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856 results for “urban studies”
Supplementary material 1 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
ESRI Shape file of SIMD DataZones
Figure 2 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Figure 2 Sampling of a single data point, postcode EH8 9AJ (orange dot) within Data Zone S01008671 (black outline). The 100, 250 and 500 metre buffer zones are shown in orange. Blue dots are other sampling points. Green shading represents greenness (NDVI).
Figure 4 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Figure 4 Mental health prescriptions vs seasonal difference at 250 m from home for pure-urban Data Zones beanplot (Kampstra 2008). Horizontal lines are individual values. Curves are density of data points at values. Thicker horizontal line is average for group.
Figure 1 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Figure 1 An area of south central Edinburgh illustrating distribution of sample points within Data Zones. Background is OpenStreetMap. Black outlines are DataZone boundaries. Blue dots are small user postcode locations that are used for sampling. Green shading represents summer-greenness (NDVI).
Supplementary material 4 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Results Data Tables
Supplementary material 3 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Small User Postcodes
Adequate vitamin A liver stores estimated by the Modified-Relative Dose Response test are positively associated with breastfeeding but not vitamin A supplementation in Senegalese urban children 9-23 months old: a comparative cross-sectional study
<p><span><span><span><span><span><span><span><span><span><span><span>Vitamin A supplementation (VAS) in 6-59 month old children is recommended but its sustainability is currently questioned. In Senegal, data suggest that VAS should be maintained, but geographic and age-related specificities need to be addressed by gathering more evidence on the impact of this program. The objective of this comparative cross-sectional study, conducted in urban settings of Dakar, was to evaluate the effectiveness of VAS on vitamin A liver stores (VALS) among 9-23 month old children. The supplemented group (n = 119) received VAS (either 100 000 UI or 200 000 UI) 2 to 6 months before evaluation while the non-supplemented group (n = 110) had not received VAS during the past 6 months. VALS were assessed using the modified-relative dose response (MRDR) test. Serum retinol concentrations (SR) and biomarkers of subclinical inflammation were measured. Children's health-related data and feeding patterns were collected. Mean MRDR values (VAS: 0.030 ± 0.017, non-VAS: 0.028 ± 0.016, P = 0.39) and inflammation-adjusted SR (VAS: 1.34 ± 0.37, non-VAS: 1.3 ± 0.35, P = 0.51) of children were adequate. Low prevalence of VALS (VAS: 5.2%, non-VAS: 5.4%) and inflammation-adjusted VAD (VAS: 2.6%, non-VAS: 0.9%) were detected despite high presence of infections and inflammation. Children were mostly still being breastfed (VAS: 85.7%, non-VAS: 77.3%) and complementary feeding indicators were similar in both groups. Only breastfeeding was associated with VALS (adjusted OR = 0.24, 95% CI: 0.07-0.8, P = 0.02). Based on MRDR values, VAS was not related to improved VALS and SR as well as VAD reduction among these children with adequate VALS. Reinforcing breastfeeding advocacy and morbidity prevention/control are essential in this setting. Scaling-back VAS in this subpopulation should be examined regarding the risk of hypervitaminosis A. However, an evaluation of dietary VA intake sufficiency and a more quantitative assessment of VALS should be prior considered.</span></span></span></span></span></span></span></span></span></span></span></p>
Raw data sharing for the study of land usecover change and ecological effects in Lanxi urban agglomeration
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Utilizing GIS and Remote Sensing for Safer Urban Transport: A Case Study of Navi Mumbai, India
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Local plant richness predicts bee abundance and diversity in a study of urban residential yards
<p>Understanding the drivers of biodiversity in cities is a central goal of urban ecology. There is currently intense scientific and public interest in the factors that influence pollinator diversity in cities and their surroundings. Existing studies point to a variety of landscape and local factors as potentially important, including urbanization (often defined as impervious surface cover in the surrounding lands), tree canopy cover and the diversity and abundance of locally flowering plants. However, few studies have sought to weigh the relative importance of these predictors of bee community metrics. Using a set of 27 residential yards chosen to represent a gradient of both urbanization and tree canopy cover at a landscape scale, we used pan trapping and netting to assess the abundance and diversity of local bee communities across the City of Ottawa, Ontario, Canada. Surprisingly, the landscape factors (urbanization and tree cover) described only a tiny fraction (< 1%) of the total variance in bee abundance and diversity across sites. This was true regardless of the scale of analysis at which the landscape factors were measured. Instead, a yard's floral richness, and, to a somewhat lesser extent, its floral abundance, emerged as the most important predictors of a yard's bee community abundance and diversity. Our study offers an important counterpoint to a growing body of work emphasizing the impacts of landscape factors on bee communities. Instead, our research suggests that improving bee floral resources by increasing the plant species richness and abundance locally is a powerful tool to support bee conservation, regardless of the level of urbanization or tree cover in the surrounding landscape. Our work highlights that the practice of promoting 'bee-friendly' plantings in private yards, currently being undertaken by a number of non-profits around the world, can play an important role in restoring and maintaining urban pollinator communities.</p>
Urban Ventilation Planning and Its Associated Benefits based on Numerical Experiments : A case study in Beijing, China
<p>dataset for Figures 1-7</p>
Figure 7 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 7. Comparison WF and WF green of WF crop of the Van province with the Upper Tigris River Basin (Muratoglu, 2019), the worldwide blue average (Mekonnen and Hoekstra, 2011b) and the Turkish average (Mekonnen and Hoekstra, 2011a).
Supplementary material 1 from: Balzan MV, Debono I (2018) Assessing urban recreation ecosystem services through the use of geocache visitation and preference data: a case-study from an urbanised island environment. One Ecosystem 3: e24490. https://doi.org/10.3897/oneeco.3.e24490
Supplementary Information
"A Study to Assess Knowledge and Opinion Regarding Adoption Among Adults Residing in Rural and Urban Area"
ClinicalTrials.gov study NCT03236402. IPD Sharing: NO. Countries: 0. Publications: 1.
Data from: Cardiovascular disease risk factors in Ghana during the rural-to-urban transition: a cross-sectional study
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Local plant richness predicts bee abundance and diversity in a study of urban residential yards
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Adequate vitamin A liver stores estimated by the Modified-Relative Dose Response test are positively associated with breastfeeding but not vitamin A supplementation in Senegalese urban children 9-23 months old: a comparative cross-sectional study
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A Multi-Centric Novel Coronavirus (COVID-19) Population-Based Age-Stratified Sero-Epidemiological Study (In Urban, Rural and Tribal Communities at Selected Centers in India: A Prospective Study)
<p>Results of population-based age stratified seroepidemiological investigation in India</p>
Figure 5. Per capita water footprint between 2004-2019 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 5. Per capita water footprint between 2004-2019 in Van province.
Study to Investigate the Causes of Fever in Children Living in Urban Dar es Salaam and Rural Ifakara
ClinicalTrials.gov study NCT01812785. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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.