Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
125
datasets available to search
ShareScore release 0.7.1
Dataset results
125 results for “Quantum Dots”
Determination of antibacterial and photothermal properties of novel composites based on graphene oxide/reduced graphene oxide, gold nanoparticles, and graphene quantum dots
<p>HR-TEM.zip - HR-TEM files, file type .jpg</p> <p>FTIR.zip - FTIR spectra, file type .spa</p> <p>Photoluminescence.zip - PL spectra, file type .opju</p> <p>UV-Vis.opju - Origin file with UV-Vis spectra combined</p> <p>Raman 532 nm.opju - Origin file with Raman spectra combined</p> <p>ABDA.opju - Origin file with singlet oxygen production measurements</p> <p>Contact angle.png - Image with contact angle values</p> <p>Antibacterial analysis.png - Image representing antibacterial growth inhibition analysis</p> <p>XRD.zip - XRD spectra, file type .dat</p>
Data and figure reproduction for paper titled 'Lateral quantum-confined Stark effect for integrated quantum dot electroabsorption modulators'
<p>The dataset contains the data behind, and instructions to reproduce, the figures in the paper titled 'Lateral quantum-confined Stark effect for integrated quantum dot electroabsorption modulators' written by the named dataset creators.</p>
Raw data: quantum interference of identical photons from remote GaAs quantum dots
<p>This is the raw data supporting the findings in the letter titled "<em>Quantum Interference of Identical Photons from Remote GaAs Quantum Dots</em>" that is published in <em>Nature Nanotechnology.</em></p>
Quantum Dots Stability Diagrams Dataset
<p>This Quantum Dots Stability Diagrams (QDSD) Dataset aggregates experimental stability diagrams of quantum dots from different research groups.</p> <p>For more information about the data processing, refer to the <a href="https://github.com/3it-inpaqt/qdsd-dataset">GitHub repository</a>.</p> <p>Only the <strong>interpolated_csv.zip</strong> and <strong>labels.json</strong> files are necessary for offline tuning or machine-learning tasks.</p> <p>Currently, only single-dot stability diagrams are labeled.</p> <p>The original data have been provided by different research groups based on the following references:</p> <ul> <li><a href="https://doi.org/10.1063/1.5091111">Rochette et al. 2019</a></li> <li><a href="https://doi.org/10.1063/1.3258663">Gaudreau et al. 2009</a></li> <li><a href="https://doi.org/10.23919/VLSICircuits52068.2021.9492427">Stuyck et al. 2021</a></li> </ul> <p>See the <strong>README.md</strong> for files description.</p>
Robust quantum dots charge autotuning using neural network uncertainty - Output data
<p>Outputs of the model training and the offline autotuning experiments presented in the paper: "<em>Robust quantum dots charge autotuning using neural network uncertainty</em>".</p> <p>For convenience, the results are splitted in several zipped files:</p> <ul> <li><strong>run_outputs_light.zip</strong>: contains only settings and results text files (sufficient for compiling result tables).</li> <li><strong>run_outputs_full_scan.zip</strong>: contains complete scan of the diagrams (for qualitative analyse)</li> <li><strong>run_outputs_part<N>.zip</strong>: contains all autotuning simulation output, grouped by seed (images and video output types might vary between seeds)</li> </ul> <p>Each folder in the zipped files represent a run that includes:</p> <ul> <li>log file</li> <li>plots / images</li> <li>run settings</li> <li>performance results</li> <li>pytorch model parameters</li> </ul> <p>See README.txt for more information about the file strucutre.</p>
Research data supporting "Duplex-Specific Nuclease-Amplified Detection of MicroRNA Using 2 Compact Quantum Dot−DNA Conjugates"
<p>Raw research data supporting the publication:</p> <p>Wang, Y. et al., 2018, ACS Applied Materials & Interfaces, "Duplex-Specific Nuclease-Amplified Detection of MicroRNA Using 2 Compact Quantum Dot−DNA Conjugates", DOI: 10.1021/acsami.8b07250.</p>
Supporting data for "Hyperfine-phonon spin relaxation in a single-electron GaAs quantum dot"
<p>Supporting data for<br> "Hyperfine-phonon spin relaxation in a single-electron GaAs quantum dot"<br> Leon C. Camenzind, Liuqi Yu, Peter Stano, Jeramy D. Zimmerman, Arthur C. Gossard, Daniel Loss & Dominik M. Zumbühl</p> <p><em>Nature Communications <strong>Volume 9</strong></em>, Article number: 3454 (2018)</p>
02. Quantum dot tunneling through single low barrier
<p>02. Quantum dot tunneling through single low barrier</p> <p>In this video Visualization of Quantum dot tunneling with light packet is performed with single low barrier. Quantum dot passes through the single low barrier.</p>
01. Quantum dot tunneling through single high barrier
<p>01. Quantum Dot Tunneling High Barrier</p> <p>In this video Visualization of Quantum dot tunneling with light packet is performed with single high barrier. Quantum dot gets back and does not passes through the barrier.</p>
Isotropic and Anisotropic g-Factor Corrections in GaAs Quantum Dots
<p>Supporting data for</p> <p>"Isotropic and Anisotropic g-factor Corrections in GaAs Quantum Dots", Phys. Rev. Lett. 127, 057701 – Published 29 July 2021</p>
Source data for the publication "SiGe quantum wells with oscillating Ge concentrations for quantum dot qubits"
<p>This repository contains data reported in the figures of the publication "SiGe quantum wells with oscillating Ge concentrations for quantum dot qubits."</p>
CdSe Quantum Dots Sonochemically Synthesized in the Presence of Oleic Acid & Oleylamine at different concentrations- UV-Vis Spectra, Photoluminescence, SAXS
<p>Conntent Smmary: </p> <ul> <li>Data from experiments in which CdSe quantum dots and magic sized clusters were sonochemically synthesized in the presence of Oleic Acid and Oleylamine at different concentration. A total of 625 unique sample conditions were tested, in triplicates using an open-hardware sonochemical materials acceleration platform (Jubilee) and a liquid handling robot ( Opentrons)</li> <li>Notebooks for loading and plotting the data</li> </ul> <p>README: </p> <p><strong>/Spectral_Data</strong></p> <p>The primary portion of te experimental dataset. UV-Vis spectroscopy was collected on a Biotek Epoch 2 micropate spectrometer. The Photoluminescence data was collected on a Biotek Synergy H1 microplate spectrometer. Notebook to visualize the data can also be found in this folder. </p> <p>The<strong> "CdSe_sample_info.csv" </strong>contains all the sample information, including:</p> <ol> <li>Metal precursors and ligands concentration (M)</li> <li>Labware information- name, OT2 deck location, plate number</li> <li>Sample code- this is a combination of plate # and well position within the plate</li> </ol> <p>The "<strong>CdSe_Summary_Final.csv"</strong> contains all the sample information, along with the key parameters extracted from the UV-Vis and Photoluminescence data. These include, first peak position & peak intensity from both spectroscopic tecniques, particle diameter ( based on the UV-Vis peak position) for all 3 replicates. </p> <p>Finally,there are 3 notebooks to visualize the data in their spectral form, for both pre and post processing of the samples, as well as the notebook to recreate the scatterplot visualization of the summary parameters from the spectroscopic techniques implemented. </p> <p><strong>/Spectral_Data/Spectral_Data_Files</strong></p> <p>Folder containing all the raw data for the pre and post processing of the CdSe samples for UV-Vis spectroscopy and Photoluminescence spectroscopy. </p> <p><strong>/SAXS_Data</strong></p> <p>Folder containing all the data small-angle X-ray scattering data. This was collected on a Xenocs Xeuss 3.0 SAXS instrument. </p> <p>The "<strong>SAXS_Sample_Composition.csv" </strong> file contains the composition of precursors and ligands for the subset of samples tested using small-angle scattering. </p> <p>Notebooks to visualized the SAXS profiles obtained for all samples characterized, as well as their comparison with UV-Vis data can also be found in this folder. </p> <p><strong>/SAXS_Data/Reduced_Raw_Data</strong></p> <p>Folder containing the 1D data obtained from the reduction of the 2D dector data for each sample tested at three different dector distances ( 50, 370, and 900 nm). </p> <p><strong>/SAXS_Data/Processed_Data</strong></p> <p>Folder containing the final process SAXS data which includes merging of the data in the 3 tested instrumental configuration and subsequent background subtraction. </p> <p><strong>/SAXS_Data/McSAS_Fit_Data</strong></p> <p>Folder containing the results obtained form fitting the SAXS data from the diluted samples using the McSAS Python software. The fitting parameters used for the samples can be found in the <strong>"SAXS_Sample_McSAS_Fitting_Parameters.csv" </strong>file.</p> <p><strong>/SAXS_Data/UV-Vis_Data/ </strong></p> <p>Folder containing the UV-Vis data of the subset of samples characterized using SAXS. </p>
Particle–hole symmetry protects spin-valley blockade in graphene quantum dots
<p> Experimental data and python scripts used to evaluate the data and to perform simulations for the publication</p> <p>" Particle-hole symmetry protects spin-valley blockade in graphene quantum dots " in Nature.</p> <p>https://doi.org/10.1038/s41586-023-05953-5</p> <p> </p>
FIGURE 4 from paper JVST-B "Formation of CdSe quantum dots from single source precursor obtained by thermal and laser treatment"
<p>The dataset includes two files: the word file describes the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure 4.</p> <p>In the following is reported the description of figure 4 as wrote in the paper.</p> <p>Effect of the precursor concentration in films containing OA (a) and OAm (b). The effect of the precursor concentration on the QD’s size in the presence of OA and OAm is reported in (c) (OA and OAm red circles and green triangles, respectively).</p>
FIGURE 5 from paper JVST-B "Formation of CdSe quantum dots from single source precursor obtained by thermal and laser treatment"
<p>The dataset includes two files: the word file describes the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure 5.</p> <p>In the following is reported the description of figure 5 as wrote in the paper.</p> <p>Effect of the time on CdSe QD’s growth monitored through the PL spectra: (a) neat precursor; (b) precursor and OA; (c) precursor and OAm; and (d) trend of the size as a function of the annealing time.</p>
Quantum cryptography with highly entangled photons from semiconductor quantum dots
<p>Measurement data used within the main manuscript and the supplementary material.</p>
Source data for the publication "Longitudinal coupling between a Si/SiGe double quantum dot and an off-chip TiN resonator"
<p>This repository contains data reported in the publication "Longitudinal coupling between a Si/SiGe double quantum dot and an off-chip TiN resonator."</p>
Flux-Tunable Hybridization in a Double Quantum Dot Interferometer
<p>Code and datasets associated with the manuscript "Flux-Tunable Hybridization in a Double Quantum Dot Interferometer". With the code and data included here, all necessary fits and analysis can be conducted to produce the figures given in the manuscript and its supplementary material.</p> <p>Version 2: Improvements to analysis code and a correction to figure S5. See README.md for details. This version corresponds to the version accompanying the paper as submitted to a scientific journal.</p> <p>Version 3: Added the code for generating the newly added Figure 7 to make_figures.ipynb.</p>
Source data for the publication "Ultra-dispersive resonator readout of a quantum-dot qubit using longitudinal coupling"
<div> <p>This repository contains data and source code for the publication "Ultra-dispersive resonator readout of a quantum-dot qubit using longitudinal coupling."</p> </div>
Impact of Andreev Bound States within the Leads of a Quantum Dot Josephson Junction
<p>This repository contains all the raw data and the code used to generate the figures of the article "Impact of Andreev Bound States within the Leads of a Quantum Dot Josephson Junction".</p> <ul> <li>The plotting.ipynb notebook creates the majority of the figures. To run it you need to install proplot. Create a fresh python environment for it, since you might need to downgrade numpy to the 1.19.5 version and matplotlib to the 3.4.3 one. To install the relevant python packages and open the notebook you can download Anaconda and use the following terminal instructions:<br> <pre><code>conda create -n andreev-trimer python=3.10 conda activate andreev-trimer conda install proplot conda install jupyterlab xarray netcdf4 tqdm jupyter lab</code></pre> extraction-3D.ipynb extracts the charge degeneracy points from 3D charge stability diagram measurements. To run it you need to install scikit-image. If you want to export additional .gif images install imageio as well:<br> <pre><code>conda install scikit-image imageio</code></pre> </li> <li>To visualize the 3D charge stability diagrams (Figure 5) you can use plotting-3D.ipynb. To run it you need to install pyvista. A dedicated environment is recommended. To install pyvista and run it with JupyterLab use the following instructions:<br> <pre><code>conda create -n pyvista python=3.9 conda activate pyvista conda install nodejs pip install jupyter pyvista trame jupyter lab</code></pre> </li> <li>Finally, simulations.ipynb computes all the theoretical simulations. To run it you need cython and you can install it in a dedicated environment using the following instructions:<br> <pre><code>conda create -n theory conda activate theory conda install jupyterlab matplotlib cython scipy jupyter lab</code></pre> </li> </ul>
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.