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Data citation, attribution, and employability

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Title: Data citation, attribution, and employability
Authors: Dailey, Meagan
Henke, Ryan
Keywords: data citation
attribution
Linguistics
Issue Date: 06 Jan 2017
Abstract: Demand from academic departments for linguists possessing data skills has remained low in the last decade despite an influx of new data-driven tools, research, and ability to manage data in ways not possible before the internet. We assessed two barometers of employability: academic job postings and course descriptions. A survey of the academic linguistic job market over 10 years reveals that despite the field becoming more reliant on digital data, employers are not asking that candidates be fluent in data management. We also surveyed course descriptions and syllabi from 25 of the top-ranked linguistics programs in the United States and abroad, finding that most universities do not offer training in basic data management, despite offering courses in data- driven subdisciplines. This poster presents the data supporting these points including data management hiring and training trends.
Description: Poster: Demand from academic departments for linguists possessing data skills has remained low in the last decade despite an influx of new data-driven tools, research, and ability to manage data in ways not possible before the internet. We assessed two barometers of employability: academic job postings and course descriptions. A survey of the academic linguistic job market over 10 years reveals that despite the field becoming more reliant on digital data, employers are not asking that candidates be fluent in data management. We also surveyed course descriptions and syllabi from 25 of the top-ranked linguistics programs in the United States and abroad, finding that most universities do not offer training in basic data management, despite offering courses in data- driven subdisciplines. This poster presents the data supporting these points including data management hiring and training trends.
Sponsor: This material is based upon work supported by the National Science Foundation under grant SMA-1447886.
Pages/Duration: 1
URI/DOI: http://hdl.handle.net/10125/43571
Rights: Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported Attribution-NonCommercial-ShareAlike 3.0 United States
Appears in Collections:Presentations from the Linguistic Society of America symposium and poster session on Data Citation and Attribution in Linguistics, 5-9 January 2017, Austin TX



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