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57 results for “Transport networks”
Data from: Downscaling pollen-transport networks to the level of individuals
1. Most plant-pollinator network studies are conducted at species level whereas little is known about network patterns at the individual level. In fact, nodes in traditional species-based interaction networks are aggregates of individuals establishing the actual links observed in nature. Thus, emergent properties of interaction networks might be the result of mechanisms acting at the individual level. 2. Pollen loads carried by insect flower-visitors from two mountain communities were studied to construct pollen-transport networks. For the first time, these community-wide pollen-transport networks were downscaled from species-species (sp-sp) to individuals-species (i-sp) in order to explore specialization, network patterns and niche variation at both interacting levels. We used a null model approach to account for network size differences inherent to the downscaling process. Specifically, our objectives were: (i) to investigate whether network structure changes with downscaling, (ii) to evaluate the incidence and magnitude of individual specialization in pollen use, and (iii) to identify potential ecological factors influencing the observed degree of individual specialization. 3. Network downscaling revealed a high specialization of pollinator individuals, which was masked and unexplored in sp-sp networks. The average number of interactions per node, connectance, interaction diversity and degree of nestedness decreased in i-sp networks, because generalized pollinator species were composed of specialized and idiosyncratic conspecific individuals. An analysis with 21 pollinator species representative of two communities showed that mean individual pollen resource niche was only c. 46% of the total species niche. 4.The degree of individual specialization was associated to inter- and intraspecific overlap in pollen use and it was higher for abundant than for rare species. Such niche heterogeneity depends on individual differences in foraging behaviour and likely has implications for community dynamics and species stability. 5. Our findings highlight the importance of taking inter-individual variation into account when studying higher–order structures such as interaction networks. We argue that exploring individual-based networks will improve our understanding of species-based networks and will enhance the link between network analysis, foraging theory and evolutionary biology.
Data from: Argentine ants (Linepithema humile) use adaptable transportation networks to track changes in resource quality
Transportation networks play a crucial role in human and animal societies. For a transportation network to be efficient, it must have adequate capacity to meet traffic demand. Network design becomes increasingly difficult in situations where traffic demand can change unexpectedly. In humans, network design is often constrained by path dependency because it is difficult to move a road once it is built. A similar issue theoretically faces pheromone-trail-laying social insects; once a trail has been laid, positive feedback makes re-routing difficult because new trails cannot compete with continually reinforced pre-existing trails. In the present study, we examined the response of Argentine ant colonies and their trail networks to variable environments where resources differ in quality and change unexpectedly. We found that Argentine ant colonies effectively tracked changes in food quality such that colonies allocated the highest proportion of foragers to the most rewarding feeder. Ant colonies maximised access to high concentration feeders by building additional trails and routes connecting the nest to the feeder. Trail networks appeared to form via a pruning process in which lower traffic trails were gradually removed from the network. At the same time, we observed several instances where new trails appear to have been built to accommodate a surge in demand. The combination of trail building when traffic demand is high and trail pruning when traffic demand is low results in a demand-driven network formation system that allows ants to monopolise multiple dynamic resources.
Data from: The multilayer temporal network of public transport in Great Britain
Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset.
Predicting barrier effects of transportation networks on Asian elephants: Implications for environmental impact assessment
<p>The rapid proliferation of transportation networks (TNs) is a threat to wide–ranged animals through habitat erosion, road–kill, and indirect socio–ecological interaction. In most cases, environmental impact assessments of TNs about animals are descriptive and targeted to direct impacts of the "linear features", while indirect and cumulative impacts are relatively neglected. Using spatially explicit data of elephant–caused damage during 2012–2015 in China, we quantified barrier effects of TN on Asian elephants and predicted the effects under a TN expansion scenario using maximum entropy algorithms. We found that TN acted as a strong barrier even for the elephants that have inhabited highly fragmented landscapes for years, with only 18% of the damage occurred on the crossed side of roads. Also, the TN expansion will reduce elephant habitats, exacerbating herd isolation and human–elephant conflict locally. Thus, we suggest that mitigation of the conflicts should be integrated into future environmental impact assessments.</p>
Data set for Coherent Hole Transport in Selective Area Grown Ge Nanowire Networks
<p>This document contains all the data and analysis used in the manuscript titled </p> <h1><span>Coherent Hole Transport in Selective Area Grown Ge Nanowire Networks</span></h1> <p><span><a title="DOI URL" href="https://doi.org/10.1021/acs.nanolett.2c00358">https://doi.org/10.1021/acs.nanolett.2c00358</a></span></p>
TEFNET24: Reference Packet Optical Network Topology for Edge to Core Transport [Invited]
<p>In this paper, we introduce TEFNET24, a reference multi-layer hierarchical network topology that spans from access to core networks, specifically designed to meet the demands of beyond-5G and and prepared for next-generation 6G communication systems. This topology, inspired by the actual network deployments of Telefónica in medium-sized countries in Europe and America, integrates both IP and optical (DWDM) layers to provide a comprehensive framework for network design, optimization, and analysis. Our primary contribution is the development of an open-source benchmarking network, accessible to both researchers and industry professionals. This resource aims to facilitate the study and advancement of integrated IP and optical networks, allowing researchers to address key challenges such as traffic aggregation, latency reduction, cost efficiency, and support for advanced applications. We provide guidelines for utilizing this benchmark network, enabling users to evaluate and enhance their solutions for AI-driven network management, ultra-reliable low-latency communication, enhanced mobile broadband, and massive machine-type communication. By sharing this detailed and practical benchmarking network, we seek to foster innovation and collaboration within the optical network community, driving forward the capabilities and performance of future communication networks. </p>
Network-Nanostructured ZIF-8 too Enable Percolation for Enhanced Gas Transport (Data Repository)
<p>Data repository </p>
Data for "Investigating molecular transport in the human brain from MRI with physics-informed neural networks"
<p>Data analyzed in Zapf <em>et al.</em> <a href="https://www.nature.com/articles/s41598-022-19157-w">Investigating molecular transport in the human brain from MRI with physics-informed neural networks</a> (Scientific Reports 2022).</p> <p>The data consists of CSF tracer concentrations in the brain subregions analyzed in the article. The data was pre-processed as described in S1.1 in the supplementarty materials. </p> <p>In Python, load the data as numpy arrays using the nibabel package as</p> <pre><code>import nibabel data = nibabel.load("068/concentrations/24h.mgz").get_fdata() domain_mask = nibabel.load("068/masks/roi.mgz").get_fdata().astype(bool)<br>And view slices of the data as:</code></pre> <div> <div><span>plt</span><span>.</span><span>figure</span><span>()</span></div> <div><span>plt</span><span>.</span><span>imshow</span><span>(</span><span>np</span><span>.take(</span><span>data</span><span>, </span><span>150</span><span>, </span><span>0</span><span>), </span><span>vmax</span><span>=</span><span>0.1</span><span>)</span></div> <div><span>plt</span><span>.</span><span>figure</span><span>()</span></div> <div><span>plt</span><span>.</span><span>imshow</span><span>(</span><span>np</span><span>.take(</span><span>data</span><span>, </span><span>100</span><span>, </span><span>1</span><span>), </span><span>vmax</span><span>=</span><span>0.1</span><span>)</span></div> <div><span>plt</span><span>.</span><span>figure</span><span>()</span></div> <div><span>plt</span><span>.</span><span>imshow</span><span>(</span><span>np</span><span>.take(</span><span>data</span><span>, </span><span>100</span><span>, </span><span>2</span><span>), </span><span>vmax</span><span>=</span><span>0.1</span><span>)</span></div> <div><span>plt</span><span>.</span><span>show</span><span>()</span></div> </div> <pre> </pre>
Dataset for "Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study"
<p>The dataset (EPANET file) which accompanies the publication </p> <p>Vrachimis, S. G., Eliades, D. G., and Polycarpou, M. M.: Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study, Drink. Water Eng. Sci., 2018</p>
A collection of public transport network data sets for 25 cities
<p>This dataset describes the public transport networks of 25 cities across the world in multiple easy-to-use data formats. These data formats include network edge lists, temporal network event lists, SQLite databases, GeoJSON files, and General Transit Feed Specification (GTFS) compatible ZIP-files.<br> <br> The source data for creating these networks has been published by public transport agencies according to the GTFS data format. To produce the network data extracts for each city, the original data have been curated for errors, filtered spatially and temporally and augmented with walking distances between public transport stops using data from OpenStreetMap. <br> <br> Cities included in this dataset version: Adelaide, Belfast, Berlin, Bordeaux, Brisbane, Canberra, Detroit, Dublin, Grenoble, Helsinki, Kuopio, Lisbon, Luxembourg, Melbourne, Nantes, Palermo, Paris, Prague, Rennes, Rome, Sydney, Toulouse, Turku, Venice, and Winnipeg.</p> <p>Contrary to the version 1.0 of this data set, this version (1.2) does not include the cities of Antofagasta and Athens, for which non-commercial usage of the data is not allowed.<br> <br> Contrary to previous versions of the data set (1.0 and 1.2), in this version (1.2) the temporal filtering of the data has been slightly adapted, so that the daily and weekly data extracts cover all trips departing between from 03 AM on Monday to 03 AM on Tuesday (daily extract) or 03 AM of the Monday next week (weekly extract). Additionally, a temporal network extract covering a full week of operations has been added for each city.<br> <br> Documentation of the data can be found in the Data Descriptor article published in Scientific Data: http://doi.org/10.1038/sdata.2018.89 <br> When using this dataset, please cite also the above-mentioned paper.</p>
Macroscopic Fundamental Diagram Based Discrete Transportation Network Design
<p>Simulation data for the research paper named "Macroscopic Fundamental Diagram Based Discrete Transportation Network Design ".</p>
Identifying contributors to PM2.5 simulation biases of chemical transport model using fully connected neural networks
<p>The processed data and codes in the study are included. </p> <p><strong>Source data:</strong></p> <p>The training and testing dataset is composed of observed and simulated data of pollutants and meteorology in the BTH and YRD regions in the whole year of 2015. The processed datasets used for training are named as "dataset_BTH" and "dataset_YRD" in the folder.</p> <ul> <li><em>The hourly observed pollution data</em> are from China National Urban Air Quality Real-time Release Platform of the National Environmental Monitoring Station</li> <li><em>The hourly simulated pollutants data</em> comes from the output of WRF-CMAQv5.2 (spatial resolution of 27 km).</li> <li><em>Meteorological observation data</em> is provided by China Meteorological Data Service Centre</li> <li><em>The meteorological simulation data</em> comes from the simulation results of the WRF model</li> </ul> <p><strong>Codes:</strong></p> <ul> <li>preprocessing of raw CMAQ data, observed pollution data and meteorological data</li> <li>bulid and train process of fully connected neural networks</li> <li>calculation of correlation between variables</li> <li>feature selection method</li> <li>contribution analysis</li> </ul>
All raw data for the transport part of 'In-plane selective area InSb-Al nanowire quantum networks'
<p>This folder contains all the raw data gathered on the devices used in the transport part of the paper "In-plane selective area InSb–Al nanowire quantum networks" https://doi.org/10.1038/s42005-020-0324-4.</p>
Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data
<p>Numerical codes and results for the article: Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data</p>
Data: Upsamling Monte Carlo Neutron Transport Simulation Tallies Using a Convolutional Neural Network
<p>This repository contains:</p> <ul> <li>openmc-data-XXXX.tar.gz - Training Data generated with the OpenMC Monte Carlo code representing neutron flux tallies in 4,400 unique light water reactor fuel assemblies in HDF5 format. Training samples consist of tallies in 64x64 pixels and 8 neutron energy groups, and tallies in 128x128 pixels and 16 neutron energy groups. Folders 0008 to 0023 contain training and validation data. Folder 0024 contains test data.</li> <li>out.mat - Upsampling results using a Convolutional Neural Network for 300 testing data samples in MATLAB format. These data include OpenMC tally uncertainties in low and high resolution tallies, scaling values used in data pre-processing, low resolution inputs to the CNN, and high resolution upsampled results as well as high resolution ground truth values.</li> </ul>
Raw data and R code for: Negative effects of urbanisation on diurnal and nocturnal pollen-transport networks
<p>Pollinating insects are declining due to habitat loss and climate change, and cities with limited habitat and floral resources may be particularly vulnerable. The effects of urban landscapes on pollination networks remain poorly understood, and comparative studies of taxa with divergent niches are lacking. Here, for the first time, we simultaneously compare nocturnal moth and diurnal bee pollen-transport networks using DNA metabarcoding and ask how pollination networks are affected by increasing urbanisation. Bees and moths exhibited substantial divergence in the communities of plants they interact with. Increasing urbanisation had comparable negative effects on pollen-transport networks of both taxa, with significant declines in pollen species richness. We show that moths are an important, but overlooked, component of urban pollen-transport networks for wild flowering plants, horticultural crops, and trees. Our findings highlight the need to include both bee and non-bee taxa when assessing the status of critical plant-insect interactions in urbanised landscapes.</p>
Predicting barrier effects of transportation networks on Asian elephants: Implications for environmental impact assessment
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Raw data and R code for: Negative effects of urbanisation on diurnal and nocturnal pollen-transport networks
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Data from: Promiscuious pollinators - evidence from an Afromontane sunbird-plant pollen transport network
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Data from: Downscaling pollen-transport networks to the level of individuals
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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.