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Data Analysis & Visualisation According to FAIR Principles

Authors: Scherer, Cédric;

Data Analysis & Visualisation According to FAIR Principles

Abstract

The slides, R script, and Jupyter notebook for the workshop lesson "Data Analysis & Visualisation According to FAIR Principles" as part of the "NFDI4Biodiversity Seasonal School 2024" and the subsequent edition as part of the follow-up course "NFDI4Biodiversity & HeFDI & iDiv Seasonal School 2025". The lesson covers the principles of the data science workflow, using R and the tidyverse, with a focus on data visualization principles and the grammar of graphics with the ggplot2 package.

Keywords

workshop, training, dataviz, FAIR principles, NFDI4Biodiversity Seasonal School, NFDI4BiodiversityiDivSeasonalSchool2024, ggplot, datascience, NFDI4BiodiversityHeFDIiDivSeasonalSchool2025, datavisualization, ggplot2, rstats

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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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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
Related to Research communities
Italian National Biodiversity Future Center