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757 results for “twins”

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edi56/100

Urban forest canopy cover, vegetation, and site characteristics, Twin Cities Metro Area, 2022 and 2023.

This data was primarily collected to assess forest quality within the Minneapolis-St. Paul (MSP) Metropolitan Area and to link above-ground and below-ground properties as part of the goals of the MSP-LTER Urban Tree Canopy research group. Here, we sampled vegetation on 48 circular plots with a 12.5 m radius distributed across 18 parks, registering the date of sampling, park and management agency names, the plot number, and geolocation (latitude, longitude, and elevation). The plots were randomly selected based on GEDI (Global Ecosystem Dynamics Investigation instrument) 2021 footprints in the MSP Metropolitan Area along accessible forested areas inside public parks, where the management agency allowed sampling. In each plot, we measured forest structure and diversity metrics, species names and abundance, DBH, height, distance from the plot center, the height where each individual canopy starts, and the relative position, exposure, and density of each canopy. We also measured understory plant structure and diversity in 4 subplots per plot, totaling 192 subplots. In these subplots, we surveyed all individual plants with heights over 20 cm, recording species names and abundance, plant basal diameter, plant height, and the total number of branches. Furthermore, we assessed the canopy openness above each subplot by calculating percent DIFN (diffuse non-interceptance) from fish eye pictures of the canopy at 1.3 meters over the subplot.

openCC (other)Feb 2025View details →
zenodo52/100

Raw Particle Number Size-Distribution Data of twin-DMPS equipped with two CPCs for nanoparticle detection for SMEAR II station, Hyytiälä, Finland, Spring 2017

<p>Raw size-Distribution data from twin-DMPS system (Aalto et al., 2001), where the nano-DMA (measuring up to 40 nm, short Hauke type DMA) is quipped with two detectors:<br> a TSI 3776 and a modified Airmodus A20 (Kangasluoma et al., 2015)</p> <p>Data acquired during in March-May 2017 at the SMEAR II station in Hyyti&auml;l&auml;, Finland.<br> Data associated with the publication Stolzenburg, Laurila et al. (2023), Atmos. Meas. Techn., &quot;Improved counting statistics of an ultrafine differential mobility particle size spectrometer system&quot;</p> <p>Files DMYYDDMM_A20.Dat contain the raw DMPS data, with YYMMDD indicating the day of the measurement.<br> Data are provided alternating between data acquired with the nano-DMA and with the long-DMA, on a scan by scan basis.<br> First line of each scan cycle (for both DMAs) always indicates the start and end times of the voltage scan.<br> Second line gives the parameters related to the DMPS as given below:<br> (sheath flow in [l per min], aerosol flow in [l per min], DMA inner electrode diameter in [m], DMA outer electrode diameter in [m], DMA classification length in [m], other parameters)<br> Following lines give<br> (for long-DMA): set voltage at DMA [in V], concentration measured by TSI3772 in [per cm3]<br> (for nano_DMA): et voltage at DMA [in V], concentration measured by TSI 3776 in [per cm3], concentration measured by mod. Airmodus A20 in [per cm3]</p> <p>File dmps_data_format_specifier.text gives a conversion from voltage to diameter and indicates the measurement time at each voltage during the stepping of the DMPS.<br> Needs to be used to convert measured concentrations in counts per set-interval.</p> <p>Files GR_J_overview.xlsx gives size-distribution derived quantities during that campaign.<br> Header defines Date, Growth Rate and Formation Rate measured at different sizes [in nm] and by the two different CPCs connected to the nano-DMA.<br> Growth rates in [nm per h], formation rate in [per cm3 per s].</p> <p>Other data related to the campaign can be obtained from the corresponding author upon reasonable request.<br> juha.kangasluoma@helsinki.fi</p> <p>References:</p> <p>Stolzenburg, Laurila et al. &quot;Improved counting statistics of an ultrafine differential mobility particle size spectrometer system&quot;,<br> Atmos. Meas. Techn., in press, 2023</p> <p>Aalto et al., &quot;Physical characterization of aerosol particles during nucleation events&quot;,<br> Tellus B, vol. 53, pp. 344-358, 2001</p> <p>Kangasluoma et al., &quot;Sub-3 nm Particle Detection with Commercial TSI 3772 and Airmodus A20 Fine Condensation Particle Counters&quot;,<br> Aerosol Sci. Techn., vol. 49, pp. 674-681, 2015</p>

opencc-by-4.0May 2023View details →
zenodo48/100

TWINS ENA flux and ion temperature data for interval on August 3, 2016

<p>This is a dataset for TWINS ENA flux and calculated ion temperatures used to prepare Figure 1 for a&nbsp;submission to GRL of a&nbsp;paper by A. M. Keesee, N. Buzulukova, C. Mouikis and E. E. Scime&nbsp; &quot;Mesoscale structures in Earth&#39;s magnetotail observed using energetic neutral atom imaging&quot;. The dataset has 56 files in .csv format.</p> <p>Files containing the ion temperature (in keV) arrays in the GSM equatorial plane used&nbsp;for Fig. 1a-d have names with format &#39;temp_YYYYMMDDHHMM.csv&#39;</p> <p>Also included in the dataset are the ENA fluxes projected to the GSM equatorial plane that were used to calculate the ion temperatures. These files have names with formal &#39;flux[energy]_YYYMMDDHHMM.csv&#39; where [energy] is in keV.&nbsp;The&nbsp;IDL scripts used to create the projections as well as the fitting algorithms used to calculate the ion temperatures&nbsp;are included in&nbsp;Keesee, Amy; Scime, Earl; Zaniewski, Anna; and Katus, Roxanne (2019). 2D Ion Temperature Maps from TWINS ENA data: IDL scripts. UNH Scholars&rsquo; Repository&nbsp;<a href="https://dx.doi.org/10.34051/c/2019.1">https://dx.doi.org/10.34051/c/2019.1</a></p>

opencc-by-4.0Dec 2020View details →
zenodo48/100

Network Digital Twin-Generated Dataset for Machine Learning-based Detection of Benign and Malicious Heavy Hitter Flows

<h3>Overview</h3> <p>This record provides a dataset created as part of the study presented in the following publication and is made <strong>publicly available for research purposes</strong>. The associated article provides a comprehensive description of the dataset, its structure, and the methodology used in its creation. If you use this dataset, please <strong>cite the following article </strong>published in the journal <strong>IEEE Communications Magazine</strong>:</p> <blockquote> <p><strong>A. Karamchandani, J. Nunez, L. de-la-Cal, Y. Moreno, A. Mozo, and A. Pastor, &ldquo;On the Applicability of Network Digital Twins in Generating Synthetic Data for Heavy Hitter Discrimination,&rdquo; IEEE Communications Magazine, pp. 2&ndash;8, 2025, DOI: 10.1109/MCOM.003.2400648.</strong></p> </blockquote> <p>More specifically, the record contains several synthetic datasets generated to differentiate between benign and malicious heavy hitter flows within a realistic virtualized network environment. Heavy Hitter flows, which include high-volume data transfers, can significantly impact network performance, leading to congestion and degraded quality of service. Distinguishing legitimate heavy hitter activity from malicious Distributed Denial-of-Service traffic is critical for network management and security, yet existing datasets lack the granularity needed for training machine learning models to effectively make this distinction.</p> <p>To address this, a Network Digital Twin (NDT) approach was utilized to emulate realistic network conditions and traffic patterns, enabling automated generation of labeled data for both benign and malicious HH flows alongside regular traffic.</p> <h3>Feature Set:</h3> <p>The feature set includes the following flow statistics commonly used in the literature on network traffic classification:</p> <ul> <li>The protocol used for the connection, identifying whether it is TCP, UDP, ICMP, or OSPF.</li> <li>The time (relative to the connection start) of the most recent packet sent from source to destination at the time of each snapshot.</li> <li>The time (relative to the connection start) of the most recent packet sent from destination to source at the time of each snapshot.</li> <li>The cumulative count of data packets sent from source to destination at the time of each snapshot.</li> <li>The cumulative count of data packets sent from destination to source at the time of each snapshot.</li> <li>The cumulative bytes sent from source to destination at the time of each snapshot.</li> <li>The cumulative bytes sent from destination to source at the time of each snapshot.</li> <li>The time difference between the first packet sent from source to destination and the first packet sent from destination to source.</li> </ul> <h3>Dataset Variations:</h3> <p>To accommodate diverse research needs and scenarios, the dataset is provided in the following variations:</p> <ol> <li> <p><strong><code>All at Once</code></strong>:</p> <ol> <li>Contains a synthetic dataset where all traffic types, including benign, normal, and malicious DDoS heavy hitter (HH) flows, are combined into a single dataset.</li> <li>This version represents a holistic view of the traffic environment, simulating real-world scenarios where all traffic occurs simultaneously.</li> </ol> </li> <li> <p><strong><code>Balanced Traffic Generation</code></strong>:</p> <ol> <li>Represents a balanced traffic dataset with an equal proportion of benign, normal, and malicious DDoS traffic.</li> <li>Designed for scenarios where a balanced dataset is needed for fair training and evaluation of machine learning models.</li> </ol> </li> <li> <p><strong><code>DDoS at Intervals</code></strong>:</p> <ol> <li>Contains traffic data where malicious DDoS HH traffic occurs at specific time intervals, mimicking real-world attack patterns.</li> <li>Useful for studying the impact and detection of intermittent malicious activities.</li> </ol> </li> <li> <p><strong><code>Only Benign HH Traffic</code></strong>:</p> <ol> <li>Includes only benign HH traffic flows.</li> <li>Suitable for training and evaluating models to identify and differentiate benign heavy hitter traffic patterns.</li> </ol> </li> <li> <p><strong><code>Only DDoS Traffic</code></strong>:</p> <ol> <li>Contains only malicious DDoS HH traffic.</li> <li>Helps in isolating and analyzing attack characteristics for targeted threat detection.</li> </ol> </li> <li> <p><strong><code>Only Normal Traffic</code></strong>:</p> <ol> <li>Comprises only regular, non-HH traffic flows.</li> <li>Useful for understanding baseline network behavior in the absence of heavy hitters.</li> </ol> </li> <li> <p><strong><code>Unbalanced Traffic Generation</code></strong>:</p> <ol> <li>Features an unbalanced dataset with varying proportions of benign, normal, and malicious traffic.</li> <li>Simulates real-world scenarios where certain types of traffic dominate, providing insights into model performance in unbalanced conditions.</li> </ol> </li> </ol> <p>For each variation, the output of the different packet aggregators is provided separated in its respective folder.</p> <p>Each variation was generated using the NDT approach to demonstrate its flexibility and ensure the reproducibility of our study's experiments, while also contributing to future research on network traffic patterns and the detection and classification of heavy hitter traffic flows. The dataset is designed to support research in network security, machine learning model development, and applications of digital twin technology.</p>

opencc-by-4.0Nov 2024View details →
zenodo48/100

Dataset for: The Effect of Loading Direction on Slip and Twinning in an Irradiated Zirconium Alloy

<p><strong>This is the dataset used in the following publication: </strong></p> <p>R. Thomas, D. Lunt, M. D. Atkinson, J. Quinta da Fonseca, M. Preuss, F. Barton, J. O&#39;Hanlon, and P. Frankel, &quot;The Effect of Loading Direction on Slip and Twinning in an Irradiated Zirconium Alloy,&quot; in&nbsp;<em>Zirconium in the Nuclear Industry: 19th International Symposium</em>, ed. A. Motta and S. Yagnik (West Conshohocken, PA: ASTM International, 2021), 233-261.&nbsp;<a href="https://doi.org/10.1520/STP162220190027">https://doi.org/10.1520/STP162220190027</a>.</p> <p><strong>Contained in this dataset are:</strong></p> <p>A Jupyter notebook which uses the open-source DefDAP Python package (https://github.com/MechMicroMan/DefDAP) to open enclosed HRDIC, EBSD and image data for non-irradiated and 0.1 dpa proton irradiated Zircaloy-4 deformed to ~3% strain, along the rolling direction and transverse direction.</p> <p>Please use the &#39;develop&#39; version of DefDAP:&nbsp;https://github.com/MechMicroMan/DefDAP/tree/f6b5d6ec33db9a45089fada17026432645044d2f</p> <p><strong>Publication abstract:</strong></p> <p>In this study, deformation experiments together with high-resolution digital image correlation were used to quantify the effect of proton irradiation on strain localization in Zircaloy-4 loaded along the rolling and transverse directions. Significant increases in strain heterogeneity were measured in the irradiated material compared to the nonirradiated material. This was a result of confinement of slip to channels in the irradiated material, which contain high effective shear strain values, with almost no strain in the regions between channels. The active slip systems in the material were also determined by comparing experimental slip trace angles from high-resolution digital image correlation with theoretical slip trace angles determined using grain orientation from electron backscatter diffraction. An increased amount of pyramidal and wavy basal slip, as well as tension twinning, were observed in the sample loaded along the transverse direction, compared to the sample loaded along the rolling direction, due to crystallographic texture. No significant change in slip system activity was observed as a result of 0.1 dpa proton irradiation, despite the dramatic change in slip pattern. The findings provide further insight into the role of irradiation on deformation behavior and provide quantitative data on slip system activation, for as-received and irradiated Zircaloy-4, against which to validate models.</p>

opencc-by-4.0Jun 2021View details →
zenodo48/100

Greenhouse gas profiles from the 2021 HEMERA-TWIN balloon launch

<p>The dataset contains mixing ratio observations of o long-lived greenhouse gase carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and (SF6) from the HEMERA TWIN gonodola launch from Kiruna (Sweden) on 12. August 2021. Profile data for CO2 and CH4 from AirCore sampling and CH4 data from the PICO IR laser spectrometer cover altitudes from the PBL to 32km. 14 cryogenic air samples collected between 14 and 31 km altitude have been analysed for all four gases.</p>

opencc-by-4.0Oct 2022View details →
zenodo48/100

Bibliographic Data from the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review

<p>This database contains all the&nbsp;bibliographic&nbsp;information about the 8673 records found after applying the Search Strategy used for the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review. Such strategy consisted on using seven&nbsp;initial keywords and similar terms of interest (namely: bridge and bridges, etc.):&nbsp;</p> <ul> <li>Bridge.</li> <li>Digital twin.</li> <li>Bridge information modelling.</li> <li>Finite elements.</li> <li>Bridge health monitoring.</li> <li>Anomaly detection algorithm.</li> <li>Cultural heritage.</li> </ul> <p>Six initial queries were done combining the first keyword with the rest of them:</p> <ul> <li>bridge* AND &quot;digital twin*&quot;</li> <li>bridge* AND (BrIM OR &quot;bridge information model*&quot;)</li> <li>bridge* AND (FEM OR FEA OR &quot;finite element method*&quot; OR &quot;finite element analy*&quot;)</li> <li>bridge* AND (&quot;bridge health monitoring&quot; OR &quot;structural health monitoring&quot;)</li> <li>bridge* AND (ADA OR &quot;anomaly detection algorithm*&quot;)</li> <li>bridge* AND (&quot;cultural heritage&quot; OR &quot;monument* bridge*&quot; OR &quot;old bridge*&quot; OR &quot;ancient bridge*&quot; OR &quot;historic* bridge*&quot;)</li> </ul> <p>As a first screening step, the combination of these 6 initial searches was&nbsp;done to obtain relevant works containing at least three of the main keywords of interest:</p> <ul> <li>#1 AND #2</li> <li>#1 AND #3</li> <li>#1 AND #4</li> <li>#1 AND #5</li> <li>#1 AND #6</li> <li>#2 AND #3</li> <li>#2 AND #4</li> <li>#2 AND #5</li> <li>#2 AND #6</li> <li>#3 AND #4</li> <li>#3 AND #5</li> <li>#3 AND #6</li> <li>#4 AND #5</li> <li>#4 AND #6</li> <li>#5 AND #6</li> </ul> <p>All records found in&nbsp;Scopus where downloaded both in .ris and .csv format and are included in this database.&nbsp;The search was conducted on 10/12/2022.</p> <p>Note: Searches 10, 14, 17 and 21 did not return any records.</p>

opencc-by-4.0Jan 2023View details →
zenodo48/100

Example of Force Digital Calibration Certificate used in ComTraForce 18SIB08 project to demonstrate Digital Twin concept

<p>Force Digital Calibration Certificate (DCC) was developed in the frameworks of 18SIB08&nbsp;ComTraForce project. It was used to demonstrate the way of data connection between the physical object (force transducer) and virtual object (Finite Element model) within&nbsp;the developed Digital Twin&nbsp;concept. The developed at PTB v3.1.2 xsd schema was used to convert analog calibration certificate to machine readable XML&nbsp;format. The DCC covers static and continuous calibration processes. Note that the current Force DCC is not a Good Practice example. Please follow further developments of force DCC Good Practice example at&nbsp;https://gitlab.com/ptb/dcc.</p>

opencc-by-4.0Apr 2023View details →
zenodo48/100

Genome-wide association study of full body nevus count in the Brisbane Twin Nevus Study (BTNS)

<p>The project uses the Brisbane Twin Nevus Study (BTNS) (N=3863)) to compare nevus counts on different anatomical sites to assess which anatomical site serves as best proxy for counting nevi on the whole body.In the project, a GWAS of nevus count on the whole body and GWAS of nevus count on the outer arm are performed.Here is the GWAS of total nevus count.</p> <p>Sample: GWAS analysis only includes samples of European ancestry. total nevus count were counted by trained research nurse.</p> <p>Genotype: All genotypes were imputed to a human haplotype map (HapMap) reference panel. Genome-wide association analyses were performed using Genome-wide Efficient Mixed model Association (GEMMA), which can account for genetically related individuals such as twins and siblings. Sex, age, age2, sex*age, sex*age2, sunburn, BSA, sun exposed hours weighted by UV index and 5 PCs, additionally two batch effect variables; were included as covariates. SNP imputation quality filter retained SNP with an INFO &gt; 0.3. minor allele frequency frequency filter was applied to retain SNP MAF &gt; 0.1</p> <p>Columns include:</p> <p>CHR: Chromosome</p> <p>BP: Base pair</p> <p>SNP: rsID</p> <p>A1: Effect allele</p> <p>A2: Non-effect allele</p> <p>A1FQ: Effect allele frequency</p> <p>HWE: Hardy-Weinberg Equilibrium</p> <p>BETA: Effect estimate (of effect allele_</p> <p>SEB: Standard error of beta</p> <p>PRB: P value</p> <p>N: Per SNP sample size</p>

opencc-by-4.0May 2023View details →
zenodo48/100

D^2EPC BIM-based Digital Twin data model example and real-time building measurements

<p>An example building digital twin data model, developed within the H2020 project D^2EPC, corresponding to the first out of six&nbsp;Case Studies&nbsp;(CERTH nZEB Smart House DIH). The following files are provided:</p><p>i) The BIM-based data model of the building parameters (.json file)</p><p>ii) Building real-time collected measurements within the project (in separate .json files):</p><ul><li>Living room: CO2, temperature, humidity, luminance, presence,&nbsp;PM2.5, TVOCs, loudness, smoke</li><li>Office: temperature, humidity, luminance, presence</li><li>Entire ground floor: HVAC system electrical energy consumption</li><li>Entire first floor: HVAC system electrical energy consumption</li><li>Entire building: electrical energy consumption (lighting &amp; appliances)</li><li>Building PV installation: electrical energy production</li></ul><p>&nbsp;</p>

opencc-by-4.0Oct 2023View details →
zenodo44/100

Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution; Supplementary Information"

<p>These files contains datasets from which the Figures appearing in the Supplementary Information have been calculated.&nbsp;</p> <p>Description of datasets:</p> <p>Datasets related to SupplFig1 contain two columns: Frequency in Hz and phase noise in rad^2/Hz</p> <p>Data_SupplFig1_stabilised_fringes: psd of the phase noise calculated from the interference fringes in a stabilised condition</p> <p>Data_SupplFig1_unstabilised_fringes: psd of the phase noise calculated from the interference fringes in an unstabilised condition</p> <p>Data_SupplFig1_roundtrip_sensing_laser: psd of the sensing laser signal after a round-trip in the interferometer, calculated&nbsp;from self-heterodyne beatnote</p> <p>Data_SupplFig1_differential_roundtrip_sensing_vs_reference_laser: psd of the difference between the round-trip self-heterodyne beatnotes at the sensing and reference laser wavelengths</p> <p>Datasets related to SupplFig2 contain two columns: time in seconds and normalised intensity (calculated as detailed in the main publication).</p> <p>Data_SupplFig2_High_power_PD_free_evol: normalised intensity of the interference signal&nbsp;obtained with classical power level at the source. This trace was recorded with&nbsp;a photodiode when no artificial phase drift was applied</p> <p>Data_SupplFig2_High_power_PD_phase_drift:&nbsp; normalised intensity of the interference signal&nbsp;obtained with classical power level at the source. This trace was recorded with&nbsp;a photodiode when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_High_power_SPD_free_evol:&nbsp;normalised intensity of the interference signal&nbsp;obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when no&nbsp;artificial phase drift was applied&nbsp;</p> <p>Data_SupplFig2_High_power_SPD_phase_drift:&nbsp;normalised intensity of the interference signal&nbsp;obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_Attenuated_SPD_free_evol:&nbsp;normalised intensity of the interference signal&nbsp;obtained with attenuated beams at the source. This trace was recorded on an SPD when no&nbsp;artificial phase drift was applied&nbsp;</p> <p>Data_SupplFig2_Attenuated_SPD_phase_drift:&nbsp;:&nbsp;normalised intensity of the interference signal&nbsp;obtained with attenuated beams at the source. This trace was recorded on an SPD when an artificial phase drift was applied (8pi/s)</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2021View details →
zenodo44/100

Underlying data - Digital Twin for Rainbow Trout (Oncorhynchus mykiss) land-based aquaculture

<p>Datasets for replicating Figures 5, 6, 7 and 8 of the article &quot;Digital twins for land-based aquaculture: a case study for rainbow trout (<em>Oncorhynchus mykiss</em>)&quot;, by Adriano C. Lima, Edouard Royer, Matteo Bolzonella, and Roberto Pastres.</p>

opencc-by-4.0Nov 2021View details →
zenodo44/100

Galactic chemical evolution of the solar neighborhood, solar twins and exoplanet indicators

<p>Galactic chemical evolution (GCE), solar analogues or twins, and peculiarities of<br> the solar composition with respect to the twins are inextricably related.<br> We examine GCE parameters from the literature and present newly derived values<br> using a quadratic fit that gives zero for a Solar age (i.e. 4.6 Gyr).&nbsp;<br> We show how the GCE parameters may be used not only to ``correct&#39;&#39; abundances<br> to the solar age, but to predict {\bf average} relative elemental&nbsp;<br> abundances as a function of age.&nbsp;<br> We address the question of whether the solar abundances<br> are depleted in refractories and enhanced in volatiles and<br> find that the answer is sensitive to the selection of a<br> representative standard. &nbsp;Our best quality data sets do<br> not support the notion that the Sun is depleted in refractories. &nbsp;<br> A simple model allows us to estimate<br> the amount of refractory-rich material missing from the sun<br> or alternately added to the average solar twin. &nbsp;The model<br> gives between zero and 1.4 earth masses. &nbsp; &nbsp;<br> &nbsp;</p>

opencc-by-4.0Feb 2022View details →
zenodo44/100

Datasets for the paper "Microstates and defects of incoherent Σ3 [111] twin boundaries in aluminum"

<p>This repository contains the raw data of the experimental EBSD analysis and the STEM imaging of the grain boundary microstates of ORI and ORII of the paper &quot;Microstates and defects of incoherent &Sigma;3 [111] twin boundaries in aluminum&quot;. Simulation data is also provided.</p> <p>See the file README.md for a detailed description.</p>

opencc-by-4.0Apr 2022View details →
zenodo44/100

theoretical and experimental study of the deposition of dielectric stacks on twin-hole silica fibers for implementation of compact all-fiber resonators

<p>This dataset includes the Matlab code to engineer theoretically a&nbsp;Bragg stack made of different dielectric layers. By changing the number of alternating stacks,&nbsp;their thickness and the refractive index of each layer it is possible to obtain&nbsp;the transmission curve of the Bragg stack versus the wavelength&nbsp;of the light in a certain range of values. The dataset includes also the experimental measurements of the resonances related to one of the fabricated compact resonators (based on the twin-hole fiber not poled) and obtained sweeping the wavelength of the input light injected through&nbsp;one of the two Bragg stacks and collecting the power at the exit of the other Bragg stack. &nbsp;</p>

opencc-by-4.0Sep 2022View details →
zenodo44/100

OpenAIRE Graph: dataset for research community in Virtual Human Twins

<p>This dataset contains metadata records of publications, research data, software and projects relevant for the research community in Virtual Twins in health.<br>The dump contains the records available in the <a href="https://dth.openaire.eu/" target="_blank" rel="noopener">OpenAIRE Gateway on Digital Twins in Health</a> of the <a href="https://www.edith-csa.eu/" target="_blank" rel="noopener">EDITH CSA project </a>of the European Commission (grant agreement n. 101083771).</p> <p>Records are identified via full-text mining and inference techniques applied to the&nbsp;<a href="https://graph.openaire.eu/">OpenAIRE Graph</a>.<br>The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications,&nbsp;datasets, software, projects, funders, and organizations, aggregating thousands of scholarly data sources world-wide.</p> <p>The dump consists of a tar archive containing gzip files with one json per line.<br>Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.10519297">https://doi.org/10.5281/zenodo.10519297</a>.</p>

opencc-by-4.0Jan 2024View details →
zenodo44/100

Twin Test 2: Wake interactions of a cluster of turbines and wake steering techniques. Wind tunnel data.

<p>The aerodynamic performance of two identical wind turbine models was characterized under various static and dynamic conditions in a synchronous configuration within the wind tunnel test section. Two experimental campaigns were performed at Technische Universit&auml;t M&uuml;nchen (TUM) and at the National Technical University of Athens (NTUA) to investigate wake flow control techniques. This document contains the necessary information to understand the performed experiments and to access and use the available data. While both experimental set ups are detailed, only data from the TUM campaign are available at the time of writing, as the NTUA campaign results will form Phase II of an ongoing blind test campaign and cannot be published.</p>

opencc-by-4.0Oct 2024View details →
zenodo44/100

Cardiac_Digital_Twin_Data

<p>Repository creation in progress.</p> <p>meta_data can be directly used to run the codes in https://github.com/juliacamps/Cardiac-Digital-Twin to generate and visualise digital twins and reproduce the results from "Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing" (https://doi.org/10.1016/j.media.2024.103361).</p> <p>The supplement of the publication mentioned earlier contains additional information on the code and data structure.</p> <p>The monodomain simulations were performed using the configuration and mesh files in&nbsp; monodomain_monoalg3D_configuration_meshes.tar and using the version of monoAlg3D that can be found at <a title="https://github.com/bergolho/monoalg3d_c/tree/t-wave-personalisation-2024" href="https://github.com/bergolho/MonoAlg3D_C/tree/t-wave-personalisation-2024" target="_blank" rel="noreferrer noopener">https://github.com/bergolho/MonoAlg3D_C/tree/t-wave-personalisation-2024</a>&nbsp;</p> <p>The specific custom functions that were implemented in the t-wave-personalisation-2024 branch of the monoAlg3D code to enable the simulations can be found in monodomain_monoalg3D_custom_functions.tar.&nbsp;</p>

opencc-by-4.0Nov 2022View details →
zenodo44/100

Carbon emission and lifecycle costs supporting digital twins for managing railway maintenance and resilience

<p>The development of railway construction increases the system complexity, which results in difficulty in management with traditional methods. Building Information Modelling (BIM) as an interoperable concept is benefits via whole life-cycle assessment (LCA) of the project, and it has been widely adopted in architecture, construction, and engineering (ACE) fields. This dataset of lifecycle cost and carbon footprint supports the&nbsp;digital twins for managing railway maintenance and resilience.</p>

opencc-by-4.0Jun 2021View details →
zenodo44/100

Data for "The Heritage Digital Twin: a bicycle made for two."

<p>The file contains the data used in the case studies of the paper &quot;The Heritage Digital Twin: a bicycle made for two. The integration of digital methodologies into cultural heritage research&quot; published on ORE.</p>

opencc-by-4.0Jan 2023View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record