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22 results for “network index”
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Traffic network routing index for the Czech Republic
<p><strong>Graph representation of road network of the Czech Republic</strong></p> <p>This dataset contains graph of the entire Czech road network. The data are derived from the Open Street Map project and stored in a HDF5 file. The file contains graph topology and metadata for edges and vertices. </p> <p>Spatial index is also included for the purpose of point snapping and other spatial queries. The spatial index is stored in a SQLite file with SpatiaLite extension.</p> <p>Creation of this dataset has been supported by the <a href="http://antarex-project.eu">Antarex project</a>.</p> <p><strong>Routing Index in HDF5</strong></p> <p><strong>File: </strong><a href="/api/files/f1fb6db8-a2e2-425b-b15e-8e401eb22d44/CZE-1528295206-proc-20180724134457.hdf?versionId=3294ae0b-c3b2-4155-ad36-311d2bb06ed9">CZE-1528295206-proc-20180724134457.hdf</a></p> <p>The index is divided into parts according to geographical boundaries defined by country borders. In this case it contains only single part - CZE. This information along with the creation time is stored as an attribute in the root group of the file.</p> <p>The graph topology is stored in the following way. Nodes have assigned a row index in the edges dataset which points to an outbound edge plus a number of the subsequent edges which are also output to this node. The edge metadata are stored in the EdgeData dataset to avoid redundancy. NodeMap dataset provides a convenient way to query nodes based on their uniqe identifiers.</p> <p><strong>File structure:</strong></p> <pre><code class="language-javascript">HDF5 "CZE-1528295206-proc-20180724134457.hdf" { GROUP "/" { GROUP "Index" { ATTRIBUTE "CreationTime" { DATATYPE H5T_STD_I64LE DATASPACE SCALAR DATA { (0): 1535451896 } } ATTRIBUTE "PartsCount" { DATATYPE H5T_STD_I32LE DATASPACE SCALAR DATA { (0): 1 } } ATTRIBUTE "PartsInfo" { DATATYPE H5T_COMPOUND { H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } "id"; H5T_STD_I64LE "nodeCount"; H5T_STD_I64LE "edgeCount"; } DATASPACE SIMPLE { ( 1 ) / ( 1 ) } DATA { (0): { "CZE\000", 904085, 2223222 } } } GROUP "CZE" { ATTRIBUTE "PartInfo" { DATATYPE H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } DATASPACE SCALAR DATA { (0): "CZE\000" } } DATASET "Edges" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "edgeId"; H5T_STD_I32LE "nodeIndex"; H5T_STD_I32LE "computed_speed"; H5T_STD_I32LE "length"; H5T_STD_I32LE "edgeDataIndex"; } DATASPACE SIMPLE { ( 2223222, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } DATASET "Nodes" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "id"; H5T_STD_I32LE "latitudeInt"; H5T_STD_I32LE "longtitudeInt"; H5T_STD_U8LE "edgeOutCount"; H5T_STD_I32LE "edgeOutIndex"; H5T_STD_U8LE "edgeInCount"; } DATASPACE SIMPLE { ( 904085, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } } DATASET "EdgeData" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "id"; H5T_STD_U8LE "speed"; H5T_STD_U8LE "funcClass"; H5T_STD_U8LE "lanes"; H5T_STD_U8LE "vehicleAccess"; H5T_STD_U16LE "specificInfo"; H5T_STD_U16LE "maxWeight"; H5T_STD_U16LE "maxHeight"; H5T_STD_U8LE "maxAxleLoad"; H5T_STD_U8LE "maxWidth"; H5T_STD_U8LE "maxLength"; H5T_STD_I8LE "incline"; } DATASPACE SIMPLE { ( 2838, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } DATASET "NodeMap" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "nodeId"; H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } "partId"; H5T_STD_I32LE "nodeIndex"; } DATASPACE SIMPLE { ( 904085, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } } } }</code></pre> <p><strong>Spatial index</strong></p> <p><strong>File: </strong><a href="https://zenodo.org/api/files/f1fb6db8-a2e2-425b-b15e-8e401eb22d44/CZE-1528295206-proc-20180829135251.sqlite?versionId=56bb1e60-ff7c-407f-8041-3c628ef78db0">CZE-1528295206-proc-20180829135251.sqlite</a><br> </p> <p>SQLite database contains two primary tables. Table <em>nodes</em> and table <em>segments</em>, that resluts of selection and projection from the tables of the same name in primary db. Both tables are suitable for <em>searching nearest lines</em> task and <em>searching closest node of nearest line</em> task. </p> <pre><code class="language-sql">CREATE TABLE nodes ( gid INTEGER, part_gid INTEGER, node_type INTEGER, geom POINT ); CREATE TABLE segments ( gid INTEGER, node_gid_from INTEGER, node_gid_to INTEGER, frc TEXT, transition_time DOUBLE, computed_speed REAL, geom_length DOUBLE, geom LINESTRING ); </code></pre> <p>There are three more tables. Table <em>segments_rt</em> is a copy of table <em>segments</em>, that exclude loops in segments, hence it supports network routing task in SpatiaLite. Next table is <em>rt_network</em> (static graph generated from table <em>segments_rt</em> suitable for routing). The last one is table <em>virtual_rt_network</em>, that is an interface for routing query. </p> <p> </p>
Figure 6. Performance plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 5 we can see that the mse curve reaches the In performance goal but it does not<br> decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these<br> results one can say that RNN is better choice than Feedforward MLP in prediction purpose.</p>
Figure 5. Performance plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 5 we can see that the mse curve reaches the In performance goal but it does not<br> decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these<br> results one can say that RNN is better choice than Feedforward MLP in prediction purpose.</p>
Figure 3. Regression plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 3 depicts the regression plot for the feedforward MLP network, analyzing it we can<br> say that Y=T regression is not so good.</p>
Figure 4. Regression plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Figure 4,depicts the regression plot for the Timedelay RNN network, analyzing it we can<br> say that Y=T regression is totally fit.<br> This paper also comprises of comparative study of performance(mse) plot of both network.</p>
The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK"
<p>The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK".</p> <p>Data collector: Runda Zheng</p>
A 5 m vertical distance to channel network index (VDCNI) across France
<p>The vertical distance to channel network index (VDCNI) expresses the vertical height (in meter) between the elevation of a pixel and the nearest channel. It was derived from the national airborne DTM (RGE ALTI ®) at 5 m spatial resolution, which is available from the website of the French National Geographic Institute (IGN) (<a href="https://geoservices.ign.fr/">https://geoservices.ign.fr/</a>), and the GIS layer of the channel network in the national hydrological database <a href="https://www.sandre.eaufrance.fr/atlas/srv/fre/catalog.search#/metadata/3b3d3c56-d9b6-4625-a57e-ba054e798274">https://www.sandre.eaufrance.fr/atlas/srv/fre/catalog.search#/metadata/3b3d3c56-d9b6-4625-a57e-ba054e798274</a>.</p> <p>Dataset includes:</p> <ul> <li>280 GeoTIFF raster files (VDCNI_000.tif) projected in the French Lambert-93 system (EPSG code 2154), each file corresponding to a 50 x 50 km tile. The number indicates the tile of interest ;</li> <li>1 vector tile index at Google Earth format (tile_index.kmz) showing the location of each tile. This file has been created to facilitate download layer only on area of interest.</li> </ul> <p>To reduce storage space and download time, each raster file has been packed at 7-Zip freeware format.</p> <p>A complete description of the dataset can be found in Panhelleux, L., Rapinel, S., Lemercier, B., Gayet, G., Hubert-Moy, L., 2023. A 5 m dataset of digital terrain model derivatives across mainland France. Data in Brief 109369. https://doi.org/10.1016/j.dib.2023.109369</p>
Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network
<p>Short Description:</p> <p>In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone.</p> <p>Code and More Details: https://github.com/lepotatoguy/aqi</p>
Indexed Data Set From Molisan Regional Seismic Network Events
<p>Abstract:</p> <p><em>After the earthquake occurred in Molise (Central Italy) on 31st October 2002 (Ml 5.4, 29 people dead), the local Servizio Regionale per la Protezione Civile to ensure a better analysis of local seismic data, through a convention with the Istituto Nazionale di Geofisica e Vulcanologia (INGV), promoted the design of the Regional Seismic Network (RMSM) and funded its implementation. The 5 stations of RMSM worked since 2007 to 2013 collecting a large amount of seismic data and giving an important contribution to the study of seismic sources present in the region and the surrounding territory. This work reports about the dataset containing all triggers collected by RMSM since July 2007 to March 2009, including actual seismic events; among them, all earthquakes events recorded in coincidence to Rete Sismica Nazionale Centralizzata (RSNC) of INGV have been marked with S and P arrival timestamps. Every trigger has been associated to a spectrogram defined into a recorded time vs. frequency domain.<br> The dataset has been fully indexed in respect of the recorded spectra: list of all records, list of earthquakes, list of multiple earthquakes records.<br> The main aim of this structured dataset is to be used for further analysis with data mining and machine learning techniques on image patterns associated to the waveforms.</em></p>
The Green View Index of Tartu street network
<p>This dataset is based on the OpenStreetMap street network for Tartu. The Green View Index (GVI) values have been calculated from Google Street View images using semantic segmentation and joined to street network data.</p>
The disruption index suffers from citation inflation and is confounded by shifts in scholarly citation practice: synthetic citation networks for bibliometric null models
Open the record for dataset details and reuse information.
Dataset and results for paper: Predicting missing links in directed networks: An investment-profit index
<p>Dataset and raw results for paper: Predicting missing links in directed networks: An investment-profit index. File "All_indices_12_dataset.mat" contains results of all 9 indices in 12 datasets. File "IP_sigma_12_dataset.mat" contains AUC and precision values when parameter sigma changes in IP index.</p>
Insulin-Glucose-Glucagon Network: Defining a Type 1 Diabetes Progression Index
ClinicalTrials.gov study NCT02663661. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Default Mode Network fMRI Maps as a Predictive Index of Hepatic Encephalopathy Outcome
ClinicalTrials.gov study NCT02083367. IPD Sharing: Not stated. Countries: 1. Publications: 27.
Data from: The index case is not enough: variation among individuals, groups, and social networks modify bacterial transmission dynamics
Open the record for dataset details and reuse information.
Modified Normalized Difference Water Index (MNDWI) in one of EDIAs's water distribution network areas during 2019
<p>Modified Normalized Difference Water Index (MNDWI) in one of EDIAs's water distribution network areas during 2019. The MNDWI was computed from Sentinel 2 images.</p>
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>
The Diagnostic Performance of BMO-MRW and RNFL Thickness and Their Combinational Index Using Artificial Neural Network
ClinicalTrials.gov study NCT03257020. IPD Sharing: YES. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.