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78 results for “Green spaces”
Supplementary material 7 from: Stanley CH, Hecht R, Cakir S, Brzoska P (2022) Approach to user group-specific assessment of urban green spaces for a more equitable supply exemplified by the elderly population. One Ecosystem 7: e83325. https://doi.org/10.3897/oneeco.7.e83325
Supplementary material 7
The boreal soil microbiome of different urban green spaces
<p>This study describes the soil microbiome in relation to the aboveground plant diversity of three types of urban green spaces: built green spaces with sown lawns, planted trees and ornamental flowerbeds, open green spaces such as meadows and pastures, and urban forests. </p> <p>Data includes information on study sites, soil physio-chemical properties, vegetation, and soil microbiome: Plot information, Soil properties, Vegetation, Meta includes column information in sheets plot and soil. In vegetation sheet column names are species. Explanations for species name abbreviations can be found in sheet meta2. Micoboal commnity composition and taxonomy in Bacteria_nmds and Fungi_nmds. </p>
Data from: Woodpeckers in the city: abundances are highest in large green spaces with complex understories
<p>We documented occurrence and relative abundance (henceforth abundance) of five woodpecker species in 36 forested green spaces in Portland, Oregon (USA), to estimate minimum area requirements (MAR; 0.5 probability of occurrence) and to test for potential influences of green space area and type (heavily treed manicured green spaces versus undeveloped natural forests), habitat features, and landscape composition on the abundance of woodpeckers. Abundances of all species increased with green space area. Manicured and natural green spaces did not differ in canopy cover or total basal area, but woodpecker abundances were greater in natural than manicured green spaces for all species except Northern Flickers (<em>Colaptes</em> <em>auratus</em>). Flickers and Downy Woodpeckers (<em>Picoides</em> <em>pubescens</em>) were most abundant; MAR of both were under 1.5 ha, and both were found in essentially all green spaces. Hairy (<em>P. villosus</em>) and Pileated (<em>Dryocopus</em> <em>pileatus</em>) woodpeckers were least abundant, and with MAR of 34 and 51 ha, respectively, exhibited pronounced area-sensitivity. Red-breasted Sapsuckers (<em>Sphyrapicus</em> <em>ruber</em>) were intermediate in abundance and MAR to the two former and two latter species. The most important habitat variables associated with variation in abundance among green spaces were the contribution of deciduous trees to the canopy (Downy Woodpecker [+], Pileated Woodpecker [+] and Northern Flicker [-]), canopy cover (Hairy Woodpecker [+] and Northern Flicker [+]), and log density (Hairy Woodpecker [+], Red-breasted Sapsucker [-]). At the landscape level, lower woodpecker abundance was associated with increased urbanization (Red-breasted Sapsucker and Northern Flicker), while increasing forest cover influenced abundance of Northern Flicker (-) and Pileated Woodpecker (+). Highest woodpecker abundances were thus found in large green spaces with complex understories that were embedded in relatively undeveloped treed landscapes. Management of forested urban green spaces should prioritize needs of the area-sensitive species because ensuring their continued presence benefits the entire avian community.</p>
Questionnaire data for assessing what determines urban residents' engagement in activities for the protection of urban green spaces (UGS)
<p>This dataset contains the core data used to investigate what determines Munich’s residents’ engagement in activities for the protection of urban green spaces (UGS). We conducted an exploratory factor analysis and a structural equation modelling based on the data from an online and in-person questionnaire.</p> <p><strong>List of data and content</strong></p> <ol> <li>Questionnaire_English: The content of the questionnaire in English (*.pdf)</li> <li>Questionnaire_RawData_ Munchen_German: Core data from the questionnaire in German (*.xlsx)</li> <li>InputEFA1: Core data for the first exploratory factor analysis with the input for analysis in Mplus (*.xlsx)</li> <li>InputEFA2: Core data for the second exploratory factor analysis with the input for analysis in Mplus (*.xlsx)</li> <li>InputSEM: Core data for the structural equation modelling with the input for analysis in Mplus (*.xlsx)</li> <li>ResultsSEM: Results of the structural equation modelling (*.xlsx)</li> <li>InputCFA1: Core data for the first confirmatory factor analysis with the input for analysis in Mplus (*.xlsx)</li> <li>ResultsCFA1: Results of the confirmatory factor analysis (*.xlsx)</li> <li>InputCFA2: Core data for the second confirmatory factor analysis with the input for analysis in Mplus (*.xlsx)</li> <li>ResultsCFA2: Results of the confirmatory factor analysis (*.xlsx)</li> <li>Acronyms_Shortings: Acronyms and shortings used in the uploaded files (*.pdf)</li> </ol> <p><strong>Data processing</strong></p> <p>The software for performing the exploratory factor analysis and structural equation modelling was Mplus 8.8 (Muthén and Muthén, 1998). The details on the methodological steps are available in the published publication. To quickly understand the <a href="https://www.statmodel.com/HTML_UG/chapter4V8.htm">EFA</a>,<a href="https://www.statmodel.com/download/usersguide/Chapter5.pdf"> CFA and SEM model and settings</a> the details are provided in the hyperlinks.</p> <p><strong>Acknowledgments</strong></p> <p>The authors thank to all participants for their participation in the online and in-person questionnaires. Data processing and analysis would not have been possible without the help of Tomomi Saito.</p> <p>This work was supported by a grant from the Alexander von Humboldt Foundation and by the Leibniz Best Minds Competition, Leibniz-Junior Research Group, Grant J76/2019.</p>
Codes, raw predation data on dummy caterpillars, and environmental data about urban green spaces across 26 Brazilian state capitals
<p>Data collected by the Urban Predation Risk Network about predation pressure on dummy caterpillars exposed for eight days in 26 small green spaces and 24 forest patches across 26 Brazilian state capitals. The raw predation data describes the type of predator and the day in which each caterpillar was found with marks, as well as the sites' coordinates. The environmental data provides information for each site about precipitation, annual mean temperature, temperature seasonality, elevation, and coordinates. This database also provides summarized information about predation at each site, such as the total number of attacked caterpillars and the number of attacked caterpillars by each predator group. The data files contain a metadata sheet with a description of each column.</p> <p>The code provided was used to assess the local and large-scale drivers of predation pressure on dummy caterpillars in small green spaces and forest patches across 26 Brazilian cities.</p>
The Emotional, Physical and Cognitive Benefits of Purposeful Green Space Activities on Seniors
ClinicalTrials.gov study NCT04913363. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Woodpeckers in the city: abundances are highest in large green spaces with complex understories
Open the record for dataset details and reuse information.
Data from: What determines how we see nature? Perceptions of naturalness in designed urban green spaces
Open the record for dataset details and reuse information.
Keep it real: Selecting realistic sets of urban green space indicators - Supplementary data
<p>This excel sheet contains, for each of the four studied cities, the conceptual framework that is described in the paper "Keep it real: Selecting realistic sets of urban green space indicators".</p> <p>Each of the cities first listed all possible indicators that they could think of. Next, they indicated how each of those indicators relates to each of the KPI; i.e. whether the indicator can not at all (0), somewhat (1) or perfectly measure (2) the KPI. Lastly, the city authorities indicated whether the indicators are implemented of not, and scored some measures of indicator quality (relevance, feasibility, clarity, and credibility)</p>
Datasets used in: Modelling eye-level visibility of urban green space: Optimising city-wide point-based viewshed computations through prototyping
<p>Research data supporting our publication. Full workflows using the R programming language have been provided on <a href="https://github.com/STBrinkmann/protoVS">GitHub</a>. Here we provide external data that has been used for our research, as well as the resulting Viewshed Greenness Visibility Index (VGVI) raster.</p> <p><strong>Datasets</strong></p> <p>Digital Terrain Model (DTM):</p> <ul> <li>Spatial Resolution: 1 m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DTM_1m.tif<br> </li> </ul> <p>Digital Surface Model (DSM):</p> <ul> <li>Spatial Resolution: 1 m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DSM_1m.tif<br> </li> </ul> <p>Landuse</p> <ul> <li>Spatial Resolution: 2 m</li> <li>Source: Land Cover Classification 2014 - 2m LiDAR</li> <li>Licence: <a href="http://www.metrovancouver.org/data">Metro Vancouver</a></li> <li>File name: Vancouver_LULC_2m.tif<br> </li> </ul> <p>VGVI map</p> <ul> <li>Spatial Resolution: 5 m</li> <li>Source: Resulting dataset from our analysis</li> <li>Licence: MIT License</li> <li>File name: vgvi_van.tif</li> </ul>
Blue-green Space Physical Activity: the "Parco Del Mare" Study
ClinicalTrials.gov study NCT06533943. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Impact of Green Spaces on the General Well-being and Stress of Students
ClinicalTrials.gov study NCT05249296. IPD Sharing: NO. Countries: 1. Publications: 0.
Work in Green Spaces and Musculoskeletal Disorders
ClinicalTrials.gov study NCT06194292. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Physiological and Mood Changes After Walking in Green Spaces. A Comparison of Environments.
ClinicalTrials.gov study NCT07114146. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
'Space for a Green Future Accelerator' session
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1665393213.9262.jpg">https://4dcity.org/imgupload/1665393213.9262.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/52184742730_e8e9b028b5_m.jpg">https://live.staticflickr.com/65535/52184742730_e8e9b028b5_m.jpg</a> <br><br><u>Image-Metadata:</u><br>Filename: 1665393213.9262.jpg<br>Image Dimensions: 240x160<br>Megapixels: 0.04 MP<br>Filesize: 26.90 KB<br><br>Copyright: MAI_<br>ExifOffset: 62<br>Artist: MAI_
Spatial characterization of microbial communities on multi-species leafy greens grown simultaneously in the Veggie vegetable production systems on the International Space Station VEG-03D
Investigates the microbial communities of plants and Veggie pillow components with culturable and non-culturable methods providing qualitative and quantitative data. This studies also stresses the food safety component of fresh crops grown on the ISS. The VEG-03D technology demonstration was completed achieving simultaneous multi-species plant growth within a Veggie unit on the International Space Station (ISS). Microbiological characterization using molecular and culture-based methods was performed on leaves and roots from two harvests of three leafy greens, red romaine lettuce (Lactuca sativa cv. ‘Outredgeous’); mizuna mustard, (Brassica rapa var japonica); and green leaf lettuce, (Lactuca sativa cv. Waldmann’s) and associated rooting pillow components and Veggie chamber surfaces. Culture based enumeration and pathogen screening indicated the leafy greens were safe for consumption. Surface samples of the Veggie facility and plant pillows revealed low counts of bacteria and fungi and are commonly isolated on ISS. Community analysis was completed with 16S rRNA amplicon sequencing. Comparisons between pillow components, and plant tissue types from VEG-03D, E, and F revealed higher diversity in roots and rooting substrate than the leaves and wick. This work provides valuable information for food production-related research on the ISS and the impact of the plant microbiome on this unique closed environment.
Spatial characterization of microbial communities on multi-species leafy greens grown simultaneously in the Veggie vegetable production systems on the International Space Station VEG-03F
Investigates the microbial communities of plants and Veggie pillow components with culturable and non-culturable methods providing qualitative and quantitative data. This studies also stresses the food safety component of fresh crops grown on the ISS. The VEG-03F technology demonstration took place within a Veggie unit on the International Space Station (ISS). Microbiological characterization using molecular methods was performed on leaves and roots from one harvest of red romaine lettuce (Lactuca sativa cv. ‘Outredgeous’) and associated rooting pillow components and Veggie chamber surfaces. Culture based enumeration and pathogen screening indicated the leafy greens were safe for consumption. Surface samples of the Veggie facility and plant pillows revealed low counts of bacteria and fungi and are commonly isolated on ISS. Community analysis was completed with 16S rRNA amplicon sequencing. Comparisons between pillow components, and plant tissue types from VEG-03D, E, and F revealed higher diversity in roots and rooting substrate than the leaves and wick. This work provides valuable information for food production-related research on the ISS and the impact of the plant microbiome on this unique closed environment.
Spatial characterization of microbial communities on multi-species leafy greens grown simultaneously in the Veggie vegetable production systems on the International Space Station VEG-03E
Investigates the microbial communities of plants and Veggie pillow components with culturable and non-culturable methods providing qualitative and quantitative data. This studies also stresses the food safety component of fresh crops grown on the ISS. The VEG-03E technology demonstration was completed within a Veggie unit on the International Space Station (ISS). Microbiological characterization using molecular and culture-based methods was performed on leaves and roots from two harvests of green leaf lettuce, (Lactuca sativa cv. Waldmann’s) and associated rooting pillow components and Veggie chamber surfaces. Culture based enumeration and pathogen screening indicated the leafy greens were safe for consumption. Surface samples of the Veggie facility and plant pillows revealed low counts of bacteria and fungi and are commonly isolated on ISS. Community analysis was completed with 16S rRNA amplicon sequencing. Comparisons between pillow components, and plant tissue types from VEG-03D, E, and F revealed higher diversity in roots and rooting substrate than the leaves and wick. This work provides valuable information for food production-related research on the ISS and the impact of the plant microbiome on this unique closed environment.
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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)
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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.