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Fig. 1 in Discoderus LeConte Species Exhibit Episodic Emergences in both Urban and Natural Environments of Arizona, USA (Coleoptera: Carabidae)
Fig. 1. Discoderus obsidianus emergence in 2022. A) Dorsal habitus, female from Mesa, AZ (scale bar 5 mm), B) Accumulation of suspected D. obsidianus in Scottsdale, AZ on a front porch over a single night.
Fig. 2 in Discoderus LeConte Species Exhibit Episodic Emergences in both Urban and Natural Environments of Arizona, USA (Coleoptera: Carabidae)
Fig. 2. Distribution map of Discoderus obsidianus. Localities based on digital records from SCAN (circles), GBIF (triangles), BugGuide (diamonds), and physical specimens from this event (squares). The Texas record is an outlier for which the identification was not confirmed by the authors.
Anonymised human location data for urban mobility research
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Pregnancy-induced hypertension and its associated risk factors among pregnant garment workers attending an urban health center in Bangladesh
<p><strong><span>ABSTRACT</span></strong></p> <p><strong><span>Background: </span></strong><span>Pregnancy-induced hypertension (PIH) can lead to severe morbidity, long-term disability, and mortality among mothers and their infants<strong>. </strong>PIH is increasing worldwide, disproportionately affecting more mothers in developing countries. <span> </span><span>This study aims to determine the prevalence of PIH and its associated risk factors among pregnant garment workers attending an urban health center in Bangladesh</span>.</span></p> <p><strong><span>Methods:</span></strong><span> This cross-sectional study was conducted among pregnant garment workers at ≥20 weeks of gestation, who visited a health center of a garment in Narayanganj, Bangladesh between July and October 2023. <span>Data on socio-demographics, clinical characteristics including body weight and blood pressure, and family history of chronic diseases were gathered. </span>Logistic regression was done to calculate crude and adjusted odds ratios (OR) to identify factors associated with PIH.</span></p> <p><strong><span>Result:</span></strong><span><span> </span><span>With an average age of 27.3 years (standard deviation 3.2) and nearly two-thirds of the 150 participants having completed secondary education or higher, the mean gestational age was 21.7 weeks. One out of ten had a family history of high blood pressure and 2% had a family history of PIH. The overall prevalence of PIH was 7.3%. Higher risk for PIH was linked to family history of PIH (aOR 18.1; 95% CI 1.3, 251.3). </span></span></p> <p><strong><span>Conclusion:</span></strong><span> <span>The study identified that one out of every 14 pregnant garment workers had PIH. The findings highlight that a family history of PIH significantly increases the likelihood of developing PIH in this population. Therefore, pregnant women with this risk factor should be prioritised by health care professionals. </span></span></p>
Data from Demonstration of quantum network protocols over a 14-km urban fiber link
<p>Datasets used to create all plots and results found in the paper "Data from Demonstration of quantum network protocols over a 14-km urban fiber link" from AG Eschner, Saarland University</p>
Data Used in Manuscript Entitled "Measurement Report: Urban Ammonia and Amines in Houston, Texas"
<p>Data for manuscript: L. Tiszenkel, J. Flynn, S. H. Lee, Measurement Report: Urban Ammonia and Amines in Houston, Texas.</p> <p>Mansucript has been accepted for publication in Atmospheric Chemistry and Physics</p> <p>Preprint and discussion are available at https://doi.org/10.5194/egusphere-2024-1230</p>
FIGURE 1 in Phylloporia minuta sp. nov. (Basidiomycota, Hymenochaetales): a remarkable species discovered in a small protected urban area of Atlantic Forest
FIGURE 1. Best-scored tree from the ML analyses, showing the phylogenetic relationships of Phylloporia species inferred from 28S sequences. The tree was rooted with Inonotus cuticularis and I. hispidus. New species is highlighted in bold. Thickened branches represents bootstrap (BS) and posterior probability (PP) support values greater than or equal to 75% or 0.95, respectively. Support values are shown above those branches (BS/PP).
FIGURE 2. Phylloporia minuta. A in Phylloporia minuta sp. nov. (Basidiomycota, Hymenochaetales): a remarkable species discovered in a small protected urban area of Atlantic Forest
FIGURE 2. Phylloporia minuta. A. Basidioma in situ indicated by a white arrow growing on Doliocarpus schottianus (FLOR 63064 –paratype); B. Detail of the sulcate surface of the basidioma (FLOR 63063–paratype); C. Minute dried basidiomata, showing the hymenophore (SP–paratype); D. Detail of the basidiomata, showing the lower context and the upper tomentum separated by a thin black line indicated by a white arrow (FURB 55088–holotype); D. Generative hyphae from the hymenophoral trama (SP –paratype); E. Mass of collapsed basidiospores, observed in some specimens (SP–paratype); F. Basidiospores (FURB 55088–holotype). Bars: A, B = 10 mm; C, D = 2 mm, E, F = 20 μm. Photos by F. Bittencourt.
FIGURES 8–11. 8. Cosmarium rostafinskii. 9. C. benedictum. 10. C. amstelodamense. 11. C in Some new and interesting desmids (Streptophyta, Desmidiales) from ephemeral puddles in the urban and industrial areas of Amsterdam (Netherlands)
FIGURES 8–11. 8. Cosmarium rostafinskii. 9. C. benedictum. 10. C. amstelodamense. 11. C. benedictum, zygote. Scalebar = 10μm.
FIGURES 12–18. 12–14. Cosmarium notabile. 15–16. C. galeatum. 17. C. fruticosum. 18. C in Some new and interesting desmids (Streptophyta, Desmidiales) from ephemeral puddles in the urban and industrial areas of Amsterdam (Netherlands)
FIGURES 12–18. 12–14. Cosmarium notabile. 15–16. C. galeatum. 17. C. fruticosum. 18. C. scutiforme. Scalebar = 10μm.
Supplemental Material for "Urban blue spaces and human health: A systematic review and meta-analysis of quantitative studies"
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Association Between Air Pollution Exposure and Respiratory Function Decline in Urban Adults: A Cross-Sectional Study
<p>In our cross-sectional study examining the association between air pollution exposure and respiratory function decline in urban adults, we found compelling evidence of a significant relationship. Over a span of six months, we assessed respiratory function using spirometry measurements among 500 participants living in densely populated urban areas with varying levels of air pollution. Our results indicate a clear correlation between higher levels of particulate matter and nitrogen dioxide exposure and decreased lung function parameters, such as forced expiratory volume in one second (FEV1) and forced vital capacity (FVC). These findings underscore the critical impact of urban air quality on respiratory health, emphasizing the need for stringent air quality regulations and public health interventions to mitigate the adverse effects of air pollution on urban populations' respiratory well-being.</p>
Montreal climate data for building simulations with urban heat island effects and nature-based solutions
<p>As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Montreal airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.</p> <p>This dataset contains hourly historical and future weather files for use in building simulations for the city of Montreal, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. </p> <p>The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. </p> <p> </p> <p>The historical and future global warming thresholds and their corresponding time periods are as follows:</p> <table> <tbody> <tr> <td> <p><strong>Global Warming Scenario</strong></p> </td> <td> <p><strong>Time Period</strong></p> </td> </tr> <tr> <td> <p><strong>Historical</strong></p> </td> <td> <p>1991-2021</p> </td> </tr> <tr> <td> <p><strong>Global Warming 0.5ºC</strong></p> </td> <td> <p>2003-2033</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.0ºC</strong></p> </td> <td> <p>2014-2044</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.5ºC</strong></p> </td> <td> <p>2024-2054</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.0ºC</strong></p> </td> <td> <p>2034-2064</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.5ºC</strong></p> </td> <td> <p>2042-2072</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.0ºC</strong></p> </td> <td> <p>2051-2081</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.5ºC</strong></p> </td> <td> <p>2064-2094</p> </td> </tr> </tbody> </table> <p> </p> <p>The following variables are included in the files:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>RUN</strong></td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td><strong>YEAR</strong></td> <td>Year associated with the record</td> </tr> <tr> <td><strong>MONTH</strong></td> <td>Month associated with the record</td> </tr> <tr> <td><strong>DAY</strong></td> <td>Day of the month associated with the record</td> </tr> <tr> <td><strong>HOUR</strong></td> <td>Hour associated with the record</td> </tr> <tr> <td><strong>YDAY</strong></td> <td>Day of the year associated with the record</td> </tr> <tr> <td><strong>DRI_kJPerM2</strong></td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DHI_kJperM2</strong></td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DNI_kJperM2</strong></td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the <em>HOUR</em> indicated)</td> </tr> <tr> <td><strong>GHI_kJperM2</strong></td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>TCC_Percent</strong></td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td><strong>RAIN_Mm</strong></td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>WDIR_ClockwiseDegFromNorth</strong></td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td><strong>WSP_MPerSec</strong></td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td><strong>RHUM_Percent</strong></td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td><strong>TEMP_K</strong></td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td><strong>ATMPR_Pa</strong></td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td><strong>SnowC_Yes1No0 </strong></td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td><strong>SNWD_Cm</strong></td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>
Toronto climate data for building simulations with urban heat island effects and nature-based solutions
<p>As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.</p> <p>This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. </p> <p>The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. </p> <p> </p> <p>The historical and future global warming thresholds and their corresponding time periods are as follows:</p> <table> <tbody> <tr> <td> <p><strong>Global Warming Scenario</strong></p> </td> <td> <p><strong>Time Period</strong></p> </td> </tr> <tr> <td> <p><strong>Historical</strong></p> </td> <td> <p>1991-2021</p> </td> </tr> <tr> <td> <p><strong>Global Warming 0.5ºC</strong></p> </td> <td> <p>2003-2033</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.0ºC</strong></p> </td> <td> <p>2014-2044</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.5ºC</strong></p> </td> <td> <p>2024-2054</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.0ºC</strong></p> </td> <td> <p>2034-2064</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.5ºC</strong></p> </td> <td> <p>2042-2072</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.0ºC</strong></p> </td> <td> <p>2051-2081</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.5ºC</strong></p> </td> <td> <p>2064-2094</p> </td> </tr> </tbody> </table> <p> </p> <p>The following variables are included in the files:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>RUN</strong></td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td><strong>YEAR</strong></td> <td>Year associated with the record</td> </tr> <tr> <td><strong>MONTH</strong></td> <td>Month associated with the record</td> </tr> <tr> <td><strong>DAY</strong></td> <td>Day of the month associated with the record</td> </tr> <tr> <td><strong>HOUR</strong></td> <td>Hour associated with the record</td> </tr> <tr> <td><strong>YDAY</strong></td> <td>Day of the year associated with the record</td> </tr> <tr> <td><strong>DRI_kJPerM2</strong></td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DHI_kJperM2</strong></td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DNI_kJperM2</strong></td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the <em>HOUR</em> indicated)</td> </tr> <tr> <td><strong>GHI_kJperM2</strong></td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>TCC_Percent</strong></td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td><strong>RAIN_Mm</strong></td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>WDIR_ClockwiseDegFromNorth</strong></td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td><strong>WSP_MPerSec</strong></td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td><strong>RHUM_Percent</strong></td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td><strong>TEMP_K</strong></td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td><strong>ATMPR_Pa</strong></td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td><strong>SnowC_Yes1No0 </strong></td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td><strong>SNWD_Cm</strong></td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>
Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning
<p>Here is the data and original code for the paper titled <em>Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning</em>. If you have any questions, please contact <a rel="noopener">202331470015@mail.bnu.edu.cn</a>.</p>
Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning
<p>Here are the relevant data and preliminary code for the paper titled 'Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning' for your reference. If you have any questions, please contact <a rel="noopener">202331470015@mail.bnu.edu.cn</a>.</p>
Assessment of carbon stocks and sequestration potential of urban bio- park under arid climatic conditions
<p>Assessment of carbon stocks and sequestration potential of urban bio- <br>park under arid climatic conditions </p>
R Data for CPBS Report 23SDSU01 - Urban Demographic Shift of Pedestrian and Bicyclist Collisions, Equity, and Police Enforcement
<p>Data for the statistical program R.</p>
Changing bird migration patterns have potential to enhance dispersal of alien plants from urban centres
<p>In this version I have uploaded the revised code and a list of plant species following reviewer comments in the revision process for the MS.</p>
Determinants of Agricultural Technology Adoption in Urban Vegetable Production: A Case Study of Chelencko and Kullubi Cities
Open the record for dataset details and reuse information.
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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.