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InteractiveResource . 2024
License: CC BY
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InteractiveResource . 2024
License: CC BY
Data sources: Datacite
ZENODO
InteractiveResource . 2024
License: CC BY
Data sources: Datacite
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Accessing Nansen Legacy data in R

Authors: Luke Marsden;

Accessing Nansen Legacy data in R

Abstract

Jupyter book showing you how to access key Nansen Legacy datasets using R. For a more generic introduction into how to extract data and metadata from NetCDF files, this other tutorial that I have created is recommended prior reading. Here, I explain at an introductory level how NetCDF files are are structured and why, discuss the CF and ACDD conventions, and show you in more detail how to access all the different components of a classic NetCDF dataset. https://lhmarsden.github.io/NetCDF_in_R_from_beginner_to_pro/01_opening_and_understanding.html Returning to this tutorial series now, each chapter is focused on how to extract data for a different dataset. The code is focused and tailored to each dataset. The book can be found here: https://lhmarsden.github.io/Accessing_Nansen_Legacy_data_in_R/intro.html

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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