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Software . 2018
Data sources: Datacite
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
Software . 2018
Data sources: Datacite
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Software . 2018
Data sources: ZENODO
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asreimer/lmfit2: FPFM Implemented as LMFIT2

Authors: Ashton Reimer;

asreimer/lmfit2: FPFM Implemented as LMFIT2

Abstract

Summary This is the first release of the First-Principles Fitting Methodology (FPFM) algorithm that can be used to fit SuperDARN rawacf data. The details of this algorithm are described in Reimer et. al. (2018) https://doi.org/10.1002/2017RS006450. This release is the version of the LMFIT2 software used in Reimer et. al. (2018). Please see the README.md for installation instructions. Modifications Since Publication No modifications have been made to the C code since before the publication of Reimer et. al. (2018). Unit testing was added to the C code, but this is extra software added in separate files with no modification to the actual C code. The point of saying this is that one should be able to use this code to reproduce the results shown in the publication. Several modifications were made to the python version of the code, with many thanks to @samuelwharton for independent testing help. These modifications include a couple bug fixes that makes the python code work almost identically to the C code, addition of unit testing. Usage This code was written with the intention that the C version would be used for any serious application. The python code was written because python is much more readable, so one can use the python code to study how the FPFM algorithm works. The python code is much slower than the C code and should only be used for testing and learning.

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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!
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