Created for the Oklahoma Department of Libraries (ODL) Public Library Academy (PLA) "How to Think Like a Librarian" course as part of the 2022 course redesign. ODL holds the copyright for this presentation.

The final presentation can be viewed here.

More information about ODL's "How to Think Like a Librarian" can be found here.

This report encompasses preliminary research, data, and statistics collected to discuss the role of McFarlin Library in the University of Tulsa's 2022 strategic initiatives. Data was gathered through publicly-available sources, such as the U.S. News and World Report rankings of colleges and universities within the United States, information gathered from websites of libraries within that ranking, and cost estimations from publicly-available sources.

This project includes unpublished documents containing rudimentary ideas regarding the role of McFarlin Library in the University of Tulsa’s Strategic Initiatives. These materials do not represent the views of the University of Tulsa or McFarlin Library at the University of Tulsa, nor does it represent implementation steps, legitimate suggestions, or advice. Those who download and consult this document do so with the understanding that it has not been implemented or consulted by the University of Tulsa’s administration.

Already facing declining staffing numbers and departmental erosion, library technical services units were disproportionately impacted by the COVID-19 pandemic. Whereas many library services could easily pivot to an online environment, technical services departments were forced to transition traditionally in-person, physical collection work to a remote environment. Seeking to better understand the lasting impact of the coronavirus pandemic on library technical services units, a survey distributed from October 2021 to January 2022 collected 829 responses to investigate the challenges faced by technical services departments and the lasting impacts of COVID-19 on these specific units.

This is a preprint of the author’s original and accepted manuscript of an article whose final form was published by Taylor & Francis in Technical Services Quarterly 39(3), pp. 241-271 on 24 July 2022, available at: https://doi.org/10.1080/07317131.2022.2082659.

Already facing declining staffing numbers and departmental erosion, library technical services units were disproportionately impacted by the COVID-19 pandemic. Whereas many library services could easily pivot to an online environment, technical services departments were forced to transition traditionally in-person, physical collection work to a remote environment. Seeking to better understand the lasting impact of the coronavirus pandemic on library technical services units, a survey distributed from October 2021 to January 2022 collected 829 responses to investigate the challenges faced by technical services departments and the lasting impacts of COVID-19 on these specific units.

Already facing declining staffing numbers and departmental erosion, library technical services units were disproportionately impacted by the COVID-19 pandemic. Whereas many library services could easily pivot to an online environment, technical services departments were forced to transition traditionally in-person, physical collection work to a remote environment. Seeking to better understand the lasting impact of the coronavirus pandemic on library technical services units, a survey distributed from October 2021 to January 2022 collected 829 responses to investigate the challenges faced by technical services departments and the lasting impacts of COVID-19 on these specific units.

The COVID-19 pandemic had a near-immediate impact on libraries worldwide. The majority of the world’s libraries shifted their work to online environments, and while this pivot was difficult for all libraries, technical services units were disproportionately impacted given the inherent physical nature of the tasks undertaken by these library units. Research on the impact of COVID-19 and libraries is still developing, but already, technical services units and tasks are being left behind. This literature review explores the early days of COVID-19 guidance for technical services units, tasks, and workers, as well as the lasting impact of COVID-19 on technical work.

This is the author’s original and accepted manuscript of an article published by Taylor & Francis in Technical Services Quarterly on 17 April 2022, available at https://www.tandfonline.com/doi/full/10.1080/07317131.2022.2045431.

The COVID-19 pandemic had a near-immediate impact on libraries worldwide. The majority of the world’s libraries shifted their work to online environments, and while this pivot was difficult for all libraries, technical services units were disproportionately impacted given the inherent physical nature of the tasks covered by these library units. Research on the impact of COVID-19 and libraries is still developing, but already, technical services units and tasks are being left behind. This literature review explores the early days of COVID-19 guidance for technical services units, tasks, and workers, as well as the lasting impact of COVID-19 on technical work

The COVID-19 pandemic had a near-immediate impact on libraries worldwide. The majority of the world’s libraries shifted their work to online environments, and while this pivot was difficult for all libraries, technical services units were disproportionately impacted given the inherent physical nature of the tasks undertaken by these library units. Research on the impact of COVID-19 and libraries is still developing, but already, technical services units and tasks are being left behind. This literature review explores the early days of COVID-19 guidance for technical services units, tasks, and workers, as well as the lasting impact of COVID-19 on technical work.

These datasets contain cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. These specific iterations of data reflect cleaning and standardization so that data can be analyzed using Python. Ultimately, the three files reflect the removal of survey begin/end times, other data auto-recorded by Qualtrics, blank rows, blank responses after question four (the first section of the survey), and non-United States responses. Note that State names for "What state is your library located in?" (Q36) were also standardized beginning in Impact_of_COVID_on_Tech_Services_Clean_3.csv to aid in data analysis. In this step, state abbreviations were spelled out and spelling errors were corrected.

This dataset contains the cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. The specific iteration of this data reflects the removal of survey begin/end times, as well as other data auto-recorded by Qualtrics. It does not reflect removed blank rows, surveys containing blank data after question four (the first survey section), and non-United States responses.

These datasets, clustered by library type, contain cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. These specific iterations of data reflect the cleaning and standardization conducted on the raw dataset retrieved from survey responses, and then cluster the data into specific library type files. All files reflect the removal of data auto-generated by Qualtrics (such as survey start/stop times), blank rows, survey responses not completed after question four (the first section of survey questions), and non-United States responses. Survey respondents were asked to identify their library type (Academic, Public, K-12 School, Special Collections and/or Archives, Other, and Blank responses). There is some duplication between files, as respondents were allowed to select more than one library type to represent the sometimes complicated governing structure within libraries. Note that these files also contain an additional cleaning steps to standardize numbers within the "How many full and part-time staff members (not student workers) were on your Technical Services team prior to the COVID-19 pandemic?" and "How many full and part-time staff members (not student workers) are on your technical services team now?" questions (Q6 and Q7). String text was removed from these fields, as well as incomplete responses (e.g. Indicating a before number but not an after number).

These files contain the initial aggregate datasets produced by University of Tulsa McFarlin Library systems tracking daily, monthly, bi-annual, or annual interactions with library patrons. Files do not contain any identifying information about library patrons in compliance with state and federal laws regarding library patron privacy and student information privacy. These files are not the final aggregate datasets utilized for this project. Files may have irregular naming conventions, dates, formats, entry methods, missing data, averaged data, or other inconsistencies because each library system outputs data in a different manner. Aggregate and formatted versions of the data are available within the University of Tulsa McFarlin Library and COVID Data, 2020-2021 Dataverse: https://dataverse.harvard.edu/dataverse/McFarlinandCOVID. All files are housed in Excel spreadsheet formats and may contain multiple tabs

This dataset reflects McFarlin Library usage from 2020-2021 in an Excel spreadsheet format. The file contains nine total spreadsheet tabs reflecting an overview of total user interactions ("Pivot"), Circulation statistics ("Circulation"), Discovery system statistics ("Discovery"), Electronic Resources usage ("Electronic Resources"), Interlibrary Loan and Document Delivery statistics ("ILL"), Instruction statistics ("Instruction"), Reference statistics ("Reference"), Special Collections interactions ("Special Collections"), and Web services statistics ("Web"). Data was hand-compiled from library systems outputs into monthly aggregations and summed by monthly user "interactions" to determine library interactions with patrons during the COVID-19 pandemic's early days.

This dataset utilized Centers for Disease Control and Prevention (CDC) data and Oklahoma Department of Health data regarding CDC cases in the State of Oklahoma and within Tulsa County to create monthly aggregations and averages of COVID-19 infections in the state and Tulsa County. Average and summation figures were aggregated month-to-month and combined with University of Tulsa McFarlin Library data to determine patterns between county or state COVID-19 cases and McFarlin Library usage statistics for 2020 and 2021. The resulting file is in an Excel format.

Copies of Anaconda 3 Jupyter Notebooks and Python script for holistic and clustered analysis of "The Impact of COVID-19 on Technical Services Units" survey results. Data was analyzed holistically using cleaned and standardized survey results and by library type clusters. To streamline data analysis in certain locations, an off-shoot CSV file was created so data could be standardized without compromising the integrity of the parent clean file. Three Jupyter Notebooks/Python scripts are available in relation to this project: COVID_Impact_TechnicalServices_HolisticAnalysis (a holistic analysis of all survey data) and COVID_Impact_TechnicalServices_LibraryTypeAnalysis (a clustered analysis of impact by library type, clustered files available as part of the Dataverse for this project).

This dataset contains the raw data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. The specific iteration of this data does not reflect the removal of survey begin/end times, or other data auto-recorded by Qualtrics, nor has it removed blank rows or non-United States responses.

These files contain the raw datasets produced by University of Tulsa McFarlin Library systems tracking daily, monthly, bi-annual, or annual interactions with library patrons. Files do not contain any identifying information about library patrons in compliance with state and federal laws regarding library patron privacy and student information privacy. These files are not the final aggregate datasets utilized for this project. Files may have irregular naming conventions, dates, formats, entry methods, missing data, averaged data, or other inconsistencies because each library system outputs data in a different manner. Aggregate and formatted versions of the data are available within the University of Tulsa McFarlin Library and COVID Data, 2020-2021 Dataverse: https://dataverse.harvard.edu/dataverse/McFarlinandCOVID. All files are housed in Excel spreadsheet formats and may contain multiple tabs.

This dataverse contains the raw and cleaned data for Elizabeth Szkirpan's research project into the measurable impact of COVID-19 on United States library technical services units. Files used for data analysis, as well as the Python scripts for data investigation, are all available within this repository.

This Excel file contains four tabs based on data hand-collected from geographical peer institutions to compare library salaries. In some instances, library salaries where provided directly to the researcher, but in most instances, the researcher relied on open data regarding state salaries to estimate and include data. This data should not be taken as perfect data, but as best estimated data using current staff member positions and names combined with university or college pay scales. Salary Comparisons: The first tab of this spreadsheet contains hand-keyed data comparing estimated salaries for library deans, library directors, librarians, library assistant IIs, and library assistant Is at geographical peer institutions. Salary Model: The second tab of this spreadsheet contains preliminary work to create a multilinear regression model to determine salary starting points at geographical peer institutions in the state of Oklahoma. Color Coded Salary Model: The third tab of this spreadsheet contains salary averages, position ranking within each library from 1-7 with 1 representing those closest to a library assistant position and 7 representing library deans or dean-type roles, and Excel formulas to represent each geographical peer on a line graph by color. Graphs: The fourth and final tab of this spreadsheet includes all graphs generated within the spreadsheet itself.

This dataverse contains raw, aggregated, and manipulated data for a 2022 research project comparing University of Tulsa McFarlin Library usage data to Oklahoma and Tulsa County COVID-19 infections for 2020-2021. This dataset and the resulting outputs represent experimental data exploration between institutional data and open data.