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Building communities of practice around environmental open data science

Authors: Julia Stewart Lowndes;

Building communities of practice around environmental open data science

Abstract

Environmental scientists are a diverse community that ranges from climatologists to geneticists, but we are united by an enormous need to work efficiently with data – and by the fact that we seldom have formal computing or data analysis training of any kind. There is great opportunity to borrow from the work of software engineers and use collaborative open tools that facilitate better science in less time. However, a fundamental shift is needed in the environmental science community that prioritizes data science and provides emerging scientific leaders training in open science tools and practices to strengthen and accelerate their work. I will discuss my work to catalyze this shift through two programs I have developed and lead at the National Center for Ecological Analysis and Synthesis (NCEAS) at the University of California at Santa Barbara. The first is the Ocean Health Index training program, which teaches international government and academic scientists how to channel the best available scientific information into marine policy using our scientific method and tools. And the second I have recently launched in January 2019 as a Mozilla Fellow: Openscapes, a mentorship program that empowers environmental scientists with open data science tools and grows the community of practice.

Keywords

ocean health index, marine ecology, community, open science, open data science, environment, mozilla, open data science, rstats

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selected citations
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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).
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.
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