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Cousins, Melanie 2022-07-11 This database contains framework matrices to describe and organize data from a content analysis which aimed to compile quantifiable data from statements made by a group of experts to help parameterize a simulation model of antimicrobial resistance (AMR) emergence and transmission in a Swedish food system context.
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Shefaza Esmail 2022-08-11 Students in a field ecosystem assessment course (ERS 340) conducted a vegetation sampling survey in two different areas on the University of Waterloo campus over two days in May 2022. They identified ground vegetation, shrubs and saplings, sub-canopy and canopy trees as well as regeneration modules within the sampling plots as per the Vegetation Sampling Protocol (VSP) methodology (http://forests-settled-urban-landscapes.org/VSP/). Data were collected and compiled by students in a third year course, students ranged from second to fourth year of study
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Alamenciak, Tim 2022-07-12 This dataset contains a spreadsheet with 308 references extracted as part of a systematic map of Canadian ecological restoration literature. The systematic map was conducted by Tim Alamenciak and Dorian Pomezanski in 2021 as part of a multi-partner knowledge synthesis project funded by the Social Sciences and Humanities Research Council. The articles were selected based on the following inclusion criteria: the study documented the outcome of ecological interventions, and the study site was located in Canada. Data was extracted in the following categories: author, publication year, title, data location, first author affiliation, action taken, intervention type, coarse ecosystem type, fine ecosystem type, fine target species group, main species, disturbance type, coarse disturbance type, reason for restoration, reference ecosystem type, research province, research city, research coordinates, time since restoration, monitoring period, outcome sentence, response variables.
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Alimohammad Rabbani; S. Huo; S. Keshav; Costin Ograda-Bratu 2020-11-16 <p><b>Data Owner</b>: Alimohammad Rabbani, S. Keshav</p> <p><b>Data Description</b>: Unlike traditional conventional centralized HVAC systems that heat or cool an entire zone, Personal Environmental Control systems can provide personalized thermal comfort for each individual but are expensive and difficult to deploy. The SPOT* system, in contrast, is an individual thermal comfort system that can be rapidly and cost-effectively deployed.</p> <p>This dataset contains data collected from a cumulative 58,000 hours of operation of 45 SPOT* systems in 15 offices. Invitations are sent out to approximately 1500 building residents of four selected campus buildings, and we distributed the systems in first-come-first-served order. Over the course of the data collection, only one person has withdrawn from the trial because they left the university, and only two failures have happened (both resolved by re-plugging the device to the power outlet fixed the problem). Details regarding the design and deployment of the SPOT* system can be found in the paper “<i>The SPOT* Personal Thermal Comfort System</i>” by A. Rabbani and S. Keshav: <a href="http://blizzard.cs.uwaterloo.ca/keshav/wiki/images/b/be/Spotstar.pdf">http://blizzard.cs.uwaterloo.ca/keshav/wiki/images/b/be/Spotstar.pdf.</a></p> <p>The AD22100 surface-mount temperature sensor with 0.1◦C resolution is used to obtain temperature readings. The AMN22111 passive infrared human detection sensor outputs analog values that are converted to values between 0 and 1000 on the Raspberry Pi. When there is no movement, the sensor output values are approximately 500. Each movement causes the sensor to first generate one value close to 1000 and then another close to 0. The closer these values are to 1000 and 0, the greater the intensity of movement. Over a 30-second window, a standard deviation close to 0 indicates almost no movement, and thus no occupancy, while higher standard deviations correspond to more movement. The user interface with SPOT* system is by means of a Web app, and we collect users’ comfort preferences to the control app. More information on the data collection process can be found in section 3.1 and 4.1 of the paper “<i>The SPOT* Personal Thermal Comfort System</i>”.</p> <ul> <li><i><b>PPVs.csv</b></i>:</li> <p><b>time</b>: Epoch Unix Time Stamp (seconds since Jan 01 1970 (UTC)) </p> <p><b>Predicted Mean Vote (ASHRAE scale)</b>: Predicted Mean Vote (PMV) model estimates an average worker’s comfort level on the 7-point ASHRAE scale using a function fpmv(·): pmv = fpmv(ta,(t_r ) ̅,var,pa,M,Icl) </p> <p><b>Predicted Personal Vote (ASHRAE scale)</b>: PPV is a generalized version of PMV, computed as a*PMV + b. During a training period, the system collects comfort votes from the user to extract two user- specific parameters a and b using least-squares regression. </p> <p><b>device_id</b>: ID of devices placed in each office </p> <li><i><b>Motions.csv</b></i>:</li> <p><b>time</b>: Epoch Unix Time Stamp (seconds since Jan 01 1970 (UTC)) </p> <p><b>standard deviation of motion sensor in the last 30s</b>: standard deviation of motion values (i.e. motion intensity) during 30-second time windows </p> <p><b>device id</b>: ID of devices placed in each office </p> <li><i><b>Occupancies.csv</b></i>:</li> <p><b>time</b>: Epoch Unix Time Stamp (seconds since Jan 01 1970 (UTC)) </p> <p><b>occupancy</b>: 0(not occupied), 1(slight chance), 2(high chance), 3(definitely occupied) </p> <p><b>device id</b>: ID of devices placed in each office </p> <li><i><b>Temperatures.csv</b></i>:</li> <p><b>time</b>: Epoch Unix Time Stamp (seconds since Jan 01 1970 (UTC)) </p> <p><b>temperature</b>: degrees C </p> <p><b>device id</b>: ID of devices placed in each office </p> </ul> <p><b>Funding</b>: Cisco Systems and the Natural Science and Engineering Research Council of Canada (NSERC).</p>
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Byl, Lauren 2020-07-07 Researchers from the U15 group of Universities in Canada were asked to answer a series of questions about their publishing, knowledge of copyright, knowledge of author addenda, their use of addenda, and the support they need to negotiate their authorship agreements. Institutional information was removed due to the limited participant pool.
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Bieniada, Aneta 2020-03-16 Tab. S2 (Suppementary Material). Physicochemical properties of 63 peat samples with methanogenic and methanotrophic sequence count above the rarefaction threshold. DOC – dissolved organic carbon (mg g-1), EC – electrical conductivity (µS cm-1), WT – water table (cm), WTFZ - water table fluctuation zone; short chain fatty acid ions and inorganic ions (µg g-1 of dry peat). Samples collected at horticultural restored and unrestored peatlands in central Alberta, Canada
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Bryan Smale; Margo Hilbrecht 2013-08 This survey monitors wellbeing among residents of the City of Kingston, Greater Napanee, Lennox and Addington County, and Frontenac County, located in South-Eastern Ontario. The survey is a joint initiative of the Canadian Index of Wellbeing in partnership with the Community Foundation for Kingston & Area and KFL&A Public Health. The primary objectives of this survey are to (a) gather data on the wellbeing of residents which could be monitored over time; and, (b) to provide information on specific aspects of wellbeing that could be used to inform policy issues and community action. The purpose of the survey is to better understand subjective perceptions of wellbeing of residents in the survey area. The survey provides information based on eight domains of wellbeing, as identified by the Canadian Index of Wellbeing: Community Vitality, Democratic Engagement, Environment, Education, Healthy Populations, Leisure and Culture, Living Standards, and Time Use. The questionnaire collected additional information about dental health, emergency preparedness, and numerous socio-economic characteristics.
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Cousins, Melanie 2022-07-11 This file contains a decision matrix used to combine data from a scoping review and statements made by experts during a participatory modelling workshop to inform a fuzzy cognitive map (FCM) of antimicrobial resistance (AMR) emergence and transmission in a Swedish food system context. It also contains the results from the scenario analysis done in FCM Expert.
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Esmail, Shefaza 2022-09-07 Students in ERS 340 collected benthic invertebrate and water quality data using the Ontario Benthic Biomonitoring Network (OBBN) protocol on 12-May-2022 at Columbia Lake as well as a nearby stream and wetland within the Environmental Reserve on University of Waterloo Campus.
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Esmail, Shefaza 2022-09-07 Students in ERS 341 assessed the site encompassed by Ring Road on the east, Conrad Grebel trail on the west, bridge between EV & St. Paul's in the north, and bridge beside MHR at the south. The site was assessed using basics of Ecological Land Classification (ELC) for terrestrial systems, including soil analysis, and through the Ontario Benthic Biomonitoring Network (OBBN) protocol for the aquatic system (i.e., Laurel Lake). The assessment was conducted over three weeks in July 2022.
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Lakshminarayanan, Vasudevan; Roy, Priyanka; Gholami, Peyman 2018-12-19 Fovea-centered OCT images of adult healthy retina
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Drescher, Michael 2019-05-09 Data for 598 private landowners analyzed to investigate the effect of participation in two different conservation incentive programs on invasive species management. Presented in: Drescher, M., Epstein, G., Warriner, K. & Rooney, R. An Investigation of the Effects of Conservation Incentive Programs on Management of Invasive Species by Private Landowners. Conservation Science and Practice (accepted).
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Pardoel, Scott; Shalin, Gaurav; Lemaire, Edward D.; Kofman, Jonathan; Nantel, Julie 2021-05-01 Wearable-sensor data with merged and separate freezing of gait episodes. Walking trials with participants with Parkinson’s disease. Contains inertial measurement unit (IMU) and plantar-pressure based features. Suited for freeze detection and prediction in Parkinson’s disease.

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