cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp3/iamcm5
title: Mapping Park and Greenspace Value Across the University of British Columbia (UBC) Vancouver Campus

description: Greenspaces on university campuses enhance well-being, yet their value varies due to accessibility and usability factors. This study mapped greenspace value across the University of British Columbia (UBC) Vancouver campus, assessing how lighting, accessibility infrastructure, topography and physical barriers influenced inclusivity and usability. Using a geospatial framework, data were collected from UBC infrastructure databases, LiDAR (Light Detection and Ranging) models, and on-site observations, analyzed through hotspot analysis, Kriging interpolation, and Weighted Overlay Analysis to generate a suitability map. Results showed central areas like West Mall and Lower Mall with high greenspace value due to dense lighting, flat terrain, and extensive accessibility features, while peripheral zones like Acadia Park scored lower due to inadequate lighting and steep slopes. The analysis revealed a central-peripheral divide, with central areas scoring 80% higher in suitability, highlighting systematic planning biases reducing equitable access. Targeted interventions like enhanced lighting and ramps were recommended to improve inclusivity. This work’s geospatial and LiDAR-based approach is transferable beyond UBC, offering a scalable model for urban parks, other campuses, and public spaces to assess accessibility. Cities globally could utilize it to address disparities in park equity, ensuring inclusivity for diverse populations, like those with mobility challenges or in underserved, remote areas. The focus on barriers and infrastructure applies to municipal planning, correcting imbalances where high-traffic zones overshadow peripheral regions. For example, suburban or rural communities could adapt this model to optimize limited greenspace resources. It also supports climate resilience by identifying areas requiring green enhancements. Applicable to sustainable development goals, this framework empowers planners to enhance social justice in urban design, making greenspaces equitable assets anywhere.
DOI: 10.5683/sp3/ukwnm9
title: Professional QOL and SPS in Psychologists

description: This spreadsheet contains data from 95 clinical and counselling psychologists, collected during the COVID-19 pandemic. Summary scores are provided on self-report measures tapping into alexithymia (20 item Toronto Alexithymia Scale, Bagby, Parker, & Taylor, 1994), sensory processing sensitivity (Highly Sensitive Person Scale, Aron & Aron, 1997; Orienting Sensitivity Subscale of Adult Temperament Questionnaire, Evans & Robthbart, 2008), self-compassion (Self-Compassion Scale; Neff, 2003); negative impacts of the COVID-19 pandemic (Coronavirus Impact Scale, Stoddard et al., 2023); professional quality of life (Professional Quality of Life Scale; Stamm et al., 2010; and mood/anxiety/stress (Depression Anxiety Stress Scale-20, Lovibond & Lovibond, 1995).
DOI: 10.5683/sp3/2m97xm
title: Data for the manuscript "The effects of pulse exposures to road salt at various stages of early development in rainbow trout (Oncorhynchus mykiss)"

description: Excel file containing all raw data for the manuscript "The effects of pulse exposures to road salt at various stages of early development in rainbow trout (Oncorhynchus mykiss)" including embryo survival, egg weight and ion composition for trout embryos exposure to multiple environmentally relevant 24h pulses of road salt. The first worksheet is a README with descriptions of the contents of the file
DOI: 10.5683/sp3/a64e3k
title: University of Regina Quick Start Guide

description: Read the attached file for Quick Start User guide to Borealis. We recommend contacting the Data Services Librarian for a one-on-one, detailed overview of Borealis, but this document will provide enough information to get you started.
DOI: 10.5683/sp3/vxf61t
title: Tracking Forest Recovery: Early Indicators of Forest Regeneration Following the 2017 Elephant Hill Wildfire in British Columbia, Canada

description: Wildfire is an increasingly prominent disturbance across forested landscapes in British Columbia, yet ecological responses and long-term vegetation recovery remain complex and spatially variable. This study investigates post-fire vegetation recovery following the 2017 Elephant Hill wildfire using Landsat-derived remote sensing data from 2015 to 2022. By analyzing the Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI) we assess vegetation change across four time periods: pre-fire, immediate post-fire, two years post-fire, and five years post-fire. Vegetation recovery was defined as achieving at least 80% of pre-fire index values by 2022. Findings indicate that substantial portions of the burn scar remained below it five years after the fire. Slower recovery was often associated with higher initial burn severity, elevation, and climatic conditions. The use of remote sensing offers a unique advantage by enabling consistent, landscape-scale monitoring especially in remote or inaccessible areas while Landsat’s 30-meter spatial resolution facilitates the detection of fine-scale recovery patterns across heterogeneous terrain. These results underscore the importance of multi-temporal satellite imagery in evaluating ecosystem resilience and identifying areas that may benefit from targeted restoration efforts in the context of more frequent and severe wildfires.
DOI: 10.5683/sp3/2ubbv8
title: Supervised Mangrove Classification in Madagascar Comparing Optical Imagery to Combined Optical and Synthetic Aperture Radar Imagery Using Random Forest on Sentinel-1 and Sentinel 2

description: Mangrove ecosystems are critical coastal habitats that provide essential services such as shoreline protection, carbon sequestration, and biodiversity support. Monitoring these ecosystems over large and often remote areas can be challenging, making remote sensing a valuable tool for effective observation and management. However, accurate classification of mangroves remains difficult due to spectral and structural complexities. This study evaluates the integration of Sentinel-1 (S1) synthetic aperture radar and Sentinel-2 (S2) optical data for supervised mangrove classification using a Random Forest (RF) algorithm across four sites in Madagascar: Velondriake, Mahajamba Bay, Belo Sur Mer, and Ambaro Bay. We compared classifications using S2 alone versus combined S1+S2 data to assess improvements in accuracy. Results showed that S1+S2 consistently outperformed S2 alone, achieving overall accuracies of 84.72%–95.53% compared to 84.09%–93.73%, with notable gains at Velondriake (92.61%vs. 89.07%) and Mahajamba Bay (95.53% vs. 93.73%). VH polarization emerged as a key discriminator, outperforming VV, contrasting with prior studies (e.g., Huang et al., 2022), likely due to our focus on seven land cover classes rather than binary mangrove/non-mangrove distinctions. The Optical and Synthetic Aperture Radar(SAR) Combined Mangrove Index (OSCMI) enhanced classification at three sites, though its importance varied (6.55%–9.89%). However, challenges persisted, particularly with closed-canopy mangroves, where user accuracy dropped below 70% at some sites, possibly due to spectral confusion, limited training data, and temporal mismatches between imagery and reference polygons. These findings highlight the value of combining S1 and S2 data for mangrove mapping, though site-specific factors and data alignment issues warrant further refinement. Enhanced field validation and updated reference data could further improve classification accuracy.
DOI: 10.5683/sp3/vaur28
title: Land Cover Mapping in Cable Bay with Sentinel 2 from 2018 to 2024

description: The Cable Bay area, south of Nanaimo, British Columbia, is an ecologically rich region with diverse ecosystems, including rare Garry oak meadows, increasingly threatened by urban expansion. This study mapped and analyzed land cover changes across six classes—Broadleaf, Conifer, Cropland, Developed, Meadow, and Water—from 2018 to 2024. We applied a Random Forest classifier to high-resolution Sentinel-2 imagery within a hybrid Python-R workflow, achieving classification accuracies of 97.0-98.6%. Spatiotemporal analyses, including transition matrices and hotspot detection, revealed a significant decline in Broadleaf forests and a net increase in Developed land. These shifts reflect urbanization’s pressure on natural habitats, threatening biodiversity and ecosystem services. The findings underscore the urgency of conservation efforts and informed land-use planning to preserve Cable Bay’s ecological integrity.
DOI: 10.5683/sp3/xqolk0
title: Evaluating Riparian Function Using LiDAR-Derived Vegetation Intactness and Cattle Accessibility in the Cariboo Region, British Columbia

description: Riparian zones provide vital ecological services, such as maintaining water quality, supporting biodiversity, and stabilizing stream banks; however, these areas in British Columbia’s Cariboo Natural Resource Region are increasingly pressured by forestry practices, cattle grazing, and private land management. Traditional cumulative effects assessments rely predominantly on indirect indicators of disturbance potential, which may inaccurately represent actual conditions, making direct quantification of riparian vegetation intactness and cattle accessibility critical for effective management. This study utilized high-resolution Light Detection and Ranging (LiDAR) data and spatial datasets to directly assess riparian conditions across four watersheds (approximately 20 km² each), addressing three objectives: (1) quantifying vegetation intactness within riparian zones on private land and Indian Reserves compared to adjacent crown land; (2) modeling cattle accessibility to riparian areas within active range tenures based on slope and vegetation density; and (3) evaluating the effectiveness of vegetation retention within forestry cutblock buffers (harvested areas less than 40 years old). Results revealed significant disparities in vegetation intactness, with crown land riparian buffers showing substantially higher mean canopy heights (7.54 m) than private and Indian Reserve lands (2.78 m). Approximately 50–60% of riparian areas within range tenures were highly accessible to cattle, with an additional 30–40% classified as medium accessibility, underscoring potential ecological risks from grazing. While 79.7% of forestry riparian buffers retained vegetation, their average canopy height (5.80 m) was significantly lower than surrounding areas (7.82 m), suggesting compromised ecological integrity. These findings emphasize the importance of leveraging remote sensing data in cumulative effects assessments, providing precise management guidance to enhance riparian protection strategies and policy efficacy.
DOI: 10.5683/sp3/sht1he
title: Dataset for self-motion and object motion axes project

description: This dataset contains data collected during psychophysical studies of visual motion perception during simulated self-motion. The dataset enables performance extraction of motion discrimination for analysis of effects of self- and object motions direction on perception during self-motion. Staircase methods were used for efficient estimation on thresholds. Data were collected and pre-processed with Psychopy package in Python programming language.
description: R, 4.4.3
description: Python, 3.8.0
description: Psychopy, 2020.2.10
DOI: 10.5683/sp3/4kd7jm
title: Assessing Baseline and Monitoring Change in Forest Carbon Projects

description: This study evaluates carbon stock changes in the Northern Scarp West Forest Reserve (project site) and Bumfuom Forest Reserve (control site) in Ghana, using remote sensing techniques and biomass estimation methodologies. The aim is to compare two carbon crediting frameworks Gold Standard V2.1 and Verra VM0047 in terms of their effectiveness in monitoring carbon sequestration in afforestation and reforestation projects.
These frameworks are fundamental for ensuring accurate carbon credit issuance, but differ in their baseline approaches: the static Gold Standard provides a conservative, fixed baseline, while the dynamic Verra framework adapts to real-time sequestration trends, allowing for more flexible monitoring. Remote sensing is critical in this context, particularly for regions like Ghana where limited ground-based data and large, inaccessible forested areas make traditional monitoring methods challenging.
The study integrates NDVI (Normalized Difference Vegetation Index) a satellite-derived metric that quantifies vegetation health by comparing red and near-infrared reflectance values along with performance benchmarks and Chloris above-ground biomass (AGB) products to estimate carbon stocks and vegetation cover changes.
Results show a significant increase in carbon stocks at the project site, with a baseline carbon stock of 36.04 tCO₂/ha. Verra VM0047’s dynamic baseline approach outperforms Gold Standard V2.1 in reflecting real-time sequestration trends. This study highlights the importance of incorporating dynamic baselines in carbon crediting methodologies, especially in ecosystems undergoing frequent land-use changes and recovery, while emphasizing the role of remote sensing in scalable, cost-effective monitoring. The findings contribute to a deeper understanding of forest carbon accounting methodologies and their application in tropical regions for climate change mitigation
description: Google Earth Engine, 2024
description: QGIS, 3.28
description: ArcGIS Pro, 3.1
description: R, 4.3.2
DOI: 10.5683/sp3/5uswyz
title: Freshwater Zooplankton Species of Newfoundland and Labrador

description: Checklist of freshwater zooplankton SPECIES of Class Branchiopoda and Class Maxillopoda, SubClass Copepoda (all Subphylum Crustacea) of insular Newfoundland and in Labrador, based on literature search and the researcher's collections. Prepared for the Provincial Species Status Advisory Committee SSAC by Dr. Christine Campbell.

For Branchiopoda: Classification based on Dodson and Frey (2001). Other classifications list Cladocera as one Order with Anomopoda, Ctenopoda, Onychopoda and Haplopoda as Suborders (Crease and Taylor 1998). Distribution of littoral species is not well-known in the province, as littoral habitats have not been as extensively sampled as pelagic.

For Copepoda: Classification based on Williamson and Reid (2001). Genus nomenclature follows Dussart and Defaye (2002) for calanoids and Hudson et al. (1998) for cyclopoids. Other classifications list Crustacea as Class, with Copepoda as Subclass (Great Lakes Science Center webpage). Data on provincial distributions and abundance are more available for Calanoida (primarily pelagic species) than for Cyclopoida (pelagic/littoral species), while little is known about Harpacticoida (small, littoral species). Littoral habitats have not been as extensively sampled as pelagic.

Given as pdf and Word documents.
DOI: 10.5683/sp3/9wqtld
title: Combining Multi-Season Multispectral Imagery and Airborne Laser Scanning Data to Improve Predicted Quercus garryana Distribution
title: Branching out: A Novel Method for Predicting Garry oaks
title: Combining Multi-Season Multispectral Imagery and LiDAR Data to Improve Predicted Quercus garryana Distribution

description: Garry oak ecosystems are one of North America's most distinctive and ecologically significant ecosystems. Currently, only 1–5% of their historical extent remains in near-natural condition. Effective conservation requires accurate information on Garry oak distribution and forest composition. Remote sensing and machine learning offer advantages over traditional methods in terms of resources and scalability. Spectral similarities with co-occurring species, rarity, and subcanopy growth present challenges in transitional forests. Previous studies have found incorporating light detection and ranging (LiDAR) and multi-season data to be advantageous in tree species classification compared to single season imagery. This research evaluates the effect of including multi-season LiDAR data and imagery on the accuracy of Garry oak identification in a random forest classification of a mixed broadleaf and coniferous forest on Vancouver Island, Canada. LiDAR improved overall accuracy by 5.21%, Garry oak producer accuracy by 19%, and user accuracy by 7.67% on average compared to imagery alone. The impact of seasonality was less clear. On average, classifications using leaf-on LiDAR outperformed multi-season and leaf-off LiDAR, while spring imagery, followed by multi-season imagery, performed best. However, that was not consistently true. Seasonality of inputs significantly impacted misclassification patterns and final proportion of predicted species. These findings highlight the benefits of integrating LiDAR data in classifications to identify Garry oaks. Further research on the impact of species composition and phenology could help optimize data acquisition timing.
DOI: 10.5683/sp3/kbctsc
title: :unav

description: :unav
DOI: 10.5683/sp3/4almzm
title: Data from: On the use of double quantile regression and visual assessment to estimate performance constraints

description: &lt;b&gt;Abstract&lt;/b&gt;&lt;br/&gt;&lt;p&gt;Quantile regression (QR) is a widely used tool for estimating performance limits in behavioral data. In &lt;em&gt;Vazquez-Cardona et al.&lt;/em&gt; (2023, &lt;em&gt;Behavioral Ecology&lt;/em&gt;), we applied mixed QR to estimate motor constraints in Adelaide’s warbler song, and measured performance as deviations from these estimated limits. A recent critique (Cardoso, 2024, &lt;em&gt;Behavioral Ecology&lt;/em&gt;) raised two concerns about that analysis: (1) QR-based deviation scores arbitrarily emphasize one of the two variables in the trade-off, and (2) uneven sampling may produce the illusion of constraint, compromising the validity of visual assessments and QR. Here, we evaluate these claims by applying double quantile regression (DQR)—Cardoso’s proposed remedy—and by testing whether rarefaction alters the observed constraints. We introduce a novel method to characterize DQR results using bisecting lines and show that, for some distributions, DQR improves fit to the constrained edge. However, DQR performs poorly under some conditions and can falsely identify constraints in unconstrained data. Rarefaction analyses reveal that uneven sampling does not account for the appearance of constraint in our bird song data, which show sharp, bounded edges unlike the smooth gradients of unconstrained simulations. Finally, we demonstrate that the warm-up effect in vocal performance, previously reported in &lt;em&gt;Vazquez-Cardona et al.&lt;/em&gt; (2023), is robust to both DQR-based estimation and rarefaction. We conclude that while DQR can be a useful complement to QR, visual inspection remains essential, and caution is warranted when applying DQR to datasets with complex geometries or potential false boundaries.&lt;/p&gt;
description: <b>Dryad version number:</b> 4
<b>Version status:</b> submitted
<b>Dryad curation status:</b> Published
<b>Sharing link:</b> http://datadryad.org/dataset/doi:10.5061/dryad.vhhmgqp5d
<b>Storage size:</b> 10193418
<b>Visibility:</b> public
DOI: 10.5683/sp3/gd0d5l
title: The Impact of Tree Shade on Building Cooling Energy Demand During Summer

description: The upsurge of climate change has exacerbated the urban heat island (UHI) effect, leading to severe consequences for human health, urban livability, and energy demand. Rising temperatures significantly increase the demand for cooling energy, as the cooling load of buildings is heavily influenced by the amount of solar radiation reaching their surfaces. One of the most effective and sustainable strategies is urban greening. These interventions can take various forms, including street trees, urban parks, and vegetation surrounding buildings, all of which contribute to heat relief and improved urban resilience. Trees offer a wide range of ecosystem benefits that improve the climate and habitability of cities, including public spaces, by acting as natural shading and wind-shielding elements around buildings. By blocking solar radiation, trees help lower the energy needed for cooling, especially in buildings with poor window-to-wall ratios.
This project contributes to the Neighborhood Climate Action Plan (NCAP) goals by quantifying tree shade on buildings on the University of British Columbia campus and analyzing its impact on building energy demand. Using high-resolution Using high-resolution Light Detection and Ranging (LiDAR) data and sun position data, a Digital Surface Model was created to represent campus elevation, and hillshade analysis was employed to simulate shade coverage at 15-minute intervals. Findings revealed that there is a negative correlation between shade coverage and cooling energy demand, highlighting the effectiveness of tree shading as a cooling strategy, as buildings with more shade tend to have lower cooling loads. Also, the analysis, although statistically significant, revealed that other confounding factors affect energy demand in a building, including the age of the building, occupancy level and behaviour, shape and orientation of the building.
The findings highlight the importance of strategic tree planting in urban environments to mitigate the urban heat island effect and enhance energy efficiency. Integrating tree shading into urban planning policies is essential for fostering sustainable and climate-resilient communities. Future research should incorporate real-time occupancy and equipment usage data, quantify transpiration contributions alongside shading effects and how the age of buildings can influence their energy demand over time.
description: ArcGIS PRO,, Version: 3.2
description: R Studio, Version: 4.3.2
DOI: 10.5683/sp3/qbko8s
title: Historically Changed 3D University of British Columbia (UBC) Buildings and Landscapes

description: This study addresses the lack of comprehensive 3D documentation of the University of British Columbia (UBC) Vancouver campus's architectural and ecological evolution from 2015 to 2024. By integrating 2D polygon shapefiles, Light Detection and Ranging (LiDAR) data, Digital Elevation Models, and open-source geographical data, an interactive time-slider enabled 3D model was developed to capture the campus's historical transformation. The methodology leverages LiDAR point clouds to extract detailed information on buildings, vegetation, and terrain, supplemented by satellite imagery and vector data for validation and calibration. Results reveal distinct spatial development patterns: stability in the academic core around Main Mall with maturing tree canopy, contrasted with significant residential expansion in peripheral areas, particularly the Wesbrook neighborhood. The visualization demonstrates UBC's implementation of sustainability policies, including Leadership in Energy and Environmental Design (LEED) certification standards for new construction, while balancing growth with ecological preservation. This model provides insights into the interplay between development and environmental planning, supporting evidence-based decision-making for campus planning while offering an educational tool for understanding the historical landscape changes. The approach establishes a replicable framework for similar historical landscape modeling efforts at other institutions seeking to integrate sustainability into their development strategies.
description: ArcGIS Pro, 3.2
description: R, 4.3.1
description: PDAL, 2.6.3