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151 results for “network scaling”
Required data for simulating a typical large-scale urban traffic network
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Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks
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Hierarchically embedded scales of movement shape the social networks of vampire bats
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Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO)
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Data from: A reusable pipeline for large-scale fiber segmentation on unidirectional fiber beds using fully convolutional neural networks
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Training data for "From small to large-scale genome comparison", a tutorial for the Galaxy Training Network
<p>This dataset comprises two sequence pairs in FASTA format, one including two mycoplasmas (<em>Hyopneumoniae</em> 232 and 7422) and the other including the first chromosome of two plant genomes (<em>Aegilops tauschii</em> and <em>Triticum aestivum</em>).</p>
Data supplementing the article "Assessing ecological status with diatoms DNA metabarcoding : scaling-up on a WFD monitoring network (Mayotte island, France)" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal
<p>These data supplement the article"Assessing ecological status with DNA metabarcoding or microscopy? Comparison using benthic diatoms in tropical rivers" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal.</p> <p>The directory contains the following files:</p> <p><strong>80 PGM sequencing libraries (raw data, fastq files).rar </strong>- contains the 80 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>80 fastq files information.xlsx</strong> - contains the information relative to the 80 samples including: the ID used in Mothur analyses (corresponding to the name of the fastq files), the sample name, the sampling site code, the name of the river, the monitoring network to which rivers belong, the year of sampling and the GPS coordinates of sampling sites.</p> <p><strong>OTU (95 percent of similarity) list of 80 Mayotte samples.xlsx</strong> - contains the final OTU list obtained after applying all the bioinformatics treatments (trimming, clustering,...): OTUs created at 95% of similarity, the number of DNA reads per sample was normalized at 5710 reads (the smallest values obtained in one sample). A DNA representative sequence and the taxonomic assignment determined using Mothur (using classify.otu command) are also provided for each OTU.</p>
Data from: Latest Ordovician (Hirnantian) brachiopod faunal lists used for non-matric multidimensional scaling (NMDS) and network analyses
<p><span>A total of 107 brachiopod genera of Hirnantian age among 42 localities worldwide are compiled into a binary dataset (Table S1; presence =1, absence = 0). The majority of the faunal lists was derived from the well-screened Hirnantian brachiopod faunal data of Rong et al. (2020). In this study, the Hirnantian faunal lists are updated for the following localities: </span><span>Anticosti Island, eastern Canada; </span><span>Edgewood region, American Mid-Continent; </span><span>Mackenzie Mountains, northwestern Canada. D</span><span>etailed discussions on these faunal update and references are provided in the main paper (section on Paleobiogeography of the Mackenzie Mountains Hirnantian fauna). </span></p>
Unraveling the cavity-nesting network at large spatial scales: The biogeographic role of woodpeckers as ecosystem engineers
<p><strong>Aim</strong>: Cavities are usually a limiting resource for several forest-dwelling vertebrates, with effects that propagate through ecological networks. Although diverse assemblages of primary excavators (e.g., woodpeckers) are assumed to increase cavities, other forest resources can also limit populations of primary excavators and cavity users, thus undermining the ecological role of excavators over different scales. We aim to test the biogeographical-scale relationships between primary excavators and cavity users by distinguishing the contribution of forest characteristics.</p> <p><strong>Location</strong>: Southern South America</p> <p><strong>Methods</strong>: We used species distribution models, which combine bioclimatic and remote sensing derived variables, to map the richness of vertebrates composing the cavity network of temperate and Mediterranean forests of South America. Based on a resampling procedure for ensuring spatial independence, we fitted structural equation models to estimate causal relationships between forest characteristics and cavity-user vertebrates.</p> <p><strong>Results</strong>: Secondary cavity users (obligated, habitat generalists and forest specialists) were positively and strongly influenced by the richness of primary excavators, while mammal richness was more influenced by tree richness. The richness of trees and <em>Nothofagus</em> tree species influenced positively the richness of primary excavators and secondary cavity users. Canopy height and net primary productivity affected positively secondary cavity users.</p> <p><strong>Main conclusions</strong>: Our results confirm the role of primary excavators as ecosystem engineers but highlight the importance of considering large spatial scales when analyzing cavity-nesting networks. Biogeographical patterns of tree diversity and forest structure can be important drivers of cavity-nesting networks that remain hidden when studies are conducted over fine spatial scales. </p>
Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona
<p>These are supplementary data for the paper "Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona". They are:</p> <ul> <li>Python code to forecast Kp index</li> <li><span>Code to construct a nerual network model</span></li> </ul>
Seismicity patterns and multi-scale imaging of Krafla (N-E) Iceland with local earthquake tomography: Raw event waveforms for all events used in the inversion and manual picks for temporary network
<p>This Data and Software were used in the submitted paper "Seismicity patterns and multi-scale imaging at Krafla (N-E Iceland) wih local earthquake tomography" by Glück et al.<br>The data and software provided here are used to compute the velocity models with TomoTV.<br>The raw data (.mseed format) can be visualised with the Python package Pyrocko/Snuffler, which was also used for the arrival time picking.<br>For the temporary network the manual picks are provided along with the code to prepare the manual picks as the input files for a localisation with NonLinLoc by weighting and quality checking the data. This resulting localsitations and the weighted traveltimes are then used for the LET.<br>The same workflow was used for the picks from the permanent network.</p> <p>Data:<br>- Raw data (\WaveformsPermanentStations): 7s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/ for the years 2021 and 2022.<br>- Raw data (\WaveformsNodes): 5s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/2022 recorded with the temporary network of 98 temporary nodes in June and July 2022.<br>- Pickfile (ManualPicks_100Nodes_Kafla2022.txt): Manual picks of the events listed in the ISOR catalogue for the evenst recorded with the temporary network.<br>- Station file (Station_file.txt): The station file includes the coordinates (Lat, Lon, Elevation) of the permanent stations (StationID starting with K...) and of the temporary nodes (StationID starting with N...).</p> <p>Software (Hyp_format.py):<br>- Weighting: The picks are weighted according to their Signal-to-Noise ratio (described in more detail in Section 2.3 in the main text of the paper)<br>- Writing the inputfile for NonLinLoc (with the selecting the mode option "PorS" in line 118), including all picks, also for those stations where not both phases were picked. The file "endfile.txt" is needed to write the picks to the NonLinLoc input format.<br>- Quality check of the picks: Computing a modified Wadati diagram from the traveltime differences of P and S phases for all the events available (with the selecting the mode option "PandS" in line 118)<br>- Python packages needed: numpy, scipy, matplotlib, pandas, obspy</p>
EnGRaiN : A Supervised Ensemble Learning Method for Recovery of Large-scale Gene Regulatory Networks
<p>EnGRaiN is a supervised machine learning method to construct ensemble networks. To benefit from the typical accuracy advantages of supervised learning methods while taking into account the impossibility of knowing true networks for training, we devised a method that uses small training datasets of true positives and true negatives among gene pairs.</p> <p>The datasets used to evaluate the performance of EnGaiN include (i) simulated datasets generated from Yeast networks and (ii) A. thaliana gene expression datasets.</p>
Dissociable Multi-scale Patterns of Development in Personalized Brain Networks
<p>The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derived personalized functional networks across 29 scales in a large sample of youths (n=693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results demonstrate that the development of functional brain networks align with and refine a hierarchy linked to cognition</p>
Large-scale dataset for the analysis of outdoor-to-indoor propagation for 5G mid-band operational networks
<p>We present a comprehensive dataset of channel measurements, performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. The dataset includes measurements of channel power delay profiles from two 5G networks operating in Band n78, i.e., 3.3--3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy, by using the Rohde & Schwarz (R&S) network scanner TSMA6 for several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics towards the definition and refinement of empirical channel propagation models.</p>
Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
<p> </p> <div> <div><a href="https://arxiv.org/search/cs?searchtype=author&query=Tello,+A">Andres Tello*</a><em>, </em><a href="https://arxiv.org/search/cs?searchtype=author&query=Truong,+H">Huy Truong*</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lazovik,+A">Alexander Lazovik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Degeler,+V">Victoria Degeler</a>. Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks. Engineering Proceedings. 2024; 69(1):50. <a href="https://doi.org/10.3390/engproc2024069050">https://doi.org/10.3390/engproc2024069050</a></div> <br> <div>(*) Both authors contributed equally.<br><br></div> <h2>Update</h2> <div>(04/09/2024): Citation is updated.<br>We have added headers for CSVs and auxiliary data (duration time, edge list, ordered names.. ) in the configuration file (JSON format). As such, corresponding INP files can be omitted when working with this version. <br>The EXN network has been included in this version, so the total number of processed networks is 11.<br>For more details, please read ZENODO_README.md.</div> <h2>Contact</h2> <div>For dataset-related questions: <a href="mailto:h.c.truong@rug.nl" target="_blank" rel="noopener">Huy Truong</a></div> <br> <div>For data acquisition: <a href="mailto:a.tello@rug.nl" target="_blank" rel="noopener">Andres Tello</a></div> <br> <div>If you use this dataset, please cite:</div> <blockquote>@article{tello2024largescale,<br> AUTHOR = {Tello, Andrés and Truong, Huy and Lazovik, Alexander and Degeler, Victoria},<br> TITLE = {Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks},<br> JOURNAL = {Engineering Proceedings},<br> VOLUME = {69},<br> YEAR = {2024},<br> NUMBER = {1},<br> ARTICLE-NUMBER = {50},<br> URL = {https://www.mdpi.com/2673-4591/69/1/50},<br> ISSN = {2673-4591},<br> DOI = {10.3390/engproc2024069050}<br>}</blockquote> </div>
Raw datasets for paper "Multi-scale hydraulic graph neural networks for flood modelling"
<p>The repository contains two zip folders for the synthetic and case study datasets (raw_datasets_mesh.zip, raw_datasets_dk15.zip). </p> <p>Each zip folder comprises 4 subfolders (DEM, Geometry, Hydrograph, Simulations), containing the elevation, boundary polygon, discharge hydrograph, and full hydrodynamic results for all simulations.</p> <p>The overview.csv file provides the seeds used for experiment replicability and the runtime of the numerical model on each simulation.</p>
Supplementary Information for Consonance-emerging Hebbian Learning neural network model predicts discreteness of musical scales and the Natural Just Intonation scale
<p><strong>The following phenomena and features are apparent in music and auditory perception in general: the discreteness of the tones in musical scales</strong> [1]<strong>, the prevalence of the tonal frequency span of one semitone (100 cents) in musical scales across cultures </strong>[1]<strong>, the list of tonal intervals ordered by consonance [2], and the musical performers’ preference of the Natural Just-Intonation scale [3] (A). However, researchers still have no agreement about the causes and the emergence of said phenomena (A). Here we show that the consonance-pattern emerging neural network model introduced in our previous study [4], predicts and yields all the said phenomena (A) with a precision of 1/100<sup>th</sup> of a semitone (1 cent). This precision is beyond the resolution of human hearing </strong>[5], [6], [7]. <strong>Since the Hebbian learning paradigm and harmonicity are the main features of our model, we propose that they are sufficient conditions for any system to yield the said phenomena (A). Therefore, they have a crucial role in processing pitch, consonance, and music perception in general. As a consequence, we additionally propose that the mentioned phenomena (A) are a balanced result of the joint workings of the Hebbian paradigm (nurture and cultural exposure) and harmonicity (auditory physics and biology).</strong></p>
Dataset (n=1671) of the publication "Small-scale private forest owners and the European Natura 2000 conservation network: Perceived ecosystem services, management practices, and nature conservation attitudes"
<p>The excel file contains the replies of small-scale private forest owners in Lower Saxony, Germany, to certain questions of a survey analyzed within the following publication: "Small-scale private forest owners and the European Natura 2000 conservation network: Perceived ecosystem services, management practices, and nature conservation attitudes”.<br> <br> The questions included in this dataset cover their objectives, management activities, attitudes and forest stand structures. Further details can be found in the second sheet of the file called “Code explanation”.<br> <br> More information about the methods of data collection can be found in the before-mentioned publication.</p>
Dataset for: Indirect nitrous oxide emission factors of fluvial networks can be predicted by dissolved organic carbon and nitrate from local to global scales
<p>Streams and rivers are important sources of nitrous oxide (N<sub>2</sub>O), a powerful greenhouse gas. Estimating global riverine N<sub>2</sub>O emissions is critical for the assessment of anthropogenic N<sub>2</sub>O emission inventories. The indirect N<sub>2</sub>O emission factor (EF<sub>5r</sub>) model, one of the bottom-up approaches, adopts a fixed EF<sub>5r</sub> value to estimate riverine N<sub>2</sub>O emissions based on IPCC methodology. However, the estimates have considerable uncertainty due to the large spatiotemporal variations in EF<sub>5r</sub> values. Factors regulating EF<sub>5r</sub> are poorly understood at the global scale. Here, we combine 4-year in situ observations across rivers of different land use types in China, with a global meta-analysis over six continents, to explore the spatiotemporal variations and controls on EF<sub>5r</sub> values. Our results show that the EF<sub>5r</sub> values in China and other regions with high N loads are lower than those for regions with lower N loads. Although the global mean EF<sub>5r</sub> value is comparable to the IPCC default value, the global EF<sub>5r</sub> values are highly skewed with large variations, indicating that adopting region-specific EF<sub>5r</sub> values rather than revising the fixed default value is more appropriate for the estimation of regional and global riverine N<sub>2</sub>O emissions. The ratio of dissolved organic carbon to nitrate (DOC/NO<sub>3</sub><sup>-</sup>) and NO<sub>3</sub><sup>-</sup> concentration are identified as the dominant predictors of region-specific EF<sub>5r</sub> values at both regional and global scales because stoichiometry and nutrients strictly regulate denitrification and N<sub>2</sub>O production efficiency in rivers. A multiple linear regression model using DOC/NO<sub>3</sub><sup>-</sup> and NO<sub>3</sub><sup>-</sup> is proposed to predict region-specific EF<sub>5r</sub> values. The good fit of the model associated with easily obtained water quality variables allows its widespread application. This study fills a key knowledge gap in predicting region-specific EF<sub>5r</sub> values at the global scale and provides a pathway to estimate global riverine N<sub>2</sub>O emissions more accurately based on IPCC methodology.</p> <p>This dataset is a global integrated N<sub>2</sub>O dataset including data from 4-year (2017-2020) in situ measurements of six large rivers in China, 3-year (2018-2020) in situ measurements of urban river networks in Beijing of China, and 825 measurements from 70 published papers over six continents. The data includes dissolved N<sub>2</sub>O concentration, biogeochemical (DOC, NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+</sup>, temperature, and DO), climatological (climate zones), and geographic (region, location, and land cover) information.</p>
Extracted Information for the Systematic Review of Survey Scales for Measuring Information Privacy Concerns on Social Network Sites
<p>The data set is part of a systematic literature review of survey scales for measuring information privacy concerns (IPCs) used in research on social network sites (SNSs).</p> <p>The results of this systematic literature review are available in Bartol, J., Vehovar, V., & Petrovčič, A. (2023). Systematic review of survey scales measuring information privacy concerns on social network sites. <em>Telematics and Informatics</em>, 102063. https://doi.org/10.1016/j.tele.2023.102063</p> <p>The article also includes a detailed description of the methods used in generating this data set.</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.