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147 results for “Spatial Network”
Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs
<p class="MsoNormal"><strong>1. </strong>Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy.</p> <p class="MsoNormal"><strong>2. </strong>Here, we demonstrate the ability of deep convolutional neural networks (CNNs) to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of annual forest change (2001-2020) and the ALOS PALSAR digital surface model, to forecast deforestation (2021). We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km<sup>2</sup> tropical forest regions in the Southern Peruvian Amazon.</p> <p class="MsoNormal"><strong>3.</strong><strong> </strong><span>The networks could predict the location of future forest loss to a high degree of accuracy (F</span><sub>1 </sub><span>= 0.58-0.71). Our best performing model (3D CNN) had the highest pixel-wise accuracy (F</span><sub>1 </sub><span>= 0.71) when validated on 2020 forest loss (2014-2019 training). Visual interpretation of the mapped forecasts indicated that the network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides).</span></p> <p class="MsoNormal"><strong>4.</strong><strong> </strong>CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in harnessing the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.</p>
A study of the spatial correlation network structure of urban innovation in Guangdong
<p>Based on the modified gravity model, a spatial correlation network of innovation was constructed among cities in Guangdong, China. Social network analysis was employed to explore their evolution characteristics during 2009–2017. The results indicate that the innovation output of prefecture-level cities in Guangdong Province shows both spatial correlations and differences. Their network shows lower density, higher efficiency, and rigid stratification properties. Based on small cluster analysis, these cities are classified into four blocks, the members of which changed. In 2017, four well-defined subgroups formed, which are "bidirectional spillover plate", "main spillover plate", "net beneficial plate", and "agent plate". With this network, the geographical characteristics of the innovation capabilities and differences among the cities in Guangdong, as well as the different positions and roles of each city in the associated network, can be properly understood. Consequently, the transmission mechanisms and development strategies of innovation in Guangdong Province can be better explored.</p>
Dataset for Spatial Variations in the Osteocyte Lacuno-canalicular Network Density and Analysis of the Connectomic Parameters
<p>This dataset is a representative case of the loaded tibia of a C57BL/6 mouse at the mid-shaft. The image pixel size is 0.303 by 0.303 um, and the z-depth is 0.296 um. </p> <p>To generate, analyse, and quantify the osteocyte lacuno-canalicular network, it requires 'Tool for Image and Network Analysis (TINA)' which can be acqruied from https://gitlab.mpikg.mpg.de/rummler/TINA.git. A demonstration has been included on using TINA.</p>
Exploring The Spatial Structure of Interregional Supply Chain: A Multilayer Network Approach
<p><span>This research aims to elucidate the organizational patterns of interregional economic interdependence to enhance our comprehension of the national economy's structure at a regional scale. Employing a multilayer network model, this study represents economic interdependence among Indonesian regions, utilizing the InterRegional Input-Output (IRIO) table. Through the application of various metrics, such as degree and strength distribution, assortativity coefficient, and global and local rich club coefficient, to the multilayer IRIO network, we uncover the organizational patterns of economic exchanges between provinces and economic sectors within Indonesia. Our findings demonstrate that a multilayer network approach reveals the heterogeneous and complex structure of the national economy at the regional level. By analyzing the assortativity pattern and global rich-club coefficient, we illustrate that the IRIO network exhibits a hierarchical organization, where significant provincial-sector nodes are interconnected and form dense rich clubs, extending from a few structural cores to peripheral regions. Additionally, we identify distinct connectivity patterns of non-rich nodes based on their incoming and outgoing relations. The insights gained from this study have implications for the macro-control of regional development.</span></p>
Inductive biases of neural network modularity in spatial navigation
<p>The brain may have evolved a modular architecture for reward-based learning in daily tasks, with circuits featuring functionally specialized modules that match the task structure. We propose that this architecture enables better learning and generalization than architectures with less specialized modules. To test this hypothesis, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the architecture that largely segregates computations of state representation, value, and action into specialized modules enables more efficient learning and better generalization. The behavior of agents with this modular architecture also resembles macaque behaviors more closely. Investigating the latent state computations in these agents, we discovered that the learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to a Kalman filter. These results shed light on the possible rationale for the brain's modular specializations and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.</p>
Results of "Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits"
<p>This dataset contains the results obtained for the article "Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits", authored by Francesc Wilhelmi, Cristina Cano, Gergely Neu, Boris Bellalta, Anders Jonsson and Sergio Barrachina. The article has been sent to Elsevier Ad-hoc Networks.</p> <p>The content of this dataset has been obtained by means of the code allocated in the following GitHub repository: <a href="https://github.com/fwilhelmi/collaborative_sr_in_wns_via_selfish_mabs">https://github.com/fwilhelmi/collaborative_sr_in_wns_via_selfish_mabs</a></p> <p>Contact information: francisco.wilhelmi@upf.edu</p>
Data from: Linking social and spatial networks to viral community phylogenetics reveals subtype-specific transmission dynamics in African lions
1.Heterogeneity within pathogen species can have important consequences for how pathogens transmit across landscapes; however, discerning different transmission routes is challenging. 2.Here we apply both phylodynamic and phylogenetic community ecology techniques to examine the consequences of pathogen heterogeneity on transmission by assessing subtype specific transmission pathways in a social carnivore. 3.We use comprehensive social and spatial network data to examine transmission pathways for three subtypes of feline immunodeficiency virus (FIVPle) in African lions (Panthera leo) at multiple scales in the Serengeti National Park, Tanzania. We used FIVPle molecular data to examine the role of social organization and lion density in shaping transmission pathways and tested to what extent vertical (i.e., father and/or mother offspring relationships) or horizontal (between unrelated individuals) transmission underpinned these patterns for each subtype. Using the same data, we constructed subtype specific FIVPle co-occurrence networks and assessed what combination of social networks, spatial networks, or co-infection best structured the FIVPle network. 4.While social organization (i.e., pride) was an important component of FIVPle transmission pathways at all scales, we find that FIVPle subtypes exhibited different transmission pathways at within- and between-pride scales. A combination of social and spatial networks, coupled with consideration of subtype co-infection, was likely to be important for FIVPle transmission for the two major subtypes, but the relative contribution of each factor was strongly subtype specific. 5.Our study provides evidence that pathogen heterogeneity is important in understanding pathogen transmission, which could have consequences for how endemic pathogens are managed. Furthermore, we demonstrate that community phylogenetic ecology coupled with phylodynamic techniques can reveal insights into the differential evolutionary pressures acting on virus subtypes, which can manifest into landscape-level effects.
Legacy effects of seed dispersal mechanisms shape the spatial interaction network of plant species in Mediterranean forests
<p>1. Seed dispersal by frugivores plays a key role in structuring and maintaining tree diversity in forests. However, little is known about how the spatial legacy of seed dispersal and early recruitment shapes spatial patterns and the spatial interaction network of plant species in mature forest communities.</p> <p>2. We analysed two fully mapped mixed Pine-Oak forest communities using spatial point pattern analysis to determine (i) the detailed structure of the intraspecific spatial patterns of saplings and adults, (ii) the intra- and interspecific spatial interaction of saplings, adults, and saplings relative to adults, (iii) the spatial patterns of species richness at the community level, and (iv) whether seed dispersal mechanisms affect the plant-plant interaction networks and the ratio of adult to sapling neighbourhood densities used as surrogate for spatial self-thinning.</p> <p>3. The intraspecific spatial patterns of saplings and adults showed in general complex nested cluster structures that were similar for sapling and adult stages, despite of substantial self-thinning in some dry-fruited species. The spatial network of saplings was characterized by positive spatial interactions. Adults of several tree species facilitated saplings in their proximity; however, adults of dry-fruited species, but not those of fleshy-fruited ones, lost almost all positive interactions that occurred at the sapling stage. Besides, interaction strength between adults was positive and often significantly stronger if both species were fleshy-fruited. At the community level, the forests were structured into multispecies clumps across all life stages.</p> <p>4. Synthesis. Our analyses highlight the importance of the spatial legacy of seed dispersal and early recruitment in the assembly of plant communities. Particularly, animal seed dispersal can lead to multispecies clusters and positive spatial associations across life stages in Mediterranean forests, with surprisingly little signatures of negative interactions. Our analysis suggests that changes of the spatial structure across plant life stages are driven by seed dispersal mechanisms and subsequent spatial self-thinning, generating a spatial footprint at the sapling stage that conditions the long-term interactions between adult plants. Combining spatial point pattern analysis with network analysis and species traits is a promising way to disentangle the processes underlying observed patterns of local diversity. </p>
Dataset : Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps
<ul> <li><strong>The three files in the dataset are:</strong></li> </ul> <p>1) Healthy Sheep Atria Fibre Orientation Dataset 300µm</p> <p>2) Heart Failure Sheep Atria Fibre Orientation Dataset 300µm</p> <p>3) Human Atria Fibre Orientation Dataset 330µm</p> <ul> <li><strong>Data is stored as numpy binary files. Given below is an example .py script to open the flat datasets:</strong></li> </ul> <p> import numpy as np<br> data = np.load("Human_330um.npy")</p> <ul> <li><strong>Volume and fibre orientation dataset stored in flat format as given below:</strong></li> </ul> <p> i, j, k, v1, v2, v3, ... repeated for each voxel </p> <p>where (i, j, k) are voxel coordinates and (v1, v2, v3) are vector components corresponding to fibre orientation within that voxel.</p>
Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization
<p>Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A. that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas or GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hotspots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat-abundance relationships, develop spatially-explicit predictions of abundance and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated high representation of this bird assemblage within the entire conservation network, but with a bias towards birds associated with moderately tall grasses relative to those associated with short or tall grasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serve as a complementary tool for conservation planning efforts globally.</p>
SI_III_4_Spatialized metabolomic annotation combining MALDI imaging and molecular network
<p>Ces documents regroupent les données supplémentaires générés lors du développement méthdologique pour la création de réseaux moléculaires par MALDI-FT-ICR IMS. Un .ppt regroupe l'ensemble des cartographies ioniques spécifiques à chaque ion.</p>
A study of the spatial correlation network structure of urban innovation in Guangdong
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Data from: Linking social and spatial networks to viral community phylogenetics reveals subtype-specific transmission dynamics in African lions
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Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization
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Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs
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Data from: Environmental and spatial effects on co-occurrence network size and taxonomic similarity in stream diatoms, insects, and fish
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Inductive biases of neural network modularity in spatial navigation
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Temporal and spatial changes in benthic invertebrate trophic networks along a salinity gradient
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Across the edge: Spatial segregation drives community structure in tri-trophic multilayer networks at a forest-grassland edge
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Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
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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.