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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2024
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
Data sources: Datacite
ZENODO
Dataset . 2024
Data sources: Datacite
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MultiDEFusion trial repository

Authors: Damian Tondaś;

MultiDEFusion trial repository

Abstract

Instructions for downloading a trial repository for the MultiDEFusion library The following repository has been created as a trial dataset for the MultiDEFusion library. The complete documentation of the MultiDEFusion repository can be found at: https://damiantondas.github.io/multidefusion/ Cloning the repository To download the MultiDEFusion trial repository, follow these steps: Open a terminal or command prompt on your computer. Use the git clone command to clone the repository to your device: git clone https://github.com/damiantondas/multidefusion_trial.git The repository will be downloaded to the current directory. You can now navigate to the repository directory using the cd command: cd multidefusion_trial To run the integration procedure in the Python environment, the initial parameters are required to be defined by the user. In the following, you can find an example script to run fusion for ALL stations in multidefusion_trial folder using forward-backward method with 0.03 mm/day^2^ noise level: from multidefusion import run_fusion integration = run_fusion(stations="ALL", path="/path/to/multidefusion_trial/", method="forward-backward", noise=0.03) More examples can be found in the library documentation.

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    Impact byBIP!
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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).
    0
    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.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
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!
0
Average
Average
Average