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3,709 results for “urbanization.”
Epigenetic variation between urban and rural populations of Darwin’s finches
GEO Series GSE87825. Geospiza fortis; Geospiza fuliginosa. 24 samples. Type: Methylation profiling by high throughput sequencing.
Hippocampus and cortex of mice exposed to urban particulate matter
GEO Series GSE252133. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
Mice Exposed in situ to Urban Air Pollution Exhibit Pulmonary Alterations in Lipid Droplet Synthesis Pathways
GEO Series GSE41698. Mus musculus. 25 samples. Type: Expression profiling by array.
Systematic surveillance of SARS-CoV-2 reveals dynamics of variant mutagenesis and transmission in a large urban population II
GEO Series GSE231415. Severe acute respiratory syndrome coronavirus 2; synthetic construct; Homo sapiens. 2054 samples. Type: Expression profiling by high throughput sequencing.
Effect of urban effluents on fathead minnow gene expression: linkages between pollutants and physiological processes.
GEO Series GSE37550. Pimephales promelas. 15 samples. Type: Expression profiling by array.
Systematic surveillance of SARS-CoV-2 reveals dynamics of variant mutagenesis and transmission in a large urban population I
GEO Series GSE224951. Homo sapiens; Severe acute respiratory syndrome coronavirus 2; synthetic construct. 39 samples. Type: Other.
Diet and multi-omics characterization in the Study of Urban and Rural Crohn Disease Evolution (SOURCE) cohort
GEO Series GSE233900. Homo sapiens. 40 samples. Type: Expression profiling by high throughput sequencing.
Systematic surveillance of SARS-CoV-2 reveals dynamics of variant mutagenesis and transmission in a large urban population III
GEO Series GSE246819. synthetic construct; Homo sapiens; Severe acute respiratory syndrome coronavirus 2. 2060 samples. Type: Expression profiling by high throughput sequencing.
Data from "Urbanization is a worrying cause of farmland moth decline"
<p>This repository contains data to reproduce the findings reported in the paper entitled <em>"Urbanization is a worrying cause of farmland moth decline"</em>.</p>
Widespread global exacerbation of extreme drought induced by urbanization
<p>The data and codes for generating the major results in the manuscript. </p> <p>Currently, the manuscript is under review.</p> <p>More information will be released.</p>
Urban monthly land dynamics Sentinel-2 benchmark dataset
<p>The public data set of the paper "<strong>Time-series land cover change detection using deep learning-based temporal semantic segmentation</strong>" uses monthly synthesized Sentinel-2 for time series semantic change detection. A total of 32894 samples were collected. Each timestamp has a land cover type annotation. Anyone can use this data set to conduct further research. We will add more areas in the future. </p> <p><strong>Data description:</strong></p> <ol> <li>The time series length of the sample is 48, 48 months.</li> <li>Label mapping: 0 is water body, 1 is woodland, 2 is grassland, 3 is bare soil, 4 is impervious surface, 5 is cropland.</li> <li>For any implementation details, you can refer to the paper or github.</li> </ol> <p><strong>Paper citations:</strong></p> <p>He H, Yan J, Liang D, Sun Z, Li J, Wang L. Time-series land cover change detection using deep learning-based temporal semantic segmentation. Remote Sensing of Environment. 2024, 305:114101.</p>
Scale-dependent effects of urbanization on avian diversity in a Neotropical region
<p>RDA_birds script: This is the script that contains the Redundancy Analysis (RDA), whose database is named: "Rda_birds.txt". RDA is a multivariate statistical technique that combines elements of Principal Component Analysis (PCA) and Multiple Regression to explore the relationships between sets of variables. This technique is often used to analyze data where there are multiple responses (dependent variables) and multiple predictors (independent variables). In this case, we used this statistical technique to explore multivariate relationships between sets of variables, where we explored how local environmental and landscape characteristics (independent variables) can explain patterns in bird species communities (dependent variables).</p> <p>MOBr_birds script: This is the script that contains the Biodiversity Measurement Analysis (Mob), whose database is named: "Mobr_birds". Mob is a multi-scale approach to dissecting the roles of species abundance distribution (SAD), total community abundance and spatial aggregation within species along continuous environmental gradients.</p>
Impact of urbanized atmosphere-land processing to the near-ground distribution of air pollution over Central Liaoning Urban Agglomeration
<pre>------------------------- Content of the dataset ------------------------- ****** the experiment case (EXP) ; the control case (CTR) ******<br> 1. ABLH.rar contains the horizontal spatial distribution data of simulated Atmospheric Boundary Layer Height (ABLH) by CTR and EXP at four typical moments chosen in the paper, which can be utilized for drawing Fig. 8.<br><br>2. TSK.rar contains the horizontal spatial distribution data of simulated near-surface temperature (TSK) by CTR and EXP at four typical moments chosen in the paper, which can be utilized for drawing Fig. 4. <br>3. PM.rar contains the vertical profile data of simulated Particulate Matter concentrations by CTR and EXP during the study period in the paper, which can be utilized for drawing Fig. 11.<br><br>4. Timerseries_data.xlsx contains the observed and simulated data for timeseries of TSK, ABLH, PM and and the vertical profile data of temperature at four typical moments chosen in the paper,which can be utilized for drawing Fig. 3、5、7、9、10、S1-S4.<br><br>5. wrfbiochemi.rar contains the biogenic emissions data for simulation both for CTR and EXP.<br><br>6. wrffirechemi.rar contains the biomass burning emissions data for simulation both for CTR and EXP.<br><br>7. wrfchemi.rar contains the Anthropogenic emissions data for simulation both for CTR and EXP.<br><br>8. The file module_sf_pkuurban.F contains the main code of the improved urban canopy model, the Multi-Scale Urban Canopy Model (MSUCM), which is coupled to WRF-Chem and used by EXP. It is located in the phys/ directory and called by surface_driver.F.<br><br>------------------------- Contact information ------------------------- Zhenxin Liu liuzhenxin@nuist.edu.cn</pre>
2013 - London LUMA KSL SLUM Spectral Library of Urban Materials
<p>2013 - London LUMA KSL SLUM Spectral Library of Urban Materials</p> <ol> <li><strong>Site:</strong> KSL</li> <li>Country\City\Location: UK London King's College London K Strand Campus S Strand Building Lab L <strong>KSL</strong></li> <li>Inside Lab work</li> </ol> <p><strong>Manual/<strong>: https://urban-meteorology-reading.github.io/other%20files/LUMA_SLUM.pdf</strong></strong></p> <p><strong>Spectral Library of Impervious Urban Materials</strong></p> <ol> <li>The radiative response of impervious urban materials is a highly influential surface property, due to its impacts on the radiation balance of incoming and outgoing long- and short-wave fluxes. Information about the material composition can be determined with data resolved to the wavelength level. Spectral reflectance in the visible- to short-wave infrared (VIS-SWIR) region is widely used in remote sensing-based land cover classification and spectral long-wave infrared (LWIR) emissivity is required for the observation of surface temperatures. The Spectral Library of impervious Urban Materials (SLUM) available from the London Urban Micromet data Archive (LUMA) includes LWIR emissivity spectra of 74 samples of impervious surfaces derived using measurements made by a portable Fourier Transform InfraRed (FTIR) spectrometer and matching short-wave reflectance spectra observed for each urban sample.</li> <li>Data are provided in csv format for short-wave reflectance and long-wave emissivity.</li> <li>Documentation contains photos, meta information for all 74 samples and plots of the short-wave reflactance (300-2500 nm) and long-wave (8-14 um) emissivity spectra.</li> <li>Manual: https://urban-meteorology-reading.github.io/other%20files/LUMA_SLUM.pdf</li> <li><strong> Final Data</strong> provided in csv format for:</li> <li>short-wave reflectance: https://urban-meteorology-reading.github.io/other%20files/LUMA_SLUM_SW.csv</li> <li>long-wave emissivity: https://urban-meteorology-reading.github.io/other%20files/LUMA_SLUM_IR.csv</li> <li>Here the raw data are provided.</li> </ol>
Urbanization dynamics of Jammu City in India: An MBRA based approach using Landsat data
<p>This dataset includes the LULC maps developed using the Landsat 8/7 datasets for the years 2002, 2013 and 2021 for the Jammu district in India. The dataset further includes the 1 km interval buffer rings for the multi-buffer ring analysis for retrieving urbanization parameters such as the urban area, urban density and urban growth rate. Additionally, a quality index is derived based on these parameters and an amenity database to compare the status of Jammu and Indore cities. </p>
An explorative study with convenience vegetables in urban Nigeria - The Veg-on-Wheels intervention
<p>This data was collected for the Veg-on-Wheels intervention study, in which ready-to-cook, washed and pre-cut green leafy vegetables (GLV) were kept cool and sold for five weeks at convenient locations near workplaces and on the open market in Akure, Nigeria. Surveys were conducted prior to the intervention with 680 consumers and during the final week of the intervention with 596 consumers near workplaces and 204 consumers at the open market. Both buyers and non-buyers of the intervention were included; 49% buyers in the workplace sample and 47% in the open market sample. It measures vegetables intake, intervention awareness, attitudes and customer satisfaction, motives and barriers for buyers and non-buyers.</p>
Empirical Data, Survey and Letter of Consent of the Study: Brouillet C. et al. "Soil extraction as an adaptation strategy to climate change - a focus on urban ecosystem services"
<ul> <li>Empirical Data (quantitative part of the results), Survey and Letter of Consent</li> <li>From the study entitled "Soil extraction as an adaptation strategy to climate change - a focus on urban ecosystem services" Brouillet C. et al. </li> <li>All documents are in French.</li> </ul>
URBAN LANDSCAPE
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1656262183.6946.jpg">https://4dcity.org/imgupload/1656262183.6946.jpg</a> <br>
Does urbanization drive up housing prices? Novel evidence from remote sensing and dynamic panel quantile regression
<p><strong>Purpose:</strong> This study aims to quantify the influence of urbanization on housing prices at the districtbased level, while also investigating the heterogeneous impacts across different quantiles of housing prices.</p> <p><br><strong>Design/methodology/approach:</strong> The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system Generalized Method of Moments is employed to evaluate how urbanization influences district-based housing prices through three spectrometrics: Urban Index (𝑈𝐼), Normalized Difference Built-up Index (𝑁𝐷𝐵𝐼), and Built-Up Index (𝐵𝑈𝐼). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo Simulation.</p> <p><br><strong>Findings:</strong> The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60𝑡ℎ percentile of housing prices, while its effect on the 10𝑡ℎ and 90𝑡ℎ percentile is comparatively weaker.</p> <p><br><strong>Originality/value:</strong> This study employs a novel method of utilizing remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.<br><br></p>
Urban Traffic Speed Dataset of Guangzhou, China
<p>This is an urban traffic speed dataset which consists of 214 anonymous road segments (mainly consist of urban expressways and arterials) within two months (i.e., 61 days from August 1, 2016 to September 30, 2016) at 10-minute interval, and the speed observations were collected from Guangzhou, China. In practice, it can be used to evaluate several missing data recovery, short-term traffic prediction and traffic pattern discovery methods.</p> <p>According to the spatial and temporal attributes, we can easily derive a third-order tensor as <span class="math-tex">\(\mathcal{X}\in\mathbb{R}^{214\times 61\times 144}\)</span> and its dimensions include road segment, day and time window (see the file <strong>tensor.mat</strong>). The total number of speed observations (or non-zero entries of the tensor <span class="math-tex">\(\mathcal{X}\)</span>) is <span class="math-tex">\(1,855,589\)</span>. If the dataset is complete, then we have <span class="math-tex">\(214\times 61\times 144=1,879,776\)</span> observations, therefore, the original missing rate of this dataset is <span class="math-tex">\(1.29\%\)</span>.</p> <p>Note that the file <strong>traffic_speed_data.csv</strong> is the original traffic speed data with four columns including road segment attribute, day attribute, time window attribute and traffic speed value. The file <strong>day_information_table.csv</strong> is a table referring to the specific date, and the file <strong>time_information_table.csv</strong> is a table expressing time window with start time and end time information.</p>
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.