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129 results for “urban greening”
Coweeta site, station Greene County, TN (FIPS 47059), study of percent urban population in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Coweeta (CWT) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.
Coweeta site, station Greene County, TN (FIPS 47059), study of population (urban) in units of number on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Coweeta (CWT) contains population (urban) measurements in number units and were aggregated to a yearly timescale.
Harvard Forest site, station Greene County, NY (FIPS 36039), study of percent urban population in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.
Harvard Forest site, station Greene County, NY (FIPS 36039), study of population (urban) in units of number on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains population (urban) measurements in number units and were aggregated to a yearly timescale.
Crowdsourcing public perceptions of urban green space quality: A case study of Rembrandt park in Amsterdam
<p><strong><em>City-dwellers are realizing the benefits of green spaces and are flocking to urban parks. City planners face the challenge of ensuring that urban green spaces are functional for all citizens. To make informed choices they need the right information and that is where the Mijn Park app can help. </em></strong></p> <p>Research shows that when considering the social functions of urban green spaces, quality is just as important as quantity. It is easy enough to map how much green spaces there are, but how do we measure their quality? How do city planners ensure that the city’s green areas are attractive, accessible and inclusive – for everyone? The Vrije Universiteit Amsterdam in collaboration with the International Institute for Applied Systems Analysis developed Mijn Park, a mobile application that will help city planners do just that. As part of the LandSense Citizen Observatory, the ‘Mijn Park’ (My Park) app asks respondents to go to several locations in a park and give subjective responses to those locations. They are then further questioned about how they use the whole park and how much they would like to see certain changes made in the park. This information provides information that can help to inform decisions about any renovations or improvements to the park.</p> <p>A pilot campaign was conducted in the summer of 2018 in Rembrandt park in Amsterdam and insights from the citizen-driven observations were shared with the Department of Planning and Sustainability of Amsterdam.</p> <p>This dataset includes responses and photographs collected by 129 unique volunteers providing 377 observations in select locations across Rembrandt Park. The following files are available:</p> <ul> <li>_Preview MijnPark-Amsterdam-LandSense.png</li> <li>Attributes-MijnPark-Amsterdam-LandSense.csv</li> <li>MijnPark-Amsterdam-LandSense.csv</li> <li>MijnPark-Amsterdam-LandSense.geoJSON</li> <li>README.txt</li> </ul> <p> </p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://www.vu.nl/">Vrije Universiteit</a> (VU), Amsterdam and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a> (IIASA).</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
Exploring and promoting green urban spaces in Vienna - can data about public perception help drive change?
<p>Citizen Science has become a vital source for data collection when the spatial and temporal extent of a project makes it too expensive to send experts into the field. However, involving citizens can go further than that – participatory projects focusing on subjective parameters can fill in the gap between local community needs and stakeholder approaches to tackle key social and environmental issues.</p> <p>The Horizon 2020 project, <a href="https://landsense.eu/">LandSense</a>, is building a modern citizen observatory for Land Use & Land Cover (LULC) monitoring, by engaging citizens to transform current approaches to environmental decision making. Citizen Observatories are community-driven mechanisms to complement existing environmental monitoring systems and can be fostered through mobile and web applications, allowing citizens to play a key role in environmental monitoring. Within this project, the City Oases mobile application, focused on the city of Vienna, has been developed that aims not only to stimulate civic engagement to monitor changes within the urban environment, but also to enable users to drive improvements by providing city planners with information about the public perception of urban spaces. <a href="https://play.google.com/store/apps/details?id=com.iiasa.cityoases">City Oases</a> was launched in March 2019.</p> <p>Where are the best places for a romantic date? Where can you skate? Where is it cool on a hot summer’s day? Open urban spaces can be used in many ways. Pick an activity in the CityOases app and we show the spots where you can do them, including the rating of previous users and pictures from the location. If you visit the spot you can rate it as well based on a few selected subjective criteria. If you know a cool spot that is openly accessible but not marked in our map yet? Just add it with a list of activities and some pictures. Additionally, you can input your perceptions including whether it is noisy, clean or if the infrastructure is attractive. The picture module within the application promotes you to take photos in the four cardinal directions. Users can search for specific activities, visit one of the points indicated on the map and then evaluate this point.</p> <p>Time period of data collection: 19/03/2019 - 10/11/2019</p> <p>Types of contributors: General Public, Students<br> Number of contributors: 50<br> Number of observations: 788<br> Number of photos: 1846</p> <p>Associated files: City Oases Vienna 2019.csv, City Oases Vienna 2019.geoJSON, City Oases Vienna 2019 Attributes.csv</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://www.umweltbundesamt.at/en/">Umweltbundesamt</a> (Environment Agency Austria) and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
Data from: Mammal diversity and metacommunity dynamics in urban green spaces: implications for urban wildlife conservation
<p>As urban growth expands and natural environments fragment, it is essential to understand the ecological roles fulfilled by urban green spaces. To evaluate how urban green spaces function as wildlife habitat, we estimated mammal diversity and metacommunity dynamics in city parks, cemeteries, golf courses, and natural areas throughout the greater Chicago, IL, USA region. We found similar a-diversity (with the exception of city parks), but remarkably dissimilar communities in different urban green spaces. Additionally, the type of urban green space greatly influenced species colonization and persistence rates. For example, coyotes (Canis latrans) had the highest, but white-tailed deer (Odocoileus virginianus) the lowest, probability of persistence in golf courses compared to other green space types. Further, most species had a difficult time colonizing city parks even when sites were seemingly available. Our results indicate that urban green spaces contribute different, but collectively important, habitats for maintaining and conserving biodiversity in cities.</p>
Data from: Pollinator-mediated gene flow connects green roof populations across the urban matrix: a paternity analysis of the self-compatible forb Penstemon hirsutus
Gene flow between populations can help maintain genetic diversity and prevent inbreeding, which is especially important for small, fragmented habitats. Many plant species rely on pollinators to move pollen between populations. In urban areas, insufficient pollinator services may result in limited gene flow, which can have negative consequences such as genetic drift and inbreeding depression. Furthermore, restored populations that are established with few founders of low genetic diversity may have limited long-term population persistence. Here, we tested the hypotheses that populations of a self-compatible forb established on urban green roofs fromnursery stock are genetically depauperate and that limited gene (pollen) flow between populations will result in increased inbreeding. We compared the neutral genetic diversity of Penstemon hirsutus, using nine microsatellite loci, between three green roof populations established from nursery stock and three natural populations. We also established ten experimental populations on green roofs and measured rates of outcrossing and inbreeding and identified the movement of pollen within and between roofs using a paternity analysis. We found that neutral genetic diversity of populations established from nursery stock was lower than that of natural populations, although the level of inbreeding was also lower on the green roofs. In our experimental populations, we found that the rates of outcrossing and inbreeding varied between the roof populations. Our results suggest that inbreeding may be correlated with cover of co-flowering species but not with any of the other measured site properties. The location of likely pollen donors suggested that on average, 75% of pollen was derived from plants within the population (including self) and 25% came from plants on different roofs. Our results document realized pollen movement within and between green roofs, demonstrating that these habitats provide important connectivity in a fragmented environment.
Fig. 2 in Remarks on Hymenoptera on urban green roofs in Belgium
Fig. 2. Green roof RPBER (Recycling park Berchem), Berchem, Belgium, with Sedum album. © Jeffrey Jacobs.
An experimental test of the impact of avian diversity on attentional benefits and enjoyment of people experiencing urban green-space
<p>Biodiversity may play a key role in generating the well-being benefits of visiting green-spaces.</p> <p>The ability of people to accurately perceive variation in biodiversity is, however, unclear and evidence supporting links between biodiversity exposure and well-being outcomes remains equivocal. In part, this is due to the paucity of controlled experimental studies that deal adequately with confounding factors that covary with biodiversity.</p> <p>Attention restoration theory (ART) proposes that natural environments contain many softly fascinating stimuli that provide visitors with a sense of separation from their normal settings and routines, switching off direct attention and allowing recovery from attention fatigue. Increased biodiversity could increase these stimuli, and ART therefore potentially provides a mediating effect linking biodiversity to well-being.</p> <p>Here, we conduct a controlled experiment in which participants virtually experience urban green-space containing high and low levels of avian biodiversity (altered by manipulating bird song).</p> <p>Respondents accurately identified the contrast in biodiversity and reported greater enjoyment of the high biodiversity treatment than the low diversity control. Higher biodiversity did not, however, elicit greater self-reported stimulation or restoration, and did not increase perceived restorativeness scores or attentional capacity (quantified using the Digit Span Backwards attention test).</p> <p>Respondents that were more connected to nature, however, had greater attentional capacity following exposure to green-space.</p> <p>Our study provides rare experimental evidence that people can accurately detect variation in biodiversity, that high avian diversity boosts visitor perceptions of urban green-space quality, and that people with increased nature connectedness show enhanced attentional capacity.</p>
Quantification of faecal glucocorticoid metabolites as a measure of stress in the rock hyrax (Procavia capensis) living in an urban green space
<p>Despite the abundance of rock hyrax (<i>Procavia capensis</i>) within South Africa's urban areas, there is not much information available about the effect of anthropogenic activities on rock hyrax wellbeing. To determine the potential impact of anthropogenic disturbance on adrenocortical activity, we conducted an ACTH challenge to identify a suitable enzyme-immunoassay (EIA) for measuring faecal glucocorticoid metabolite (fGCM) concentrations in the rock hyrax. This study identified an 11β-hydroxyaetiocholanolone EIA as the most suitable assay in this regard. The fGCM levels measured, indicate the physiological stress response in different rock hyrax populations, living in an area with varying degrees of anthropogenic activity (low, medium, high) within the National Botanical Garden of Pretoria, South Africa. The species' habituation to human numbers <span>(weekly mean number of people</span>) was examined by determining individual flight initiation distance (FID). Seasonally, there were overall higher fGCM concentrations in late spring compared to winter. The fGCM concentrations, although not significantly different but possibly biologically relevant, in the section with the lowest anthropogenic disturbance were ~10% higher compared to those in the section with medium disturbance, and ~20% higher compared to those in the section with the highest disturbance. Animal FID did not differ significantly between seasons but they did differ significantly between sections, and decreased in accordance with fGCM concentrations. The non-invasive approach established in this study provides a foundation for assessing rock hyrax wellbeing, and can help better understand how anthropogenic presence is perceived as a stressor in this species.</p>
Supplementary material 3 from: Schmidt K, Walz A (2021) Ecosystem-based adaptation to climate change through residential urban green structures: co-benefits to thermal comfort, biodiversity, carbon storage and social interaction. One Ecosystem 6: e65706. https://doi.org/10.3897/oneeco.6.e65706
Mean values of microclimatic parameters between 9am and 9pm, based on measurements in the four courtyards (CY): CY 1: light green, CY 2: dark green, CY 3: orange, CY 4: red
Supplementary material 6 from: Schmidt K, Walz A (2021) Ecosystem-based adaptation to climate change through residential urban green structures: co-benefits to thermal comfort, biodiversity, carbon storage and social interaction. One Ecosystem 6: e65706. https://doi.org/10.3897/oneeco.6.e65706
Results from tree mapping and allometric equations, indicating above-ground biomass and carbon stocks
Supplementary material 5 from: Schmidt K, Walz A (2021) Ecosystem-based adaptation to climate change through residential urban green structures: co-benefits to thermal comfort, biodiversity, carbon storage and social interaction. One Ecosystem 6: e65706. https://doi.org/10.3897/oneeco.6.e65706
Results from habitat mapping and biodiversity scores. Domin values = 1: < 4% cover with few individuals; 2: < 4% with several individuals; 3: < 4% with many individuals; 4: 4–10%; 5: 11–25%; 6: 26–33%; 7: 34–50%; 8: 51–75%; 9: 76–90%; 10: 91–100% cover
UGS-1m: Fine-grained urban green space mapping of 34 major cities in China based on the deep learning framework
<p>Urban green space (UGS) is an important component in the urban ecosystem and has great significance to the urban ecological environment. The UGS-1m product provides the fine-grained UGS maps of 34 major cities/areas in China, which is generated based on a deep learning (DL) framework.</p> <p>The DL framework consists of a generator and a discriminator. The generator is a fully convolutional network designed for UGS extraction (UGSNet), which integrates attention mechanisms to improve the discrimination to UGS, and employs a point rending strategy for edge recovery. The discriminator is a fully connected network aiming to deal with the domain shift between images. To support the model training, an urban green space dataset (UGSet) with a total number of 4,454 samples of size 512×512 is provided. Code for the UGSet and the UGSet will be soon available at: https://github.com/liumency/UGS-1m. </p> <p>The main steps to obtain UGS-1m can be summarized as follows: a) Firstly, the UGSNet will be pre-trained on the UGSet in order to get a good starting training point for the generator; b) After pre-training on the UGSet, the discriminator is responsible to adapt the pre-trained UGSNet to different cities/areas through adversarial training; c) Finally, the UGS results of the 34 major cities/areas in China (UGS-1m) are obtained using 2,343 Google Earth images with a data frame of 7'30" in longitude and 5'00" in latitude, and a spatial resolution of nearly 1.1 meters. Evaluating the performance of the proposed approach on samples from Guangzhou city shows the validity of the UGS-1m products, with an overall accuracy of 87.4% and an F1 score of 81.14%. </p>
Data from: Balanced spatial distribution of green areas creates healthier urban landscapes
<div> <span>The benefits of green infrastructure on human well-being in urban areas are already well established, with strong evidence of the positive effects of the amount and proximity to green areas. However, the understanding of how the spatial distribution and type of green areas affect health is still an open question. <br>Here, we explore how different spatial configurations of green and built-up areas, through a land sharing and sparing framework, and how different types of green areas affect cardiovascular and respiratory hospitalizations in São Paulo city, Brazil. <br>Sharing/sparing indicators were selected as the main explanatory factors in the control of all groups of diseases. Land sharing appeared as a favourable spatial condition to prevent cardiovascular hospitalization, while land sparing and arboreal vegetation were relevant to reduce hospitalization by lower respiratory diseases. <br>For upper respiratory diseases, forests seem to provide a disservice, once they were associated with increased rates of hospitalization by respiratory allergies causes.<br>Considering that hospitalization rates and severity of cardiovascular diseases are substantially higher than those of upper respiratory ones, dense vegetation tends to provide more services than disservices. The land sharing configuration, which is characterized by green areas spread throughout the urban network (in streets, gardens, small squares, or parks), should lead to higher exposure and use of the benefits of green areas, which may then explain the greater prevention of cardiovascular diseases. <br>These novel results indicate that a more balanced distribution of green areas across built-up areas creates healthier urban spaces, and thus can be used as an urban planning strategy to leverage the health benefits provided by green infrastructure. <br>Policy implications: </span><span><span>Aiming to reduce hospitalizations by cardiovascular and pulmonary causes, urban planning should promote the spreading of green areas across the cities, in order to increase daily contact with natural attributes, giving preference to distribution over total quantity of green in urban landscape.</span></span> </div>
Urban greenness and hedonic dataset in Busan, South Korea
<p>This dataset is aggregated to investigate the association between green amenities and property prices in a metropolitan city. The provided data file contains a total of 52,644 observations with 27 variables, retrieved from the Korea Transport Database, Statistics Korea, Ministry of Land, Infrastructure and Transport, and the Spatial Information Portal. Specifically, the green index is quantified using a large amount of Google street view images, obtained from the download tool (https://svd360.istreetview.com). In this procedure, we carry out a three-step process and then employ the spatial interpolation method to alleviate the nonuniform distribution of source images. We encourage the reusability of our dataset and approaches used in this work by providing supplementary information including code files, which is available at Github (https://github.com/Quantitative-Finance-Lab/Green-Index). </p>
Supplementary material 1 from: Unterweger PA, Klammer J, Unger M, Betz O (2018) Insect hibernation on urban green land: a winter-adapted mowing regime as a management tool for insect conservation. BioRisk 13: 1-29. https://doi.org/10.3897/biorisk.13.22316
Table with all captured species / morphotypes sorted by order, family and species / morphotype. : Explanation note: Collection: University of Tübingen, Evolutionary Biology of Invertebrates, Auf der Morgenstelle 28, 72076 Tübingen, Germany. Individuals with scientific species name that were checked by a taxonomic expert were counted as taxonomic species (s); all the other determinations were counted as morphotypes (m). Morphotypes are defined by the lowest practical taxonomic level (e.g. Hanula et al. 2009; Kutschbach-Brohl et al. 2010). In some cases, the family or the morphometric body length (in mm, numbers in column C, Mini: smaller than 1 mm) was counted as a morphotype (Daly 1985). In cases for which the determination was not validated by a taxonomic expert, our species determination was checked for plausibility in terms of its geographical occurrence via the Entomofauna Germanica (http://www.colkat.de, 2017.11.06). Alternatively (if no taxonomic name could be found), a classification letter / number was assigned for a morphotype. The provided author name refers to the lowest practical taxonomic level (e.g. Hanula et al. 2009; Kutschbach-Brohl et al. 2010). Validation: name of scientific expert who checked the taxonomic determination. Management type of meadow in autumn: mown /unmown. Plant compartment: flower head, stem, tuft, leaves. All numbers represent total sums of all sample sites over the entire study period. Brown-labelled species names are thought to have hibernated in the soil. Green-labelled species names could only be found in flower heads and stems. Black-labelled species occurred in all plant compartments without any preference for a specific plant compartment.
Urban pluvial flood maps under different green cover scenarios
<p>This dataset provides pluvial flood water depth maps for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece as a part of the REACHOUT project. The maps are generated using a Pluvial Flood Tool for different return periods estimated based on observations and EURO-CORDEX future climate change scenarios (Logroño only) under different nature-based green cover scenarios, depending on the city.</p> <p>Technical Info</p> <p>The pluvial flood hazard maps are generated for each event using rainfall intensity as input for the hydrostatic inundation model SaferRAIN (Samela et al., 2020). This is a simplified raster-based model based on a hierarchical filling and spilling algorithm, identifying inundated areas on the basis of high-resolution digital elevation model. It accounts for spatially distributed rainfall input and infiltration, building upon the pixel-based Green-Ampt model (Green and Ampt, 1911). It is suitable for applications over large urban areas.</p> <p>Rainfall input for the pluvial flood model is computed for return periods (RPs) of 2-, 5-, 10-, 25-, 50-, 100-, 200-years based on the historical rainfall data. Different datasets have been utilized in various cities to tailor the analysis to their specific needs. More specifically:</p> <ul> <li> <p>In the city of Gdynia, historical local station data (Climate data IMGW 1960-2021: https://danepubliczne.imgw.pl/) are used to estimate RPs and assess different precipitation events. </p> </li> </ul> <ul> <li> <p>For the cities of Milan and Athens, 2.2-km ERA5 downscaled data are employed to assess historical precipitation events under different RPs (Essenfelder et al., 2021). </p> </li> <li> <p>In the city of Logroño, historical local station data (SOS-Logroño precipitation data 1999-2022: https://www.larioja.org/emergencias-112/es/meteorologia/datos-actuales-rioja/detalle-estacion?homepage=9&cod_muni=89) are used to estimate RPs and assess different precipitation events. Additionally, here, future climate change projections have been analyzed. These projections are based on the precipitation Intensity-Duration-Frequency (IDF) curves computed from the EURO-CORDEX data (REF to zenodo dataset, Pal J et al., 2024). Observations are then scaled according to the changes simulated between future and historical scenarios, using the median and 90th percentile values estimated from the EURO-CORDEX ensemble.</p> </li> </ul> <p>Different urban green cover maps are used as input for the model to simulate the pluvial flood maps under the current land cover conditions and for different nature-based adaptation scenarios for each city to estimate their benefits. Nature-based adaptation scenarios are the result of codesign processes carried out within REACHOUT, involving local stakeholders, experts and representatives of local administrations. Urban green cover scenarios were identified based on areas that could be converted from built-up areas and concrete surfaces (no water infiltration) to green areas allowing for rainwater infiltration. In addition, during this process, local station precipitation, high-resolution digital elevation model and high-resolution land cover data were collected to configure and run the pluvial flood model.</p> <p>Description of the datase: This dataset contains urban pluvial flood maps for return periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-years for hourly and 15-minute events for different urban green cover scenarios and climate change scenarios depending on the city.</p> <p>Format:</p> <p>The format of this dataset is organized in a ZIP file: PluvialFloodMap_{Cityname}.zip. The zip file is organised into sub-folders, one for each urban green cover scenario, including raster (Tiff) files for the rainfall event associated with each return period.</p> <p>Logrono:</p> <ul> <li> <p>Precipitation events historical: 15-minute events – 9.79 mm (RP2), 13.51mm (RP5), 16.27 mm (RP10), 20.15 mm (RP25), 23.33 mm (RP50), 26.77 mm (RP100), 30.50 mm (RP200)</p> </li> <li> <p>Precipitation events climate change: 15-minute events – CC_Q50 (median): 10.49 mm (RP2), 14.91 mm (RP5), 18.32 mm (RP10), 23.18 mm (RP25), 26.61 mm (RP50), 31.25 mm (RP100), 35.40 mm (RP200): CC_Q90 (90th percentile): 11.83 mm (RP2), 16.76 mm (RP5), 20.96 mm (RP10), 26.87 mm (RP25), 32.27 mm (RP50), 38.99 mm (RP100), 46.65 mm (RP200)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS planned: baseline + additional 4 bioswales/ponds (= 29,850 m3) and a green corridor (5.3 km x 5 m) in the southern part of the city.</p> </li> <li> <p>NBS planned plus: NBS planned scenarios + additional small ponds/rain gardens (depth 0.5 m, 13,500 m3)</p> </li> <li> <p>All Green: baseline + all open spaces converted to green</p> </li> </ul> <p>Milan</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 33.36 mm (RP5), 38.52 mm (RP10), 45.04 mm (RP25), 49.88 mm (RP50), 54.68 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>DMG_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise economic damage reduction</p> </li> <li> <p>POP_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise exposed population reduction</p> </li> </ul> <p>* Each green conversion scenario considers four different incremental conversion percentages: 25%, 50%, 75%, and 100% of all potential green areas.</p> <p>Gdynia</p> <ul> <li> <p>Precipitation events historical: 6-hours events – 24.89 mm (RP2), 35.93 mm (RP5), 43.55 mm (RP10), 53.19 mm (RP25), 60.60 mm (RP100), 75.74 mm (RP200) </p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + bioswales/ponds (+ 50,000 m3)</p> </li> <li> <p>All green: baseline + all open spaces converted to green</p> </li> <li> <p>NBS All green: all green + NBS</p> </li> </ul> <p>Athens</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 28.05 mm (RP5), 34.08 mm (RP10), 42.28 mm (RP25), 48.83 mm (RP50), 55.74 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + ponds/rain gardens in existing green spaces (depth 1m) in the northern district of the city</p> </li> <li> <p>All green: baseline + all open spaces (>100 m2) converted to green</p> </li> </ul>
Informing the design of urban green and blue spaces through an understanding of European's usage and preferences
<p><span>In light of global climate change and the biodiversity crisis, making cities more resilient through an adjusted design of urban green and blue spaces is crucial. Nature-based solutions help address these challenges while providing opportunities for nature experiences, and providing cultural ecosystem services that support public health. The COVID-19 pandemic and its associated stressors highlighted the interrelated socio-ecological services provided by nature-based solutions like urban green and blue spaces. </span><span>This pan-European study therefore aimed to enhance the socio-ecological understanding of green and blue spaces to support their design and management. Using an online survey, green and blue space preferences, usage, and pandemic-related changes in greenspace visit and outdoor recreation frequencies were examined. </span><span>Greenspace visit and outdoor recreation frequencies were associated with respondents' (N=584 from 15 countries) geographic location, dominant type of neighborhood greenspace, and greenspace availability during the pandemic, but not greenspace perceptions or sociodemographic background. </span><span>Greenspace visit and outdoor recreation frequencies were generally high, however Southern Europeans reported lower greenspace visit and outdoor recreation frequencies both before and during the pandemic than Northern Europeans. Many Southern Europeans also reported having few neighborhood greenspaces and low greenspace availability during the pandemic. </span><span>The most common outdoor recreational activity among respondents before the pandemic was walking or running with the most frequently stated purpose of time spent outdoors being restorative in nature (i.e. relaxing or calming down). Most Europeans had positive perceptions of green and blue spaces with preferences for structurally diverse and natural or unmanaged green elements. </span><span>This highlights the importance of accessible green and blue spaces both in everyday life and during times of crisis. Stakeholders, their preferences, and regional and cultural differences should be included in the co-design of urban green and blue spaces to maximize their potential for both people and nature.</span></p>
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.