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SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Shallow Groundwater Intrinsic Vulnerability Map for Northeast BC
Federated Research Data Repository / dépôt fédéré de données de recherche
Allen, Diana M.; Holding, Shannon — 2016-11-30 Simon Fraser University developed a Shallow Groundwater Intrinsic Vulnerability Map of Northeast British Columbia using the DRASTIC assessment method. The assessment was conducted in response to mounting concerns surrounding water management and protection in Northeast BC in relation to shale gas development. The mapping characterizes the intrinsic vulnerability of near surface geological materials to contamination originating at land surface. Format of data is NAD 83 BC Environment Albers and content type is GIS data. Data was compiled from BC GIS data warehouse. Software used was ESRI ArcGIS. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Supplementary Dataset for trophic magnification of legacy persistent organic pollutants in an urban terrestrial food-web
Federated Research Data Repository / dépôt fédéré de données de recherche
Fremlin, Katharine — 2019-12-31 This Excel workbook contains separate data sheets for Appendices 1 - 11 for a manuscript published in STOTEN: Trophic magnification of legacy persistent organic pollutants in an urban terrestrial food web (https://doi.org/10.1016/j.scitotenv.2020.136746). For a detailed description of what is included in each appendices, please see the accompanying ReadMe file (ReadMe.txt). Cite as: Fremlin, K.M. 2019. Supplementary Dataset for trophic magnification of legacy persistent organic pollutants in an urban terrestrial food-web. Science of the Total Environment. Content type is chemical concentration data, stable isotope data, and list of chemicals analysed. Software used was Microsoft Excel. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Wastewater Regulations Database
Federated Research Data Repository / dépôt fédéré de données de recherche
Allen, Diana M.; Notte, Chelsea — 2015-04-01 This MS Access database contains details on various Acts and Regulations for several jurisdictions in North America that pertain to wastewater produced during hydraulic fracturing and shale gas production. The database was produced as part of Chapter 3 in Goss et al. (2015) Unconventional Wastewater Management: A Comparative Review and Analysis of Hydraulic Fracturing Wastewater Management Practices Across Four North American Basins. (http://www.cwn-rce.ca/reports/report/unconventional-wastewater-management-a-comparative-review-and-analysis-of-hydraulic-fracturing-wastewater-management-practices-across-four-north-american-basins). This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Improving Mental Health Act rights advice communications and practices
Federated Research Data Repository / dépôt fédéré de données de recherche
Cheung, Iva W. — 2021-04-05 Clinicians taking part in training sessions on patient rights advice were asked to complete pre-session and post-session questionnaires and evaluations. This dataset contains anonymized focus group data and questionnaire data taken over 5 sessions in 2018, at hospitals in Metro Vancouver. Please see "Interpreting CPD" file in this dataset for additional information.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Caveolae and scaffold detection from single molecule localization microscopy data
Federated Research Data Repository / dépôt fédéré de données de recherche
Khater, Ismail M. — 2019-06-19 Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. In the absence of CAVIN1/PTRF, Cav1 forms non-caveolar membrane domains called scaffolds. In this work, we train machine learning models to automatically distinguish between caveolae and scaffolds from single molecule localization microscopy (SMLM) data. We apply machine and deep learning algorithms to discriminate biological structures from SMLM data. Our work is the first that is leveraging machine and deep learning approaches to automatically identifying biological structures from SMLM data. In particular, we develop and compare three binary classification methods to identify whether or not a given 3D cluster of Cav1 proteins is a caveolae. The first uses a random forest classifier applied to 28 hand-picked features, the second uses a convolutional neural net (CNN) applied to a projection of the point clouds onto three planes, and the third uses a PointNet model, a recent development that can directly take point clouds as its input. We validate our methods on a dataset of super-resolution microscopy images of PC3 prostate cancer cells labeled for Cav1. Specifically, we have images from two cell populations: 10 PC3 and 10 CAVIN1/PTRF-transfected PC3 cells (PC3-PTRF cells) that form caveolae. We obtained a balanced set of 1714 different cellular structures. Our results show that both the random forest on hand selected features and the deep learning approach achieve high accuracy in distinguishing the intrinsic features of the caveolae and non-caveolae biological structures. More specifically, both random forest and deep CNN classifiers achieve classification accuracy reaching 94% on our test set, while the PointNet model only reached 83% accuracy. We also discuss the pros and cons of the different approaches. Additional information about individual files is in the accompanying CSV file (item_metadata.csv). This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
City of Vancouver Intangible Transit Costs
Federated Research Data Repository / dépôt fédéré de données de recherche
Zuehlke, Brett — 2017-02-28 Intangible cost of transportation modes (walking, cycling, transit, and driving) within the City of Vancouver. Dataset contains City of Vancouver Cycling Quality Data (cycling.zip), City of Vancouver Road Quality Data (road.zip), City of Vancouver Transit Quality Data (transit.zip), and City of Vancouver Walking Quality Data (walk.zip). Information about Input Data Sources as well as software used for mapping and analysis is included in the README.txt file. Content type is GIS data. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Fraser River Gravel Sand Transition
Federated Research Data Repository / dépôt fédéré de données de recherche
Venditti, Jeremy G. — 2019-03-13 This data archive contains data collected through the gravel-sand transition of the Fraser River. This dataset is composed of the 2007 Fraser GST Survey (Metadata_Fraser_GST_Topography_2007.zip) and the Fraser River Gravel Sand Transition Summary Figures (Summary_Figures_from_Venditti_et_al_2015_and_2019.xlsx).This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Probability gain versus information gain in category learning: eye movements
Federated Research Data Repository / dépôt fédéré de données de recherche
Meier, Kim; Stepanova, Ekaterina — 2013-03-28 Column information and descriptions are included in README.txt. Confidentiality declaration: Data has been anonymized and is non-identifying. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
SFU-HW-Objects-v1: Object labelled dataset on raw video sequences
Federated Research Data Repository / dépôt fédéré de données de recherche
Choi, Hyomin; Hosseini, Elahe; Ranjbar Alvar, Saeed; Cohen, Robert; Bajić, Ivan — 2020-06-24 SFU-HW-Objects-v1 is a dataset of object labels for a set of raw video sequences. The dataset can be useful for the cases where both object detection accuracy and video coding efficiency need to be evaluated on the same dataset.This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
The Growth of Dunes in Rivers
Federated Research Data Repository / dépôt fédéré de données de recherche
Bradley, Ryan — 2018-12-13 Data archive for Bradley, R., and J. G. Venditti, (2019), The Growth of Dunes in Rivers, Journal of Geophysical Research: Earth Science. This dataset includes Growth Time Series (Growth_Time_Series.xlsx), Bed Maps (BedMapsXYZ.zip), and a ReadMe file. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Web-scale Discovery Service Adoption in Canadian Academic Libraries
Federated Research Data Repository / dépôt fédéré de données de recherche
Wong, Sandra — 2020-07-31 This dataset includes library profile data and codebook for a survey of web-scale discovery service adoption in Canadian post-secondary academic libraries. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Social Enterprise Sector Survey
Federated Research Data Repository / dépôt fédéré de données de recherche
Hall, Peter — 2016-05-23 This collection contains anonymized survey data collected from 1350 responding Social Enterprises in all Canadian provinces and territories, with the exception of Quebec. The surveys were conducted in 2014 and 2015. For the purposes of these surveys, social enterprises were defined as follows: "A social enterprise is a business venture owned or operated by a non-profit organization that sells goods or provides services in the market for the purpose of creating a blended return on investment, both financial and social/environmental/cultural". The purpose of the surveys, and of making the data available, is to support the development of the social enterprise sector in Canada by highlighting the size, scope and impact of social enterprises. Funding for the surveys has included the Social Sciences and Humanities Research Council, the Institute for Community Prosperity, Mount Royal University, Enterprising Non-Profits Canada, the TRICO Foundation of Calgary, and Employment and Social Development Canada, and generous local sponsors and supporters. These surveys have been undertaken with the tremendous support, dedication and enthusiasm of provincial umbrella groups that want to see social enterprises develop and flourish in Canada. Without these organizations this initiative would not have been possible. The collections consists of 11 SPSS-format Data Files, 11 excel-format Variable Keys and 10 pdf-format Questionnaires. Geographical information for each individual file can be found in item_metadata.csv. The researchers who created this dataset would be pleased to hear from you and how you have used this data (pvhall@sfu.ca and pelson@uvic.ca). Software used was SPSS. Confidentiality declaration: Use of this anonymized survey microdata has been approved by the SFU Research Ethics Board. Survey respondents were assured that their names would be kept confidential, as would the individual answers they provide. Hence all identifying variables, as well as open-response text fields and almost all financial data (except total revenue and expense) have been deleted. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Granular Dynamics of Natural Sediment
Federated Research Data Repository / dépôt fédéré de données de recherche
Dudill, Ashley; Venditti, Jeremy G.; Church, Michael; Frey, Philippe — 2019-10-21 This archive contains data and videos collected in experiments examining the influence of a finer grain input to a coarser granular bed driven by a shallow fluid flow. The experiments were undertaken in the River Dynamics Laboratory at Simon Fraser University between October 2015 and February 2016. The first set of runs was undertaken using spherical glass beads. The second set with natural fluvial sediment. The file 'Experimental_Conditions_Dudill_etal_Granular_Dyanmics_Natural_Sediment_Oct_21_2019.xlsx' contains information on sediment properties, flume dimensions, imposed hydraulic conditions, observed infiltration behavior, and measured parameters including flow depth and bed slope. Each run listed in this spreadsheet has a video file(s) showing characeristic granular motions. These data were published in Dudill, A., J.G. Venditti, M. Church, P. Frey (2020). Comparing the behavior of spherical beads and natural grains in bedload mixtures. Earth Surface Processes and Landforms, 45: 831-840. The file 'Supporting_Material_for_Church_etal_April_1_2020.xlsx' contains observations from select experiments in the River Dynamics Laboratory at Simon Fraser University, including some not reported in Dudill et al. (2020). Also included are select laboratory experiments conducted at the Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture (IRSTEA, now INRAE) in Grenoble, France and reported in Dudill et al., (2017; 2018; full references in document). The combined data set are published in Church M., A. Dudill, J.G. Venditti, P. Frey (2020). Are results in geomorphology reproducible? Journal of Geophysical Research: Earth Surface. https://doi.org/10.1029/2020JF005553. Additional descriptions and dates for individual files can be found in item_metadata.csv. Software used was Excel. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Employer Transit Subsidy Study
Federated Research Data Repository / dépôt fédéré de données de recherche
Hall, Peter — 2020-10-15 This collection contains anonymized survey data collected in three waves from employees of seven downtown Vancouver hotel workers. The questionnaire collected data on commute behaviour, including mode choice for the most recent commute to and from work, employment, quality of life and demographics. As part of an experimental study, differential transit subsidies were offered to workers at some hotels between the survey waves. The three survey waves were conducted in March 2018 (n=774), September 2018 (n=905) and March 2019 (n=902). The collections consists of 3 SPSS-format Data Files, 3 excel-format Variable Keys and 3 pdf-format Questionnaires. In order to protect the anonymity of respondents, some variables from the survey responses are not included. Dataset inventory: Employer Transit Subsidy Study Wave 1 Data (ETSS_surveydata_wave1_RADAR.sav), Employer Transit Subsidy Study Wave 1 Questionnaire (ETSS_questionnaire_Wave_1.pdf), Employer Transit Subsidy Study Wave 1 Variable Guide (ETSS_Wave_1_Variable_Key.xlsx), Employer Transit Subsidy Study Wave 2 Data (ETSS_surveydata_wave2_RADAR.sav), Employer Transit Subsidy Study Wave 2 Questionnaire (ETSS_questionnaire_Wave_2.pdf), Employer Transit Subsidy Study Wave 2 Variable Guide (ETSS_Wave_2_Variable_Key.xlsx), Employer Transit Subsidy Study Wave 3 Data (ETSS_surveydata_wave3_RADAR.sav), and Employer Transit Subsidy Study Wave 3 Questionnaire (ETSS_questionnaire_Wave_3.pdf). The researcher who created this dataset would be pleased to hear from you and how you have used this data (pvhall@sfu.ca). Confidentiality declaration: This study was approved by SFU Research Ethics Board (study number 2017s0591). The individual consent form signed by all respondents included the following statement: "Your responses will be entered into a database which will be stored on a secure server at SFU and on the researcher's SFU computer during data collection and analysis. At the end of the study the information you provided, which cannot be used to identify you, will become part of an open-access dataset that can be shared among researchers, policy actors, and other stakeholders who may also be interested in studying travel patterns." This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
The Canadian Bikeway Comfort and Safety (Can-BICS) Classification System: A Common Naming Convention for Cycling Infrastructure, Supplement 1 Index and Datasets
Federated Research Data Repository / dépôt fédéré de données de recherche
Winters, Meghan; Zanotto, Moreno; Butler, Gregory — 2019-03-14 Supplement 1 to The Canadian Bikeway Comfort and Safety (Can-BICS) Classification System: A Common Naming Convention for Cycling Infrastructure manuscript. Can-Supplement 1 (BICS_Supplement_1_10JUN2019.xlsx) lists the cycling facility names extracted from open datasets and bicycle maps from 44 municipalities across Canada. Can-BICS cycling nomenclature is applied to each of the records. Codebook for the data is given in the ReadMe.txt file. The zipped file (Can-BICS_Supplement_1_Datasets_10JUN2019.zip) contains the open datasets and cycling maps we used to create Supplement 1. Content type is GIS data and source of data is Canadian municipal open data catalogues and websites. Software used was QGIS and ArcGIS. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
JeL project data from Fun Palace June 25, 2019
Federated Research Data Repository / dépôt fédéré de données de recherche
Riecke, Bernhard E.; Stepanova, Ekaterina; Desnoyers-Stewart, John; Pasquier, Philippe — 2020-04-23 This data set contains data in relation to an exhibition of immersive installation JeL. One part of the data is a survey of 12 participants who have interacted with JeL and agreed to complete a post experience questionnaire (Interactive_Exhibit_Survey.csv). The other part of the data contains the breathing data recordings from pairs of participants interacting with the installation throughout the night (JeL_2019-06-25_19-16-47.csv). This data was collected on June 25th, 2019 at Centre for Digital Media during the Fun Palace Carnival of Mixed Realities. Content type is survey data and respiration data and software used was Survey Monkey. Source of data was attendees of Fun Palace Carnival of Mixed Realities. Confidentiality declaration: all data is de-identified. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
The Pulse Breath Water System: Exploring Breathing as an Embodied Interaction for Enhancing the Affective Potential of VR
Federated Research Data Repository / dépôt fédéré de données de recherche
Pasquier, Philippe; Prpa, Mirjana; Tatar, Kıvanç; Riecke, Bernhard E. — 2017-01-09 We study Pulse Breath Water, an immersive virtual environment (VE) with affect estimation in sound. We employ embodied interaction between a user and the system through the user's breathing frequencies mapped to the system's behaviour. In this study we investigate how two different mappings (metaphoric, and "reverse") of embodied interaction design might enhance the affective properties of the presented system. We build on previous work in embodied cognition, embodied interaction, and affect estimation in sound by examining the impact of affective audiovisuals and two kinds of interaction mapping on the user's engagement, affective states, and overall experience. The insights gained through questionnaires and semi-structured interviews are discussed in the context of participants' lived experience and the limitations of the system to be addressed in future work. Content type is respiratory sensor data synched with audio and format of files is .mubu (see MAX Sound Box Library by IRCAM). Software used was MAX 7 by Cycling74. Confidentiality declaration: The data is anonymous. This dataset was originally deposited in the Simon Fraser University institutional repository.
SFU Research Data (FRDR) Translation missing: fr.blacklight.search.logo
Southern Oscillation-driven rainfall pulse amplifies predation by owls on seabirds via apparent competition with mice
Federated Research Data Repository / dépôt fédéré de données de recherche
Thomsen, Sarah; Mazurkiewicz, David; Stanley, Thomas; Green, David — 2018-08-02 Data used in Thomsen Sarah K., Mazurkiewicz David M., Stanley Thomas R. and Green David J. 2018 El Niño/Southern Oscillation-driven rainfall pulse amplifies predation by owls on seabirds via apparent competition with mice. Proc. R. Soc. B.2852018116120181161 http://doi.org/10.1098/rspb.2018.1161. This dataset was originally deposited in the Simon Fraser University institutional repository.

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