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51 results for “Urban green space”
Supplementary material 4 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 4
Supplementary material 8 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 8
Supplementary material 1 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 1
Supplementary material 3 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 3
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>
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>
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>
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.