R and RStudio are installed, open RStudio.

Step 3: Install Shiny Package

Install the shiny package if it's not already installed. You can do this by running the following command in the RStudio console:

r install.packages('shiny')

Step 4: Run iMESc

To start using the iMESc app, run the following code in the RStudio console:

r shiny::runGitHub('iMESc','DaniloCVieira', ref='main')

Note: When you use the iMESc app for the first time, it will automatically install all the necessary packages, which may take several minutes to complete. However, once the first installation is finished, subsequent access of the app will be much faster. If the required packages are not already loaded, they typically take several seconds to load. On the other hand, if the packages are already loaded, iMESc will start almost instantly.

Option 2: Running iMESc with Docker

For a hassle-free setup, especially for users unfamiliar with RStudio, iMESc is available as a Docker container. The Docker image includes all dependencies, ensuring a consistent environment for running iMESc.

Prerequisites

  • Install Docker by following the instructions for your operating system on the Docker website.

Run the Docker Image

bash docker pull vieiradc/imesc docker run -d -p 3838:3838 vieiradc/imesc

Access the App

Once the container is running, open your browser and navigate to:

http://localhost:3838

Desktop Version for Windows Users

If you are a Windows user, you can enhance your experience with the standalone iMESc desktop version. Visit the iMESc-Desktop repository to find more details and download the installer.

Tutorial

For a detailed walkthrough of the application and its features, visit the tutorial page: iMESchelp [<https://danilocvieira.github.io/iMESchelp/>]

License

This project is licensed under the CC BY-NC-ND 4.0 license. To view a copy of this license, visit CC BY-NC-ND 4.0.

Owner

  • Login: DaniloCVieira
  • Kind: user

Citation (citation.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  iMESc: An Interactive Machine Learning App for
  Environmental Science
message: >-
  Please cite this software using the metadata from
  'preferred-citation'.
type: software
authors:
  - given-names: Danilo Cândido
    family-names: Vieira
    email: vieiradc@yahoo.com.br
    orcid: 'https://orcid.org/0000-0002-7989-6553'
    affiliation: Universidade Federal de São Paulo
  - orcid: 'https://orcid.org/0000-0001-8625-4279'
    given-names: Gustavo
    family-names: Fonseca
    email: gfonseca.unifesp@gmail.com
    affiliation: Universidade Federal de São Paulo
version: 2.1.0.1
date-released: '2022-11-03'
url: 'https://github.com/DaniloCVieira/iMESc'
doi: 10.5281/zenodo.7278042

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Dependencies

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