Index Catalog // Scholar@UC

Type:: Dataset
Description/Abstract:: 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] / Chicago Novel Corpus [nvl] / Newspaper Corpus [nws] 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
Creator/Author:: McCabe, Erin E.
Submitter:: Erin E. McCabe
Date Uploaded:: 08/10/2022
Date Modified:: 11/11/2022
Date Created:: 2022
License:: Open Data Commons Attribution License (ODC-By)

Type:: Dataset
Description/Abstract:: 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
Creator/Author:: McCabe, Erin E.
Submitter:: Erin E. McCabe
Date Uploaded:: 08/10/2022
Date Modified:: 08/10/2022
Date Created:: 2022
License:: Open Data Commons Public Domain Dedication and License (PDDL)

Type:: Dataset
Description/Abstract:: This file contains poet metadata for a dataset of poems collected from Poetry Foundation. This metadata relates to a poetry corpus collected from Kaggle available on that site: https://www.kaggle.com/datasets/ultrajack/modern-renaissance-poetry Sean Ayres collected publication metadata and generated other relevant metadata fields based on his subject matter expertise. The original Kaggle poetry dataset contains approximately X lines of poetry. The metadata for this dataset references X poets
Creator/Author:: Ayres, Sean and McCabe, Erin E.
Submitter:: Erin E. McCabe
Date Uploaded:: 07/11/2022
Date Modified:: 07/11/2022
Date Created:: 2022
License:: Open Data Commons Attribution License (ODC-By)

Type:: Dataset
Description/Abstract:: This file contains book metadata for a Project Gutenberg dataset of poetry metadata. This metadata relates to a poetry corpus collected by Allison Parrish available on GitHub: https://github.com/aparrish/gutenberg-poetry-corpus Sean Ayres collected the publication metadata and generated other relevant metadata fields based on his subject matter expertise. The poetry dataset contains approximately three million lines of poetry The metadata for the dataset references X hundreds of books.
Creator/Author:: Ayres, Sean; McCabe, Erin E., and Parrish, Allison
Submitter:: Erin E. McCabe
Date Uploaded:: 07/11/2022
Date Modified:: 07/11/2022
Date Created:: 2022
License:: Open Data Commons Attribution License (ODC-By)

Type:: Dataset
Description/Abstract:: Data and code used for the analysis of the effects of weather on the fall migration of eastern monarch butterflies.
Creator/Author:: Matter, Stephen F.; Guerra, Patrick ; Parlin, Adam ; Rich, Jeremy, and Taylor, Orley R.
Submitter:: Stephen F. Matter
Date Uploaded:: 07/03/2022
Date Modified:: 11/28/2022
Date Created:: 2022
License:: Attribution-NonCommercial-NoDerivs 4.0 International

Type:: Media
Description/Abstract:: Belinda Reynolds (music), Ting Luo (piano and poetry), Charles Woodman (images) WORDS is a multimedia piano work with spoken words and visuals. The vocal part consists of an aural collage of Miss Luo, reciting in Mandarin and English, a poem written by herself, about the story of her grandfather, a composer during the late 20th century in China. The interplay between the audio collage, the video, and the piano part, create a multimedia composition that immerses the audience in a blanket of enticing, reflective sound-visual experiences.
Creator/Author:: Woodman, Charles
Submitter:: Charles Woodman
Date Uploaded:: 01/12/2022
Date Modified:: 01/12/2022
Date Created:: 2022

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