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9,152 results for “Networking”
Utah FORGE 2024 stimulations: Microseismic event catalog from seismic surface network
<p>This microseismic catalog of the 2024 stimulations at Utah FORGE is based on the surface monitoring network consisting of 5 permanent seismic stations deployed by the University of Utah Seismograph Stations and a temporary deployment of 144 nodal geophones.<br> <br>The catalog and its compilation are described in:<br>Niemz et al. (2024). Mapping Fracture Zones with Nodal Geophone Patches: Insights from Induced Microseismicity during the 2024 Stimulations at Utah FORGE. Submitted to Seismological Research Letters.<br> <br>The local coordinate system is relative to well 16A(78)-32 (lat=38.50402147, lon=-112.8963897, elev=1650m). The magnitudes in this catalog were calibrated based on the local magnitude provided by the University of Utah Seismograph Stations. They are not authoritative.</p>
Source Code Accompanying the Paper "More on network approaches in Historical Chinese Phonology (音韻學)"
<p>First version of the source code and data accompanying the paper "More on Network Approaches in Historical Chinese Phonology".</p> <p>This paper is available here:</p> <ul> <li>List, Johann-Mattis (2018): <strong>More on network approaches in Historical Chinese Phonology (音韻學)</strong>. Paper prepared for the <em>LFK Society Young Scholars Symposium</em>. Taibei: Li Fang-Kuei Society ofr Chinese Linguistics. URL: <a href="https://hal.archives-ouvertes.fr/hal-01706927">https://hal.archives-ouvertes.fr/hal-01706927</a>.</li> </ul> <pre><code>@InProceedings{List2018a, author = {List, Johann-Mattis}, title = {{More on Network Approaches in Historical Chinese Phonology (音韻學)}}, booktitle = {{LFK Society Young Scholars Symposium}}, year = {2018}, publisher = {Li Fang-Kuei Society for Chinese Linguistics}, pdf = {https://hal.archives-ouvertes.fr/hal-01706927/file/main.pdf}, url = {https://hal.archives-ouvertes.fr/hal-01706927}, address = {Taipei}, hal_id = {hal-01706927}, } </code></pre> <p>See the README.md for mor information.</p> <ul> <li> </li> </ul>
Supplementary material for 'Revealing patterns of nocturnal migration using the European weather radar network'
<p>This package contains data, filters and visualizations from <a href="https://doi.org/10.1111/ecog.04003">Nilsson and Dokter et al. (2019)</a>.</p> <p><strong>Files</strong></p> <p><strong>radar_metadata.csv</strong>: Metadata for the 84 European radars considered for this study. Includes radar code (<code>odim_code</code> = <code>country</code> + <code>odim_code_3char</code> and alternative radar code <code>vp_radar</code>), radar site location (<code>location</code>, <code>latitude</code>, <code>longitude</code>), radar site elevation (<code>site_altitude_asl</code> in meters above sea level) and radar altitude range used in this study (<code>min_height_cut_asl</code> and <code>max_height_cut_asl</code> in meters above sea level).</p> <p><strong>vp.zip</strong>: Vertical profiles of birds (vp) data, processed from the radar volume data following procedures described by Dokter et al. (2011), using the vol2bird algorithm in the R package bioRad. Zip file includes vp data for the 84 European radars considered for this study from September 19 to October 9, 2016 (21 days). This time period is characterized by strong passerine migration throughout Europe. Files are organized in radar (= <code>odim_code</code>), date and hour directories and follow the <a href="https://github.com/adokter/vol2bird/wiki/ODIM-bird-profile-format-specification">ODIM bird profile format specification</a>. Data can be read with the <a href="https://github.com/adokter/bioRad/">R package bioRad</a>.</p> <p><strong>vp_processing_settings.yaml</strong>: Data selection setting for this study, based on data quality criteria. File lists for each radar the altitudes to include (<code>include_heights</code>), time periods to exclude (<code>exclude_datetimes</code>) and reasons for exclusion (comments). 70 of the 84 radars were retained after filtering.</p> <p><strong>vp_processed_70_radars_20160919_20161009.csv</strong>: Processed vp data for 70 radars. Is the result of processing <code>vp.zip</code> with <code>vp_processing_settings.yaml</code> and <code>radar_metadata.csv</code> using <a href="https://doi.org/10.5281/zenodo.1173544">vp-processing</a> (Desmet & Nilsson 2018). Note: includes all timestamps: day and night & those marked for exclusion (marked in <code>exclusion_reason</code>). This data file forms the basis for analysis in the study.</p> <p>Headers are:</p> <ul> <li><code>radar_id</code>: odim_code of the radar</li> <li><code>datetime</code>: timestamp</li> <li><code>HGHT</code>: lower altitude of altitude bin (m above sea level)</li> <li><code>u</code>: bird ground speed towards east (m/s)</li> <li><code>v</code>: bird ground speed towards north (m/s)</li> <li><code>dens</code>: bird density (birds/km3)</li> <li><code>dd</code>: bird flight direction (degrees from north)</li> <li><code>ff</code>: bird ground speed (m/s)</li> <li><code>DBZH</code>: reflectivity factor (dBZ) in horizontal polarisation</li> <li><code>mtr</code>: migration traffic rate (birds/km/h)</li> <li><code>day_night</code>: timestamp occurs during <code>day</code> or <code>night</code> (based on sunrise/sunset)</li> <li><code>date_of_sunset</code>: date at sunset, with night timestamps between midnight and sunrise belonging to the previous date</li> <li><code>exclusion_reason</code>: reason timestamp is excluded in vp_processing_settings.yaml (if applicable). Excluded timestamps have <code>NA</code> values for <code>u</code>, <code>v</code>, <code>dens</code>, <code>dd</code>, <code>ff</code>, <code>DBZH</code>, and <code>mtr</code>.</li> </ul> <p><strong>vp_flowviz.csv:</strong> Input data for visualizations. Is the result of processing <code>vp_processed_70_radars_20160919_20161009.csv</code> using <code>vp-to-flowviz.Rmd</code> in <a href="https://doi.org/10.5281/zenodo.1173544">vp-processing</a> (Desmet & Nilsson 2018). Aggregates data in hourly bins for 200-2000m (<code>altitude_band</code> = 1) and above (<code>altitude_band</code> = 2). Only altitude band 1 is used in visualizations.</p> <p><strong>flowviz.mov:</strong> Screencast of <code>vp_flowviz.csv</code> visualized with <a href="https://doi.org/10.5281/zenodo.57472">Bird migration flow visualization v2</a> (Desmet et al. 2016, Shamoun-Baranes et al. 2016). The visualization extrapolates the migration over the entire sampling range (cropped in the screencast due to technical limitations and thus excluding the Bulgarian radar), not taking topography or water bodies into account, and shows the ground speed (length of arrows) and direction of migration over time. Note that density is not shown: low density movements can therefore appear as strong as high density movements when ground speeds are similar.</p> <p><strong>cartoviz.mov:</strong> Screencast of <code>vp_flowviz.csv</code> visualized as an interactive map with <a href="https://carto.com">CARTO</a>. Visualization shows migration density (size of circles) and mean direction (colour) over time. The interactive map is available at <a href="https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed">https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed</a>.</p>
Navigating protected areas networks for improving diffusion of conservation practices
<p>The Natura 2000 protected area network is the cornerstone of European Union's biodiversity conservation strategy. These protected areas range across multiple biogeographic regions, and they include a diversity of species assemblages along with a diversity of managing organizations, altogether making difficult to pool relevant sites to facilitate the flow of knowledge significant to their management. Here we introduce an approach to navigating protected area networks that has the potential to foster systematic identification of key sites for facilitating the exchange of knowledge and diffusion of information within the network. To demonstrate our approach, we abstractly represented Romanian Natura 2000 network as a co-occurrence network, with individual sites as nodes and shared species as edges, further combining into our analysis network topology, community detection, and network reduction methods. We identified most representative Natura 2000 sites that may increase the transfer of information within the national network of protected areas, detected clusters of sites and key sites for maintaining network cohesiveness, and highlighted the subsample of sites that retain the characteristics of the entire network. Our analysis provides implications for protected area prioritization by proposing a network perspective approach to collaboration rooted in ecological principles.</p>
Network Measurements while Uploading 5.6 KB Files from Moving Buses to Cellular Networks in Varmland, Sweden.
<p>The dataset and the collection methodology are described and used in the following papers:</p> <ul> <li>Ben Abdesslem, Fehmi, Henrik Abrahamsson, and Bengt Ahlgren.<br> "<em><strong>Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications" </strong></em><br> Network Traffic Measurement and Analysis Conference (TMA'18), Vienna, Austria (2018).</li> <li>Ben Abdesslem, Fehmi, Henrik Abrahamsson, and Bengt Ahlgren.<br> "<em><strong>Cellular Network Multi-Access Measurements on the Roads of Värmland, Sweden.</strong></em>" <br> <em>arXiv preprint arXiv:1805.06814</em> (2018).</li> <li>Henrik Abrahamsson, Ben Abdesslem, Fehmi, Bengt Ahlgren, Anna Brunstrom, Ian Marsh and Mats Björkman.<br> "<em><strong>Connected Vehicles in Cellular Networks: Multi-access versus Single-access Performance</strong></em>" <br> 2nd Workshop on Mobile Network Measurement (MNM’18), Vienna, Austria (2018).</li> </ul> <p>The CSV file has the following columns:</p> <ul> <li>Index: Unique number for the transaction</li> <li>Timestamp: Time and date of the transaction</li> <li>Interface: Interface used by the transaction [op0, op1 or op2]</li> <li>TransactionTime: Duration of the transaction (in sec)</li> <li>Status: Result of the transaction [failed, senderror, timeout, or number of received bytes acknowledged]</li> <li>GpsTimestamp: Time and date of the GPS coordinates</li> <li>GpsLatitude: Last GPS latitude known</li> <li>GpsLongitude: Last GPS longitude known</li> <li>ModemTimestamp: Time and date of the modem properties</li> <li>ModemOperator: Name of the operator [op0, op1, op2]. The original names (Telia, Telenor, 3) have been replaced in a different order.</li> <li>ModemRSSI: RSSI (in dBm)</li> <li>ModemCID: Cell ID</li> <li>ModemDeviceMode: <ul> <li>UNKNOWN (0).</li> <li>DISCONNECTED (1).</li> <li>NO_SERVICE (2).</li> <li>2G (3).</li> <li>3G (4).</li> <li>LTE (5).</li> </ul> </li> <li>ModemDeviceSubmode: <ul> <li>UNKNOWN (0).</li> <li>UMTS (1).</li> <li>WCDMA (2).</li> <li>EVDO (3).</li> <li>HSPA (4).</li> <li>HSPA+ (5).</li> <li>DC HSPA (6).</li> <li>DC HSPA+ (7).</li> <li>HSDPA (8).</li> <li>HSUPA (9).</li> <li>HSDPA+HSUPA (10).</li> <li>HSDPA+ (11).</li> <li>HSDPA+HSUPA (12).</li> <li>DC HSDPA+ (13).</li> <li>DC HSDPA + HSUPA (14).</li> </ul> </li> <li>ModemLAC: Location Area Code</li> <li>ModemRSRP: RSRP (in dBm)</li> <li>ModemFrequency: Frequency in Mhz</li> <li>ModemRSRQ: RSRQ (in dBm)</li> <li>ModemBand: LTE band</li> <li>ModemPCI: LTE Physical Cell ID</li> <li>ModemECIO: Ec/Io</li> <li>ModemENODEBID: eNodeB ID</li> <li>ModemRSCP: RSCP (in dBm)</li> <li>bus: Bus number (head node number in Monroe)</li> <li>country: Country of operation [Sweden]</li> <li>protocol: protocol used [UDP, TCP or HTTPS]</li> <li>experiment: Experiment ID (one hour experiments)</li> <li>diff: Max time difference between the three simultaneous uploads (in ms)</li> <li>TransactionTime200: Transaction duration if timeout=200ms</li> <li>TransactionTime1000:Transaction duration if timeout=1000ms</li> <li>TransactionTime6000: Transaction duration if timeout=6000ms</li> <li>bestAvailability: Best availability over the whole experiment ID (%)</li> <li>bestAvailability200: Best availability over the whole experiment ID (%) if timeout=200ms</li> <li>bestAvailability1000: Best availability over the whole experiment ID (%) if timeout=1000ms</li> <li>best: Best duration (in sec)</li> <li>best1000: Best duration (in sec) if timeout=1000ms</li> <li>availability: Availability over the whole experiment ID (%)</li> <li>availability200: Availability over the whole experiment ID (%) if timeout=200ms</li> <li>availability1000: Availability over the whole experiment ID (%) if timeout=1000ms</li> <li>DayOfWeek: Day of the Week [Monday, ..., Sunday]</li> </ul>
Exemple of a valorization network based on a 10% collection rate from potential biowaste in the Grand Lyon territory (France).
<p>This study, conducted in the frame of the H2020 DECISIVE project, aims at developing a method to design a decentralized and small-scale AD (mAD) network in urban and peri-urban areas. A mixed integer linear program (MILP) was set up based on the proposed system. It aims at minimizing the impacts of the biowaste and the digestate transportation by minimizing their payload-distances while taking into account notably the technical constraints of the newly developed micro-AD. A Geographic Information System (GIS) based methodology was developed to feed the MILP model with very fine-scale data required to optimized a proximity treatment system. The method allows to locate and estimate the biowaste generation and to locate the digestate outlets, the agricultural areas, and to estimate the maximal amount of digestate usable. The candidate sites for mAD are identified with a GIS multi-criteria analysis that includes the environmental regulations, some urban planning rules and the site accessibility and heat outlet valorization. The method developed is successfully applied in the territory of The Grand Lyon Metropole (534 km²), located in France. The MILP model succeeds at providing a solution even with the very large problem studied (≥10<sup>8</sup> possible combination). Different scenarios can be easily tested to meet the potential needs of the stakeholder: the quantity of biowaste to treat, the type of sources to target, the synergy with the current treatment solution, etc.</p> <p>The data are provided through the open, non-proprietary GeoPackage files (GPKG) commonly recognized in GIS tools. A complementary xml file describe the metadata in compliance with the Inspire directive. A complementary pdf file describe the fields of the datasets.</p> <p> </p>
Data from Automated plankton image analysis using convolutional neural networks
<p>Datasets and code from Luo et al., "Automated plankton image analysis using convolutional neural networks." Limnology and Oceanography Methods.</p> <p>Data include:</p> <p>1) 42,564 item training library, sorted in 108 classes,</p> <p>2) 42,548 item test set for filtering thresholds, sorted into 38 groups. These images are independent from the training library, and are used for setting the thresholds for post-classification filtering.<br> CSV file: Luo_etal_FT_images_pred.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p>3) 75,000 item fully random, validated set for confusion matrix calculations, sorted into 38 groups. This set is a representation of the full dataset, selected at random after classification. <br> CSV file: Luo_etal_confusionmatrix_images.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p> </p> <p>Scripts and programs:</p> <p>1) Segmentation.zip contains the scripts and executables for the segmentation program.</p> <p>2) Plankton_template.zip contains the archived version of the SparseConvNet program used in manuscript (current version available at: https://github.com/btgraham/SparseConvNet or https://github.com/facebookresearch/SparseConvNet)<br> Note that google-sparsehash is necessary for running SparseConvNet.<br> Also, plankton_epoch-150.cnn are the weights from the training used in the manuscript, and should be placed in the /weights folder if you want to replicate the classifications.</p>
Third harmonic generation images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)
<p>Data set for 11 samples in 3 groups of Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains THG images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.</p>
Confocal fluorescence microscopy images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)
<p>This data set provides complementary measurements to a separate THG data set of the same study: doi: 10.5281/zenodo.1475906</p> <p>Data set for 1 sample of each of the 3 groups: Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains confocal fluorescence microscopy images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.</p>
WikiLinkGraphs: A complete, longitudinal and multilanguage dataset of the Wikipedia link networks
<p>This dataset contains yearly snapshots of the Wikipedia's internal link network for the 9 largest language edition (de, en, es, fr, it, nl, pl, ru, sv). The dataset spans over 17 years, from the creation of Wikipedia in 2001 to March 2018. The snapshots are taken on March 1st of every year.</p> <p>The graphs include the links extract from the wikitext of each page (i.e in the form [[wikilink]]). Links transcluded from templates are not included. Redirects are resolved to their target page.</p> <p>More detailed information and supporting datasets are available at: http://disi.unitn.it/~consonni/datasets/.</p> <p><strong>IMPORTANT NOTICE</strong></p> <p>Gzipped files are compressed two times by Zenodo, the MD5 provided by Zenodo and the SHA512 sums provided in the `.sha512sums.txt` files, match with the files compressed once. In other words, when you download a `.gz` file save it as `.gz.gz`, uncompress it once and it should match both the MD5 provided by Zenodo and the SHA512 sum provided by us. We have opened a bug report for this behavior on Zenodo's repository at: https://github.com/zenodo/zenodo/issues/1705</p> <p> </p>
Web Experience in Mobile Networks: Lessons from Two Million Page Visits
<p>Measuring and characterizing web page performance is a challenging task.</p> <p>When it comes to the mobile world, the highly varying technology characteristics coupled with the opaque network configuration make it even more difficult.</p> <p>Aiming at reproducibility, we present a large scale measurements study of web page performance collected in eleven commercial mobile networks spanning four countries.</p> <p>We build a dataset of nearly two million web browsing sessions to we shed light on the impact of different web protocols, browsers, and mobile technologies on the web performance.</p> <p>We find that the impact of mobile broadband access is sizeable.</p> <p>For example, the median page load time using mobile broadband increases by a third compared to wired access.</p> <p>Mobility clearly stresses the system, with handover causing the most evident performance penalties.</p> <p>Contrariwise, our measurements show that the adoption of HTTP/2 and QUIC has practically negligible impact.</p> <p>Our work highlights the importance of large-scale measurements.</p> <p>Even with our controlled setup, the complexity of the mobile web ecosystem is challenging to untangle.</p> <p>For this, we are releasing the dataset as open data for validation and further research.</p> <p>We also release together with the datasets we collected the scripts we use to produce the analysis we present in the paper. Please use plot_all.sh script to generate the plots in the paper, using the separate scripts from the "scripts" archive. </p> <p>Should you use any of these resources, please also make an attribution using the following reference (provided here in bibtex format):</p> <pre>@inproceedings{rajiullah2019web, title={{Web Experience in Mobile Networks: Lessons from Two Million Page Visits}}, author={Rajiullah, Mohammad and Lutu, Andra and Khatouni, Ali Safari and Fida, Mah-Rukh and Mellia, Marco and Brunstrom, Anna and Alay, Ozgu and Alfredsson, Stefan and Mancuso, Vincenzo}, booktitle={The World Wide Web Conference}, pages={1532--1543}, year={2019}, organization={ACM}, address = {San Francisco, CA, USA}, keywords = {Web Experience, HTTP2, QUIC, TCP, Mobile Broadband, Measurements} }</pre>
Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing (code, data and scripts to reproduce paper results)
<p>This repository contains the data, code, and scripts required to reproduce the results of the paper "Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing" by Daniele De Sensi, Salvatore Di Girolamo and Torsten Hoefler, presented at the 2019 International Conference for High Performance Computing, Networking, Storage, and Analysis. </p> <p>This repository does not contains the code of the library used to automatically tune the routing algorithm, which can be found at http://doi.org/10.5281/zenodo.3372785</p>
Studibach stream networks
<p>These files contain the data for the following publication:</p> <p>van Meerveld, H. J. I., Kirchner, J. W., Vis, M. J. P., Assendelft, R. S., and Seibert, J.: Expansion and contraction of the flowing stream network changes hillslope flowpath lengths and the shape of the travel time distribution, Hydrol. Earth Syst. Sci., <a href="https://doi.org/10.5194/hess-2019-218">https://doi.org/10.5194/hess-2019-218</a></p> <p>The data set includes:</p> <p>*) Mapped_networks.shp: Shapefile with mapped stream networks in the upper Studibach catchment (Alptal, Switzerland) for three different dates (columns in the attribute table, yyyy_mm_dd):</p> <ul> <li>Aug 21, 2018: Extremely dry conditions</li> <li>Nov 2, 2016: Dry conditions</li> <li>Oct. 25, 2016: Wetting-up conditions (during a low intensity rainfall event)</li> </ul> <p>The stream state was classified as:</p> <ol> <li>Dry streambed</li> <li>Standing water</li> <li>Weakly trickling</li> <li>Trickling</li> <li>Weakly flowing</li> <li>Flowing</li> </ol> <p>*) Topo_network.shp: The stream network shown on the topographic map (derived from the Federal Office of Topography (Swisstopo) National Map 1:25,000 (Pixelkarte 25))</p> <p>*) Boundary.shp: The catchment boundaries</p>
Structure and dynamics of growing networks of Reddit threads
<p>Data used in the paper "<a href="https://doi.org/10.1007/s41109-024-00654-y" target="_blank" rel="noopener">Structure and dynamics of growing networks of Reddit threads</a>".</p> <p>This dataset is made of 6366 threads collected from the r/AmITheAsshole community on Reddit. The dataset contains a total of 6,372,251 comments. The collected threads constitute the “top” submissions — those having the highest score, measured as the difference between upvotes and downvotes of a post. We downloaded them using PRAW, running 10 different queries across various temporal scopes, and then cleaning the obtained dataset by removing duplicated threads. Please refer to the paper, specifically to <a href="https://appliednetsci.springeropen.com/articles/10.1007/s41109-024-00654-y/tables/3" target="_blank" rel="noopener">Table 3</a>, for more details about the dataset.</p> <p><strong>If you use this data, please cite the following source:</strong> Goglia, D., Vega, D. Structure and dynamics of growing networks of Reddit threads. Appl Netw Sci 9, 48 (2024). https://doi.org/10.1007/s41109-024-00654-y</p>
SCG Dataset from Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
<p><strong>Abstract:</strong> Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical ML and other deep learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.</p>
Horizon projects network
<p>Horizon 2020 and Horizon Europe are key EU initiatives fostering collaborative research and innovation across Europe. The data about Horizon programmes can be explored via the CORDIS Data Lab at <a href="https://cordis.europa.eu/datalab/" target="_blank" rel="noopener">https://cordis.europa.eu/datalab/ </a> and can be accessed trough <a href="https://data.europa.eu/data/datasets/cordish2020projects" target="_blank" rel="noopener">https://data.europa.eu/data/datasets/cordish2020projects</a> for Horizon 2020 and <a href="https://data.europa.eu/data/datasets/cordis-eu-research-projects-under-horizon-europe-2021-2027" target="_blank" rel="noopener">https://data.europa.eu/data/datasets/cordis-eu-research-projects-under-horizon-europe-2021-2027</a> <br>for Horizon Europe..</p> <p>The <strong>Horizon projects network</strong> <strong>dataset </strong>is a collection of case studies created with the <strong>Horizon intelligence</strong> software [DOI: 10.5281/zenodo.11276687]. Its purpose is to provide an additional method for analyzing the impact of Horizon projects, specifically in terms of collaborations among European organizations, leveraging network analysis and community detection techniques. The dataset has been enriched with NUTS3 geolocation data and name tags for Italian organizations and is segmented by year, covering the period from 2015 to 2029.</p> <p>The dataset includes 3 case studies, each focused on a specific topic, selected using the EuroSciVoc taxonomy: <em>hydrogen energy</em>, <em>electron microscopy</em>, and <em>pandemics</em>. For each case study, the dataset includes:</p> <ul> <li> <p>A set of CSV files:</p> <ul> <li><code>O.csv</code>: Contains <em>organisation </em>unique identifier and attributes.</li> <li><code>P.csv</code>: Contains <em>project </em>unique identifier and attributes.</li> <li><code>W.csv</code>: Describes the <em>participation </em>of each organization in a project for each specific year. The participation is weighted according to the "total cost" of the project, shared proportionally based on the project's duration within that year.</li> </ul> </li> <li> <p>An additional CSV file, <code>activity_type.csv</code>, which provides a description of the codes used to define the activity types associated with each organization.</p> </li> <li> <p>A set of network files representing the collaboration between organizations in any given year (from 2015 to 2029), in interoperable format <code>.graphML</code>. These yearly graphs include centrality measures (degree, strength, coreness) and community labels. More details on <code>.graphML</code>format are provided in <code>format-info.txt</code> file.</p> </li> </ul> <p>The file <code>sample-networks-hydrogen-energy</code> showcases a visual representation of the networks for the first case study. </p>
Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study
<p>This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented.</p> <p>For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables. </p> <p> </p>
Supplementary File: Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia's most popular social network VK
<p>Supplementary file and dataset for the paper "Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia’s most popular social network VK"</p>
Input Dataset for Estimating Continuous Soil Water Retention Curves Using Physics-Informed Neural Networks
<p>This dataset was used as input to a physics-informed neural network (PINN) model developed to estimate continuous soil water retention curves (SWRCs). It includes basic soil properties such as particle-size distribution (sand, silt, clay), organic carbon content (OC), bulk density (BD), and measurements of soil water retention at various matric potentials. These inputs allow the model to learn the relationship between soil properties and water retention, via both data and embedded physical constraints. This data set consists of 4,200 Danish soil samples with measurements spanning the wet and dry ends of the SWRC. </p>
5D-NP-FABTECH_ALD - Open Dataset for: "ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic-inorganic structures"
<p>This is the open dataset for the paper: "L. Demelius, L. Zhang, A. M. Coclite and M. D. Losego, ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic–inorganic structures, <em>Mater. Adv.</em>, 2024, <strong>5</strong>, 8464–8474."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
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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.