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11 results for “Green Networks”
Real-Time Sensor Network of Detroit Green Infrastructure: Datasets and Code
<ol> <li><strong>MonitoredRainGardens_PaperTable.xlsx: </strong>Excel spreadsheet where the "Garden" sheet contains the 14 monitored green infrastructure (GI) sites with their design and physiographic features and the "Field Log" sheet contains the installation and field maintenance trips.</li> <li><strong>WaterWells.xlsx: </strong>an Excel file containing all of the water wells in the Detroit region uses for interpolating groundwater levels.</li> <li><strong>GI_GIS_Analysis.aparx:</strong> ArcGIS Pro project file which includes the 14 monitored GI sites and the GIS data for Detroit (percent imperviousness, elevation, slope, land use type, wells, interpolated groundwater levels, hydrologic soil group).</li> <li><strong>Code.zip: </strong>Zip folder containing another folder titled "Code" which holds: (1) a folder titled "SensorData" containing 16 csv files with the raw pressure transducer data for the 16 monitored GI sites during the measurement period (including the two excluded sites); (2) a csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features used in the correlation analysis; (3) a csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period; (4) a csv file titled "GLWA_RainGaugesforStudy.csv" that contains rainfall from 9 rain gauges during the measurement period; (5) a Jupyter notebook titled "storm_constants_analysis.ipynb" which provides the code for calculating the decay constants for the monitored GI; (6) a Jupyter notebook titled "storm_constants.ipynb" which provides the code for analyzing the decay constants including the correlation analysis and surface plots; and (7) a Jupiter notebook titled "modeled_response.ipynb" which provides the code for plotting the drawdown curves based on the decay constant.</li> </ol>
UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network
<p>The UBGG dataset provides easily access and leverage to researchers and analysts, which is stored in the following Zenodo repository (<a href="https://doi.org/10.5281/zenodo.8053333">https://doi.org/10.5281/zenodo.8352777</a>). The UBGG dataset consists of two main components:</p> <ul> <li><strong>UBGG-3m: the fine-grained UBGG map product of 36 metropolises in China.</strong> The UBGG-3m dataset captures the intricate urban landscape features with remarkable precision, providing a detailed representation at an impressive 3-meter resolution. Fig. 1 in User Guides shows the classification results for 36 Chinese metropolises. Researchers can delve into the nuances of the UBGG continuum, gaining invaluable insights into the interplay between the blue, green, and gray elements of urban environments in each metropolis.</li> </ul> <ul> <li><strong>UBGGset:</strong> <strong>the large-volume sample dataset to support the UBGG deep learning research.</strong> Complementing the UBGG-3m dataset, UBGGset serves as a large-volume sample dataset specifically tailored to support and foster UBGG research endeavors (Fig. 2). The UBGGset consists of 14,627 sample images (without data augmentation), with dimensions of 256 pixels in length and width, covering an urban area of approximately 2,272 km<sup>2</sup>. The UBGGset was constructed with co-registered pairs of 3 m Planet images and fine-annotated urban landscapes labeled on 1 m Google Earth image. This dataset encompasses 15 typical cities, offering researchers a rich and diverse resource to drive exploration, analysis, and innovation in the field of urban landscape studies.</li> </ul> <p> </p> <p><strong>Citation format for paper and dataset:</strong></p> <p>[1] Zhiyu Xu, Shuqing Zhao. Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network. <em>Sci Data</em> 11, 266 (2024). https://doi.org/10.1038/s41597-023-02844-2</p> <p>[2] Zhiyu Xu, Shuqing Zhao, Fine-grained urban landscape mapping reveals broad-scale homogeneity in urban environments,<br>Science Bulletin, (2024). https://doi.org/10.1016/j.scib.2024.03.060</p> <p>[3] Zhiyu Xu, Shuqing Zhao. UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network (v1.0) [Data set]. (2023). Zenodo. https://doi.org/10.5281/zenodo.8352777</p>
Data and scripts for: Green turtles highlight connectivity across a regional marine protected area network in West Africa
<p>Data derivates and analysis scripts (in R) used for the paper on analyzing green turtle MPA coverage and connectivity in West Africa.</p>
Maps of ecosystem multifunctionality and ecological connectivity for identifying Green Infrastructure networks in the European Alps
<p>High resolution raster datasets (20 meters) containing the results of an ecological connectivity and an ecosystem multifunctionality assessment for identifying Green Infrastructure networks in 10 pilot regions of the European Alps, modelled as part of the LUIGI Interreg Alpine Space project. Pilot regions include: department of Isère (FR), departments of Savoie and Haute-Savoie (FR), Munich Metropolitan Region (DE), Central Area of Salzburg (AT), South Burgenland (AT), Goriška region (SI), South Tyrol (IT), canton of Grisons (CH), Metropolitan City of Milan (IT), and Metropolitan City of Turin (IT). For a preview of the data and the results available for each pilot region <a href="https://www.alpine-space.org/projects/luigi/en/project-results/d.t1.2.1-pilot-regions-policy-briefs">click here</a></p> <p>Further information on the LUIGI project is available at: <a href="https://www.alpine-space.org/projects/luigi/en/home">https://www.alpine-space.org/projects/luigi/en/home</a></p> <p><a href="https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf">https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf </a></p> <p>The datasets include:</p> <ul> <li>a map for ecosystem service-based multifunctionality calculated out of the average of 11 standardized ecosystem service indicators: water provision, crop potential, timber production, fodder provision, pollination potential, carbon sequestration, nitrogen retention, natural hazard mitigation, runoff retention, outdoor recreation, and landscape aesthetics.</li> <li>a map of the modelled Ecological Network composed of core areas and ecological corridors. Corridors are modelled for medium-large forest mammal species and represent least-cost pathways connecting core areas. Different classes indicate areas with different levels of current ecological connectivity starting from core areas to areas in cities or anthropized land with no connectivity. Modeled corridors are presented in two classes to mirror different levels of prioritization and management actions.</li> <li>a map of the resistance of the landscape to the movement of forest mammal species. The landscape resistance raster has been developed by reclassifying and aggregating a high resolution (5m) land use and land cover map. Resistance values have been determined in relation to the naturalness of different land use and land cover classes. In this context, land use or landscape resistance is intended as the opposite of habitat suitability.</li> </ul>
PREDICTING THE PERFORMANCE OF GREEN STORMWATER INFRASTRUCTURE USING MULTIVARIATE LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK
<p>The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, a LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data.</p>
Effect of green infrastructure on restoration of pollination networks and plant performance in semi-natural dry grasslands across Europe
<p>Agricultural intensification, afforestation and land abandonment are major drivers of biodiversity loss in semi-natural grasslands across Europe. Reversing these losses requires the reinstatement of plant-animal interactions such as pollination. Here we assessed the differences in species composition and patterns of plant-pollinator interactions in ancient and restored grasslands and how these patterns are influenced by landscape connectivity, across three European regions (Belgium, Germany and Sweden). We evaluated the differences in pollinator community assemblage, abundance, and interaction network structure between 24 ancient and restored grasslands. We then assessed the effect of surrounding landscape functional connectivity (i.e. green infrastructure, GI) on these variables and tested possible consequences on the reproduction of two model plants, Lotus corniculatus and Salvia pratensis. Neither pollinator richness nor species composition differed between ancient and restored grasslands. A high turnover of interactions across grasslands was detected but was mainly due to replacement of pollinator and plant species. The impact of grassland restoration was consistent across various pollinator functional groups, whereas the surrounding GI had differential effects. Notably, bees, butterflies, beetles, and dipterans (excluding hoverflies) exhibited the most significant responses to GI variations. Interestingly, networks in restored grasslands were more specialised (i.e. less functionally redundant) than in ancient ones and also showed a higher number of insect visits to habitat-generalist plant species. Landscape connectivity had a similar effect, with habitat-specialist plant species receiving fewer visits at higher GI values. Fruit set in S. pratensis and L. corniculatus was unaffected by grassland type or GI. However, the fruit set in the specialist S. pratensis increased with the number of pollinator visits, indicating a positive correlation between pollinator activity and reproductive success in this particular species. Synthesis and applications. Our findings provide evidence of the necessity to enhance ecosystem functions while avoiding biotic homogenization. Restoration programs should aim at increasing landscape connectivity which influences plant communities, pollinator assemblages, and their interaction patterns. To avoid generalist species taking over from specialists in restored grasslands, we suggest reinforcing the presence of specialist species in the latter, for instance by means of introductions, as well as increasing the connectivity to source populations.</p>
Dataset for: Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE
<p><strong>Based on the Github respostitory: <a href="https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main">https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main</a></strong></p> <p>This repository contains the data used for the paper "Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE"<br> You'll find the csv files for the three simulated (Hydrus 1D) scenarios explained in the paper. These files were processed from the 'Nod_Inf.out' files to csv format.</p> <p><strong>Acknowledgments</strong><br> The publicly available data used for this study (scenario 1 & 2) as well as the code for the second PINN architecture (based on Dr. Maziar Raissi PINN code) and the code used to transform “Nod_inf.out” files from Hydrus 1D to csv files created by Dr. Toshiyuki Bandai and Dr. Teamrat A. Ghezzehei were helpfulfor this study.</p>
The Green View Index of Tartu street network
<p>This dataset is based on the OpenStreetMap street network for Tartu. The Green View Index (GVI) values have been calculated from Google Street View images using semantic segmentation and joined to street network data.</p>
Effect of green infrastructure on restoration of pollination networks and plant performance in semi-natural dry grasslands across Europe
Open the record for dataset details and reuse information.
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
Dataset related to article: Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials
<p>This repository contains the dataset to train and test the GeTe Machine Learning Interatomic Potential (MLIP). The computational details are given in the manuscript. </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.