Copy edits/spellcheck -- removed stat min sentence
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Vicky Steeves
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\citation{29}
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\citation{30}
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\citation{31}
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\citation{32}
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\@writefile{toc}{\contentsline {section}{\numberline {4}Research Methods}{\thepage }{section.4}}
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\citation{33}
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\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Code categories, their frequencies and sub-codes from the document analysis.\relax }}{\thepage }{table.caption.4}}
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@ -74,10 +73,10 @@
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\bibcite{3}{3}
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\bibcite{4}{4}
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\bibcite{6}{6}
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\@writefile{toc}{\contentsline {section}{\numberline {7}Implications and Future Work}{\thepage }{section.7}}
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\@writefile{toc}{\contentsline {section}{\numberline {9}References}{\thepage }{section.9}}
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\bibcite{6}{6}
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\bibcite{8}{8}
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\bibcite{9}{9}
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\bibcite{29}{29}
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\bibcite{30}{30}
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\bibcite{31}{31}
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\bibcite{32}{32}
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\bibcite{33}{33}
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\bibcite{33}{32}
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@ -195,11 +195,6 @@ M.~Denscombe.
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projects}.
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\newblock Open University Press, Maidenhead, 5. ed edition, 2014.
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\bibitem{32}
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M.~R. Sirkin.
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\newblock {\em Statistics for the social sciences}.
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\newblock Sage Publications, Thousand Oaks, Calif, 3rd ed edition, 2006.
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\bibitem{33}
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F.~J. Fowler.
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\newblock {\em Survey research methods}.
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@ -16,45 +16,45 @@ Warning--entry type for "26" isn't style-file defined
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--line 265 of file 2016-10-03_iPRES_NDSRLongPaperBiblio.bib
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This is pdfTeX, Version 3.1415926-2.5-1.40.14 (TeX Live 2013/Debian) (format=pdflatex 2016.4.1) 1 JUL 2016 16:08
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This is pdfTeX, Version 3.1415926-2.5-1.40.14 (TeX Live 2013/Debian) (format=pdflatex 2016.4.1) 6 JUL 2016 15:47
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[]\T1/aer/m/n/9 The au-thors' fi-nal con-cern was with send-ing in-di-vid-ual e
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ymity
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[]
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l skills\T1/aer/m/n/9 ; \T1/aer/m/it/9 Knowl-
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[]
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[5 <./total_skills.png> <./modal_average.png>]
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l.266
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l.267 \section{Conclusions}
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[6]
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\T1/aer/m/n/9 re-search op-por-tu-ni-ties, com-mu-ni-ca-tion skills, and
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\T1/aett/m/n/9 what-[]does-[]it-[]take-[]to-[]be-[]a-[]well-[]rounded-[]digital
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@ -174,7 +174,7 @@ The authors used Qualtrics\footnote{Produced by Qualtrics: https://www.qualtrics
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Initially, four survey invitations were sent to the list of participants using the Qualtrics email function, or "mailer." The mailer allows for complete anonymity in the data collection: the authors could not see who had completed or not completed the survey. This also allowed the authors to send out individualized, anonymous links, to separate respondents in bulk. When the survey was scheduled to close, there were still nine participants of the original thirty-five who did not partake. To get as close to a full dataset as possible, each author sent a follow-up email to four-to-seven participants. The link to the survey included in these emails was still anonymous and did not record IP, but was no longer unique to each recipient.
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The authors acknowledge several methodological issues with the data collection for this study. The first is that the authors are included in the dataset as participants. The most significant issue is that the authors effectively studied themselves; they designed, tested, and discussed the survey before deployment. As a result, they did not take the survey blind. Not only did this differentiate them from the rest of the participants, which could potentially skew the data, but it also introduced the potential for nonresponse bias\cite{31}. However, the authors randomized the questions to mitigate the latter issue. Although the authors recognize that participating in their own research is unorthodox, they felt that it was essential to equally represent all of the different NDSR projects, locations, and cohorts in the survey results. Were they to have recused themselves from the study, it would have been impossible to achieve the generally accepted statistical minimum of 35\cite{32}. Moreover, because the authors all belonged to the same 2014-15 NDSR in New York cohort, those projects would not have been represented in the survey results. The authors felt that the benefits of including their responses outweighed the potential costs of excluding their responses from the dataset.
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The authors acknowledge several methodological issues with the data collection for this study. The first is that the authors are included in the dataset as participants. The most significant issue is that the authors effectively studied themselves; they designed, tested, and discussed the survey before deployment. As a result, they did not take the survey blind. Not only did this differentiate them from the rest of the participants, which could potentially skew the data, but it also introduced the potential for nonresponse bias\cite{31}. However, the authors randomized the questions to mitigate the latter issue. Although the authors recognize that participating in their own research is unorthodox, they felt that it was essential to equally represent all of the different NDSR projects, locations, and cohorts in the survey results. Moreover, because the authors all belonged to the same 2014-15 NDSR in New York cohort, those projects would not have been represented in the survey results. The authors felt that the benefits of including their responses outweighed the potential costs of excluding their responses from the dataset.
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Another potential problem was the fact that fifteen of the participants took the survey before they completed their residencies. This introduced a possibility for survey bias \cite{33}. They might not have been able to answer the optional questions regarding 1) post-NDSR job functions, and 2) additional skills necessary to complete their residencies. However, since the current residents could answer all the required questions (they were more than halfway through their residencies during data collection), they were still included in the participant population.
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@ -281,7 +281,7 @@ Digital stewards are qualified to manage, preserve, and provide access to variou
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The authors conclude that while there are some fundamental competencies required of digital stewards, digital stewardship also encompasses niche skills that are role-specific. Several \textit{Technical skills} were far more important to some projects than to others, and therefore could be considered specialized, rather than fundamental skills. There was a clear bimodal distribution for \textit{Technical skills} (sub-codes in this category were deemed "Not at all important" 84 times and "Essential" 85 times). The authors posit that while job postings often list \textit{Technical skills} as being essential, this study indicates that they are not always essential to all jobs in practice.
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These split distributions apply to \textit{Technical skills} sub-codes as well. For example, respondents were evenly split when gauging the importance of both \textit{Hardware/software implementation} and \textit{Qualitative data analysis}. These skills were unambiguously important to half of the respondents, but it unambiguously unimportant to the other half. \textit{Web archiving} distinguishes itself in this regard as a particularly niche skill--"Essential" to four respondents, but "Not important at all" to eighteen. By contrast, \textit{Workflow enhancement} is a universally important skill, having been deemed "Essential" twenty-one times and "Not important at all" only once.
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These split distributions apply to \textit{Technical skills} sub-codes as well. For example, respondents were evenly split when gauging the importance of both \textit{Hardware/software implementation} and \textit{Qualitative data analysis}. These skills were unambiguous important to half of the respondents, but it unambiguous unimportant to the other half. \textit{Web archiving} distinguishes itself in this regard as a particularly niche skill--"Essential" to four respondents, but "Not important at all" to eighteen. By contrast, \textit{Workflow enhancement} is a universally important skill, having been deemed "Essential" twenty-one times and "Not important at all" only once.
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By analyzing the project descriptions of the National Digital Stewardship Residencies, the authors enumerated the competency areas that define digital stewardship across a broad swath of applications. By surveying the residents responsible for successfully completing these residencies, they were also able to highlight fundamental competency areas that therefore belong in any profile of an effective digital steward.
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