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Dataset results
163 results for “superconductivity”
Dataset of the publication : "Misfit layer compounds as ultra-tunable field effect transistors: from charge transfer control to emergent superconductivity"
<p>Dataset of the publication : "Misfit layer compounds as ultra-tunable field effect transistors: from charge transfer control to emergent superconductivity"</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>
Data and Codes for "Non-local transport measurements in hybrid quantum Hall - superconducting devices"
<p>Datasets and processing code for reproduction of "Non-local transport measurements in hybrid quantum Hall - superconducting devices".</p>
Data from "Superconducting Diode Effect Sign Change in Epitaxial Al-InAs Josepshon Junctions"
<p><strong>Full Changelog</strong>: https://github.com/ShabaniLab/arXiv-2303.01902/commits/v1.0.0</p>
Data for the manuscript "Correlation between two distant quasiparticles in separate superconducting islands mediated by a single spin"
<p>Data for the manuscript "Correlation between two distant quasiparticles in separate superconducting islands mediated by a single spin", https://doi.org/10.48550/arXiv.2203.00104</p> <p>The archive contains the experimental data and the results of numerical calculations as well as plotting scripts/notebooks for the figures presented in the manuscript.</p>
Replication Data for: One-dimensional proximity superconductivity in the quantum Hall regime
Open the record for dataset details and reuse information.
Optical Bias and Cryogenic Laser Readout of a Multipixel Superconducting Nanowire Single Photon Detector
<p>The complete datasets of the Publication are updloaded in this repository.</p> <p>Have a look into the ReadMe files for further information.</p> <p>Info about the Spice Model: ReadME LTSPICE</p> <p>Info about the Detector Efficiency Analysis: ReadMe Countrate</p> <p>Info about the PNR Extraction: ReadMe PNR Extraction</p> <p>Info about the SNSPD traces: ReadMe Traces </p> <p>Dataset of the Laser Diode Characterisation: IVP_Laserdiode.txt</p> <p> </p> <p> </p> <p> </p>
Raw data for "Deep mouse brain two-photon near-infrared fluorescence imaging using a superconducting nanowire single-photon detector array"
<p>Two-photon microscopy (2PM) has become an important tool in biology to study the structure and function of intact tissues in-vivo. However, adult mammalian tissues such as the mouse brain are highly scattering, thereby putting fundamental limits on the achievable imaging depth, which typically resides around 600-800um. In principle, shifting both the excitation as well as (fluorescence) emission light to the shortwave near-infrared (SWIR, 1000-1700 nm) region promises substantially deeper imaging in 2PM, yet has proven challenging in the past due to the limited availability of detectors and probes in this wavelength region. To overcome these limitations and fully capitalize on the SWIR region, in this work we introduce a novel array of superconducting nanowire single-photon detectors (SNSPDs) and associated custom detection electronics for the use in near-infrared 2PM. The SNSPD array exhibits high efficiency and dynamic range, as well as low dark-count rates over a wide wavelength range. Additionally, the electronics and software permit seamless integration into typical 2PM systems. Together with a fluorescent dye emitting at 1105 nm, we report imaging depth of > 1.1mm in the in-vivo mouse brain, limited only by available labeling density and laser power. Our work further establishes SWIR 2PM approaches and SNSPDs as promising technologies for deep tissue biological imaging. </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>
Dataset and processing scripts for manuscript titled "Broadband polarization insensitivity and high detection efficiency in high-fill-factor superconducting microwire single-photon detectors"
<p>The manuscript "Broadband polarization insensitivity and high detection efficiency in high-fill-factor superconducting microwire single-photon detectors" contains results for high-efficiency, low-polarization-sensitivity superconducting microwire single-photon detectors. All of the data gathered and the python scripts used for processing is being provided as a zip archive, along with a digital signature file. The dataset contains crucial procedures regarding the equipment calibrations used for accurate device efficiency measurements.</p>
Datasets for "A superconducting dual-rail cavity qubit with erasure-detected logical measurements"
<p>Title of Dataset: Demonstrating a superconducting dual-rail cavity qubit with erasure-detected logical measurements<br>---</p> <p>Included are data shown in Figures 1-4 of the main text and Extended Data Figures 2 and 3 in the Methods.<br>Data was collected by running quantum programs on hardware deployed at Quantum Circuits, Inc. The results of these experiments are decoded and assigned an appropriate label (discussed below and in the manuscript). We provide various levels of processing: number of counts, fraction of counts, and the logical assignment. Full labeled shot-by-shot outcomes can be provided upon request.</p> <p>## Description of the data and file structure</p> <p>Description of column labels<br>- xs: sweep variable (if applicable)<br>- Counts:<br> - 00_counts, 01_counts, 10_counts, 11_counts: Number of runs with outcome labeled "00", "01", "10", "11", respectively<br> - A_counts: Number of runs with outcome labeled as ambiguous outcome<br> - FSP_counts: Number of runs with outcome labeled as a failed state preparation<br> - all_shots: Total number of runs<br> - total_counts: Number of runs with a successful state preparation (e.g. all_shots - B_counts)<br>- Outcome fraction<br> - 00, 01, 10, 11, A, FSP: Fraction of counts normalized by total_counts<br> - 00_err, 01_err, 10_err, 11_err, A_err, FSP_err: Standard error for all above<br>- Logical outcomes<br> - 0L, 1L, erasures, Z_L: Computed logical dual-rail outcome for "0_L", "1_L", erasures, and expectation value of logical sigma_z <Z>, respectively<br> - 0L_err, 1L_err, erasures_err, Z_L_err: Standard error for all above</p> <p>State assignment datasets:<br>- Fig2_state_assignment_1msmts.csv: Data for Figure 2<br>- ExtendedDataFig2_state_assignment_2msmts.csv: Data for Extended Data Figure 2</p> <p>Bit-flip datasets: <br>- Each row corresponds to a different delay specified in the xs column in units of microseconds<br>- Counts and Outcome fraction are plotted in Figure 3A<br>- Logical outcomes are plotted in Figure 3B<br>- Detail on datasets:<br> - Fig3_bit_flip_0L_1ms.csv: Data for Figure 3, left panel<br> - Fig3_bit_flip_0L_20us.csv: Data for Figure 3, left panel inset<br> - Fig3_bit_flip_1L_1ms.csv: Data for Figure 3, right panel<br> - Fig3_bit_flip_1L_20us.csv: Data for Figure 3, right panel inset</p> <p>Phase error datasets: <br>- Each row corresponds to a different delay specified in the xs column in units of microseconds<br>- Counts and Outcome fraction are plotted in the top panels for Figure 4A and 4B<br>- Logical outcomes are plotted in the bottom panel for Figure 4A and 4B<br>- Short time data are shown in the inset of bottom panel for Figure 4A and 4B. <br>- We provide additional detail for the short time data in Extended Data Figure 3 for short-time Ramsey<br>- Detail on ramsey_short_time.csv: Data includes an additional dimension where the Ramsey phase angle is swept<br> - values in radians are enumerated in ExtendedDataFig3_ramsey_short_time_phases.csv<br>- Detail on datasets:<br> - Fig4_ramsey_long_time.csv: Data for Figure 4A<br> - ExtendedDataFig3_ramsey_short_time.csv, ExtendedDataFig3_ramsey_short_time_phases.csv: Data for Figure 4A, inset (bottom panel); Data for Extended Data Figure 3<br> - Fig4_echo_long_time.csv: Data for Figure 4B<br> - Fig4_echo_short_time.csv: Data for Figure 4B, inset (bottom panel)</p>
Data for publication titled "Effect of ion irradiation on superconducting thin films"
<p>Here is the data to support the findings in the article titled "Effect of ion irradiation on superconducting thin films".</p> <p>There is a .txt file showing the room temperature sheet resistance measurement results for different wafers (measured by a four point probe in multiple positions of the wafer and the value in the table is the average of those measurements).</p> <p>The .csv files show resistance of the films scaled by the room temperature resistance value (R_RT) as a function of temperature. The first column is temperature (K) and the second column is R(T)/R_RT. For some wafers, there are multiple measurement results, labeled _a/_b/_c.</p> <p>The TEM images are the .tif files and FFT graphs derived from some of the TEM images are shared as .png files (titles show which TEM image has been used).</p> <p>EDS data is also shared for some wafers. The corresponding wafer is in the file names, and the data contains a .rpl, a .raw and a .png file with same main names.</p> <p> </p>
Data and codes for the work: "Quasiparticle dynamics in a superconducting qubit irradiated by a localized infrared source"
<p>All data and codes used for the work can be found here. </p> <p> </p> <ul> <li>For figures 2 and SM6, one must unzip the files and change the directory in the codes accordingly.</li> <li>For figures 3, SM7 and SM8, one should use the file "Figure3_data.h5", already containing the analysis of the raw data of the pulsed experiment, which is also contained inside the zip file.</li> <li>Comsol 6.2 was used to create the simulation file for the sample temperature.</li> </ul>
Data for "Luther-Emery liquid and dominant singlet superconductivity in the hole-doped Haldane spin-1 chain"
<p>This archive contains the data presented in the paper "Luther-Emery liquid and dominant singlet superconductivity in the hole-doped Haldane spin-1 chain", Phys. Rev. B <strong>110</strong>, 064515 (2024), <a href="https://doi.org/10.1103/PhysRevB.110.064515">https://doi.org/10.1103/PhysRevB.110.064515</a>, with preprint arXiv:2311.13440, <a href="https://doi.org/10.48550/arXiv.2311.13440">https://doi.org/10.48550/arXiv.2311.13440</a></p>
Figures code and data of "Coulomb blockade in open superconducting islands on InAs nanowires"
<p>Electrons in closed systems can exhibit Coulomb blockade (CB) oscillations due to charge quantization. Here, we report CB oscillations in aluminum superconducting islands on InAs nanowires in the open regime. The Al island is connected to the source/drain leads through two contacts: One is fully transmitting while the other is tuned into the tunneling regime. This device configuration is typical for tunneling spectroscopy where charging energy is generally considered negligible. The oscillation periods are 2e or 1e, depending on the gate settings. A magnetic field can induce the 2e to 1e transition. Our result is reminiscent of the ``mesoscopic Coulomb blockade'' in open quantum dots caused by electron interference. </p>
All-optical superconducting qubit readout
<p>Here you can find data and its analysis used in the creation of the publication "All-optical superconducting qubit readout". https://arxiv.org/abs/2310.16817</p>
SPM Images and dI/dV Data for Nanoscale Control of Quantum States in Radical Molecules on Superconducting Pb (111)
<p>This archive contains raw data files for the paper titled "Nanoscale Control of Quantum States in Radical Molecules on Superconducting Pb (111)"-----arXiv preprint arXiv:2408.05115/https://arxiv.org/abs/2408.05115.<br>If you have any questions, please contact chao.li@unibas.ch</p>
Probing enhanced superconductivity in van der Waals polytypes of V$_x$TaS$_2$
<p>Data associated with the publication "Probing Enhanced Superconductivity in van der Waals Polytypes of V$_x$TaS$_2$".</p> <p>The content and details are outlined in the README file.</p>
Data and code for the article "Superconducting microsphere magnetically levitated in an anharmonic potential"
<p>Data and code for the article "Superconducting microsphere magnetically levitated in an anharmonic potential", consisting of all the measurements, spectra and code for data analysis.</p>
Tunable Capacitor For Superconducting Qubits Using an InAs/InGaAs Heterostructure
<p>COMSOL, HFSS simulation files, along with Python analysis / plotting code related to the work titled "Tunable Capacitor for Superconducting Qubits Using an InAs/InGaAs Heterostructure."</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.