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1,721 results for “network data”
Dataset for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
Open the record for dataset details and reuse information.
Supplementary data and code for "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"
<p>This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". Files are password protected during the revision process. A completely public version of the repository will be availble after the revision process is completed. </p> <p>In detail, the uploaded archive folder contains the following data sources:</p> <ul> <li>the relevant code and supporting data (code_to_upload and supporting_data);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures)</li> </ul> </li> </ul>
Supplementary data (CC BY-NC-SA 4.0): Migration of Zeolite-Encapsulated Subnanometre Platinum Clusters via Reactive Neural Network Potentials
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p> <ul> <li>Trajectory files containing structures, energies and forces of CHA, MWW (including MWW*), TON, MFI (Pt1, Pt3, Pt5 at 750, 1000, 1250 K) as (extended) xyz files readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE)</li> <li>Animated gif files of Pt1 migration between double-six rings in CHA, Pt3 jump through an eight-ring in CHA, and insertion of Pt1 into a t-pen unit in MFI</li> <li>Neural Network Potential (NNP) files readable by <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li> </ul>
Data from: Research on potential disruptive technology identification based on technology network
<p><span>Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, the paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the LDA topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil UAVs as an example to prove the feasibility and effectiveness of the model. The results show that the potential disruptive technology in this field is the main equipment, data acquisition, and information transmission.</span></p>
Data for Airlines network analysis on an air-rail multimodal system. Journal of Open Aviation Science, 1(2)
<h2>About</h2> <p>This dataset contains all the input data required to generate the analysis and results of the article Delgado, L., Trapote-Barreira, C., Montlaur, A., Bolić, T., & Gurtner, G. (2023). <em>Airlines’ network analysis on an air-rail multimodal system</em>. Journal of Open Aviation Science, 1(2). <a href="https://doi.org/10.59490/joas.2023.7223" rel="nofollow">https://doi.org/10.59490/joas.2023.7223</a></p> <p>The code used is available on GitHub: <a href="https://github.com/UoW-ATM/joas_air_rail_network_analysis">https://github.com/UoW-ATM/joas_air_rail_network_analysis</a></p> <p>The path to the input data can be modified in the scripts provided in the GitHub repository. With the default setting, the input is in a folder called data.</p> <h2>Dataset structure</h2> <ul> <li>data_computed <ul> <li><em>rail_used_emissions.csv</em></li> <li><em>rail_used_emissions_2.csv</em></li> <li><em>routes_emissinos_v2.csv</em></li> </ul> </li> <li>flights_data4 <ul> <li>year=2023 <ul> <li>month=05 <ul> <li><em>1st_week_0523.csv</em></li> </ul> </li> </ul> </li> </ul> </li> <li>renfe <ul> <li>renfe_mid_long <ul> <li><em>agency.txt</em></li> <li><em>calendar.txt</em></li> <li><em>calendar_dates.txt</em></li> <li><em>routes.txt</em></li> <li><em>stops.txt</em></li> <li><em>stop_times.txt</em></li> <li><em>trips.txt</em></li> </ul> </li> </ul> </li> <li><em>aircraftDatabase.csv</em></li> <li><em>airport_static.csv</em></li> <li><em>code_seats.csv</em></li> <li><em>manual_fixed_airports.csv</em></li> <li><em>mat_corrected.csv</em></li> <li><em>type_code_missing.csv</em></li> </ul> <h2>Data description</h2> <h3>data_computed</h3> <p>This folder contains pre-computed values by the authors on rail and air emissions. These are estimated:</p> <ul> <li>For rail using <a href="http://ecopassenger.hafas.de/" rel="nofollow">EcoPassenger</a>.</li> <li>For flights based on the emission model from Montlaur, A., Delgado, L., & Trapote-Barreira, C. (2021). <a href="https://doi.org/10.3390/su131810401" rel="nofollow"><em>Analytical Models for CO<sub>2</sub> Emissions and Travel Time for Short-to-Medium-Haul Flights Considering Available Seats</em></a>. Sustainability 13.18 (2021) and from some specific flights using EUROCONTROL's <a href="https://www.eurocontrol.int/platform/integrated-aircraft-noise-and-emissions-modelling-platform">IMPACT </a>model.</li> </ul> <h3>flights_data4</h3> <p>Information from flights_data4 table from <a href="https://opensky-network.org/data/impala">OpenSky</a>. Please refer to the <a href="https://opensky-network.org/about/terms-of-use">terms of use of OpenSky</a> for the restrictions on the further use of these data.</p> <p>The file <em>1st_week_0523.csv </em>contains the information of the table flights_data4 from OpenSky for the week of the 1st May 2023 (01/05/2023 to 07/05/2003). This was downloaded with the SQL query </p> <p>SELECT * FROM flights_data4 WHERE lastseen >= 1682899200 AND firstseen <= 1683504000;</p> <p>Note that the lastseen and firstseen are in UnixTime and correspond to 2023-05-01 00:00:00 UTC and 2023-05-08 00:00:00 UTC respectively. This ensures capturing all flights landing on 01/05/2023 even if they departed the day before and departing on 07/05/2023 even if landing the day after.</p> <h3>renfe</h3> <p>renfe folder contains the General Transit Feed Specification (GTFS) data from the <a href="https://data.renfe.com/dataset/horarios-de-alta-velocidad-larga-distancia-y-media-distancia">Renfe</a> rail operator with the high-speed, long and medium distances timetables. Note that Renfe provides the data under a <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">Creative Commons Attribution 4.0</a> license. </p> <h3>Other datasets</h3> <ul> <li><em>aircraftDatabase.csv </em>: Database containing information on aircraft information (type, manufacturer, license, etc.) as a function of their transponder icao24 code. Obtained from <a href="https://opensky-network.org/aircraft-database">OpenSky</a>.</li> <li><em>airport_static.csv</em>: Airport ICAO code, latitude and longitude.</li> <li><em>code_seats.csv</em>: allows each aircraft model to be related to the seats in the cabin. It has been extracted from airline websites and other sources.</li> <li><em>manual_fixed_airports.csv</em>: List of manually modified airports for arrival/departure to fix wrong rotations from OpenSky data. For each airport ICAO code, it provides the one that should be used instead and some information on that airport (e.g., name)</li> <li><em>mat_corrected.csv</em>: identifies the rotation of each aircraft in the week of study. This is especially relevant for fleet analysis as it is necessary to know the start and end airport of each rotation for each day. It has been identified with an ad-hoc algorithm, and some data has been corrected with <a href="https://www.flightradar24.com/">FlightRadar24</a> data support.</li> <li><em>type_code_missing.csv</em>: relates the transponders' icao24 identifiers missing from OpenSky to the aircraft type (complement aircraftDatabase), compiled from <a href="https://www.flightradar24.com/" rel="nofollow">FlightRadar24</a>.</li> </ul>
Data used in "Biologically informed deep neural network for prostate cancer discovery" publication
<p>Data used in the publication titled "<strong>Biologically informed deep neural network for prostate cancer discovery </strong>" </p> <p>Elmarakeby, Haitham A., et al. "Biologically informed deep neural network for prostate cancer discovery." <em>Nature</em> 598.7880 (2021): 348-352.</p> <p>These datasets were derived from the following public domain resources:</p> <ol> <li>Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, et al. The long tail of oncogenic drivers in prostate cancer. Nat Genet. 2018;50: 645–651. DOI: <a href="https://doi.org/10.1038/s41588-018-0078-z">10.1038/s41588-018-0078-z</a></li> <li>Fraser M, Sabelnykova VY, Yamaguchi TN, Heisler LE, Livingstone J, Huang V, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541: 359–364. https://doi.org/10.1038/nature20788</li> <li>Robinson DR, Wu Y-M, Lonigro RJ, Vats P, Cobain E, Everett J, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548: 297–303. https://doi.org/10.1038/nature23306</li> <li>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46: D649–D655. DOI: <a href="https://doi.org/10.1093/nar/gkv1351">10.1093/nar/gkv1351</a></li> </ol> <p> </p>
Data set for Coherent Hole Transport in Selective Area Grown Ge Nanowire Networks
<p>This document contains all the data and analysis used in the manuscript titled </p> <h1><span>Coherent Hole Transport in Selective Area Grown Ge Nanowire Networks</span></h1> <p><span><a title="DOI URL" href="https://doi.org/10.1021/acs.nanolett.2c00358">https://doi.org/10.1021/acs.nanolett.2c00358</a></span></p>
Mombasa TEOM and Clarity network data
<p>data from TEOM and clarity sensors in Mombasa kenya. </p>
Simulation data for the manuscript "Characterizing Optimal Signal Propagation in the Human Brain Network."
<p>See <a href="https://github.com/kuffmode/OI-and-CMs">https://github.com/kuffmode/OI-and-CMs</a></p>
Data for "Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition"
<p>Data for "Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition".</p> <p>neutron_data.zip --> neutron data in .nxspe form. S(Q,E) calculated using the DAVE software. Includes logbook spreadsheet. </p> <p>Training_Data.xyz --> xyz file containing training data used to generate Allegro machine learning forcefield in the paper</p> <p>nh3_pimd.deploy --> Trained Allegro model to that can be used in LAMMPS and RXMD software a ML forcefield </p> <p>POSCAR_UNIT_CELL_AMMONIA --> NH3 unit cell in solid phase in POSCAR format that can be read by the VASP software used to perform the DFT simmulations.</p>
Datasets for "Physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data"
<div> <p>Datasets used for implementing the <a href="https://github.com/huankoh/PSICHIC">PSICHIC</a> experiments shown in the <a href="https://doi.org/10.1101/2023.09.17.558145">manuscript</a>.</p> <p> </p> </div>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Data for DualNetGO: A Dual Network Model for Protein Function Prediction via Effective Feature Selection
<p>Data used in the paper, including annotation files, graph embeddings from TransformerAE, and protein attributes for both human and mouse, and for cafa3 data. Extract and place them in the <em>data </em>folder.</p>
data - ERα Inhibits the Progression of Hepatocellular Carcinoma by Regulating the CircRNA/miRNA/SMADs Network
Open the record for dataset details and reuse information.
Data for Deriving WMO cloud classes from ground-based RGB pictures with a residual neural network ensemble
Open the record for dataset details and reuse information.
Supplementary data: Optimising centralisation and decentralisation in distribution networks for perishable products through mathematical modelling, parametric analysis, and machine learning
<p><span>The success of distribution companies for perishable products is enabled by optimally configuring distribution networks, which allows for reducing total logistic costs while ensuring reduced product spoilage and high service levels. Since customer demand for perishable products varies over time, the network configuration should not be optimised once, but periodically reviewed. Among the decisions to be reviewed, determining whether to centralise or decentralise inventory (i.e., stock allocation in distribution centres) is crucial. However, the literature overlooks stock allocation decisions, and existing methodologies to compare the economic performance of centralised, decentralised, and hybrid policies neglect important cost items, also requiring advanced computational technologies and skills to be applied. This paper addresses these gaps by providing two contributions. In this dataset, a comparison has been made between the cost performance of centralized, decentralized, and hybrid stock allocation policies in distribution networks for perishable products. The dataset comprises 100,000 realistic case studies generated through a Sobol quasi-random low discrepancy series.</span></p>
Data for "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes"
<p>This is sampling data of "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes".</p> <p>'qv_nn_in' for the large scale specific humidity, [kg/kg]<br> 'T_nn_in' for the large scale temperature, [K]<br> 'dqvls_nn_in' for the large scale moisture advection, [kg/kg/s]<br> 'dTls_nn_in' for the large scale moisture advection, [K/s]<br> 'qtend_check' for the moistening rate by CRM, [kg/kg/s]<br> 'stend_check' for the heating rate by CRM, [K/s]<br> 'SOLS' for direct shorwave solar radiation down to surface, [W/m2]<br> 'SOLSD' for diffusive shortwave solar radiation down to surface, [W/m2]<br> 'SOLL' for direct near infrared solar radiation down to surface, [W/m2]<br> 'SOLLD' for diffusive near infrared solar radiation down to surface, [W/m2]<br> 'SOLIN' for insolation at model top, [W/m2]<br> 'FSNS' for net shortwave radiation at model surface, [W/m2]<br> 'FSNT' for net shortwave radiation at model top, [W/m2]<br> 'FLNS' for net longwave radiation at model surface, [W/m2]<br> 'FLNT' for net longwave radiation at model top, [W/m2]<br> 'SPPS' for surface pressure, [Pa]</p> <p>To download the full dataset of the SPCAM simulation in 1998. Please click the dropbox link: https://www.dropbox.com/s/p841v1tw00rokdy/SPCAM_VAR_1998.tar.gz?dl=0</p>
Data in support of: Species-specific interactions in an avian-bryophyte dispersal network
<p>Animal dispersal of plant propagules fundamentally alters the success of dispersal events, and thus shapes plant community composition through time. While this is well-documented in seed plants, spore-bearing plants have received little attention with regard to this phenomenon. Birds are particularly attractive as a potential bryophyte dispersal vector given their highly motile nature as well as their association with bryophytes when foraging and building nests. Despite this, species-specific dispersal relationships between birds and bryophytes have never been examined. We captured birds in Gifford Pinchot National Forest in the Pacific Northwest of the United States to sample their legs and tails for bryophyte spores. We found 24 bryophyte species across 34 species of bird. We examined the level of specialization 1) within the overall interaction network to assess community-level patterns and 2) at the plant species level to determine the effect of bird behavioral type on the plant-animal interaction. Our results suggest that associations within the network are more constrained (specialized) than expected by chance. Additionally, we found that avian foraging guild impacted the variety of bryophytes found on an individual bird. Foliage gleaners and ground foragers had particularly specialized associations within the overall disperser-bryophyte network. Our findings suggest that diffuse bird-bryophyte dispersal networks are likely to be common in habitats where birds readily encounter bryophytes and that further work aimed at understanding individual bird-bryophyte species relationships may prove valuable in determining nuance within this newly described dispersal mechanism.</p>
NicheNet networks update data
<p>cf separate doc for explanation</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.