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- Type:
- Dataset
- Description/Abstract:
- 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.
- Creator/Author:
- Choudhary, Chandrashekhar ; Omaghomi, Toju; Buchberger, Steven; Wang, Tianshuo, and Tao, Li
- Submitter:
- Toju Omaghomi
- Date Uploaded:
- 12/19/2022
- Date Modified:
- 12/19/2022
- Date Created:
- 2022-12
- License:
- Open Data Commons Open Database License (ODbL)
- Type:
- Dataset
- Description/Abstract:
- Classifier algorithms use the features (collectively known as Feature Vectors) of each item in a dataset to assess the classification to which that item belongs. In this classifier approach, each item represents one document containing the application essay combined with unstructured language describing relevant activities of a single applicant. For privacy, the full text of this document is not provided. Instead, each document is represented only by its features. The feature vector for this classifier is based on the term frequency for each of the identified terms. E.G. Doc_A contains 0 occurrences of any terms identified as family medicine vocabulary, and 10 occurrences of terms from the the non-family-medicine vocabulary.
- Creator/Author:
- Boylan, Andrew and McCabe, Erin E.
- Submitter:
- Erin E. McCabe
- Date Uploaded:
- 05/14/2021
- Date Modified:
- 05/14/2021
- License:
- Open Data Commons Public Domain Dedication and License (PDDL)
- Type:
- Dataset
- Description/Abstract:
- W2V takes terms from a large corpus of text and models them onto a vector space, based on word associations from your dataset. These Word Associations take into account each word's immediate context (its ten neighboring words). Following the data modeling (large-scale unstructured text), The platform then generates a visualization of this vector space, which lets us perform analysis e.g. detect synonymous/synonym-ish words and highlight related words. At the heart of this project, is W2V's ability to identify key words that were more frequent - and more unique - to each group using results from 2 different W2V models – one for each group's application texts. We coded these Key Terms into categories, then analyzed those categories for overarching themes.
- Creator/Author:
- McCabe, Erin E.
- Submitter:
- Erin E. McCabe
- Date Uploaded:
- 05/14/2021
- Date Modified:
- 05/14/2021
- License:
- Open Data Commons Public Domain Dedication and License (PDDL)
- Type:
- Dataset
- Description/Abstract:
- The dataset includes all the data used to generate figures for the article submitted to the journal of Neuron. This includes individual figure panels and the raw data used to generate each figure panel, as well as the statistical analyses for each experiment.
- Creator/Author:
- Zhang, Jun-Ming
- Submitter:
- Jun-Ming Zhang
- Date Uploaded:
- 01/28/2025
- Date Modified:
- 02/26/2025
- Date Created:
- 2022-2025
- License:
- All rights reserved
- Type:
- Dataset
- Description/Abstract:
- Varieties of International Cyber Strategies (VoICS): Text Analysis of National Cybersecurity Documents is a project that compares and contrasts the three main approaches to conceptualize national cybersecurity strategies (NSS): deterrence, norm-based approach (NBA) and cyber persistence engagement (CPE). Scholars and policymakers have initially conceptualized NSS in terms of deterrence or NBA. More recent academic research has demonstrated that these frameworks are inadequate for cyber space. As a result, Cyber Persistence Engagement (CPE) emerged as a third option. The first version (1.0) of the VoICS database on National Cybersecurity Strategies focuses on nations in Europe and North America and includes a total of 77 NCS of the states in the North Atlantic Area—NATO allies, EU members and Switzerland—released from 2003 until the end of 2023. It consists of 27 variables, including country and strategy identifiers, EU and NATO membership, their respective accession dates, and total length of the documents. VoICS include eighteen variables representing different measures of relative and absolute weights of the three NSS types—deterrence, NBA and CPE. The text analysis is based on official NSS documents provided by the NATO Cooperative Cyber Defence Centre of Excellence library (2024) and ENISA’s interactive map for National Cyber Security Strategies (2023). Both sources rely on voluntary submission from the member states. Unfortunately, some official documents were not available or accessible or were not listed at all. Authors have used various sources and contacts with a variety of cyber attachés in Brussels to determine if any additional strategies were released and to obtain the missing documents. The 18 text analysis variables compare and contrast the extent to which different NCS are associated with a specific strategy. They represent different frequency scores based either on words, phrases, or words and phrases combined. These calculations are associated with either deterrence, NBA, or CPE in each strategy. The authors have generated respective vocabularies for the three strategic ideas through which each of these approaches are operationalized. We have conducted a text analysis using WordStat text analysis software by Provialis ( https://provalisresearch.com/products/content-analysis-software/). A detailed codebook for NSS Dataset 1.0 along with a NSS Dictionary 1.0 have been included in this collection/ repository. The process of generating vocabulary associated with the three cybersecurity approaches involved several steps. First, upon reviewing the literature, the authors generated independently a list of words and phrases associated with each type of cybersecurity strategy. Second, the authors compared their lists to determine the degree of overlap in vocabulary. Those words and phrases that included in at least two different lists were reviewed and, if there was consensus, were incorporated in the dictionary. Finally, words and phrases which were identified in only one of lists were once again reviewed and, in case there was a consensus among the authors, these were also included in the dictionary. Third, the three vocabularies were updated on several instances when it was unanimously agreed that these words or phrases should be included in the analysis.
- Creator/Author:
- Millard, Matthew; Kovac, Igor, and Ivanov, Ivan Dinev
- Submitter:
- Ivan Ivanov
- Date Uploaded:
- 05/12/2025
- Date Modified:
- 05/12/2025
- Date Created:
- 2025-04-18
- License:
- All rights reserved
- Type:
- Dataset
- Description/Abstract:
- 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
- Creator/Author:
- McCabe, Erin E.
- Submitter:
- Erin E. McCabe
- Date Uploaded:
- 11/12/2022
- Date Modified:
- 11/12/2022
- Date Created:
- 2022
- License:
- Open Data Commons Attribution License (ODC-By)
- Type:
- Dataset
- Description/Abstract:
- Dataset Summary: This dataset studies the main challenges that students in these institutions faced during the transition from face-to-face (f2f) to remote mode of instruction and the resources that they used to minimize these adversities. In order learn about their experiences during this transition, I surveyed at the end of the Spring Semester students enrolled in two Political Science (POL) classes. The results showed that majority of students struggled with stress caused by moving away from campus and self-quarantine leading to deteriorating mental and physical health. Concerns about student health along with distraction at home were identified as top adversities for student well-being. Survey results also showed that educational resources can have varying impact on student learning in introductory and upper-level courses. For example, lecture notes, power point presentations and online videos can be better resources for remote instruction in an introductory class, while class meetings via video conferencing platforms can be the preferred resource of instruction in upper-level courses. Below is the questionnaire used for this study: Survey Questionnaire: Transition to Remote Instruction During COVID-19 Crisis: Qualtrics Link for POL1080: https://artsciuc.co1.qualtrics.com/jfe/form/SV_bd7cF1OF6eNeYBv Qualtrics Link for POL2074: https://artsciuc.co1.qualtrics.com/jfe/form/SV_3xegnXy4LFSC2t7 1. As you know, the University of Cincinnati has transitioned from face-to-face to remote instruction for Spring Semester since March 14, 2020 due to COVID-19. Once it was decided to switch to remote instruction, how did you expect that this decision would impact your performance in this class? I thought it would improve my performance I thought it would impair my performance I did not think that it would impact my performance I don’t know 2. Based on your experience with remote instruction, how do you think the new form of instruction impacted your performance in this class? I did better in this class after we switched to remote teaching I did worse in this class after we switched to remote teaching The switch to remote teaching had no impact on my performance I don’t know. 3. Do you agree or disagree with the following statement: “I felt that the instructor in this class provided timely instructions and information about the switch from face-to-face to remote form of content delivery in the class”? Completely agree Partially agree Partially disagree Completely disagree Not sure/ don’t know. 4. Do you agree or disagree with the following statement: “I felt that the instructor in this class cared about my performance in the class once we switched from face-to-face to remote form of content delivery in the class”? Completely agree Partially agree Partially disagree Completely disagree Not sure/ don’t know. 5. Which of the following course resources (if available) helped you ease the transition from face-to-face to remote instruction (check all that apply)? Online instructional videos created or made available by the instructor Instructor-led class meetings via a web-conferencing platform (e.g. Webex, Zoom, MS Teams, Skype) Meetings with the instructor via a web-conferencing platform (e.g. Webex, Zoom, MS teams, Skype) during their office hours Instructor’s lecture notes and presentation materials (e.g. Power Point Slides) Online quizzes or interactive questions administered via web platforms (e.g. Canvas, Blackboard, Echo 360 or others). Online forums made available for this course Assigned course readings Book publisher’s online resources (websites, book ancillaries, etc.) Supplemental assistance from teaching assistants (e.g. office hours, online sessions, etc.) Supplemental peer-led review sessions (e.g. Learning Assistant Sessions, Supplemental Instruction Sessions, etc.) Group activities with peers enrolled in the class (e.g. study sessions via conference platforms) Others (please list) _________. 6. Which one of the following course resources was most helpful to you in the transition from face-to-face to online mode of content delivery (select only one)? Online instructional videos created or made available by the instructor Instructor-led class meetings via a web-conferencing platform (e.g. Webex, Zoom, MS Teams, Skype) Meetings with the instructor via a web-conferencing platform (e.g. Webex, Zoom, MS teams, Skype) during their office hours Instructor’s lecture notes and presentation materials (e.g. Power Point Slides) Online quizzes or interactive questions administered via web platforms (e.g. Canvas, Blackboard, Echo 360 or others). Online/ web discussion forums made available for this course Assigned course readings Textbook publisher’s online resources (websites, book ancillaries, etc.) Supplemental assistance from teaching assistants (e.g. office hours, online sessions, etc.) Supplemental peer-led review sessions (e.g. Learning Assistant Sessions, Supplemental Instruction Sessions, etc.) Group activities with peers enrolled in the class (e.g. study sessions via web-conferencing platforms) Others (please list) _________. 7. Which of the following, do you think, impacted negatively your performance in this class during the transition from face-to-face to remote instruction (please select all relevant options)? I had to move away from campus in the middle of the semester My physical or mental health deteriorated after we switched to remote instruction I missed face-to-face interaction with the instructor, the TAs and the undergrad assistant (SI) I did not have stable and reliable Internet connection at home I had a lot of distraction at home I lost my job/ income due to the COVID-19 epidemic I had to take an additional job to support myself and/ or my family Self-quarantine and/ or social distancing caused me a lot of stress The news about the COVID-19 epidemic and concerns about my health and the health of my loved ones caused me a lot of stress Other (please list) ___________. 8. Which of the following, do you think, impacted negatively your performance in this class during the transition from face-to-face to remote instruction (please select only one options)? I had to move away from campus in the middle of the semester My physical or mental health deteriorated after we switched to remote instruction I missed face-to-face interaction with the instructor, the TAs and the undergrad assistant (SI) I did not have stable and reliable Internet connection at home I had a lot of distraction at home I lost my job/ income due to the COVID-19 epidemic I had to take an additional job to support myself and/ or my family Self-quarantine and/ or social distancing caused me a lot of stress The news about the COVID-19 epidemic and concerns about my health and the health of my loved ones caused me a lot of stress Other (please list): 9. Based on your experience with this course’s transition from face-to-face to remote instruction for Spring Semester 2020, what aspects of this transition had greatest values for you? Open ended question: 10. Based on your experience with this course’s transition from face-to-face to remote instruction for Spring Semester 2020, what changes would you recommend to ease this transition in the future? Open ended question: 11. What is your gender? Male Female Other/ prefer not to disclose 12. What is your major? Political Science International Affairs Interdisciplinary/ Cyber Strategy and Policy Interdisciplinary/ Law and Society Another major (please specify) 13. What is your class level? First year (freshman) Second year (sophomore) Third year (junior) Fourth year (senior) 14. What is your race or ethnicity? White Black or African American Asian American Indian or Alaska Native Native Hawaiian or Pacific Islander International student Other 15. What do you think your grade will be for this course? A or A- B+, B or B- C+, C or C- D+, D or D- F Nor sure/ don't know
- Creator/Author:
- Ivanov, Ivan
- Submitter:
- Ivan Ivanov
- Date Uploaded:
- 05/14/2020
- Date Modified:
- 05/14/2020
- Date Created:
- 2020-05-13
- License:
- All rights reserved
- Type:
- Dataset
- Description/Abstract:
- This data set includes the raw rare earth element data for all fluorite and calcite samples analyzed by Josh Bergbower for work on his thesis project titled "Trace and Rare Earth Element Chemistry of Fluorite from the Illinois-Kentucky Fluorspar District and its Implications for the Origins of Mineralizing Fluids".
- Creator/Author:
- Bergbower, Joshua and Dietsch, Craig
- Submitter:
- Joshua Bergbower
- Date Uploaded:
- 05/25/2018
- Date Modified:
- 05/25/2018
- Date Created:
- 2018
- License:
- Open Data Commons Public Domain Dedication and License (PDDL)
- Type:
- Dataset
- Description/Abstract:
- Six topic models were generated using Latent Dirichlet Allocation, an algorithm that considers the probability of words co-occurring in a document given a collection of documents. The collection of documents that these particular models are based on include 599 articles that include the term 'bone' from two archaeology journals, Ancient Mesoamerica and Latin American Antiquity.
- Creator/Author:
- McCabe, Erin E. and Jackson, Sarah E.
- Submitter:
- Erin E. McCabe
- Date Uploaded:
- 07/29/2020
- Date Modified:
- 07/29/2020
- Date Created:
- 2019-11-01
- License:
- Open Data Commons Attribution License (ODC-By)
- Type:
- Dataset
- Description/Abstract:
- This CSV file contains the topic distribution of each EIN as uncovered using six parallel Latent Dirichlet Allocation (LDA) Topic Models. Each row depicts a topic and topic-score associated with an Ohio NPO (identified by Employer Identification Number) generated from one model run. The sum of topic scores possible for every row associated with an EIN therefore will not exceed 6.0 (6 models x 100%) Topic scores below .01 (1%) are not included. Each topic from the models is further identified as Essential/Non-Essential by subject matter expert, Dr. Michael Jones, guided by the official IRS definition. The topic models are generated on unstructured text language from the mission statement and activities language taken from the 2019 tax forms of Ohio non-profit organizations.
- Creator/Author:
- McCabe, Erin E.
- Submitter:
- Erin E. McCabe
- Date Uploaded:
- 05/06/2021
- Date Modified:
- 09/20/2021
- Date Created:
- 2020-10-03
- License:
- Open Data Commons Public Domain Dedication and License (PDDL)