Session Type

40-minute concurrent session

Start Date

29-4-2024 2:30 PM

End Date

29-4-2024 3:10 PM

Keywords

tdm, text analysis, research data, data analysis, digital humanities, data science, data literacy, digital literacy, computational literacy

Abstract

The ability to comprehend and communicate with text-based data is essential to future success in academics and employment, as evidenced in a recent survey from Bloomberg Research Services which shows that nearly 97% of survey respondents now use data analytics in their companies and 58% consider data and text mining a business analytics tool (https://www.sas.com/content/dam/SAS/bp_de/doc/studie/ba-st-the-current-state-of-business-analytics-2317022.pdf). This has fueled a substantial growth in text analysis research (involving the use of technology to analyze un- and semi-structured text data for valuable insights, trends, and patterns) across disciplines and a corresponding demand on academic libraries to support text analysis pedagogy and text analysis research.

In this session, we will introduce text analysis, the methodologies it employs, and various examples of each methodology in research. Then, the Head of Digital Scholarship at a large state university will discuss how her library is beginning to support text analysis across campus including running workshops out of the library, working with faculty to integrate it into class materials, and supporting research on campus. She will discuss the challenges inherent in supporting this new territory, ranging from budgetary concerns to issues of data management, access, and reproducibility.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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Apr 29th, 2:30 PM Apr 29th, 3:10 PM

Supporting text and data analysis across campus from the academic library

The ability to comprehend and communicate with text-based data is essential to future success in academics and employment, as evidenced in a recent survey from Bloomberg Research Services which shows that nearly 97% of survey respondents now use data analytics in their companies and 58% consider data and text mining a business analytics tool (https://www.sas.com/content/dam/SAS/bp_de/doc/studie/ba-st-the-current-state-of-business-analytics-2317022.pdf). This has fueled a substantial growth in text analysis research (involving the use of technology to analyze un- and semi-structured text data for valuable insights, trends, and patterns) across disciplines and a corresponding demand on academic libraries to support text analysis pedagogy and text analysis research.

In this session, we will introduce text analysis, the methodologies it employs, and various examples of each methodology in research. Then, the Head of Digital Scholarship at a large state university will discuss how her library is beginning to support text analysis across campus including running workshops out of the library, working with faculty to integrate it into class materials, and supporting research on campus. She will discuss the challenges inherent in supporting this new territory, ranging from budgetary concerns to issues of data management, access, and reproducibility.