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184 results for “Nb”
EBSD Dataset of the Alpha and Beta Phase Orientations for a Hot-Rolled Zr-2.5Nb Alloy
<p>A set of ex-situ electron backscatter diffraction (EBSD) datafiles following rolling of a Zr-2.5Nb alloy at different temperatures (700C, 750C, 775C, 800C, 825C, 850C, 900C) and rolling reductions (50%, 75%, 87.5%). Data for annealing of the material (750C for 2 hours) and following rolling at 800C from a different ‘as-forged’ starting texture is also included. Note, phases in the ctf files marked as Titanium Cubic refer to measurement of the Zirconium Cubic phase. </p> <p>The data in the 'Beta ctf' folder includes a reconstruction of the high temperature beta-phase orientations where possible, reconstructed from the alpha phase orientations using a software based on the Burgers relationship.</p> <p>Please see the accompanying paper for the interpretation of crystallographic texture changes;</p> <p>C.S. Daniel, P.D. Honniball, L. Bradley, M. Preuss, J. Quinta da Fonseca, A detailed study of texture changes during alpha–beta processing of a zirconium alloy, J. Alloys Compd. 804 (2019) 65–83, <a href="https://doi.org/10.1016/j.jallcom.2019.06.338">10.1016/j.jallcom.2019.06.338</a></p>
2D EBSD Dataset of the Alpha and Beta Phase Orientations for a Hot-Rolled Model Zircaloy-4 with 7 wt.% Nb Alloy
<p>A set of electron backscatter diffraction (EBSD) data files for a model Zircaloy-4 with 7 wt.% Nb addition alloy following hot-rolling. </p> <p>The 2D data set contains EBSD maps recording the microstructure and texture evolution, from a beta-processed (and annealed) starting condition, following rolling at a temperature of 725C to 50% and 75% reduction. Data for annealing of the rolled materials (750C for 2 hours) is also included. <em>Note, phases in the ctf files marked as 'Titanium Cubic' refer to measurement of the 'Zirconium Cubic' phase.</em></p> <p>Please see our accompanying paper for analysis of the 2D measurements - as well as analysis of a 3D reconstruction from <a href="https://doi.org/10.5281/zenodo.3785084">10.5281/zenodo.3785084</a> - and for interpretation of the coupled crystallographic texture evolution;</p> <p>C.S. Daniel, A. Garner, P.D. Honniball, L. Bradley, M. Preuss, P.B. Prangnell, J. Quinta da Fonseca, Co-deformation and dynamic annealing effects on the texture development during alpha–beta processing of a model Zr-Nb alloy, Acta Materialia 205 (2021) 116538. <a href="https://doi.org/10.1016/j.actamat.2020.116538">10.1016/j.actamat.2020.116538</a></p>
Figure 1. The comparison of the Area Under the ROC Curve (AUC) for fuzzy Diagnosis, KNN and NB-Comparison of Fuzzy Diagnosis with K-Nearest Neighbor and Naïve Bayes Classifiers in Disease Diagnosis
<p>The area under the receiver operating characteristic (ROC) curve (AUC) is used to measure<br> the performance of fuzzy diagnosis, KNN and NB. In order to show the difference between AUC<br> for the three methods, a single figure which combines the three AUC for the three methods was<br> used for comparison as shown in figure 1 below.</p>
Dataset: NB Bancorp, Inc. (NBBK) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: NioCorp Developments Ltd. (NB) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
High Temperature Compression Studies of a Zr-2.5Nb Alloy using Deformation Dilatometer
<p>Data recorded in uniaxial compression for a Zr-2.5Nb alloy deformed at temperatures of 650C, 675C, 700C, 725C, 750C, 775C, 800C, 825C and 850C, at strain rates of 10-2.5, 10-2, 10-1.5, 10-1, 10-0.5 and 1 s-1, to 50% height reduction, using TA Instruments DIL 805 A/D/T Quenching and Deformation Dilatometer. The cylindrical samples measured 5 mm diameter and 10 mm height. The Zr-2.5Nb specimens were machined from the centre of an as-received forged plate manufactured at Wah Chang, with a beta-transformed starting microstructure. Si3N43 platens were used for all tests, with graphite lubricant applied at the ends of the sample to minimise friction. Tests were conducted in an inert He gas atmosphere. Temperature was controlled using an S-Type thermocouple spot-welded to the centre of the samples.</p> <p>Data recorded at high acquisition frequency during deformation is stored in the 'deformation_files' folder and saved with the format: ' test number (001 to 191)_temperature_log(strain rate)_repeat number (01 or 02)'. Data in the 'basic_files' folder is recorded at a lower acquisition frequency, but includes recording of the entire themomechanical cycle, including both heating and cooling stages, as well as deformation. The 'software_files' folder includes metadata stored in the form of a parameter file (.par and .pad), along with a Windows data file (.D5D) that can be loaded and analysed within the dilatometer user interface.</p> <p>An <a href="https://doi.org/10.5281/zenodo.3673105">accompanying python script</a> will allow the user to plot the stress-strain data using the Jupyter Notebook application, along with generating 'processing maps' of the material. A critical assessment of the application of 'processing maps' is included in the accompanying paper;</p> <p>C. S. Daniel, P. Jedrasiak, C. J. Peyton, J. Quinta da Fonseca, H. R. Shercliff, L. Bradley, and P. D.Honniball, “Quantifying Processing Map Uncertainties by Modeling the Hot-Compression Behavior of a Zr-2.5Nb Alloy,” in Zirconium in the Nuclear Industry: 19th International Symposium, ed. A. T. Motta and S. K. Yagnik (West Conshohocken, PA: ASTM International, 2021), 93–122. <a href="https://doi.org/10.1520/STP162220190031">10.1520/STP162220190031</a></p>
NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies
<p><strong>NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies</strong></p> <p>This dataset contains the raw energy measurements as well as R scripts to reproduce the energy consumption plot for the corresponding paper.</p> <p>Each .csv file contains a specific set of measurements and we provide a script to read, process and plot the contained data.</p> <p><strong>Figure 3</strong></p> <p>Mean energy consumption of the different phases for Authentication for NB-IoT and LTE-M.</p> <p>Due to the fact that the duration of <em>Idle Connected</em> in the measurement scripts was 30 seconds and 60 seconds for <em>Idle Not Connected</em>, the D-value and the mean power consumption are divided by 2.</p> <ul> <li>Data – energy_measurements_fig3.csv</li> <li>Code – fig3.R</li> </ul> <p><strong>Figure 4</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for NB-IoT and LTE-M for 1KB of data in HTTP.</p> <p>The delay between the measurements for Figure 4 were all 30 seconds long, but the identified <em>Standby</em> and <em>Idle</em> phases have different lengths. Therefore, the <em>Idle</em> phase values for both access technologies have been normalized and calculated for 20 seconds each.</p> <ul> <li>Data – energy_measurements_fig4.csv</li> <li>Code – fig4.R</li> </ul> <p><strong>Figure 5</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for HTTP and MQTT for 1KB of data in NB-IoT.</p> <p>In this scenario the delay between the measurements were different again. For <em>MQTT</em> the delay was 150 seconds and for <em>HTTP</em> 30 seconds. Therefore, the data during the <em>Idle</em> and <em>Standby</em> (only for <em>MQTT</em>) phase is normalized and calculated for 20 seconds and 10 seconds, respectively. During the <em>MQTT</em> <em>Idle</em> phase measurements, the device disconnects. This is not taken into account for the evaluation, which is why these energy values are discarded for this figure.</p> <ul> <li>Data – energy_measurements_fig5.csv</li> <li>Code – fig5.R</li> </ul> <p><strong>Contact</strong></p> <p>For questions or issues with this code, please contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de) or any of the authors of the related publication.</p>
Atomic-scale environment of niobium in minerals as revealed by X-ray absorption spectroscopy at the Nb K-edge
<p>This data set provides the raw EXAFS and XRD data of the manuscript '<strong>Atomic-scale </strong><strong>environment of niobium in minerals as revealed b</strong><strong>y </strong><strong>X-ray absorption spectroscop</strong><strong>y at the Nb </strong><strong>K</strong><strong>-edge'</strong> submitted to European Journal of Mineralogy.</p> <p>- EXAFS data files were converted into .txt for more accessibility. <em>Pcl</em> is the abbreviation for <em>pyrochlore. </em>The files are accoridngly labelled.</p> <p>- XRD data files for synthetic Nb-doped compounds start with 'XRD_...'.</p> <p>- The refined crystal structure of hydropyrochlore is 'hydropcl_01.cif' file.</p> <p> </p> <p> </p>
A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements
<p>Mobile networks have become highly complex systems. In order to better understand how network features affect performance and suggest additional improvements, it is crucial to examine them from an empirical perspective. In the following, we present a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT) and 5G New Radio (NR) connectivity. We collected our dataset during a period of seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization tasks.</p> <p><br>If you use our dataset in your research, we kindly request that you cite the following paper:</p> <p>K. Kousias <em>et al</em>., "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," in <em>IEEE Communications Magazine</em>, vol. 62, no. 5, pp. 44-49, May 2024, doi: 10.1109/MCOM.011.2200707.</p>
Data of normal spectral emissivity measurements for Ta, Mo, W and Nb
<p>Data aquired during my master thesis about the normal spectral emissivty of Ta, Mo, W and Nb measured with an ohmic pulse heating apparatus and a us-DOAP</p>
The complete genome sequence and comparative genomic analyses of four phages (NJ-P3, NB-P21, NC-P34 and NN-P42)
<p>We downloaded and reanalyzed the raw data of four phages genomes (NJ-P3, NB-P21, NC-P34, NN-P42). This is the reassembled whole genomes and comparative genomic analyses of four phages.</p>
Data for "Laser Nanostructured Metasurfaces in Nb Superconducting Thin Films"
<p>Dataset for the article entitled "Laser Nanostructured Metasurfaces in Nb Superconducting Thin Films", submitted to the journal Applied Surface Science.</p><p>Contains original experimental data:</p><p>- SEM images with EDS</p><p>- TEM images</p><p>- SQUID magnetic measurements</p>
Superconducting flip-chip devices using indium microspheres on Au-passivated Nb or NbN as under-bump metallization layer
<p>Data used for figures in "Superconducting flip-chip devices using indium microspheres on Au-passivated Nb or NbN as under bump metallization layer" by A. Paradkar et al. <a href="https://doi.org/10.1063/5.0235266">Appl. Phys. Lett. <strong>126</strong>, 022601 (2025)</a></p>
Outdoor NB-IoT and 5G coverage and channel information data in urban environments
<p>This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G).</p> <p>Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: <a href="https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements">https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements</a> </p> <p>The dataset includes a metadata file providing the following information for each campaign: </p> <ul> <li>date of collection;</li> <li>start time and end time of collection;</li> <li>length;</li> <li>type (walking/driving).</li> </ul> <p>Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization.</p> <p>The dataset contains the following data for NB-IoT:</p> <ul> <li>Raw data for each campaign, stored in two .csv files. For a generic campaign <X>, the files are: <ul> <li>NB-IoT_coverage_C<X>.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);</li> <li> NB-IoT_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <NPCI, Operator ID, eNodeB-ID> unique combination detected at the coordinates of the data point.</li> <li>Estimated positions of eNodeBs, stored in a csv file for each city;</li> <li>A matlab script and a function to extract and generate processed data from the raw data for each city.</li> </ul> <p>The dataset contains the following data for 5G:</p> <ul> <li>Raw data for each campaign, stored in two .xslx files. For a generic campaign <X>, the files are: <ul> <li>5G_coverage_C<X>.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);</li> <li> 5G_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <PCI, Beamforming Index, Operator ID> unique combination detected at the coordinates of the data point.</li> <li>A matlab script and a supporting function to extract and generate processed data from the raw data.</li> </ul> <p>In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches:</p> <ul> <li>A campaign-by-campaign linear interpolation (both NB-IoT and 5G);</li> <li>A bidimensional interpolation on all campaigns combined (NB-IoT only).</li> </ul> <p>A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in:</p> <p>L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. <span>DOI: </span><a href="https://doi.org/10.3390/s23094266" target="_blank" rel="noopener"><span>10.3390/s23094266</span></a>.</p> <p>The second interpolation approach is instead introduced and described in:</p> <p>L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation.</p> <p>Positioning using the 5G data was furthermore in investigated in: </p> <p>K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," <span>IEEE Communications Magazine, Volume 62, Issue 5, pp</span><span>. 44-49, May</span><span> 202</span><span>4</span><span>. DOI: </span><a href="https://doi.org/10.1109/MCOM.011.2200707" target="_blank" rel="noopener"><span>10.1109/MCOM.011.2200707</span></a><span>.</span></p> <p><span>G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: <a href="https://doi.org/10.1109/OJCOMS.2024.3499370" target="_blank" rel="noopener"><span>10.1109/OJCOMS.2024.3499370</span></a>.</span></p> <p>Please refer to the above publications when using and citing the dataset. </p>
Extracted synthesis conditions for Nb:TiO2 films from scientific papers
<p>The csv file contains the dataset for synthesis conditions for Nb doped TiO2 films extracted from scientific papers using a knowledge graph.</p>
Use of Localized NB-UVB (Levia®) in the Treatment of Plaque-psoriasis
ClinicalTrials.gov study NCT02107482. IPD Sharing: NO. Countries: 1. Publications: 1.
Ab Initio amorphous Nb and Ta oxide structures
Open the record for dataset details and reuse information.
3D EBSD Dataset of the Alpha and Beta Phase Orientations for a Hot-Rolled Model Zircaloy-4 with 7 wt.% Nb Alloy
<p>A set of serial-section electron backscatter diffraction (EBSD) data files and a 3D reconstruction of a model Zircaloy-4 with 7 wt.% Nb addition alloy following hot-rolling.</p> <p>The 3D data set contains measurements of the material rolled at 725C to 75% reduction. The measurements include indexing of both the alpha and the beta phases in 441 sequential slices, each of 0.1 μm, through a small section of the material, taken using the dual beam Thermo Scientific Helios Xe<sup>+</sup> plasma focused ion-beam scanning electron microscope (PFIB-SEM). The 3D EBSD data set includes EBSD measurements in the form of ctf files, binary data files, an Aztec project file, and accompanying images for each slice.</p> <p>A 3D volume was reconstructed from the 3D EBSD data set using a customised pipeline within the DREAM.3D software. The results of this analysis are included in the 'dream3d' folder, which includes a description of the pipeline, the final fully reconstructed dream3d data file, an xdmf file used for visualising the data in ParaView, a h5ebsd file containing results for the reconstruction, and grain averaged data for each of the identified features.</p> <p>Please see our accompanying paper for analysis of the 3D reconstruction - as well as analysis of the 2D measurements from <a href="https://doi.org/10.5281/zenodo.3784460">10.5281/zenodo.3784460</a> - and for interpretation of the coupled crystallographic texture evolution;</p> <p>C.S. Daniel, A. Garner, P.D. Honniball, L. Bradley, M. Preuss, P.B. Prangnell, J. Quinta da Fonseca, Co-deformation and dynamic annealing effects on the texture development during alpha–beta processing of a model Zr-Nb alloy, Acta Materialia 205 (2021) 116538. <a href="https://doi.org/10.1016/j.actamat.2020.116538">10.1016/j.actamat.2020.116538</a></p>
Simulation data for CHARMM36 POPC bilayer, 100 lipids/leaflet, 450 mM CaCl2 ("NB-Fix" used), 310K, GROMACS 5.1.4
<p>Simulations of a POPC bilayer with 450 mM of CaCl_2. </p> <p>The second from the set of 6 simulations.</p> <p>The goal was to study the effect of scaling the CHARMM FF on the ion binding.</p> <p>Done for the NMRlipids project, see <br> http://nmrlipids.blogspot.fi for more information.</p> <p>A POPC bilayer consisting of 200 lipids (100 per leaflet) <br> is simulated in the presence of 450 mM CaCl_2. The Charmm36 <br> model is employed for lipids, the Charmm compatible variant <br> of the tip3p model for water, and the default Charmm<br> ion parameters (type CAL) for CaCl_2. The new extra nonbonded parameters were used for trating the calcium bonding (NB-Fix)</p> <p> </p> <p>The Charmm36 force field parameters were obtained from http://charmm-gui.org/</p> <p>––––––––––––––––––––––––––––––––––––––––––––––––––––––</p> <p>The files are in GROMACS format. Trajectory (.xtc) is <br> 360 ns long with data saved every 100 ps.</p> <p>the initial structure (.gro), topology (.top), index file (.ndx), <br> simulation paremeter file (.mdp), binary run input file <br> for GROMACS v. 5.1–> (.tpr) and the energy output file <br> (.edr) are provided. </p>
CHARMM36, NB-Fix approaches, without NBFIX, POPC membrane, Ca, Na ions,
<p>Different versions of NB-Fix from CHARMM-Gui and simulations without NB-Fix (called original charmm)</p> <p>200 POPC lipids, TIP3P - charrmm water model,</p> <p> </p>
ScienceDex guides
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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.