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Farzan, Azadeh; Klumpp, Dominik; Podelski, Andreas — 2023-11-13 This archive contains the benchmark programs used in the POPL'24 paper "Commutativity Simplifies Proofs of Parameterized Programs" by A. Farzan, D. Klumpp and A. Podelski. https://doi.org/10.1145/3632925 A preprint of the paper can be found at https://arxiv.org/abs/2311.02673. https://creativecommons.org/licenses/by-sa/4.0/legalcode

Pochailo, Nick; Viliani, Leonardo; Stolar, Jessica; Stralberg, Diana; Nielsen, Scott — 2025-01-20 Pochailo N., Viliani L., Stolar J., Stralberg D., Nielsen S., (2025). Assessing the vulnerability and conservation potential of old growth forest in British Columbia, Canada. In review Climate change is predicted to have widespread effects on the distribution of many species and ecosystems, including old growth forests. Because of the slow development time of old growth forests, it is especially important for their management to map and understand areas of relative climatic stability, or “climate-change refugia”. British Columbia (BC), Canada, holds globally significant areas of old growth forests with varying levels of climate change threat. To better understand these threats, we used Maxent to model climate niches of BC’s ecosystem types, as represented by Biogeoclimatic Ecosystem Classification units. We then projected ecosystem changes for the 2050s climate period and overlaid them with current and potential future old growth forests (old forests projected to become old growth in absence of fire) to identify where these forests are likely to persist (“old growth refugia) and develop a Provincial Refugia Probability Index. Finally, we assessed to what extent BC’s current protected areas network represents current and future old growth forest. Our analysis identified 110,545 km² of current old growth in BC, which has the potential to increase via natural succession by up to an additional 69,410 km² by 2055, barring future wildfires or other disturbances. We also showed that up to 54% of the province and 63% of current old growth fell within the projected area of maximum total refugia for the 2050s climate period. Less than 12% of these forests were within refugia and already conserved, with <0.2% protected in areas with “high” probability of refugia. Thus, we demonstrate that almost all old growth in BC is susceptible to climate change, human development, or both, highlighting the continued vulnerability of these forests into the middle of the century. Overall, over 51% of BC’s old growth was identified as susceptible to human development and within the projected area of maximum total refugia. We suggest future planning to focus on conserving elements of these areas, as their projected climate stability potentially translates into efficient, long-term protections. We provide a framework for forest and conservation managers to assess the future effects of climate change on old growth forests in BC, or beyond.

David, Dorian; Dakota, Gustafson; Ryan, Quinn; Robert, Bentley; Paul, Dorian; Jack, Goodman; Jason, Fish; Kim, Kim A. Connelly — 2023-11-07 Aptamer-Based Proteomics Profiling: The modified aptamer binding reagents, SOMAscan assay technique, performance characteristics, and analysis have previously been described. In brief, protein levels for ~7,000 analytes (Supplementary Table II) in the archived dipotassium ethylenediaminetetraacetic acid plasma samples were measured by the SOMAscan platform, which uses single‐stranded DNA‐based aptamers to translate protein concentrations into DNA signals measurable by standard DNA detection methodologies. Plasma samples had a single controlled freeze-thaw cycle on ice prior to proteomics profiling which was required for aliquoting and shipping purposes. Target annotation and mapping of aptamers to UniProt accession numbers as well as Entrez gene identifiers were provided by SomaLogic; normalization information has been provided in the associated manuscript. The associated proteomics dataset can be requested from the corresponding authors, which, following institutional data sharing approval, is analyzable using SOMALogic's analysis suite (https://stats.somalogic.com/studyqc). https://creativecommons.org/licenses/by/4.0/legalcode

Kublik, Kristina; Currie, Claire; Pearson, D. Graham — 2025-01-10 This repository contains examples of SOPALE input files used to generate the geodynamic models for the paper Lateral Accretion of Cratons--The Influence of Surface Processes on Lithosphere Geodynamics.   Contents: L-A_inputFile_50Myr_sopale_nested_i: input file for compression phase (0-50 Myr) of model L-A M-A_inputFile_50Myr_sopale_nested_i: input file for compression phase (0-50 Myr) of model M-A H-A_inputFile_50Myr_sopale_nested_i: input file for compression phase (0-50 Myr) of model H-A L-A1_inputFile_sopale_nested_i: input file for post-compression phase (50-2000 Myr) of model L-A1   The provided examples are for models with 0 mm/yr erosion and sedimentation rates. To apply surface processes, line 317 in the input file is changed to 2 (for slope-dependent erosion), and rate of sedimentation and erosion in m/s is entered in line 318. https://creativecommons.org/licenses/by/4.0/legalcode

Li, Hao; Bezemer, Cor-Paul; Hassan, Ahmed E. — 2024-12-27 Replication package for the paper "Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models" http://www.apache.org/licenses/LICENSE-2.0

Kapil, Rudraksh; Marvasti-Zadeh, Seyed Mojtaba; Erbilgin, Nadir; Ray, Nilanjan — 2024-01-04 This is the RT-Trees dataset proposed and used in the paper titled, "Shadowsense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection From RGB-Thermal Drone Imagery", published at the IEEE/CVF WACV 2024 conference. Due to the size of the dataset and Zenodo's 50GB limit, the dataset is partitioned into two separate uploads. This upload contains the evaluation splits (test & val), along with the labelled subset of RGB training images used for a supervised training experiment, and the much larger set of unlabelled RGB images used for fully-unsupervised training.  The second upload includes the corresponding unlabelled thermal images used for unsupervised training. https://creativecommons.org/licenses/by/4.0/legalcode

Kapil, Rudraksh; Marvasti-Zadeh, Seyed Mojtaba; Erbilgin, Nadir; Ray, Nilanjan — 2024-01-04 This is the RT-Trees dataset proposed and used in the paper titled, "Shadowsense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection From RGB-Thermal Drone Imagery", published at the IEEE/CVF WACV 2024 conference. Due to the size of the dataset and Zenodo's 50GB limit, the dataset is partitioned into two separate uploads. This upload contains the unlabelled thermal images used for unsupervised training The first upload includes evaluation splits (test & val), along with the labelled subset of RGB training images used for a supervised training experiment, and the much larger set of unlabelled RGB images used for fully-unsupervised training. https://creativecommons.org/licenses/by/4.0/legalcode

Lanti-Traikovski, Alexander — 2024-09-06 https://creativecommons.org/licenses/by/4.0/legalcode

Piccolroaz, Sebastiano; Zhu, Senlin; Ladwig, Robert; Carrea, Laura; Oliver, Samantha; Piotrowski, Adam P.; Ptak, Mariusz; Shinohara, Ryuichiro; Sojka, Mariusz; Woolway, Richard I.; Zhu, David Z. — 2024-01-11 This repository contains the raw and processed data used to produce Figure 7 in "Lake water temperature modelling in an era of climate change: data sources, models, and future directions" - Reviews of Geophysics, by Piccolroaz et al. (2024). https://creativecommons.org/licenses/by/4.0/legalcode

Puliti, Stefano; Lines, Emily; Müllerová, Jana; Frey, Julian; Schindler, Zoe; Straker, Adrian; Allen, Matthew J.; Lukas, Winiwarter; Rehush, Nataliia; Hristova, Hristina; Murray, Brent; Calders, Kim; Terryn, Louise; Coops, Nicholas C.; Höfle, Bernhard; Junttila, Samuli; Krucek, Martin; Krok, Grzegorz; Král, Kamil; Levick, Shaun R.; Luck, Linda; Missarov, Azim; Mokroš, Martin; Owen, Harry; Stereńczak, Krzysztof; Pitkänen, Timo; Puletti, Nicola; Saarinen, Ninni; Hopkinson, Christopher D.; Torresan, Chiara; Tomelleri, Enrico; Weiser, Hannah; Astrup, Rasmus — 2024-08-07 Description Data for benchmarking tree species classification from proximally-sensed laser scanning data. Data split and usage The data is split into: Development data (dev): these includes 90% of the trees in the dataset and consists of individual tree point clouds (*.laz) named according to the treeID column available in the tree_metadata_dev.csv file, from which tree_species labels are available. These data are meant to be used for model development and can thus be further split into training and validation datasets. Test data (test): these are 10% of the trees (balanced sample) and include individual tree point clouds (*.laz) but, for benchmarking purposes, the species labels are witheld for benchmarking purposes. Thus to make use of the test data the users should predict species on the test trees, and output a table (.csv file) with a row per predicted tree and two columns (treeID and predicted_species). This table can then be used to create a new submission in the FOR-species20K Codabench benchmarking platform and obtain the evaluation metrics corresponding to the test data. Cite Any scientific publication using the data should cite the following paper: Puliti, S., Lines, E., Müllerová, J., Frey, J., Schindler, Z., Straker, A., Allen, M.J., Winiwarter, L., Rehush, N., Hristova, H., Murray, B., Calders, K., Terryn, L., Coops, N., Höfle, B., Krůček, M., Krokm, G., Král, K., Luck, L., Levick, S.R., Missarov, A., Mokroš, M., Owen, H., Stereńczak, K., Pitkänen, T.P., Puletti, N., Saarinen, N., Hopkinson, C., Torresan, C., Tomelleri, E., Weiser, H., Junttila, S., and Astrup, R. (2024) Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset. ArXiv; available here https://creativecommons.org/licenses/by/4.0/legalcode

Douzal, Clara; Jones, Sarah; Chemarin, Charlotte; Mosnier, Aline; Orduña-Cabrera, Fernando; Adenäuer, Lucie; Vittis, Yiorgos; Cozza, Davide; Diaz, Maria; Javalera Rincón, Valeria; Rios, Alejandro; Sandoval, Marcial; Sanchez, Andrea; Navarrete Frias, Carolina; Obersteiner, Michael; Declerck, Fabrice; Frank, Federico; Monjeau, Adrian; Bertranou, Camila; Navarro Garcia, Javier; Marcos-Martinez, Raymundo; Costa, Wanderson; Ramos, Fernando; Reyes, René; Zerriffi, Hisham; Paradis, Gregory; Maloney, Avery; Chavarro, John; Peña, Andres; Arguello, Ricardo; Escobar, Jorge; Marimon Bolivar, Wilfredo; Højte, Simone; Skou Fertin, Regitze; Fraas, Emil; Nyord, Tavs; Getaneh, Yonas; Nigussie, Yirgalem; Bekele, Mekonnen; Mulatu, Kalkidan; Abera, Wuletawu; Balcha, Yodit; Anshiso, Desalegn; Mohammed, Jemal; Assefa, Beneberu; Kebede, Kaleab; Eshetae, Meron; Hamza, Tagay; Tesfaye, Getachew; Tamene, Lulseged; Lehtonen, Heikki; Rämö, Janne; Rasche, Livia; Schneider, Uwe; Steinhauser, Jan; Landis, Conrad; Dellis, Konstantinos; Ioannou, Alexandra; Chatzigiannakou, Maria Angeliki; Laspidou, Chrysi; Koundouri, Phoebe; Saha, Ankit; Singh, Vartika; Das, Prantika; Joshi, Aditi; Jha, Chandan Kumar; Ghosh, Ranjan Kumar; Lotze-Campen, Hermann; Stevanović, Miodrag; Fuad, habiburrachman; Gonzalez-Abraham, Charlotte; Olguín, Marcela; Rodriguez Ramirez, Sonia; McCord, Gordon; Torres-Rojo, Juan Manuel; Flores-Martinez, Arturo; Cardenas Hernandez, Oscar; Avila Ortega, Daniel; Basnet, Shyam; Pradhan, Prajal; Acharya, Sushant; Uprety, Rajendra; Pokhrel, Pashupati; Khatri, Dil; Basnet, Ram; Van Oort, Bob; Daloz, Anne-Sophie; Strokov, Anton; Imanirareba, Dative; Hall, Marianne; Fetzer, Ingo; Tacer Caba, Zeynep; Kesici, Müge; Özuyar, Pinar; Smith, Alison; Lynch, John; Harrison, Paula; Jones, Sarah; Whittaker, Freya; Wu, Grace C.; Baker, Justin; Wade, Christopher — 2024-06 This database contains key parameters and variables from the 2023 Scenathon which has been run by the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Consortium. A scenathon - a scenario marathon - is a multi-objective challenge that allows a decentralized global modelling approach with multiple models developed by different teams in the world at national and regional scales, and a methodology to link them ensuring international trade consistency and tracking collective progress towards the achievement of global sustainability targets.  A description and analysis of the Scenathon 2023 pathways has been published in Sachs et al. (2024). The Scenathon 2023 database includes results at the global, country and rest of the world regions levels, for indicators related to food and nutrition security, land and biodiversity, GHG emissions from agriculture and land use change, and input use in agriculture. It also includes key parameters that can be used to explain the results, such as the evolution of productivity and all supply and use balance items at the commodity level. It is possible to visualise some of the key results on the Scenathon dashboard. Scope of the 2023 database: Pathways: The Current Trends (CT) pathway, reflecting a low-ambition future shaped by existing policies; The National Commitments (NC) pathway, projecting how national strategies, pledges, and targets for climate, biodiversity, and food systems would shape future outcomes. The Global Sustainability (GS) pathway, identifying additional actions necessary to align national and regional pathways with global sustainability targets. Countries and regions: Argentina, Australia, Brazil, Canada, China, Colombia, Denmark, Ethiopia, Finland, Germany, Greece, India, Indonesia, Mexico, Norway, Nepal, Russia, Rwanda, Sweden, Türkiye, the UK, and the United States and the rest of the world regions Rest of Asia and Pacific, Rest of Central and South America, Rest of European Union, Rest of Europe non-EU, Rest of Sub-Saharan Africa. Time: 2000-2050 with 2020 being the last calibration year. Results are provided for each 5 year-time step. Trade adjustment: results are provided before total exports and total imports are balanced or after. The readme worksheet provides all the relevant information on the indicators and acronyms definition used in the database. Contact author: clara.douzal@unsdsn.or and info.fable@unsdsn.org The FABLE Calculator has been used to compute all these results. You can review the FABLE Calculator 2020 documentation and get an Open FABLE Calculator. There is one FABLE Calculator per country or region. The FABLE Scenathon 2023 has been supporte by: The Food Systems, Land Use and Restoration (FOLUR) Impact Program, Norway's International Climate and Forest Initiative (NICFI), and The International Climate Initiative (IKI). https://creativecommons.org/licenses/by/4.0/legalcode

Hackett, Finn; Beschastnikh, Ivan — 2024-09-18 http://www.apache.org/licenses/LICENSE-2.0

Woo, Jeonghyun — 2025-01-07 Ramulator2 traces for the code artifact (Zenodo) for the paper "QPRAC: Secure and Practical PRAC-based Rowhammer Mitigation using Priority Queues", which will appear in HPCA 2025. https://creativecommons.org/licenses/by/4.0/legalcode

Liu, Liyang; Wan, Xue; Karaaslan, Muzaffer; Hua, Qi; Shen, Fei; Sipponen, Mika; Renneckar, Scott — 2024-12-12 This data set consisted of the original data set for the paper ''Circular poly(ethylene terephthalate) with lignin-based toughening additives''. It has two folders: lignin characterization and fiber characterization. The former lignin characterization includes 13C Nuclear Magnetic Resonance, 31P Nuclear Magnetic Resonance, Gel Permeation Chromatography combined with three detectors: multi-angle light scattering, intrinsic viscometer, and differential refractive index, thermal gravimetric analysis (TGA), and differential scanning calorimetry (DSC). The latter fiber characterization for LignoPET composite includes scanning electron microscopy (SEM) images, dynamic mechanical analysis (DMA), Instron tensile tests, differential scanning calorimetry (DSC), and optical microscopy. https://creativecommons.org/licenses/by/4.0/legalcode

Birch, Ruth; Britton, Ben; Poole, Warren — 2024-08-27 Improving parent-austenite twinned grain reconstruction using electron backscatter diffraction in low carbon austenite  Ruth M. Birch1*, T. Ben Britton1, W. J. Poole1 1.                   Department of Materials Engineering, University of British Columbia, Frank Forward Building, 309-6350 Stores Road, Vancouver, BC, Canada V6T 1Z4 *corresponding author: ruth.birch@ubc.ca --- Abstract:  Thermomechanical controlled processing (TMCP) is widely used to optimize the final properties of high strength low alloy (HSLA) steels, via microstructure engineering. The room temperature microstructures are influenced by the high temperature austenite phase, and the austenite microstructure is commonlycan be accessed by reconstruction using electron backscatter diffraction (EBSD) data of the final microstructure. A challenge for reconstruction of the PAG parent austenite grain (PAG) microstructure and subsequent austenite grain size measurement is the presence of austenite-phase annealing twins, and we address this challenge with a new ‘re-sort’ algorithm. Our algorithm has been validated using the retained austenite regions (which were recovered via advanced pattern matching of EBSD patterns). We demonstrate that the re-sort algorithm improves the PAG reconstruction significantly, especially for the grain boundary network and correlation with other methods of grain size assessment and development of TMCP steels. --- Dataset includes: Higher quality figures EBSD dataset with/without pattern matching: 1mm map Specimen 1 Site 1 Map Data 1-Subset 1.h5oina 1mm map Specimen 1 Site 1 Map Data 1-Subset 1-PatternMatching.h5oina Code bundle https://creativecommons.org/licenses/by/4.0/legalcode

Hergt, Lukas Tobias — 2024-11-08 This zip file contains the Monte Carlo Markov chains (MCMC), the plotting codes, and the figures for the paper "Some Times in Standard Cosmology" by Lukas Tobias Hergt and Douglas Scott (arXiv:2411.07703): The folder MCMC__LCDM__PR3_lowlTT_PR4_LoLLiPoP-lowlEE_HiLLiPoP-TTTEEE_lensing contains the MCMC run generated with Cobaya. For details about theoretical model, input parameters, and likelihoods refer to the .yaml files therein or to information in the corresponding paper. The some_times_in_standard_cosmology.py file was used for the post-processing of the MCMC chains; specifically, to compute the physical times, which are not standard derived parameters. The some_times_in_standard_cosmology.ipynb file contains the plotting code. We additinally provide the some_times_in_standard_cosmology.html file to allow for easy read access via a browser. The PDF files some_times_in_standard_cosmology.pdf and some_times_in_standard_cosmology_2d.pdf are the two figures published in the paper. https://creativecommons.org/licenses/by/4.0/legalcode

Yang, Chen Xi; Sin, Don; Ng, Raymond — 2024-11-08 Source code and simulated datasets used in manuscript "SMART: Spatial transcriptomics deconvolution using marker-gene-assisted topic model" https://www.gnu.org/licenses/gpl-3.0-standalone.html

Gu, Tianhong; Xu, Yilun; Britton, Ben; Gourlay, Christopher; Dunne, Fionn — 2024-10-22 The data released here is for the paper "Understanding the deformation creep and role of intermetallic compound-microstructure in Sn-Ag-Cu solders". DOI: 10.1016/j.msea.2024.147429. Tianhong Gu*1,2, Yilun Xu*1.3, Christopher M. Gourlay1, Fionn P.E. Dunne1 and T. Ben Britton1,4 1Department of Materials, Imperial College London, SW7 2AZ, UK.2Department of Civil Engineering and XJTLU Advanced Materials Research Center(AMRC), Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China.3 Institute of High Performance Computing (IHPC), Agency for Science, Technology andResearch (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632,Republic of Singapore.4Department of Materials Engineering, University of British Columbia, Vancouver,British Columbia, V6T 1Z4, Canada.*Corresponding author: Tianhong.gu@xjtlu.edu.cn; Xu_yilun@ihpc.a-star.edu.sg  The data bundle was prepared by Tianhong Gu https://creativecommons.org/licenses/by/4.0/legalcode

Ahmad, Syed Ishtiaque — 2024-07-26 The repo-data folder contains 53 .json files, each corresponding to one of the 53 Java open-source projects. Each file contains various metrics for methods in the project.   { "hawtio-3976.json": { "Age": 794, "sloc": [11,11,11], "slocAsItIs": [11,14,14], "slocNoCommentPretty": [11,11,11], "diffSizes": [0,7,0 ], "bodychanges": [0,1,0], "newAdditions": [0,5,0], "isGetter": [false,false,false], "isSetter": [false,false,false], "changeDates": [0,3,794], "isEssentialChange": [false,true,false], "isBuggy": [false,false,false], "changeTypes": ["Yintroduced","Ybodychange","Yfilerename"], "filename": "hawtio-3976.json", "authors": ["X","Y","Z"], "editDistance": [0, 68, 0], "repo": "hawtio" }, "method_id": {...}, "method_id": {...} }   The above method with id hawtio-3976.json has total 3 revisions which is why the array of values for a particular metric (e.g., sloc: [11,11,11]) are of length 3. Index 0 of the array represents the introduction value of a particular metric for the above method. Description of the metrics Age: Age of the method in days sloc: Source line of code of a method without comment and blank lines slocAsItIs: Source line of code of a method with comment and blank lines slocNoCommentPretty: Source line of code pretty printed without comment and blank lines diffSizes: Total number of lines added + removed in git diff bodychanges: Contains value 0 or 1; where 1 implies occurrence of body change newAdditions: Total number of lines added in git diff isGetter: Contains true or false; where true indicates it is a get method isSetter: Contains true or false; where true indicates it is a set method changeDates: Contains the date difference in days from when the method was introduced. Index 0 is always 0 which indicates the introduction date isEssentialChange: Contains true or false; where true indicates it is an essential change. Essential change includes: Ybodychange, Ymodifierchange, Yexceptionschange, Yrename, Yparameterchange, Yreturntypechange and Yparametermetachange detected by CodeShovel isBuggy: Contains true or false; where true indicates the method bug was fixed at a particular revision changeTypes: All transformations applied to the method at each revision. The full list of transformation that is detected by CodeShovel are: Ybodychange, Ymodifierchange, Yexceptionschange, Yrename, Yparameterchange, Yreturntypechange, Yparametermetachange, Yannotationchange, Ydocchange, Yformatchange, Yfilerename and Ymovefromfile filename: It is the method id bugData folder contains 53 .json files with bug information, each belonging to one of the 53 Java open-source projects. The sample JSON schema of a file is given below: { "hawtio-3976.json":{ "exactBug0Match": [false, false, false], "exactBug1Match": [false, false, false], "exactBug2Match": [false, false, false], "exactBug3Match": [false, false, false], "regExBug0": [false, false, false], "regExBug1": [false, false, false], "regExBug2": [false, false, false], "regExBug3": [false, false, false] }, "method_id": {...}, "method_id": {...}, } The above method can be mapped to its metrics dataset using the method_id. For e.g., the above method with id hawtio-3976.json in bugData/hawtio.jsonthat has 3 revision can be found in the metric dataset using the same id hawtio-3976.json in the file repo-data/hawtio.json.Description of bug dataset Each key in the above example contains value true or false indicating if a method was buggy or not at each revision. The "hawtio-3976.json method has 3 revisions (including method's introduction) which is why the array length is 3. The keys in the above json output represent bug-fix classification based on buggy keywords adopted from prior work. We identified bug-fix commit using two approaches:     Exact case insensitive match of buggy keywords from the commit message (keys prefix wih exact represent this)    Partial case insensitive substring match (using regular expression) excluding words that ends with fix or bug. (keys prefix with regEx represent this)     Bug0: This is the approach that we have used for classifying bug-fix commit. Buggy keyword list: ["error", "bug", "fixes", "fixing", "fix", "fixed", "mistake", "incorrect", "fault", "defect", "flaw"]    Bug1: Same keyword list as exactBug0Match with the addition of keyword issues    Bug2: Buggy keyword list from prior work: ["bug", "fix", "error", "issue", "crash", "problem", "fail", "defect", "patch"]    Bug3: Buggy keyword list from prior work: ["error", "bug", "fix", "issue", "mistake", "incorrect", "fault", "defect", "flaw", "type"] https://creativecommons.org/licenses/by/4.0/legalcode

Ren, Shuoqi; Giang, Amanda — 2024-07-06 DemoEnPoC2016.csv/DemoEnPoC2006.csv: This is a table including environmental and demographic (Census variables) data at postal code level for Metro Vancouver in the year 2006 and 2016. The environmental data (SO2 metrics, PM2.5 metrics, Calculated ozone metrics, NO2 data, NDVI metrics, and Canadian Active Living Environments Index (Can-ALE) indexed to DMTI Spatial Inc. postal codes) were extracted from CANUE (Canadian Urban Environmental Health Research Consortium). The demographic data is extracted from Canadian Census analyzer (https://datacentre.chass.utoronto.ca/), the deprivation index is downloaded from from the Institut national de santé publique du Québec (INSPQ).  DGRwithLable: This is the Dissemination Geographies Relationship File for the 2021 census year (Statistics Canada, 2021) with the lable of urban or rural, indicating which dissemination area (DA) is identified as urban and included in this study. The urban area is named as population certer.  Aggregation and SS Determination: This script contains code for: Aggregating postal code level data to the Dissemination Area (DA) level. Eliminating rural DAs. Converting environmental data into ordinal categories using quartile and even break methods. Identifying sweet and sour spots for each DA based on these methods. SSEJ Analysis: This script includes code for: Creating violin and box plots to illustrate descriptive statistics of demographic groups across different environmental categories (sweet, sour, risky, and medium). Performing linear regression analyses between environmental categories and demographic variables. SS Heatmap: This script comprises code for: Summarizing the results of the linear regression analyses. Assessing changes in inequities among demographic groups between 2006 and 2016. Visualizing regression coefficients through heatmaps. https://creativecommons.org/licenses/by/4.0/legalcode