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ZENODO
Dataset . 2020
License: CC BY
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
Dataset . 2020
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 . 2020
License: CC BY
Data sources: Datacite
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Claim

RYDL: the sample data of the RY product for deep learning applications

Research datakeyboard_double_arrow_right Dataset 29 Jan 2020Publisher:Zenodo
Authors: Ayzel, Georgy;

doi: 10.5281/zenodo.3629951 , 10.5281/zenodo.3629950

RYDL: the sample data of the RY product for deep learning applications

Abstract

RYDL is the sample data of the RY product provided by the German Weather Service (DWD). This data has been utilized for training the RainNet model -- a deep convolutional neural network for radar-based precipitation nowcasting. You can find the latest RY product data on DWD open data repository: https://opendata.dwd.de/weather/radar/. The RainNet model is available on GitHub: https://github.com/hydrogo/rainnet.

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Keywords

radar, RY, Germany, DWD, RainNet, deep 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!
views
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