Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
32
datasets available to search
ShareScore release 0.7.1
Dataset results
32 results for “Toronto”
Oculus Pavilion, Toronto
The Oculus Pavilion sits next to Humber River Trail in Toronto's west end. The Oculus was designed by architect Alan Crossley and Laurence Cazaly in 1959 in the mid-century modern style. It has fallen into disrepair in recent decades, but is presently undergoing restoration and revitalization. [Read more about it here.](http://spacing.ca/toronto/2016/07/22/toronto-humber-park-pavilion/) The model is a little rough around the edges, but I might be able to clean it some in Blender. # Model Details: * **Camera**: Olympus OM-D E-M10 * **Photo Count**: 371 * **Software**: Agisoft Metashape # Controls: * **Rotate**: left click and drag * **Zoom**: scroll wheel, **or** hold CTRL, left click and drag * **Pan**: right click and drag * **Rotate light source**: hold ALT, left click and drag Source: Objaverse 1.0 / Sketchfab
Toronto Thromboprophylaxis Patient Safety Initiative
ClinicalTrials.gov study NCT01869075. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Extending Preservation and Assessment Time of Donor Lungs Using the Toronto EVLP System™ at a Dedicated EVLP Facility
ClinicalTrials.gov study NCT02234128. IPD Sharing: Not stated. Countries: 1. Publications: 25.
Guild Park & Gardens : Toronto Star Building
Images of art and artifacts from Guild Park & Gardens, 201 Guildwood Pkwy., Toronto. With thanks to Friends of Guild Park. Captured with S20+ 4k 60fps video, and Mavic Air 2. Processed in Reality Capture and Cleaned up in Medium, Modo, and Substance Source: Objaverse 1.0 / Sketchfab
Toronto Emergency Response - Open Data
<p>This upload contains data and documentation for the Python analysis undertaken in Google Colab as part of Episode 1 of the webinar series, conducted by Sambodhi's Center for Health Systems Research and Implementation (CHSRI). You can find the link to the Google Colab notebook <a href="https://colab.research.google.com/drive/1dFBokIJytUe1qWJePoTof1u2rxkIhaHQ?usp=sharing" target="_blank" rel="noopener">here.</a></p> <p>All the data uploaded here is open data published by the Toronto Police Public Safety Data Portal and the Ontario Ministry of Health. </p>
Toronto climate data for building simulations with urban heat island effects and nature-based solutions
<p>As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.</p> <p>This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. </p> <p>The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. </p> <p> </p> <p>The historical and future global warming thresholds and their corresponding time periods are as follows:</p> <table> <tbody> <tr> <td> <p><strong>Global Warming Scenario</strong></p> </td> <td> <p><strong>Time Period</strong></p> </td> </tr> <tr> <td> <p><strong>Historical</strong></p> </td> <td> <p>1991-2021</p> </td> </tr> <tr> <td> <p><strong>Global Warming 0.5ºC</strong></p> </td> <td> <p>2003-2033</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.0ºC</strong></p> </td> <td> <p>2014-2044</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.5ºC</strong></p> </td> <td> <p>2024-2054</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.0ºC</strong></p> </td> <td> <p>2034-2064</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.5ºC</strong></p> </td> <td> <p>2042-2072</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.0ºC</strong></p> </td> <td> <p>2051-2081</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.5ºC</strong></p> </td> <td> <p>2064-2094</p> </td> </tr> </tbody> </table> <p> </p> <p>The following variables are included in the files:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>RUN</strong></td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td><strong>YEAR</strong></td> <td>Year associated with the record</td> </tr> <tr> <td><strong>MONTH</strong></td> <td>Month associated with the record</td> </tr> <tr> <td><strong>DAY</strong></td> <td>Day of the month associated with the record</td> </tr> <tr> <td><strong>HOUR</strong></td> <td>Hour associated with the record</td> </tr> <tr> <td><strong>YDAY</strong></td> <td>Day of the year associated with the record</td> </tr> <tr> <td><strong>DRI_kJPerM2</strong></td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DHI_kJperM2</strong></td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DNI_kJperM2</strong></td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the <em>HOUR</em> indicated)</td> </tr> <tr> <td><strong>GHI_kJperM2</strong></td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>TCC_Percent</strong></td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td><strong>RAIN_Mm</strong></td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>WDIR_ClockwiseDegFromNorth</strong></td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td><strong>WSP_MPerSec</strong></td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td><strong>RHUM_Percent</strong></td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td><strong>TEMP_K</strong></td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td><strong>ATMPR_Pa</strong></td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td><strong>SnowC_Yes1No0 </strong></td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td><strong>SNWD_Cm</strong></td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>
Toronto Cherry Street Building
Source: Objaverse 1.0 / Sketchfab
Preventing and Addressing At-obesity-Risk Early Years INtervention with Home Visits in Toronto (PARENT): a Pragmatic RCT
ClinicalTrials.gov study NCT03219697. IPD Sharing: NO. Countries: 1. Publications: 1.
A Group-level Intervention to Reduce HIV/STI Risk for Women Who Have Sex With Women in Calgary and Toronto
ClinicalTrials.gov study NCT02067845. IPD Sharing: Not stated. Countries: 1. Publications: 2.
PRe-Exposure Prophylaxis Acceptability & Readiness Assessments for Toronto gaY, Bisexual and Other Men Who Have Sex With Men-5
ClinicalTrials.gov study NCT02149888. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Toronto Bedside Swallowing Screening Test (TOR-BSST) - A Bedside Swallowing Screening for Stroke Patients
ClinicalTrials.gov study NCT00141752. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Stigma Intervention Among Primary Health Workers in Toronto
ClinicalTrials.gov study NCT03043417. IPD Sharing: NO. Countries: 1. Publications: 1.
Screening for Swallowing Problems in Patients Following Prolonged Intubation: Validation of the Toronto Bedside Swallowing Screening Test (TOR-BSST©)
ClinicalTrials.gov study NCT01411306. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Decentralizing PrEP delivery: implementation and dissemination strategies to increase PrEP uptake among MSM in Toronto, Canada
Open the record for dataset details and reuse information.
The list of vascular plants for the city of Toronto
<p>Urban areas have become epicenters for applied ecological and conservation research and policy. Yet, most urban areas have surprisingly little consolidated information about their biota, including species-at-risk and invasive species.</p> <p>I used multiple data sources to compile a list of vascular plants for the greater metropolitan Toronto region. This data not only includes taxonomic information, but also global and national status ranks, growth from, native status, threatened status, abundance estimates and year of first observation for non-indigenous species.</p> <p>The list includes 1937 taxa from 146 families, of which 822 are non-indigenous. The majority of native species were ranked as abundant and widespread both globally and provincially. However, non-indigenous species ranks were bimodal, likely to be either extremely restricted in the province, or very widespread.</p> <p>This database provides a robust list of plant taxa in Canada's largest city. It will inform global urban ecology analyses and local and regional management and policy.</p>
Labour market perceptions and experiences among Ghanaian–Canadian second-generation youths in the Greater Toronto Area
<p>The study examines the perceptions and experiences of second-generation Ghanaian–Canadian immigrant youths in the Greater Toronto Area labour market and highlights some of the challenges they faced. Specifically, the study explores how some of these youths viewed and expressed their feelings in their process of navigating the labour market as well as the connections between their neighbourhood of residence and their performance in the labour market. In-depth interviews and focus group discussions were used to procure empirical data from youths residing in two localities in the Greater Toronto Area. The participants most frequently identified unemployment or underemployment as the main issues that highlighted the differences between the two study localities and that there were differences between male and females in this respect. The findings indicate that the youths’ performance in the labour market was influenced by a multitude of factors, including inefficient transportation, discrimination, lack of information, and dress code. In addition, the findings shed light on how neighbourhood characteristics influenced employment outcomes.</p>
The list of vascular plants for the city of Toronto
Open the record for dataset details and reuse information.
Radical prostatectomy specimens with and without TMPRSS2:ERG fusion from 139 patients with prostate in a Toronto cohort
GEO Series GSE18655. Homo sapiens. 139 samples. Type: Expression profiling by array.
Jarus bird lane mural, Toronto
Tool: http://display.land About the mural: https://bretkelly.com/young-jarus-bird-uber5000-cat-murals Source: Objaverse 1.0 / Sketchfab
Toronto Western Hospital (TWH) Waste Audit
ClinicalTrials.gov study NCT06680284. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.