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1,721 results for “network data”
Data from: Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas
<p><strong><span>General Information</span></strong></p> <p><span>This dataset was used in the study titled "Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas". The study employs citizen science data collected from the Infomedusa application to assess the presence of jellyfish on beaches along the Andalusian coast, along with environmental data to analyze the factors influencing jellyfish distribution. The study aims to employ machine learning techniques, specifically a Multi-Layer Perceptron (MLP) neural network, to classify user comments on the presence or absence of jellyfish and analyze how environmental factors such as sea surface temperature, wind direction, and wind speed influence jellyfish distribution.</span></p> <p><strong><span>Dataset Columns</span></strong></p> <ul> <li><strong><span>Fecha</span></strong><span>: Timestamp of the comment made by the user in the Infomedusa application.</span></li> <li><strong><span>Municipio</span></strong><span>: Name of the municipality where the beach mentioned in the comment is located.</span></li> <li><strong><span>Jellyfish</span></strong><span>: Binary variable indicating the presence (1) or absence (0) of jellyfish according to the user’s comment.</span></li> <li><strong><span>Comunidad</span></strong><span>: Autonomous community to which the municipality belongs.</span></li> <li><strong><span>Provincia</span></strong><span>: Province to which the municipality belongs.</span></li> <li><strong><span>Latitud</span></strong><span>: Geographical latitude of the municipality where the comment was made.</span></li> <li><strong><span>Longitud</span></strong><span>: Geographical longitude of the municipality where the comment was made.</span></li> <li><strong><span>Set</span></strong><span>: Set of grouped beaches for geographical analysis. Each set includes beaches close to each other and the nearest weather station.</span></li> <li><strong><span>Month</span></strong><span>: Month when the comment was made.</span></li> <li><strong><span>Longitud_sea</span></strong><span>: Longitude of the nearest point in the sea for which environmental data was available.</span></li> <li><strong><span>Latitud_sea</span></strong><span>: Latitude of the nearest point in the sea for which environmental data was available.</span></li> <li><strong><span>SST</span></strong><span>: Sea Surface Temperature at the nearest point in the sea to the municipality, obtained from the Copernicus Marine Environment Monitoring Service.</span></li> <li><strong><span>Wind_dir</span></strong><span>: Wind direction measured at the weather station closest to the municipality, provided by the Spanish Meteorological Agency (AEMET).</span></li> <li><strong><span>Wind_speed</span></strong><span>: Wind speed measured at the weather station closest to the municipality, provided by the AEMET.</span></li> </ul> <p><strong><span>Data Sources</span></strong></p> <ul> <li><strong><span>Infomedusa APP</span></strong><span>: Application developed by the Provincial Council of Malaga and Aula del Mar of Malaga to monitor the presence of jellyfish through citizen participation.</span></li> <li><strong><span>Copernicus Marine Environment Monitoring Service (CMEMS)</span></strong><span>: Provides data on sea surface temperature with an hourly temporal resolution and a spatial resolution of 0.0625° x 0.0625°.</span></li> <li><strong><span>Agencia Estatal de Meteorología (AEMET)</span></strong><span>: Provides daily data on wind direction and speed.</span></li> </ul>
Data for constructing China's co-invention network across 371 cities and depicting the regional context of 19 city-regions under COVID-19
<p>co-inventions.xlsx contains the application records of China's invention patents with more than one applicants in 371 cities from 2005 to 2021.</p> <p>determinants.xlsx contains statistical data depicting the socioeconomic background, innovation conditions, and COVID-19 pandemic severity of 19 city-regions in China.</p>
A collection of public transport network data sets for 25 cities
<p>This dataset describes the public transport networks of 25 cities across the world in multiple easy-to-use data formats. These data formats include network edge lists, temporal network event lists, SQLite databases, GeoJSON files, and General Transit Feed Specification (GTFS) compatible ZIP-files.<br> <br> The source data for creating these networks has been published by public transport agencies according to the GTFS data format. To produce the network data extracts for each city, the original data have been curated for errors, filtered spatially and temporally and augmented with walking distances between public transport stops using data from OpenStreetMap. <br> <br> Cities included in this dataset version: Adelaide, Belfast, Berlin, Bordeaux, Brisbane, Canberra, Detroit, Dublin, Grenoble, Helsinki, Kuopio, Lisbon, Luxembourg, Melbourne, Nantes, Palermo, Paris, Prague, Rennes, Rome, Sydney, Toulouse, Turku, Venice, and Winnipeg.</p> <p>Contrary to the version 1.0 of this data set, this version (1.2) does not include the cities of Antofagasta and Athens, for which non-commercial usage of the data is not allowed.<br> <br> Contrary to previous versions of the data set (1.0 and 1.2), in this version (1.2) the temporal filtering of the data has been slightly adapted, so that the daily and weekly data extracts cover all trips departing between from 03 AM on Monday to 03 AM on Tuesday (daily extract) or 03 AM of the Monday next week (weekly extract). Additionally, a temporal network extract covering a full week of operations has been added for each city.<br> <br> Documentation of the data can be found in the Data Descriptor article published in Scientific Data: http://doi.org/10.1038/sdata.2018.89 <br> When using this dataset, please cite also the above-mentioned paper.</p>
Selected CO2 Data from BErkeley Atmospheric CO2 Observation Network
<p>Selected CO<sub>2</sub>data from BErkeley Atmospheric CO<sub>2</sub> Observation Network (BEACO<sub>2</sub>N) for use in the characterization of the heterogeneity of greenhouse gas concentrations around the San Francisco Bay Area and the constraint of CO<sub>2 </sub>emissions from mobile sources.</p>
Discovering and explaining the learning process of neural networks: A study on EEG data. The animated visualizations.
<p>In the paper 'Discovering and explaining the learning process of neural networks: A study on EEG data' frames of animated visualizations are presented. Here, the full animations are made available so they can be used for interpretation.</p>
Data-driven brain network models differentiate variability across language tasks
<p>Data and script associated with the manuscript titled "Data-driven brain network models differentiate variability<br> across language tasks". </p>
Historic Caddo Network Analysis Data
<p>Ceramic and lithic types and counts used in the Historic Caddo network analysis for northeast Texas. This dataset is incomplete, and does not include the site locations used to plot the network.</p>
Data for "Efficient neural decoding of self-location with a deep recurrent network"
<p>Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the article</p> <p>"Efficient neural decoding of self-location with a deep recurrent network".</p> <p> </p> <p>This data should be used with the code found in https://github.com/NeuroCSUT/RatGPS and should be placed in the Bayesian/Data folder of the codebase.</p> <p> </p>
raw data and simulation scripts for "Exotic states in a simple network of nanoelectromechanical oscillators"
<p>Presented are raw data and simulation python scripts used to create figures from the manuscript "Exotic states in a simple network of nanoelectromechanical oscillators". Data is formatted by {time, mag_1, phase_1, mag_2, phase_2, mag_3, phase_3, mag_4, phase_4, mag_5, phase_5, mag_6, phase_6, mag_7, phase_7, mag_8, phase_8} for 17 column data files, and {time, phase_1, phase_2, phase_3, phase_4, phase_5, phase_6, phase_7, phase_8} for 9 column data. The data is organized by Figure number within manuscript and supplementary information.</p>
Input data and scripts for "Spatial conservation prioritization for the East Asian islands: a balanced representation of multi-taxon biogeography in a protected area network"
<p>This release contains the input files 'input_data.zip' for the spatial conservation prioritization analysis by Zonation software, which are conducted in Lehtomäki et al. Input data includes biodiversity features (species distribution maps from vascular plants, mammals, birds, reptiles, amphibians, and freshwater fishes), habitat condition map (human influence index), priority mask information (the categorized protected area distribution) and the Japanese prefecture polygons in GeoTiff format, and the list of species attributes for conservation weighting in CSV format. Note that endangered rare species have been excluded from this dataset, though they were reflected in the output files. The Zonation setting files and R scripts for pre- and post analyses are included in 'japan-zsetup-1.0.zip' and also placed at GitHub : https://github.com/cbig/japan-zsetup</p> <p>The output files (priority score maps and removal curves) from the original Zonation analyses are summarized in 'output_from_original.data.zip'</p>
Data and results for Cochrane systematic review and network meta-analysis on hepatorenal syndrome
<p>This contains the data and the raw results for the Cochrane systematic review and network meta-analysis on hepatorenal syndrome (https://doi.org/10.1002/14651858.CD013103). Please unzip the file and read the instructions before using the data.</p>
Data and results for Cochrane systematic review and network meta-analysis on treatment of spontaneous bacterial peritonitis
<p>This contains the data and the raw results for the Cochrane systematic review and network meta-analysis on the treatment of spontaneous bacterial peritonitis (https://doi.org/10.1002/14651858.CD013120). Please unzip the file and read the instructions before using the data.</p>
Data for "Convergent temperature representations in artificial and biological neural networks"
<p>Data for "Convergent Temperature Representations in Artificial and Biological<br> neural networks" by Haesemeyer M, Schier AF and Engert F, 2019</p> <p>The corresponding python code is available at:<br> <a href="https://github.com/haesemeyer/GradientPrediction">https://github.com/haesemeyer/GradientPrediction</a></p> <p>All zip files should be extracted in the same folder as the python files. This<br> will create a sub-folder structure for the model data.<br> ZIP File Contents (Note: These are used by the code and not necessarily useful<br> by themselves):<br> model_data.zip<br> Contains tensorflow checkpoints on all naive and fully trained models, test<br> errors during training as well as evolution weights where applicable.<br> model_cluster_assignments.zip<br> For the trained models in model_data.zip the response cluster assignment<br> for each individual unit.<br> zebrafish_data.zip<br> The zebrafish brain and behavior data used in the paper comparisons. This<br> archive also contains the temperature stimulus file stimFile.hdf5<br> training_data.zip<br> The generated training data used during predictive network training<br> test_data.zip<br> The generated test data used to evaluate predictive network training</p>
Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network [Version 2]
<p>This package provides material that can be openly published for the paper "Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network". It consists in the code used to generate results and figures as well as the weights of the deep convolutional neural networks trained to segment water in the surveillance camera images.</p>
Data and results for Cochrane systematic review and network meta-analysis on 'Induction immunosuppression in adults undergoing liver transplantation: a network meta-analysis'
<p>This contains the data and the raw results for the Cochrane systematic review and network meta-analysis on Induction immunosuppression in adults undergoing liver transplantation: a network meta-analysis (<a href="https://doi.org/10.1002/14651858.CD013203">https://doi.org/10.1002/14651858.CD013203</a>). Please unzip the file and read the instructions before using the data.</p>
Data for the "Systems NMR: simultaneous quantification of RNA, protein, and metabolite reaction dynamics for biomolecular network analysis."
<p>This dataset contains raw NMR data used in the publication.</p> <p>Detailed protocol for the presented NMR setup and analysis is included in the publication, and at https://github.com/systemsnmr/ivtnmr.</p> <p>v0.2 includes the integr_results_31P_pure_PO4.txt files - phosphate-spectra integration files which were missing in v0.1 submission.</p>
Image data used for publication "Species-level image classification with convolutional neural network enable insect identification from habitus images "
<p>Image-crops of specimens from insect drawers</p> <p>In 2017 we scanned 208 insect drawers containing the collection of british carabids from the Natural History Museum London and extracted crops from the scanned images. This database contain 63.364 specimens that we used to train, validate and test a convolutional neural network.</p> <p>Each folder is named as the gbif id number. E.g. Carabus problematicus is 4470555: https://www.gbif.org/species/4470555</p>
Fig. 2. A–G, Aphaena discolor. A–B in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 2. A–G, Aphaena discolor. A–B, specimen on his host-tree, Kirirom, 9.V.2015 (J. Constant). C, habitat in Kirirom, 9.V.2015 (J. Constant). D, Pursat, Cardamom, 2.I.2009 (J. Holden). E, Koh Kong, Tatai, 5.III.2012 (G. Chartier). F–G, Dichoptera sp. Chambok, 5.V.2015 (J. Constant). H, Kalidasa nigromaculata, Siem Reap, Angkor, 10.VIII.2014 (S. De Greef). I, Penthicodes atomaria tended by a cockroach, Cardamom Mts, 4.VIII.2013 (A. Anker). J, P. pulchella, Siem Reap, 18.IX.2013 (S. De Greef). K, P. variegata, Mondulkiri, O Reang District, 19.V.2015 (B. Barca). L–M, Polydictya tricolor, Siem Reap, Angkor, 1.VIII.2013 (S. De Greef). N–O, Polydictya sp., 8 km NNW Angkor, 6.XI.2013 (E. Smith).
Fig. 1 in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 1. Call to collaboration to the study of Fulgoridae of Cambodia posted on Facebook on May 18th, 2015.
Fig. 3. A–B, Pyrops candelaria. A in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 3. A–B, Pyrops candelaria. A, Chambok, 1.IX.2014 (S. Phauk). B, Kampong Tralach, 21.IX.2013 (O. Rodriguez). C–D, P. coelestinus, Chambok, 1.IX.2014 (S. Phauk). E, P. condorinus, Koh Kong, Tatai, 24.V.2015 (G. Chartier). F, P. ducalis, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). G–I, P. peguensis. G, Tumpor, Cardamom, 12.VIII.2009 (J. Holden). H, Chambok, 5.V.2015 (J. Constant). I, idem, biotope. J, P. spinolae, Ratanakiri, Veun Sai Siem Pang, 23.II.2015 (Marduk). K, P. viridirostris, Chambok, 7.V.2015 (J. Constant). L–N, Saiva gemmata. L, nymph, Chambok, 7 May 2015 (J. Constant). M, adult tended by a cockroach, Chambok, 7.V.2015 (J. Constant). N, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). O–Q, Zanna sp. O–P, Koh Kong, Tatai, 1.XI.2012 (G. Chartier). Q, Kampot, 21.XII.2013 (K.W. Meier-Doernberg).
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.