cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp/qjmwq2
title: RAVDESS stimulus files

description: Contact Information If you experience any issues downloading the RAVDESS, or if would like further information about the database, please contact us at ravdess@gmail.com. Description This dataset contains the complete set of 7356 RAVDESS files (total size: 24.8 GB). To facilitate downloading, each of the 24 actors in the RAVDESS is provided as a separate .zip download (~ 1GB each). Each actor's .zip file contains the complete set of validated recordings for that actor, consisting of video (720p H.264, AAC 48kHz, .mp4) and audio (16bit, 48kHz .wav) files. Each of the 24 zip files must be downloaded individually. Checking multiple files and clicking download does not work (exceeds the server's 1GB zipping limit). License information The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NA-SC 4.0 How to cite the RAVDESS Academic citation If you use the RAVDESS in an academic publication, please use the following citation (note, new paper in early 2018): Livingstone, S. R., Peck, K., & Russo, F. A. (2012). RAVDESS: The Ryerson Audio-Visual Database of Emotional Speech and Song. Paper presented at the 22nd Annual Meeting of the Canadian Society for Brain, Behaviour and Cognitive Science (CSBBCS), Kingston, ON. All other attributions If you use the RAVDESS in form other than an academic publication, such as a blog post, school project, non-commercial product, please use the following attribution:"The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)" by Livingstone & Russo is licensed under CC BY-NA-SC 4.0. File naming convention Each RAVDESS file has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics: Filename identifiers Modality (01 = full-AV, 02 = video-only, 03 = audio-only). Vocal channel (01 = speech, 02 = song). Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised). Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion. Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door"). Repetition (01 = 1st repetition, 02 = 2nd repetition). Actor (01 to 24. Odd numbered actors are male, even numbered actors are female). Filename example: 02-01-06-01-02-01-12.mp4 Video-only (02) Speech (01) Fearful (06) Normal intensity (01) Statement "dogs" (02) 1st Repetition (01) 12th Actor (12) Female, as the actor ID number is even.
DOI: 10.5683/sp/j59uug
title: Interpersonal Swearing Dictionary

description: The dictionary was developed based on the two primary sources: (a) public lists of English swear words shared freely on websites such as www.noswearing.com, and (b) a custom-built dictionary of swear words and abbreviations (e.g., smfh, stfu, wtf, wth) derived from the manual reviews of over 60,000 Twitter messages by the research team. The inter-coder reliability of the Twitter-derived swear words achieved 92.04% agreement, with kappa alpha = .87.
DOI: 10.5683/sp2/9mcjjh
title: Social Media and Political Engagement in Canada

description: The report examines the ways online Canadian adults are engaging politically on social media. This is the third and final report based on a census-balanced survey of 1,500 Canadians using quota sampling by age, gender, and geographical region. The other two reports in this series are: "The State of Social Media in Canada 2017" and "Social Media Privacy in Canada". The series is published by the Social Media Lab, an interdisciplinary research lab at Ted Rogers School of Management, Ryerson University. The lab studies how social media is changing the ways in which people communicate, share information, conduct business and how these changes are impacting our society.
DOI: 10.5683/sp2/z9mpgf
title: Case Study: The Ethics of Hiring with Targeted Ads

description: This case study is part of a series on the ethics of social media data use by third parties.
DOI: 10.5683/sp2/2kkj8g
title: Case Study: The Ethics of Preventing Crimes with Social Media Data

description: This case study is part of a series on the ethics of social media data use by third parties.
DOI: 10.5683/sp2/2qhub7
title: Case Study: The Ethics of Political Microtargeting

description: This case study is part of a series on the ethics of social media data use by third parties.
DOI: 10.5683/sp2/bjirdy
title: Case Study: The Ethics of Cybervetting

description: This case study is part of a series on the ethics of social media data use by third parties.
DOI: 10.5683/sp2/dzm0gz
title: Case Study: The Ethics of Preventing Suicide with Social Media Data

description: This case study is part of a series on the ethics of social media data use by third parties.
DOI: 10.5683/sp2/2dttge
title: Case Study: The Ethics of Sharenting

description: This case study is part of a series on the ethics of social media data use by third parties.
DOI: 10.5683/sp2/3x82xc
title: Case Study: The Ethics of Live streams on Social Media

description: This case study is part of a series on the ethics of social media data use by third parties.
DOI: 10.5683/sp2/skdxje
title: Case Study: The Ethics of Government Use of Social Media

description: This case study is part of a series on the ethics of social media data use by third parties.
DOI: 10.5683/sp2/ytlfie
title: Case Study: The Ethics of Native Advertising on Social Media

description: This case study is part of a series on the ethics of social media data use by third parties.
DOI: 10.5683/sp2/zpw4ik
title: Subreddit AskScience - 1% sample from 2016 - Learning in the Wild Coding Schema

description: Data Collection: Data was collected using a custom web application (Communalytic, available at: https://communalytic.com/) that used Reddit’s public API (https://www.reddit.com/dev/api/). We sampled one percent of public Reddit comments posted in 2016 from AskScience. Since the dataset was collected retroactively, it does not include comments deleted by authors or moderators. Manual Coding: The sample comments were then manually coded using the 'Leaning in the Wild' Coding Schema by three independent coders, each of whom had first completed a schema tutorial training-module. Each coder (1) reviewed a submission that started a thread and was often framed as a question (see the "submissions_title" column) and then (2) assigned up to three applicable codes to the reply message (see the "text" column). The values stored under columns C1-C8 represent the number of coders who agreed on a given code.
DOI: 10.5683/sp2/9vsrhu
title: Manual Coding of Toxic and Insulting Tweets during the 2019 Federal Election in Canada

description: Data collection: Using Twitter's Search API, we collected 363,706 public tweets (in English) mentioning #elxn43 and directed at 1,116 candidates running for office during the 2019 Federal election in Canada. Tweets were collected between September 29 and October 28, 2019. Manual coding: The data set contains a random sample of 3,637 tweets (1% sample) hand coded as either 'toxic' or 'insulting' by using three coders. Only tweets that were flagged by all three coders were considered as either 'toxic' (TOXICITY_3CODERS_AGREE=1) or 'insulting' (INSULT_3CODERS_AGREE = 1).
DOI: 10.5683/sp2/imispe
title: COVID-19 Fact-checkers Dataset

description: The COVID-19 Fact Checkers Dataset is a comprehensive list of over 200 active fact-checking organizations and groups that verify COVID-19 misinformation. The dataset is maintained by the Ryerson University’s Social Media Lab as part of an international initiative to study the proliferation of COVID-19 misinformation and to map fact-checking activities around the world in partnership with the World Health Organization (WHO). It was created to provide the public with a better understanding of the COVID-19 fact-checking ecosystem and is intended for use by policy makers and others to make data-informed decisions in the fight against COVID-19 misinformation.
DOI: 10.5683/sp2/vpysis
title: COVID-19 Fact-checked Claims Dataset

description: The dataset contains COVID-related claims reviewed by fact-checking organizations from around the world. The dataset was collected between January 22, 2020 and January 31, 2020 using a custom Python script by querying Google Fact Check Tools API on a daily basis using search keywords: "Coronavirus" or "COVID". Notes: 1. Only claims indexed by the Google Fact Check Tools are included. See more information about this resource at https://toolbox.google.com/factcheck/about 2. Google Fact Check Tools aggregates data from over 100 fact-checking organizations and each of them uses slightly different textual ratings, titles, and annotations. As a result, the dataset may contain links to multiple reviews of the same claim, reviewed by different fact-checkers. 3. Since the dataset contains claims in different languages, we used Google's Translation API to translate some fields of non-English claims (fields: "text_en", "claimant_en" and "textualRating_en"). We kept the original values as well.
DOI: 10.5683/sp2/bmcgi3
title: Twitter Dataset: Replies to President Trump’s Tweet about contracting the Novel Coronavirus

description: On October 2nd, 2020, U.S. President Donald Trump tweeted that he and the First Lady of the United States (FLOTUS), Melania Trump, both tested positive for COVID-19 (https://twitter.com/realDonaldTrump/status/1311892190680014849). Within seconds, his tweet received thousands of replies. The dataset consists of 298,172 replies to Donald Trump’s tweet announcing his COVID-19 diagnosis, posted on October 2nd between 6am and 12:30pm (ET). It contains tweet ids, the toxicity scores (as calculated by Google's Perspective API via https://Communalytic.com) and tweet availability status values (via twarc library). Following Twitter’s API policy, we stripped metadata associated with each tweet. So, if you’d like to examine potential relationships between other metadata elements, you would need to recollect original tweets using tools like DocNow’s Hydrator first. The only downside of this approach is that tweets that have been blocked or deleted will not be recollected. To help you get started, we also shared our Exploratory Data Analysis (EDA) Python script at https://github.com/RUSocialMediaLab/toxicityanalysis
DOI: 10.5683/sp2/bsgqgs
title: #FilmYourHospital Twitter Dataset: a COVID-19 conspiracy theory on Twitter

description: The dataset contains 99,039 Tweet IDs of Twitter posts with #FilmYourHospital. It was collected using Netlytic.org between March 28 and April 9, 2020, by querying Twitter Search API (ver.1) very 15 minutes. NOTES: 1) In accordance with Twitter API Terms, only Tweet IDs are provided as part of this dataset. 2) To recollect tweets based on the list of Tweet IDs contained in these datasets, you will need to use tweet 'rehydration' programs like Hydrator (https://github.com/DocNow/hydrator) or Python library Twarc (https://github.com/DocNow/twarc). For more info about this dataset, read the following paper: https://doi.org/10.1177/2053951720938405
DOI: 10.5683/sp2/pllx6f
title: Employer Talent Recruitment Strategies and Newcomer Employment Outcomes - Research Report

description: Employer Talent Recruitment Strategies and Newcomer Employment Outcomes - Research Report
DOI: 10.5683/sp2/sljnh7
title: Replication data for Survey Recruitment Messages and Reported Turnout: An Experimental Study

description: These files allow or replication of all analyses in the this article.
DOI: 10.5683/sp2/bnarsz
title: FRAGSTATS DATABASE: Showcasing relationships between neighborhood design and Wellbeing Toronto indicators

description: A research study across the 140 neighborhood‐landscapes (streetscapes) of Toronto was presented through three main intentions. Its foundational goal was to calculate landscape ecology metrics from the 2007 land cover dataset for the City of Toronto; for use in sustainable development planning strategies and to bolster its Wellbeing Toronto data dashboard. In doing so, 130 landscape ecology metrics were computed to serve as a foundational suite for the City of Toronto: 18 class configuration metrics across seven of the City’s eight land cover categories and four landscape diversity metrics. Metrics for agriculture were not included due to very limited neighborhood representation. The 18 class configuration metrics computed for each of the seven land cover types were: class area (CA), percentage of landscape (PLAND), patch density (PD), largest patch index (LPI), landscape shape index (LSI), mean patch area (AREA_MN), area-weighted mean patch area (AREA_AM), area‐weighted mean shape index (SHAPE_AM), area‐weighted mean patch fractal dimension (FRAC_AM), perimeter‐area fractal dimension (PAFRAC), area‐weighted core area distribution (CORE_AM), area‐weighted core area index (CAI_AM), area‐weighted mean Euclidean nearest neighbor distance (ENN_AM), clumpiness index (CLUMPY), percentage‐of‐like‐adjacency (PLADJ), patch cohesion index (COHESION), landscape division index (DIVISION), and effective mesh size (MESH). Additionally, the four landscape diversity metrics were: Patch richness density (PRD), Relative patch richness (RPR), Shannon’s diversity index (SHDI), and Shannon’s evenness index (SHEI). Note that other relationships await discovery using this free database; thus, forthcoming germane research should consider its adoption. The landscape ecology database is provided here via GIS shapefile format and can be used freely with citation.
DOI: 10.5683/sp2/smn7au
title: 2014 Toronto Election Study

description: This submission consists of the 2014 TES dataset (Stata and SPSS versions), codebook, and methodology report.
DOI: 10.5683/sp2/tcikkq
title: Historic Ontario Road, Rail, Municipality, County and District Shapefiles

description: This is a dataset of historic southern Ontario road (line), rail (line), municipality, (point) county (polygon) and district (polygon) shapefiles. The shapefiles were created based on georeferenced historic maps of Ontario from the years 1800, 1818, 1837, 1852, 1861, 1879, 1901, 1922, and 1955.
DOI: 10.5683/sp2/uccfvq
title: Historical Canadian Railroads

description: Historical Canadian railway lines, places, and transactions in GIS formats. HR_rails_NEW contains historical railway lines with dates of activity. Modified from a proprietary dataset produced by ESRI Canada. hr_places_all was also created from an ESRI base layer. Points were repositioned using the ESRI base as reference, and new points were also added from the railway atlas.This set of place names was used as an intermediary layer when making the final dataset of railway lines. The places named are points where railway construction dates change.
DOI: 10.5683/sp2/kxal88
title: Acadian Deportation and Return, 1750-1803

description: Contains all data and maps for the unit Acadian Deportation and Return, 1750-1803 in the Historical Atlas of Canada Online Learning Project: http://www.historicalatlas.ca/website/hacolp/defining_episodes/social/UNIT_42/index.htm The unit contains two maps: "Acadian Deportation Experience, 1755-1785" and "Acadian Population Distribution: 1750, 1803" Data is best viewed in tree view. It is divided into shapefiles, arcmap documents, and supporting documents, including data tables, legends, and snapshots of the maps as displayed on the website. For documentation explaining the data, see file: "HACOLP_Acadian_Distribution_Info_20161205.pdf". This document gives the locations for data pertaining to the different maps within the file structure.
DOI: 10.5683/sp2/sbzvfy
title: Native Canada, ca 1820

description: Contains all data (in shapefile form) and Arcmap documents (.mxd) for the map "Native Population, Economies and Movement, ca 1820" from the unit Native Canada, ca 1820 in the Historical Atlas of Canada Online Learning Project. From the Historical Atlas Online Learning Project Website: "On this map, population is shown by circles, colour coded by linguistic groups, and scaled according to estimated population size. This gives the general picture of population distribution at this time. These were greatly affected by disease and migration resulting from European contact. Information on patterns of population movement is also depicted - both traditional seasonal movements, and large scale migration due to warfare or other disruptive influences. Native economic zones are broadly characterized by how traditional economies had been affected by European contact. These range from those severely disrupted by European settlement and resource depletion, to traditional economies relatively untouched by contact. Information on European commercial activity is also shown, to put the former into context: both trading posts, and routes of maritime commerce are depicted." For documentation explaining the data, see file: HACOLP_Nat_Canada_ca_1820_Distribution_Info_20161207.pdf To view the full maps, download all the files preserving file hierarchy, and unzip the shapefiles to the folder HACOLP_Nat_Canada_1820/GISData/HACOLP/Data/UNIT_14/Shapefiles/
DOI: 10.5683/sp2/iq7e0x
title: Territorial Evolution, 1670-2001

description: Contains all data for the maps "Boundary Changes, 1670-2001" and "Newfoundland Joins Confederation, 1949" from Unit 17: Territorial Evolution, 1670-2001 in the Historical Atlas of Canada Online Learning Project. Data is best viewed in tree view. It is divided into shapefiles, arcmap documents, and supporting documents, including data tables, legends, and snapshots of the maps as displayed on the website. For documentation explaining the data, see the file: "HACOLP_Territorial_Evolution_Distribution_Info_20161205.pdf". This document gives the locations within the file structure for data pertaining to the different maps
DOI: 10.5683/sp2/ozoixg
title: 'Exploration' Theme

description: Contains all data for maps in the "Exploration" theme in the Historical Atlas of Canada Online Learning Project. Legend and Sources pages for each unit are captured from the HACOLP website as they were available in 2020. Exploration routes compiled for the Historical Atlas of Canada printed volumes; attribute data added for the Historical Atlas of Canada Online Learning Project. Documentation and explanations of the data are in the file: HACOLP_Explorers_Distribution_Info_20161205.pdf Contents: UNIT 5: Exploring the Atlantic Coast, 16th, 17th Centuries Principal Explorations, 1497-1632 UNIT 6: French Exploration, 17th, 18th Centuries French Exploration, 1603-1751 UNIT 7: Exploration from Hudson Bay, 17th to 19th Centuries Exploration from Hudson Bay, 1610-1821 UNIT 8: Exploration in the Far West, 18th, 19th Centuries Exploration of the Far West, 1741-1821 UNIT 8.5: Exploration to Mid-19th Century Exploration, 1818-1851 UNIT 9: Exploration and Assessment to 1944 Arctic Exploration, 1851-1944 UNIT 9.5: Summary of Exploration through Four Centuries Summary of Exploration, 1497-1891 NOTE: All of these maps show the selected explorers' complete routes for the time period, with the exception of Unit 9.5, which shows the section of the selected explorer's route which explored an area 'for the first time'.
DOI: 10.5683/sp2/oweboj
title: geohist.ca website files/fichiers du site web geohist.ca

description: Archive of the Geohistory/Géohistoire website and related files. Captured July 17, 2020.
DOI: 10.5683/sp/gycpm7
title: 2011 National Household Survey, Shelter Costs at the Dissemination Area (DA) Level in the City of Toronto Census Subdivision (CSD)

description: This dataset is a custom tabulation of the 2011 National Household Survey containing shelter cost statistics (rent, electricity, fuel, water and other municipal services, mortgage payments, property taxes and condominium fees) for private households in non-band non-farm non-reserve occupied private dwellings in the City of Toronto.
DOI: 10.5683/sp/3mfhi5
title: 2016 Census of Canada, Place of Work Status by Industry (North American Industry Classification System NAICS 2012) and Work Activity During the Reference Year, at the Census Tract (CT) Level [custom tabulation]

description: Full table title: Place of Work Status (5), Industry - North American Industry Classification System (NAICS) 2012 (21) and Work Activity During the Reference Year (4) for the Employed Labour Force Aged 15 Years and Over in Private Households of Census Metropolitan Areas, Tracted Census Agglomerations and Census Tracts, 2016 Census - 25% Sample Data
DOI: 10.5683/sp/dgbnkt
title: 2016 Census of Canada, Place of Work Status by Occupation (National Occupational Classification NOC 2016) and Work Activity During the Reference Year, at the Census Track (CT) Level [custom tabulation]

description: Full table title: Place of Work Status (5), Occupation - National Occupational Classification (NOC) 2016 (11) and Work Activity During the Reference Year (4) for the Employed Labour Force Aged 15 Years and Over in Private Households of Census Metropolitan Areas, Tracted Census Agglomerations and Census Tracts, 2016 Census - 25% Sample Data
DOI: 10.5683/sp/yaa5b4
title: 2016 Census of Canada, Selected Labour Force, Demographic, Cultural and Income Characteristics for the Employed Labour Force, by Place of Work at the Census Tract (CT) Level [custom tabulation]

description: Full table title: Selected Labour Force, Demographic, Cultural and Income Characteristics (92) for the Employed Labour Force Aged 15 Years and Over Having a Usual Place of Work, in Private Households, for Census Metropolitan Areas, Tracted Census Agglomerations and Census Tracts of work, 2016 Census - 25% Sample Data
DOI: 10.5683/sp2/qno5jg
title: 1971, 1981-2011 Census Profiles and 2011 National Household Survey Profile aggregated to 2016 Dissemination Area Level geographies [Semi-Custom Tabulation]

description: This dataset consists of semi-custom tabulations of the 1971, 1981, 1986, 1991, 1996, 2001, 2006 and 2011 Censuses as well as the 2011 National Household Survey (NHS) containing Census and NHS Profile variables aggregated to 2016 Dissemination Area boundaries for the Toronto and Vancouver CMAs.
DOI: 10.5683/sp2/ewhm0y
title: Historical Waste Disposal Site Inventory

description: This dataset provides point data showing active and inactive waste disposal sites in Ontario as of 1990, digitized from paper maps in the Ontario Ministry of the Environment publication "Waste Disposal Site Inventory"(1991). For each UTM zone there is a shapefile for inactive sites and a shapefile for active sites. Layer-specific metadata is included in .xml format in each ZIP file. Datum: NAD83 Projection: UTM zones 15, 16, 17, 18
DOI: 10.5683/sp2/2aheth
title: Lost Rivers -- Disappearing Rivers of Toronto

description: Data used to make the Disappearing Rivers web map. Includes historical maps, and GIS data for digitized rivers. This data, and the webmap, were developed through a partnership between the Lost Rivers project, and Geohistory-Géohistoire Canada. More information about the web-mapping project is available here.
DOI: 10.5683/sp2/yrussp
title: Neptis Foundation Geospatial Data

description: Geospatial Data for various Neptis projects conducted for the Neptis foundation
DOI: 10.5683/sp2/7aznty
title: 2016 Census of Canada, Visible Minority, Generation Status, Age and Sex for the Population in Private Households

description: This dataset contains four data tables. Tables 1 & 2 provide data at the CMA & Census Tract levels of geography, and Tables 3 & 4 provide data at the Census Division & Census Subdivision levels of geography. Tables 1 & 3 provide standard Statistics Canada age ranges, whereas tables 2 & 4 provide additional custom age ranges designed based on eligibility for criminal charges: 0-11 (cannot be charged), 12-17 (young offenders), and 18-24 (young adult).