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1,721 results for “network data”
180-Bus Medium Voltage Distribution Network Data
<p>The data describes a realistic medium-voltage distribution network with 180 buses, 90 loads (6799 consumers), 5 EV parking lots, 2 Energy Storage Systems, 42 Wind Farms, 33 PV parks, 3 biomasses, and 1 substation supplied by a transformer with 1 MVA and 30 kV.</p> <p>The network characteristics, such as cable resistances, reactances, and thermal limits, are available.</p> <p>Demand, EV demand, and PV/Wind generation profiles are presented for 64 scenarios.</p>
Data associated to the paper "Reduced basis surrogates for quantum spin systems based on tensor networks"
<p>Within the reduced basis methods approach, an effective low-dimensional subspace of a quantum many-body Hilbert space is constructed in order to investigate, e.g., the ground-state phase diagram. The basis of this subspace is built from solutions of snapshots, i.e., ground states corresponding to particular and well-chosen parameter values. Here, we show how a greedy strategy to assemble the reduced basis and thus to select the parameter points can be implemented based on matrix-product-state calculations. Once the reduced basis has been obtained, observables required for the computation of phase diagrams can be computed with a computational complexity independent of the underlying Hilbert space for any parameter value. We illustrate the efficiency and accuracy of this approach for different one-dimensional quantum spin-1 models, including anisotropic as well as biquadratic exchange interactions, leading to rich quantum phase diagrams.</p>
Data for Aligning rTWT with 802.1Qbv. A Network Calculus Approach
<div>This project stores Matlab scripts to solve the equations from <a href="https://dl.acm.org/doi/10.1145/3565287.3617606" target="_blank" rel="nofollow noreferrer noopener">Aligning rTWT with 802.1Qbv: a Network Calculus Approach </a></div> <div>This information can also be found in the next link, please check it for updates: <a href="https://gitlab.netcom.it.uc3m.es/predict-6g/twtscheduler" target="_blank" rel="noopener">PREDICT 6G / Aligning rTWT with 802.1Qbv. A Network Calculus Approach · GitLab (uc3m.es)</a>. </div> <div> </div> <div><em>This work has been partially funded by the European Commission Horizon Europe SNS JU <a href="https://predict-6g.eu/" target="_blank" rel="nofollow noreferrer noopener">PREDICT-6G</a> (GA 101095890) Project and the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D <a href="https://unica6g.it.uc3m.es/6g-edgedt/" target="_blank" rel="nofollow noreferrer noopener">6G-EDGEDT</a> and 6G-<a href="https://unica6g.it.uc3m.es/6g-datadriven/" target="_blank" rel="nofollow noreferrer noopener">DATADRIVEN</a>. </em></div> <div> </div>
Data for "Synapses without Tension Fail to Fire in an In Vitro Network of Hippocampal Neurons."
<p>Neurons in the brain communicate with each other at their synapses. It has long been understood that this communication occurs through biochemical processes. Here, we reveal a previously unrecognized paradigm wherein mechanical tension in neurons is essential for communication. Using <em>in vitro</em> rat hippocampal neurons, we find that (1) neurons become tout/tensed after forming synapses resulting in a contractile neural network, and (2) without this contractility, neurons fail to fire. To measure time evolution of network contractility in 3D (<em>not</em> 2D) extracellular matrix, we developed an ultra-sensitive force sensor with 1 nN resolution. We employed Multi-Electrode Array (MEA) and iGluSnFR, a glutamate sensor, to quantify neuronal firing at the network and at the single synapse scale, respectively. When neuron contractility is relaxed, both techniques show significantly reduced firing. Firing resumes when contractility is restored. This discovery highlights the essential contribution of neural contractility in fundamental brain functions and has implications for our understanding of neuronal physiology.</p>
Data from: Urbanisation and agricultural intensification modulate plant-pollinator network structure and robustness
<p>Land use change is a major pressure on pollinator abundance, diversity, and plant-pollinator interactions. Far less is known about how land use alters the structure of plant-pollinator networks and their robustness to plant-pollinator coextinctions.</p> <p>We analyzed the structure of plant-pollinator networks sampled in 12 landscapes along an urbanisation and agricultural intensity gradient, from early spring to late summer 2021, and used a stochastic coextinction model to correlate plant-pollinator coextinction risk with network structure (species and network-level metrics) and landscape context.</p> <p>Networks in intensively managed (i.e. agricultural and urban) landscapes had a lower risk of initiating a coextinction cascade, while networks in less-intensively managed landscapes may be less robust. Network structure modulated the frequency and severity of coextinctions and species loss, while the strength of species interactions increased robustness.</p> <p>Urban networks were more species-rich and symmetrical due to the high diversity of ornamental plants, while intensively managed agricultural landscapes had smaller, more tightly connected, and nested networks.</p> <p>Network structure modulated the frequency of extinctions, which was decreased by greater linkage density, interaction asymmetry, and interaction dependence in the networks, while once an extinction occurred, nestedness and linkage density propagated the degree of the coextinction cascade and species loss. At the species level, species strength was inversely correlated with extinction risk, implying that generalist species with a high number of interactions with specialists had the lowest extinction risk.</p>
Data from: Jeans and language: social networks and reproductive success are associated with the adoption of outgroup norms
<p><span>Traditional norms of human societies in rural China may have changed due to population expansion, rapid development of the tourism economy and globalization since </span><span>the</span><span> 1990s; people from different ethnic groups might adopt cultural traits from out</span><span>side</span><span> their group or lose their own culture at different rates. Human behavioural ecology can help to explain adoption of outgroup cultural values. We compared the adoption of four cultural values, specifically speaking outgroup languages/mother tongue and wearing jeans, in two co-residing ethnic groups, the Mosuo and Han. Both groups are learning outgroup traits, including each other's languages through contact in economic activities, education and social networks, but only the Mosuo are starting to lose their own language. Males are more likely to adopt outgroup values than females in both groups. Females of the two groups are no different in speaking Mandarin and wearing jeans, whereas males do differ, with Mosuo males being keener to adopt them than Han males. </span><span>The reason might be that Mosuo men experience more reproductive competition over mates than others, as Mosuo men have larger reproductive skews than others. Moreover, Mosuo men but not others gain fitness benefits from the adoption of Mandarin (they start </span><span>reproducing</span><span> earlier than non-speakers).</span></p> <p><span>This article is part of the theme issue 'Social norm change: from evolution to policy intervention'.</span></p>
Flower-visitor and pollen-load data provide complementary insight into species and individual network roles
<p>Most animal pollination results from plant-insect interactions, but how we perceive these interactions may differ with the sampling method adopted. The two most common methods are observations of visits by pollinators to plants and observations of pollen loads carried by insects. Each method could favour the detection of different species and interactions, and pollen load observations typically reveal more interactions per individual insect than visit observations. Moreover, while observations concern plant and insect individuals, networks are frequently analysed at the level of species. Although networks constructed using visitation and pollen-load data have occasionally been compared in relatively specialised, bee-dominated systems, it is not known how sampling methodology will affect our perception of how species (and individuals within species) interact in a more generalist system. Here we use a Diptera-dominated high-Arctic plant--insect community to explore how sampling approach shapes several measures of species' interactions (focusing on specialisation), and what we can learn about how the interactions of individuals relate to those of species. We found that species degrees, interaction strengths, and species motif roles were significantly correlated across the two method-specific versions of the network. However, absolute differences in degrees and motif roles were greater than could be explained by the greater number of interactions per individual provided by the pollen-load data. Thus, despite the correlations between species roles in networks built using visitation and pollen-load data, we infer that these two perspectives yield fundamentally different summaries of the ways species fit into their communities. Further, individuals' roles generally predicted the species' overall role, but high variability among individuals means that species' roles cannot be used to predict those of particular individuals. These findings emphasize the importance of adopting a dual perspective on bipartite networks, as based on the different information inherent in insect visits and pollen loads.</p>
Data from: A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images
<p>Accurate species identification is a prerequisite to assess the medical relevance of a mosquito specimens. In monitoring or surveillance programs, mosquitoes are typically identified based on morphological characters, which can be supported by molecular biological assays. Both methods require intensive experience of the observers and well-equipped laboratories. The use of convolutional neural networks (CNNs) to identify species based on images may be a cost-effective and reliable alternative. In this proof-of-concept study, we developed a CNN to identify seven <em>Aedes</em> species by wing images, only. While previous studies used images of the whole mosquito body, the nearly two-dimensional wings may facilitate standardized image capture and thereby reduce the complexity of the CNN implementation.</p> <p>Mosquitoes were sampled from different sites in Germany. Their wings were mounted and photographed with a professional stereomicroscope. The data set consisted of 1,155 wing images from seven <em>Aedes</em> species, including the exotic species <em>Aedes albopictus</em> und six native <em>Aedes</em> species, as well as 554 wings from different non-<em>Aedes </em>mosquitoes. The wing images were used to train a CNN to differentiate between <em>Aedes</em> and non-<em>Aedes</em> mosquitoes and to classify the seven <em>Aedes </em>species. The training was conducted separately for grayscale and RGB images. Image processing, data augmentation, training, validation and testing were conducted in python using deep-learning framework PyTorch. </p> <p>For both input images, i.e. grayscale and RGB images, our best-performing CNN configuration achieved an accuracy of 100% to discriminate <em>Aedes</em> from non-<em>Aedes </em>mosquito species<em>. </em>The accuracy to predict the <em>Aedes</em> species reached 93% for grayscale images and 96% for RGB images. <em>Aedes albopictus</em> could be identified with an accuracy of 100%. </p> <p>In conclusion, wing images are sufficient to identify mosquito species by CNN based image classification. Thus, wing images can represent a useful complement for CNN-based image classification, e.g. for damaged mosquito specimens. Larger training data sets with further mosquito species and a greater variety of images are required to improve and test broad applicability.</p>
Receiver function data from Jammu and Kashmir seismological NETwork
<p>This data constitutes Radial component P-wave receiver functions computed at Gaussian width 2.5 for teleseismic earthquakes recorded at 20 broadband stations of the Jammu and Kashmir Seismological NETwork (JAKSNET). These P-RFs are used for modeling the crustal structure of the Jammu and Kashmir Himalaya.</p>
Data from: Heat stress conditions affect the social network structure of free‐ranging sheep
<p>Extreme weather conditions, like heatwave events, are becoming more frequent with climate change. Animals often modify their behaviour to cope with environmental changes and extremes. During heat stress conditions, individuals change their spatial behaviour and increase the use of shaded areas to assist with thermoregulation. Here, we suggest that for social species, these behavioural changes and ambient conditions have the potential to influence an individual's position in its social network, and the social network structure as a whole. We investigated whether heat stress conditions (quantified through the temperature humidity index) and the resulting use of shaded areas, influence the social network structure and an individual's connectivity in it. We studied this in free‐ranging sheep in the arid zone of Australia, GPS‐tracking all 48 individuals in a flock. When heat stress conditions worsened, individuals spent more time in the shade and the network was more connected (higher density) and less structured (lower modularity). Furthermore, we then identified the behavioural change that drove the altered network structure and showed that an individual's shade use behaviour affected its social connectivity. Interestingly, individuals with intermediate shade use were most strongly connected (degree, strength, betweenness), indicating their importance for the connectivity of the social network during heat stress conditions. Heat stress conditions, which are predicted to increase in severity and frequency due to climate change, influence resource use within the ecological environment. Importantly, our study shows that these heat stress conditions also affect the animal's social environment through the changed social network structure. Ultimately, this could have further flow on effects for social foraging and individual health since social structure drives information and disease transmission.</p>
The data for "Search for gravitational-lensing signatures in the full third observing run of the LIGO–Virgo network"
<p>This material is part of several data products associated with the publication "Search for gravitational-lensing signatures in the full third observing run of the LIGO–Virgo network" from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://arxiv.org/abs/2304.08393">arXiv</a>) and the related material linked from this page.</p><p><strong>Data release</strong></p><p>This data release contains plotting scripts, jupyter notebooks and datasets for the figures / tables in the aforementioned paper. Each script/notebook have detailed instructions and comments.</p><p><strong>How to download all files from this page</strong></p><p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p><p>pip install zenodo_get zenodo-get RECORD_ID_OR_DOI</p><p>where the record ID for the most recent version of this page is v5 and IDs for other versions can be found in the Versions section at the side of this page.</p>
Data from: Recreational vessel networks reveal potential hot spots for marine pest introduction and spread
<p>Recreational vessels are an important pathway for spreading marine non-indigenous species (NIS) around coastal environments globally. However, most vessels are not tracked, limiting our ability to map their movements and identify locations at greater risk of NIS introductions. Using New Zealand as a case study, we quantified spread and risk patterns of recreational vessel movements, using a web-based survey allowing the more than 1,800 respondents to map significant trips with over 12,000 visits. These vessel routes were used to build a network model representing nationwide recreational vessel movements. Two proxies were used to quantify the risk of introduction of marine NIS: (i) incoming hull length and (ii) cumulative residency periods at sites. There was significant variation in the distances travelled, the destinations they visited, and the duration of their stays. New Zealand's recreational boating network contained 317 destinations with over 4,000 unique connections, concentrated within two distinct areas of the country. Network-based metrics and risk proxies quantified the relative importance of domestic locations as incursion or spreading hubs for NIS. This approach highlighted several areas that pose high relative biosecurity risk within the national network, but are underrepresented within current surveillance programmes. Synthesis and applications. Our study demonstrates how the movement dynamics of recreational vessels can be quantified at a regional scale to inform proactive management. The identification of spreading hubs and locations at particular risk of NIS introductions, enables managers to design risk-based and effective surveillance and monitoring programmes. Our network-based approach to determine the biosecurity implications posed by recreational vessels is transferable to other parts of the world. It enables managers to understand the distribution of risk within an area of interest (e.g., a jurisdiction) and develop optimised approaches for mitigating impact.</p>
California MPA network ROV data set and code
<p>Dataset of remotely operated vehicle (ROV) surveys conducted across California's MPA network between 2005 and 2021 and code to conduct analyses and produce plots in the manuscript "Diving deep into the network: quantifying protection effects across California's marine protected area network using a remotely operated vehicle".</p>
Data and code for the GECCO 2024 paper: "Understanding fitness landscapes in morpho-evolution via local optima networks"
<p>The LON and algorithm run data is available in data/ </p> <p>To run the LON extraction:</p> <p>From gymrem2d-lons/ModularER_2D, run python3 setup.py</p> <p>Direct encoding: python3 lons.py --file direct.cfg</p> <p>LSystem: python3 lons.py --file lsystem.cfg </p> <p>CPPN: python3 lons.py --file cppn.cfg</p> <p> </p>
Code and partial data used in "Vertically recurrent neural networks for sub-grid parameterization"
<p>This repository contains the RNN training and evaluation code used in the paper<em> Vertically recurrent neural networks for sub-grid parameterization</em></p> <p> </p> <ul> <li> The radiative transfer emulation data can be accessed with through a Climetlab plugin (<a href="https://pypi.org/project/climetlab-maelstrom-radiation/">Climetlab-maelstrom-radiation</a>). <p>Datasets are downloaded and explained in the demo notebook here <a href="https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb" rel="nofollow">https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb</a></p> In addition the full training and testing code used in the paper is uploaded here (<em>pu-maelstrom-radiation.tar.gz</em>).</li> <li> </li> </ul> <p>Three parameterization problems from earlier studies are also included (we have modified the code from these papers to incorporate RNNs): </p> <ul> <li>non-orographic gravity wave drag (<a href="https://doi.org/10.1029/2021MS002477">Chantry et al. 2021</a>) <ul> <li>Based on TensorFlow</li> <li>This repository uses the <em>CliMetLab </em>plugin and<strong> downloads the data from the European Weather Cloud</strong></li> </ul> </li> <li>non-local parameterization (<a href="https://doi.org/10.1029/2022MS002984">Wang et al. 2022</a>) <ul> <li>The new code is based on TensorFlow, so you'll need both PyTorch and TensorFlow to run everything</li> <li><strong>See original paper for data access</strong></li> </ul> </li> <li>moist physics (Han et al. <a href="https://doi.org/10.1029/2022MS003508">2023</a>, <a href="https://doi.org/10.1029/2020MS002076">2020</a>) <ul> <li>Based on TensorFlow and PyTorch. This one has the most additions, e.g. code to generate a TensorFlow TFRecord dataset from the raw netCDF data archived in the original paper, autoregressive training and experimental model architectures in PyTorch</li> <li><strong>See original paper for data access</strong></li> </ul> </li> </ul> <p>Each of the code repos (unpack the tars) have an updated README.</p> <p>References:</p> <table> <tbody> <tr> <td> <div>Chantry, M., Hatfield, S., Dueben, P., Polichtchouk, I., & Palmer, T. (2021). Machine learning emulation of gravity wave drag in numerical weather forecasting. <em>Journal of Advances in Modeling Earth Systems</em>, <em>13</em>(7), e2021MS002477</div> <div> </div> <div> <div>Han, Y., Zhang, G. J., Huang, X., & Wang, Y. (2020). A moist physics parameterization based on deep learning. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>(9), e2020MS002076.</div> </div> <div> </div> <div>Han, Y., Zhang, G. J., & Wang, Y. (2023). An ensemble of neural networks for moist physics processes, its generalizability and stable integration. <em>Journal of Advances in Modeling Earth Systems</em>, <em>15</em>(10), e2022MS003508</div> <div> </div> <div>Wang, P., Yuval, J., & O’Gorman, P. A. (2022). Non‐local parameterization of atmospheric subgrid processes with neural networks. <em>Journal of Advances in Modeling Earth Systems</em>, <em>14</em>(10), e2022MS002984.</div> </td> </tr> <tr></tr> </tbody> </table> <div> </div>
Data: Core-periphery detection in multilayer networks
Open the record for dataset details and reuse information.
Supplementary data for manuscript "Genetic risk converges on regulatory networks mediating early type 2 diabetes"
<p>Supplementary data for manuscript "Genetic risk converges on regulatory networks mediating early type 2 diabetes" Nature 624, 621–629 (2023). <a href="https://doi.org/10.1038/s41586-023-06693-2">https://doi.org/10.1038/s41586-023-06693-2</a></p> <p>Brief description of the included files is given below. Please visit the manuscript website for latest updates: <a href="http://theparkerlab.org/manuscripts/2021_islet-rfx6/">http://theparkerlab.org/manuscripts/2021_islet-rfx6/</a></p>
Data: Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD
<p>This archive contains all scripts, resource data and results, including intermediate results to reproduce the results for "Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD". </p>
Dataset for the article "Can we use seismic reflection data to infer the interconnectivity of fracture networks?"
<p>This package contains the effective stiffness coefficients of the fractured rock samples explored in the paper of Rubino et al. "Can we use seismic reflection data to infer the interconnectivity of fracture networks?".</p>
Data belonging to: Decreasing relatedness among mycorrhizal fungi in a shared plant network increases fungal network size but not plant benefit
<p>Dataset beloning to the publication: "Decreasing relatedness among mycorrhizal fungi in a shared plant network increases fungal network size but not plant benefit" in Ecology letters (2021). R script used to analyse the data in the .csv files</p>
ScienceDex guides
Understand access before you commit
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.