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iMESc: An Interactive Machine Learning App for Environmental Science

Authors: Vieira, Danilo Cândido; Fonseca, Gustavo;

iMESc: An Interactive Machine Learning App for Environmental Science

Abstract

Developed by Danilo Candido Vieira and Gustavo Fonseca, the iMESc is a cutting-edge application designed to assist researchers and professionals in the field of Environmental Sciences. Harnessing the power of machine learning, this interactive tool aims to streamline the data analysis process, facilitating a deeper understanding and insight into environmental data. As a user-friendly platform, iMESc integrates seamlessly with R and RStudio, offering a range of functionalities that simplify the execution of complex tasks. From data visualization to predictive modeling, users can leverage the advanced features of this app to enhance their research projects. Key Features: Interactive Interface: Engage with your data like never before, through an intuitive and interactive interface. Machine Learning Integration: Utilize machine learning algorithms to derive meaningful insights from your data. Open Source: As a commitment to the scientific community, iMESc is available under the CC BY-NC-ND 4.0 license, promoting collaboration and knowledge sharing.

Please cite this software using the metadata from 'preferred-citation'.

Related Organizations
Keywords

Machine Learning, Data Analysis, Open Source, User-Friendly Interface, Environmental Sciences, Shiny App, R Programming

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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1
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4