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  • Fighting Post-Tenure Fatigue: Reclaiming Your Time and Redefining Leadership

    Document thumbnail: Fighting Post-Tenure Fatigue: Reclaiming Your Time and Redefining Leadership
    Type:
    Document
    摘抄:
    Fighting Post-Tenure Fatigue: Reclaiming Your Time and Redefining Leadership offers insights, strategies and personal examples of how the two mid-career librarians have managed to navigate academic librarianship as first-generation, BIPOC, cis-gendered women while also balancing parenthood on the road to achieving tenure. The chapter describes their bouts with post-tenure fatigue, a phenomenon familiar to many mid-career librarians. Early in their careers, academic librarians focus on fulfilling the requirements of the promotion process at their institution. These requirements often leave little room for personal fulfillment once day-to-day responsibilities and designated service requirements are met. This process can provoke feelings of stress and anxiety as they struggle to find their footing within the institution, and the larger areas of librarianship and academia. Most institutions provide a support system and other mentorship for professional growth and career development. Achieving promotion and/or tenure is an accomplishment to be celebrated and followed by a sigh of relief. However, in the days, months, and years afterward, tenured librarians can start feeling a lack of motivation, support, and career guidance. They often struggle to answer the question, “What next?” The co-authors will highlight strategies for dealing with this kind of post-tenure burnout. One strategy for mid-career librarians is to be intentional about how one wants to spend their time. Day-to-day responsibilities and leadership expectations tend to increase exponentially the longer one stays in a position. Therefore, mid-career librarians need to learn when and how to say no in some cases, in order to say yes to other possibilities. Readers will be encouraged to look beyond their core responsibilities and think about expanding their time into areas like strategizing, skill building, acting creatively, and strengthening relationships. The chapter will include questions the reader can use to reflect on how they are spending their time, as well as tips for examining and adjusting their time to align with their intentions. Another strategy for mid-career librarians is to take advantage of unexpected leadership opportunities. Rather than looking for a new position with formal leadership responsibilities, one can develop leadership skills by leading projects inside and outside of their institution. Examples include participating in initiatives with specific goals or professional service in the form of short-term committees. The chapter will include questions the reader can use to examine opportunities for new leadership roles. To illustrate these strategies, the co-authors will share their experience using the mid-career point as an inspiration to invest time into the BIPOC in LIS community. Through service on the ALA Spectrum Advisory Committee, the co-authors were able to lead projects, establish virtual networking events, support new librarians, and experience professional growth. As a result of this work, they were also introduced to new opportunities and professional relationships. This experience models how one can be more intentional about time and energy and grow leadership skills without changing jobs and moving into a leadership or management position.
    作者:
    Lam, Nia; McKinney, Michelle; West, Brandon K. (editor), and Galoozis, Elizabeth (editor)
    提交者:
    Michelle McKinney
    上传日期:
    01/24/2023
    更改日期:
    05/16/2023
    创建:
    2023-01-06
    证书:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • Wireless Sensor Monitoring Water Use at Fixtures

    Dataset thumbnail: Wireless Sensor Monitoring Water Use at Fixtures
    Type:
    Dataset
    摘抄:
    The Dataset contains raw data that indicates the start and stop time of water flowing at fixtures in the Marian Spencer Hall Cafeteria restroom during hours of operation. The data were collected as part of an effort to develop and test a novel method of measuring flow to calculate the probability that the fixture is busy (fixture p-value). The fixture p-value is one of the parameters necessary to predict peak demand in buildings for pipe sizing purposes. There are two .csv files, a README file and a sample of the data collection template with contact information. The dataset also contains a MATLAB code written to accept data in the suggested format and estimate the fixture probability of use.
    作者:
    Choudhary, Chandrashekhar ; Omaghomi, Toju; Buchberger, Steven; Wang, Tianshuo, and Tao, Li
    提交者:
    Toju Omaghomi
    上传日期:
    12/19/2022
    更改日期:
    12/19/2022
    创建:
    2022-12
    证书:
    Open Data Commons Open Database License (ODbL)

  • Coherence_Evaluations - nvl_pollution.csv

    Dataset thumbnail: Coherence_Evaluations - nvl_pollution.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one from) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] / News [nws] SearchTerm[s] = (one from) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/12/2022
    更改日期:
    11/12/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Coherence_Evaluations - nvl_earth.csv

    Dataset thumbnail: Coherence_Evaluations - nvl_earth.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one from) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] / News [nws] SearchTerm[s] = (one from) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/12/2022
    更改日期:
    11/12/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Coherence_Evaluations - nvl_climate.csv

    Dataset thumbnail: Coherence_Evaluations - nvl_climate.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one from) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] / News [nws] SearchTerm[s] = (one from) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/12/2022
    更改日期:
    11/12/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Coherence_Evaluations - nvl_environmental.csv

    Dataset thumbnail: Coherence_Evaluations - nvl_environmental.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one from) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] / News [nws] SearchTerm[s] = (one from) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/12/2022
    更改日期:
    11/12/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • TV News Transcripts of Outer Space Coverage

    Dataset thumbnail: TV News Transcripts of Outer Space Coverage
    Type:
    Dataset
    摘抄:
    Text and Metadata for 14,399 newspaper articles. Transcripts collected from Internet Archive Date Range: 2010-2022 File includes meta/data: - Unique-id (uid) - Title (incl. search term) - Date - Link (url) - Abstract - Text Text matching the following terms: - space explor* - space mission - space science - spaceship - space tour* - space transport* - spacecraft - space shuttle - outer space - astronom* - astrop* - astrona* - planet - NASA - star trek - star wars - lunar - space flight
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/12/2022
    更改日期:
    11/12/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Newspaper coverage of Outer Space Theme

    Dataset thumbnail: Newspaper coverage of Outer Space Theme
    Type:
    Dataset
    摘抄:
    Text and Metadata for 9,061 newspaper articles. Newspapers included: New York Times, Wall Street Journal, & Washington Post Date Range: 2017-2022 File includes meta/data: - Unique-id (uid) - Title (incl. source paper & section name) - Date - Link (url) - Author - Text Text matching the following terms: - space explor* - space mission - space science - spaceship - space tour* - space transport* - spacecraft - space shuttle - outer space - astronom* - astrop* - astrona* - planet - NASA - star trek - star wars - lunar - space flight
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/12/2022
    更改日期:
    11/12/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Coherence_Evaluations - nvl_random5k.csv

    Dataset thumbnail: Coherence_Evaluations - nvl_random5k.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/05/2022
    更改日期:
    11/11/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Coherence_Evaluations - pmc_environmental.csv

    Dataset thumbnail: Coherence_Evaluations - pmc_environmental.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/04/2022
    更改日期:
    11/04/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)