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Advancing Interdisciplinary Data Science Training for the Humanities and Social Sciences

Authors: Zeffiro, Andrea; Brodeur, Jason J; Whitwell, Sarah; Van, Allison; Dion, Michelle; Holtz Schramek, Evangeline; Litt, Veronica;

Advancing Interdisciplinary Data Science Training for the Humanities and Social Sciences

Abstract

This report documents the outcomes and key learnings that stemmed from the 2020-2021 SSHRC funded The Relevant Research Series. A workshop series jointly organized and hosted by the Sherman Centre for Digital Scholarship and Spark: a centre for social research innovation at McMaster University. The recommendations can inform future interdisciplinary data science training initiatives at McMaster University and beyond.

Social Sciences and Humanities Research Council of Canada

Country
Canada
Related Organizations
Keywords

data fluency, knowledge mobilization, interdisciplinary data science training, data literacy, open educational resources, research support and acceleration, data acumen

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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
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