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138 results for “Urban ecosystems”
Supplementary material 2 from: Wübbelmann T, Bouwer LM, Förster K, Bender S, Burkhard B (2022) Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosystem 7: e87458. https://doi.org/10.3897/oneeco.7.e87458
Table of the scaled Flood Regulating Ecosystem Services indicators and categories
Supplementary material 1 from: Wübbelmann T, Bouwer LM, Förster K, Bender S, Burkhard B (2022) Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosystem 7: e87458. https://doi.org/10.3897/oneeco.7.e87458
Ratio of FRES supply and flood hazard
Supplementary material 1 from: Sprondel N, Donner J, Mahlkow N, Köppel J (2016) Urban climate and heat stress: how likely is the implementation of adaptation measures in mid-latitude cities? The case of façade greening analyzed with Bayesian networks. One Ecosystem 1: e9280. https://doi.org/10.3897/oneeco.1.e9280
Questionnaire for Bayesian network analysis
Supplementary material 4 from: Nedkov S, Zhiyanski M, Dimitrov S, Borisova B, Popov A, Ihtimanski I, Yaneva R, Nikolov P, Bratanova-Doncheva S (2017) Mapping and assessment of urban ecosystem condition and services using integrated index of spatial structure. One Ecosystem 2: e14499. https://doi.org/10.3897/oneeco.2.e14499
Contains descriptions of urban ecosystem subtypes and their relation to EUNIS habitat classess
Supplementary material 3 from: Nedkov S, Zhiyanski M, Dimitrov S, Borisova B, Popov A, Ihtimanski I, Yaneva R, Nikolov P, Bratanova-Doncheva S (2017) Mapping and assessment of urban ecosystem condition and services using integrated index of spatial structure. One Ecosystem 2: e14499. https://doi.org/10.3897/oneeco.2.e14499
Map of urban ecosystem condition representing an example of map sheets that cover the whole country
Thessaloniki's urban waterfront: decision-making and governmental aspects for adding metropolitan ecosystem functions
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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
Supplementary material 1 from: Barton DN (2023) Value 'generalisation' in ecosystem accounting - using Bayesian networks to infer the asset value of regulating services for urban trees in Oslo. One Ecosystem 8: e85021. https://doi.org/10.3897/oneeco.8.e85021
Bayesian Belief Network
Dataset on 'Green in grey: ecosystem services and disservices perceptions from small-scale green infrastructure along a rural-urban gradient in Bengaluru, India'
<p>This dataset includes the raw data of a survey of 649 residents of 61 villages along a rural-urban gradient in Bengaluru, India. It presents socio-demographic characteristics (village, village rural-urban state, age, gender, level of education, caste, and sources of household income) and Likert-scale answers (ranging from 1, i.e., not important to 5, i.e., very important) to rank the perceived importance of ecosystem services and disservice from five types of small-scale green infrastructure: Domestic trees (DT), Farm trees (FT), Street trees (ST), Platform trees (PT), Temple trees (TT).</p> <p>The complete method is described in Thapa, P., Torralba, M., Bhaskar, D., Nagendra, H., Plieninger, T. (2023): Green in grey: ecosystem services and disservices perceptions from small-scale green infrastructure along a rural-urban gradient in Bengaluru, India. Ecosystems and People, in press.</p>
Hierarchical Bayesian scaling of soil properties across urban, agricultural, and desert ecosystems in central Arizona-Phoenix
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Figure 1 from: Lapin K, Bindewald A, Kraxner F, Marinšek A, La Porta N, Hazarika R, Chakraborty D, Oettel J, Berger F, Detry P, Kolesaric G, Baric D, Abluton S, Ulrici G, Georges I, Schweinzer KM, Schüler S (2020) A transnational cooperation for sustainable use and management of non-native trees in urban, peri-urban and forest ecosystems in the Alpine region (ALPTREES). Research Ideas and Outcomes 6: e53038. https://doi.org/10.3897/rio.6.e53038
Figure 1 ALPTREES Regions.
Dataset accompanying Korányi et al. 2021 Urban Ecosystems
<p>Dataset containing species abundance of birds and environmental background data and Google Earth file showing bird survey points. For details please see the original publication:<br> Korányi, D., Gallé, R., Donkó, B., Chamberlain, D.E. & Batáry, P. (2021). Urbanization does not affect green space bird species richness in a mid-sized city. Urban Ecosystems. DOI: 10.1007/s11252-020-01083-2</p>
Examining the distributional equity of urban tree canopy cover and ecosystem services across United States cities - PLOS ONE Data
<p>Shapefile data used in the analysis for each of the 9 cities examined (New York, Philadelphia, Washington, Cleveland, Pittsburgh, Chicago, Los Angeles, San Diego, Sacramento). Datasets provided have been trimmed following the procedures outlined in the Methods and represent the data used in the analysis.</p>
FIGURE 5 in Long-Term Avifaunal Survey In An Urban Ecosystem From Southeastern Brazil, With Comments On Range Extensions, New And Disappearing Species
FIGURE 5: Distribution of bird species according to habitat use and patterns of movements and dispersal on the campus of the Pontifícia Universidade Católica de Minas Gerais, Belo Horizonte, Minas Gerais, southeastern Brazil.
FIGURE 2 in Long-Term Avifaunal Survey In An Urban Ecosystem From Southeastern Brazil, With Comments On Range Extensions, New And Disappearing Species
FIGURE 2: Aerial photographs of the campus of the Pontifícia Universidade Católica de Minas Gerais in 1960 (left) and 2003 (right). Red arrows indicate the forest fragment (PUC forest).
FIGURE 1 in Long-Term Avifaunal Survey In An Urban Ecosystem From Southeastern Brazil, With Comments On Range Extensions, New And Disappearing Species
FIGURE 1: Map showing the study area of the campus of the Pontifícia Universidade Católica de Minas Gerais (yellow) in the municipality of Belo Horizonte, Minas Gerais, southeastern Brazil. The municipality is in a transitional zone between the Atlantic Forest and the Cerrado.
FIGURE 3 in Long-Term Avifaunal Survey In An Urban Ecosystem From Southeastern Brazil, With Comments On Range Extensions, New And Disappearing Species
FIGURE 3: Saffron-billed Sparrow (Arremon flavirostris) mistnetted in the PUC forest. Photo: J.E.M. Dias.
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>
ScienceDex guides
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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.