These Centrality measurements were generated with NetworkX, a Python package for networks. The specific algorithms used for this paper are Betweenness Centrality (where Degree Centrality considers individual topics).
Complete Centrality Data for this research can be found at https://scholar.uc.edu/show/6t053h21x
This dataset contains quantitative and qualitative data about the archaeological remains of fish-salting and fulling workshops throughout the ancient Mediterranean world (Europe, North Africa, and Western Asia), primarily dating to the Roman period. The data provided the basis for the two case studies in the author's dissertation (Motz, C.F. 2021. "The Knowledge Networks of Workshop Construction in the Roman World." Ph.D. diss., University of Cincinnati).
The tables contained in this dataset were exported from the author's FileMaker database. Detailed information about the structure and contents of this dataset may be obtained by consulting Chapter 2 of the author's dissertation.
Data collected to identify use of special education vouchers in OH, GA and FLA and if information provided regarding loss of least restrictive environment civil rights.
The files in this work represent the presentations and workshop content from the 5th UC Data Day held 2020-10-23.
The theme was “World Changing Data: How Digital Data Will Change Our Future”.
The Keynote speaker was Glenn Ricart, of US Ignite - "Smart Runs on Data"
Interactive Panel featuring: Michael Dunaway (moderator) - Whitney Gaskins (Asst Dean, CEAS - Incl Excellence & Comm Engagmnt) - Zvi Biener (Assoc Professor, A&S Philosophy) - Prashant Khare (Asst Professor, CEAS - Aerospace Eng & Eng Mechanics)- Sam Anand (Professor, CEAS - Mechanical Eng) - Achala Vagal
(Professor Clinical - GEO, COM Radiology Neuroradiology)
Power Sessions:
George Turner - Indiana University - High-Performance Computing at UC
Erin McCabe - University of Cincinnati - Text Mining, Natural Language Processing & AI
link to slides - https://bit.ly/dataday_slides
link to code - https://bit.ly/dataday_code
Videos of the day can be found on the UC Libraries STRC1 youtube channel - https://www.youtube.com/c/STRC1/videos
The data sets were derived from coronavirus related scientific literature using the CORD-19 dataset released by the Allen Institute of Artificial Intelligence as of July 14, 2020, using the Elasticsearch engine hosted by the Digital Scholarship Center (DSC). Through indexing the full-text and the metadata of the article corpus, the research team generated a full-corpus model and 7 different models corresponding to key viral outbreaks from the past several decades' coronaviruses (SARS-CoV, MERS-CoV, and SARS- CoV-2) and non-coronaviruses (HIV, Zika, H1N1, and Ebola). The targeted subsets of the articles used two or more occurrences of virus-specific keywords drawn from conventions established by the World Health Organization.
The data sets were derived from coronavirus related scientific literature using the CORD-19 dataset released by the Allen Institute of Artificial Intelligence as of July 14, 2020, using the Elasticsearch engine hosted by the Digital Scholarship Center (DSC). Through indexing the full-text and the metadata of the article corpus, the research team generated a full-corpus model and 7 different models corresponding to key viral outbreaks from the past several decades' coronaviruses (SARS-CoV, MERS-CoV, and SARS- CoV-2) and non-coronaviruses (HIV, Zika, H1N1, and Ebola). The targeted subsets of the articles used two or more occurrences of virus-specific keywords drawn from conventions established by the World Health Organization.
The data sets were derived from coronavirus related scientific literature using the CORD-19 dataset released by the Allen Institute of Artificial Intelligence as of July 14, 2020, using the Elasticsearch engine hosted by the Digital Scholarship Center (DSC). Through indexing the full-text and the metadata of the article corpus, the research team generated a full-corpus model and 7 different models corresponding to key viral outbreaks from the past several decades' coronaviruses (SARS-CoV, MERS-CoV, and SARS- CoV-2) and non-coronaviruses (HIV, Zika, H1N1, and Ebola). The targeted subsets of the articles used two or more occurrences of virus-specific keywords drawn from conventions established by the World Health Organization.
