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9,152 results for “Networking”
Simulated random habitat networks and generic networks
<p>Simulated random habitat networks and generic networks</p>
Figure 2. CAD haplotype network. TCS Network based off 814 in Low Variation in Nuclear and Mitochondrial DNA Inhibits Resolution of Invasion Pathways across the Pacific for the Coconut Rhinoceros Beetle (Scarabeidae: Oryctes rhinoceros)
Figure 2. CAD haplotype network. TCS Network based off 814 base pairs of the CAD gene region from a total of 117 samples (8 to 39 from any given location) representing 234 total haplotypes. Hash marks represent a single base pair change. A PHASE algorithm was used to generate haplotypes from ambiguities present in the sequence data of a multi-copy nuclear gene, resulting in twice as many haplotypes as samples.
Figure 1 in The SISBIOTA-Diptera Brazilian Network: A long-term survey of Diptera from unexplored Brazilian Western Arc of Amazon, Cerrado, and Pantanal
Figure 1 Map of the main biomes of Brazil showing collecting points and sampling sites in the states of Rondônia (RO), Mato Grosso (MT), Mato Grosso do Sul (MS).(a) geographical limits of the Brazilian states and main biomes (left map) and satellite images of each area, depicting all collecting points (white circles on the left and yellow circles on the right); (b) general landscape of forest in Cacaulândia, Rondônia; (c) forest along a river in Cacaulândia, Rondônia; (d) gate of Transpantaneira road in Poconé, Mato Grosso; (e) open vegetation area with patches of forests in Poconé, Mato Grosso; (f) open vegetation area with a mosaic of forest over the rocky formation in Aquidauana, Mato Grosso do Sul; (g) general landscape in Bodoquena, Mato Grosso do Sul.
Figure 11 in The SISBIOTA-Diptera Brazilian Network: A long-term survey of Diptera from unexplored Brazilian Western Arc of Amazon, Cerrado, and Pantanal
Figure 11 Map of the area of study in a private site in Bodoquena, Mato Grosso do Sul. (a) limits of all municipalities of the state, highlighting the limits of Bodoquena and the sampling site; (b) satellite image of Fazenda Califórnia, Serra da Bodoquena, showing the sampling points where Malaise traps 4, 5, and 6 were installed; (c) Malaise trap number 6 installed in a seasonal decidual forest fragment.
Figure 9 in The SISBIOTA-Diptera Brazilian Network: A long-term survey of Diptera from unexplored Brazilian Western Arc of Amazon, Cerrado, and Pantanal
Figure 9 Comparison of generic richness rankings of the top 10 families collected and identified of Mato Grosso, Mato Grosso do Sul, Rondônia, the three states combined, and Brazil (1also includes Lauxaniidae and Tephritidae in the same position, with 24 genera known.). Horizontal lines and bars represent equal ranking; each family is represented by a different color.
Building Resilient Business For SMES: The Role of Financial Management Skill, Networking Capabilities, and Business Model Innovation
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Dataset for Mitogenome Assembly Training, part of the Galaxy Training Network (GTN)
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Social Network. El infinito en un junco. Papyrus: The Invention of Books in the Ancient World
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Traning set for "Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks"
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Dynamic multi-dose vaccination with initial immunity on higher-order networks
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An Interdependence Analysis of the Trade Network of Key Exporting Countries: Focusing on the Asia-Pacific Region (U.S., China, India, Japan, and South Korea)
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IV-YOLO: A Lightweight Dual-Branch Object Detection Network
<p>IV-YOLO 的网络和数据存储</p>
Database files 1 for network described in "Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach"
<p>Contains part 1/2 of the database files required for running the database described in "Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach" that can be run with https://github.com/stephwills/docker-neo4j.</p>
Database files 2 for network described in "Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach"
<p>Contains part 2/2 of the database files required for running the database described in "Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach" that can be run with https://github.com/stephwills/docker-neo4j.</p>
FIGURE 4 in Delimitation of a novel member of genus Metarhizium (Clavicipitaceae) by phylogenetic and network analysis
FIGURE 4. Reconstruction of Neighbor-Net network of Metarhizium synnematis and Metarhizium sp.
HiPHD: Hierarchical Classification for Protein Remote Homology Detection using Graph Neural Networks and Language Models
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Database of ionospheric rate of TEC change index (ROTI) map derived from Indonesian GNSS receiver network
<blockquote> <p><strong>USERS CAN ALSO ACCESS THE ROTI MAPS IN THIS DATABASE LINK:</strong></p> <p><strong>https://gatotkaca.brin.go.id/petaionosfer/</strong></p> </blockquote> <p> </p> <p><strong>Introduction</strong></p> <p>Indonesia, located near the magnetic equator in Southeast and East Asia, is essential for studying ionospheric phenomena, particularly equatorial plasma bubbles (EPBs). The Indonesian Geospatial Information Agency (BIG) has deployed a network of Global Navigation Satellite System (GNSS) receivers as part of the Indonesia Continuously Operating Reference Stations (Ina-CORS) across this country. This network has enabled the creation of detailed ionospheric irregularities maps based on the Rate of Total Electron Content (TEC) Change Index (ROTI). These maps are crucial for understanding EPBs in Southeast and East Asia.</p> <p><strong>GNSS network and ROTI map generation</strong></p> <p>The Ina-CORS consists of over 300 GNSS receivers strategically placed across Indonesia (see attached figure "geographic map_receivers.jpg"), spanning from 95°E to 140°E and from 5°N to 10°S. These receivers continuously gather GNSS observable data with a time resolution of 30 seconds in a Receiver Independent Exchange (RINEX) file. This data is then processed to generate the ROTI maps from the magnetic equator to the southern low-latitude region in Southeast/East Asia.</p> <p>The GNSS data in the RINEX file is processed to calculate the Total Electron Content (TEC) using the open software developed by Seemala (2023). The software can be found at https://seemala.blogspot.com/2020/12/gps-tec-program-version-3-for-rinex-3.html. ROTI is derived by measuring the standard deviation of the rate of change of TEC over a 5-minute interval (Pi et al., 1997). This index is a critical indicator of ionospheric irregularities with kilometers of spatial scales inside the EPBs.<br>Using ROTI data plotted at the Ionospheric Pierce Point (IPP) altitude of 350 km, 2-dimensional (2D) latitude-longitude ROTI maps are generated. The grid size of the ROTI map is 0.25° × 0.25°. The ROTI map is smoothed by a boxcar average of 5 × 5 grid data regarding geographic latitude and longitude. In the map, sunset and sunrise terminators at altitudes of 110 km (red curve), 350 km (green curve), and 650 km (black curve) are plotted. The ROTI map is generated at each interval of 10 minutes from 9:00 to 23:50 UT. The name file of the zipped map in one day indicates the year and day of the year. For example, s_2024122_map.rar indicates the maps on day 122 in 2024.</p> <p><strong>Purpose</strong></p> <p>Sharing GNSS data in RINEX files from the CORS could be strictly limited. Sharing the ROTI map derived from the CORS of Southeast Asian countries can be an alternative solution. This database aims to store the ROTI maps over Indonesia derived from GNSS data of the Ina-CORS network. The ROTI map database is also freely accessible and can be used for educational and scientific purposes. It is an academic/scientific resource and promotes a deeper understanding of EPB phenomena and their impact on navigation and communication systems. This database enables continuous monitoring and analysis of ionospheric conditions, particularly EPB occurrence. The database supports scientific research, enhances GNSS applications, and contributes to space weather forecasting by providing a high-resolution ROTI map. This ROTI map database has been developed to encourage research collaboration between researchers globally and in Indonesia.</p> <p><strong>Attribution</strong></p> <p>Users must appropriately credit the data source in this database in any publications, presentations, or products derived from it. When using the ROTI maps in this database, please cite the database. The users acknowledge the Indonesian Geospatial Information Agency (Badan Informasi Geospasial, BIG) for providing the GNSS data to make the ROTI map. Users are also encouraged to collaborate with ionospheric researchers in Indonesia.</p> <p><strong>References</strong></p> <p>Gopi K. Seemala (2023), Chapter 4 - Estimation of ionospheric total electron content (TEC) from GNSS observations, Editor(s): A.K. Singh, S. Tiwari, In Earth Observation, Atmospheric Remote Sensing, Elsevier, 2023, Pages 63 - 84, doi.org/10.1016/B978-0-323-99262-6.00022-5.</p> <p>Pi, X., Mannucci, A. J., Lindqwister, U. J., and Ho, C. M. (1997), Monitoring of global ionospheric irregularities using the worldwide GPS network, Geophys. Res. Lett., 24, 2283–2286.</p> <p> </p> <p><strong>More Information</strong><br>Feel free to reach out via email for more information. Your feedback is invaluable to us, and we encourage users to share their experiences and suggestions for research ideas and further improvements.<br>Correspondence: P. Abadi, Dr.; Researcher at Research Center for Climate and Atmosphere, BRIN; email: pray001[at]brin.go.id (replace "[at]" with "@").</p> <p> </p> <p> </p>
Multiple stressors drive multitrophic biodiversity and ecological network dynamics in a shrinking sandy lake
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Comparative Efficacy of various Procedures of eccentric exercises for Midportion Achilles Tendinopathy: A Systematic Review and Network Meta-analysis
<p>supplementary materials<span><span> </span><span> 补充材料</span></span></p>
A flexible lightweight signcryption scheme for underwater wireless sensor networks
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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.