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Dataset results
501 results for “Charging”
Charge Exchange Cross sections for He+, Ne+, Ar+, Kr+ on N2 from 0.3 - 5.0 keV
<p>Energy resolved absolute charge exchange cross sections for He+, Ne+, Ar+, Kr+ incident on N2 from 0.3 - 5.0 keV.</p>
Data for the publication "Singular charge fluctuations at a magnetic quantum critical point"
<p>Data sets of the figures in the publication "Singular charge fluctuations at a magnetic quantum critical point"</p> <p>Preprint: arXiv:1808.02296</p>
Data release for the "First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K"
<p>### On-/Off-Axis Data Release<br>#### (Version 1.0.1, dated 2024/08/12)</p> <p>This tar archive contains the data release for ‘First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K’. It contains the cross-section data points and supporting information in ROOT and text format, which are detailed below:</p> <p>+ `onoffaxis_xsec_data.root`<br>This ROOT file contains the extracted cross section and the nominal MC prediction as TH1D histograms for both the flattened 1D array of bins and in the angle binning for the analysis. The ROOT file also contains both the covariance and inverted covariance matrix for the result stored as TH2D histograms. The angle bin numbering and the corresponding bin edges are detailed at the end of the README.</p> <p>+ `flux_analysis.root`<br>This ROOT file contains the nominal and post-fit flux histograms for ND280 and INGRID. Two different binnings are included: a fine binned histogram (220 bins) and a coarse binned histogram (20 bins). The coarse binned histogram corresponds to the flux parameters detailed in the paper (and bin edges listed in the appendix).</p> <p>+ `xsec_data_mc.csv`<br>The extracted cross-section data points and the nominal MC prediction for each bin is stored as a comma-separated value (CSV) file with header row.</p> <p>+ `cov_matrix.csv` and `inv_matrix.csv`<br>The covariance matrix and the inverted covariance matrix are both stored as CSV files with each row stored as a single line and columns separated by commas (there is no header row). Matrix element (0,0) corresponds to the first number in the file.</p> <p>+ `nd280_analysis_binning.csv` and `ingrid_analysis_binning.csv`<br>The analysis bin edges are included as CSV files. The columns are labeled with a header row and denote the linear bin index and the lower and upper bin edge for the angle and momentum bins. The units are in cos(angle) for the angle bins and in MeV/c for the momentum bins.</p> <p>+ `calc_chisq.cxx`<br>This is an example ROOT script to calculate the chi-square between the data and the nominal MC prediction using the ROOT file in the data release. To run, open ROOT and load the script (`.L calc_chisq.cxx`) and execute the function `calc_chisq("/path/to/file.root")`.</p> <p>+ `calc_chisq.py`<br>This is an example Python script to calculate the chi-square between the data and the nominal MC prediction using the text/CSV files in the data release. The code requires NumPy as an external dependency, but otherwise uses built-in modules. To run, execute using a Python3 interpreter and give the file paths to the data/MC text file and the inverse covariance text file as the first and second arguments respectively -- e.g. `python3 calc_chisq.py /path/to/xsec_data_mc.csv /path/to/inv_matrix.csv`</p> <p>+ ND280 angle bin numbering<br> - 0: `-1.0 < cos(#theta) < 0.20`<br> - 1: `0.20 < cos(#theta) < 0.60`<br> - 2: `0.60 < cos(#theta) < 0.70`<br> - 3: `0.70 < cos(#theta) < 0.80`<br> - 4: `0.80 < cos(#theta) < 0.85`<br> - 5: `0.85 < cos(#theta) < 0.90`<br> - 6: `0.90 < cos(#theta) < 0.94`<br> - 7: `0.94 < cos(#theta) < 0.98`<br> - 8: `0.98 < cos(#theta) < 1.00`</p> <p>+ INGRID angle bin numbering<br> - 0: `0.50 < cos(#theta) < 0.82`<br> - 1: `0.82 < cos(#theta) < 0.94`<br> - 2: `0.94 < cos(#theta) < 1.00`<br> <br>### Changelog</p> <p>#### v1.0.1<br>Fix transcription error in INGRID momentum binning. The lowest momentum bin edge is at 350 MeV/c, not 300 MeV/c.</p>
Raw Data and Codes for the Article "Strain-Affected Ferroelastic Domain Walls in RbMnFe Charge-Transfer Materials undergoing collective Jahn-Teller Distortion"
<p>Dataset for the article "Strain-Affected Ferroelastic Domain Walls in RbMnFe Charge-Transfer Materials undergoing collective Jahn-Teller Distortion", containing:</p> <ul> <li>The data and the codes used to generate the figures</li> </ul>
Experimental online quantum dots charge autotuning using neural networks - Output data
<p>Outputs of the model training and the online autotuning experiments presented in the paper: "Experimental online quantum dots charge autotuning using neural networks".</p> <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>
Correlating the Photoshunt with Charge-Collection Losses in Organic Solar Cells
<p>Data of figures shown in the main and supporting information of the paper "Correlating the Photoshunt with Charge-Collection Losses in Organic Solar Cells".</p>
Data and simulation scripts for the manuscript "Patchy charge distribution affects the pH in protein solutions during dialysis."
<p>A zip archive containing the following data:</p> <ul> <li>README.md file describing in detail how to navigate the archive</li> <li>LICENSE.md file describing the license under which the data can be used or reproduced</li> <li>python scripts required to reproduce the plots presented in the manuscript</li> <li>processed simulation results used by the plotting scripts</li> <li>python scripts required to run the simulations in order to reproduce the results</li> </ul> <p> </p> <p> </p>
Impact of RbF and NaF Postdeposition Treatments on Charge Carrier Transport and Recombination in Ga-Graded Cu(In,Ga)Se2 Solar Cells
<p>Excel file with data to all figures published in our article found at <a href="https://doi.org/10.1002/adfm.202103663">https://doi.org/10.1002/adfm.202103663 </a>(Advanced Functional Materials)</p>
Data release for the "Measurement of the charged-current electron (anti-)neutrino inclusive cross-sections at the T2K off-axis near detector ND280"
<p>This data release is associated with the publication "Measurement of the charged-current electron (anti-)neutrino inclusive cross-sections at the T2K off-axis near detector ND280". It is currently available on arXiv and in JHEP:</p> <p><a href="https://arxiv.org/abs/2002.11986">arXiv:2002.11986 [hep-ex]</a> and <a href="https://doi.org/10.1007/JHEP10(2020)114">J. High Energ. Phys. 10, 114 (2020)</a></p> <p><strong>When citing this data release, please cite as well the paper.</strong></p> <p><em>The full author list and acknowledgements for the T2K collaboration are described in the article.</em></p> <p>The data release contains:</p> <ul> <li>cross-section measurements with NEUT 5.3.2 (fraction and total with covariances)</li> <li>cross-section measurements with GENIE 2.8.0 (fraction and total with covariances)</li> <li>smearing matrices for selected electron/positron momentum</li> </ul> <p><strong>Description:</strong></p> <p>The cross-section measurements are provided in the form of text files and a PDF summary. The detailed method and results are presented in the paper (especially section 8).</p> <p>The smearing matrices are provided as one ROOT file with two 2D histograms showing the electron/positron smearing matrices for momentum and angle, obtained using the selection from the ND280 nue CC inclusive analysis. It is similar to the figure 10 of the paper, but with more statistics and finer binning. They are accompanied with a README file presenting how to use these matrices and the related caveats. <strong>Please read it carefully.</strong></p> <p><strong>We strongly encourage any users of the matrices to present these caveats alongside any public comparison to T2K data.</strong></p> <p> </p> <p><strong>Full abstract:</strong></p> <p>The electron (anti-)neutrino component of the T2K neutrino beam constitutes the largest background in the measurement of electron (anti-)neutrino appearance at the far detector. The electron neutrino scattering is measured directly with the T2K off-axis near detector, ND280. The selection of the electron (anti-)neutrino events in the plastic scintillator target from both neutrino and anti-neutrino mode beams is discussed in this paper. The flux integrated single differential charged-current inclusive electron (anti-)neutrino cross-sections, dσ/dp and dσ/dcos(θ), and the total cross-sections in a limited phase-space in momentum and scattering angle (p>300 MeV/c and θ≤45<sup>∘</sup>) are measured using a binned maximum likelihood fit and compared to the neutrino Monte Carlo generator predictions, resulting in good agreement.</p>
A Josephson relation for fractionally charged anyons
<p>Data file corresponding to the figures 2, 3, S2 to S7 of a Research Report to be published in the review SCIENCE</p>
Dataset for the paper "Boosting Charge Carrier Mobilities in Upgraded Metallurgical Grade Silicon by Phosphorous Diffusion Gettering"
<p>Dataset related to the publication "Boosting Charge Carrier Mobilities in Upgraded Metallurgical Grade Silicon by Phosphorous Diffusion Gettering" in Advanced Energy and Sustainable Research 2022, 2200077 (https://doi.org/10.1002/aesr.202200077)</p>
SourceData for "Correlating the charge transfer gap to the maximum transition temperature in Bi2Sr2Can-1CunO2n+4+x"
<p>SourceData for "Correlating the charge transfer gap to the maximum transition temperature in Bi2Sr2Can-1CunO2n+4+x"</p>
Open data for publication: Advanced catalyst for CO2 photo-reduction: From controllable product selectivity by architecture engineering to improving charge transfer using stabilized Au clusters
<p>Original data for publication: Advanced catalyst for CO2 photo-reduction: From controllable product selectivity by architecture engineering to improving charge transfer using stabilized Au clusters, published in Small, 2023.</p> <p>The dataset is organized according to the Figures in the manuscript.</p>
Data Set "Efficient automatic construction of atom-economical QM regions with point-charge variation analysis"
<p>This data set accompanies the publication "Efficient automatic construction of atom-economical QM regions with point-charge variation analysis" by Felix Brandt and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p>- PDB files of the reactant and product starting structure</p> <p>- modified AMBER95 force field file</p> <p>- AMS fragment files for the ligands and ions</p> <p>- AMS input files for all geometry optimizations and single point calculations</p>
Zinc(II) Complexes with Triplet Charge-Transfer Excited States Enabling Energy-Transfer Catalysis, Photoinduced Electron Transfer, and Upconversion
<p>Raw data to the graphs of the publication</p>
Automated Earthquake Catalogues for CHARGE & CHARSME/CSN networks in South America (Pampean Flat Slab)
<p>This repository includes:</p> <p>1. Gzipped automated catalogues of earthquake locations with P and S arrival times for:</p> <ul> <li>The CHARGE network - charge.data.gz</li> <li>The CHARSME and CSN networks - charsme_csn.data.gz</li> </ul> <p>2. A README explaining the information in each column </p>
Data underpinning "Disorder-induced spin-charge separation in the 1-D Hubbard model"
<p>Many-body localisation is believed to be generically unstable in quantum systems with continuous non-Abelian symmetries, even in the presence of strong disorder. Breaking these symmetries can stabilise the localised phase, leading to the emergence of an extensive number of quasi-locally conserved quantities known as local integrals of motion, or l-bits. Using a sophisticated non-perturbative technique based on continuous unitary transforms, we investigate the one-dimensional Hubbard model subject to both spin and charge disorder, compute the associated l-bits and demonstrate that the disorder gives rise to a novel form of spin-charge separation. We examine the role of symmetries in delocalising the spin and charge degrees of freedom, and show that while symmetries generally lead to delocalisation through multi-particle resonant processes, certain subsets of states appear stable.</p>
Single-electron-charge transfer into putative Majorana and trivial modes in individual vortices
<p>Supporting data for Jian-Feng Ge, et al. “Single-electron-charge transfer into putative Majorana and trivial modes in individual vortices”.</p> <p>The following data files are used for the following figures.</p> <p> Fig. 1 a Illustration figure, no data used<br> b NbSe2_04_220202_0184.txt<br> c FeTeSe_08_210604_0614.txt</p> <p> Fig. 2 a NbSe2_04_220202_0133_raw.txt<br> b NbSe2_04_220202_dIdV_0033_0037_raw.txt<br> c NbSe2_04_220202_0133_raw.txt<br> d NbSe2_04_220202_0133_deconv.txt<br> e NbSe2_04_220202_dIdV_0033_0037_deconv.txt<br> f NbSe2_04_220202_0133_deconv.txt</p> <p> Fig. 3 a FeTeSe_08_210604_0188_raw.txt<br> b FeTeSe_08_210604_dIdV_0121_0122_raw.txt<br> c FeTeSe_08_210604_0188_raw.txt<br> d FeTeSe_08_210604_0188_deconv.txt<br> e FeTeSe_08_210604_dIdV_0121_0122_deconv.txt<br> f FeTeSe_08_210604_0188_deconv.txt</p> <p> Fig. 4 a 220210_NbSe2_04_2.3K_spectrum_06_08.txt<br> b qeff_NbSe2.txt<br> c 210622_FeTeSe08_2.3K_spectrum_06_04.txt<br> d qeff_FeTeSe.txt</p> <p>Supplementary Fig. 1 a PbtipPb111.txt<br> b PbtipAu111.txt<br> c Pbtipfits.txt</p> <p>Supplementary Fig. 2 a Illustration figure, no data used<br> b linecut_raw.txt<br> c linecut_deconv.txt<br> d peak_pos.txt<br> e peak_amp.txt</p> <p>Supplementary Fig. 3 a NbSe2_04_220202_0098_raw.txt<br> b NbSe2_04_220202_0098_c_33_31_a_63_0_raw.txt<br> c NbSe2_04_220202_0098_raw.txt<br> d 220210_NbSe2_04_2.3K_spectrum_04_03.txt<br> e NbSe2_04_220202_0098_deconv.txt<br> f NbSe2_04_220202_0098_c_33_31_a_63_0_deconv.txt<br> g NbSe2_04_220202_0098_deconv.txt<br> h 220210_NbSe2_04_2.3K_spectrum_04_03_qeff.txt<br> i NbSe2_04_220202_0172_raw.txt<br> j NbSe2_04_220202_0098_c_33_35_a_0_63_raw.txt<br> k NbSe2_04_220202_0172_raw.txt <br> l 220210_NbSe2_04_2.3K_spectrum_10_11.txt<br> m NbSe2_04_220202_0172_deconv.txt<br> n NbSe2_04_220202_0098_c_33_35_a_0_63_deconv.txt<br> o NbSe2_04_220202_0172_deconv.txt<br> p 220210_NbSe2_04_2.3K_spectrum_10_11_qeff.txt</p> <p>Supplementary Fig. 4 a FeTeSe_08_210604_0355_raw.txt<br> b FeTeSe_08_210604_0355_c_26_30_a_63_0_raw.txt<br> c FeTeSe_08_210604_0355_raw.txt<br> d 210622_FeTeSe08_2.3K_spectrum_15_14.txt<br> e FeTeSe_08_210604_0355_deconv.txt<br> f FeTeSe_08_210604_0355_c_26_30_a_63_0_deconv.txt<br> g FeTeSe_08_210604_0355_deconv.txt<br> h 210622_FeTeSe08_2.3K_spectrum_15_14_qeff.txt<br> i FeTeSe_08_210604_0463_raw.txt<br> j FeTeSe_08_210604_0463_c_26_27_a_0_0_raw.txt<br> k FeTeSe_08_210604_0463_raw.txt <br> l 210622_FeTeSe08_2.3K_spectrum_27_24.txt<br> m FeTeSe_08_210604_0463_deconv.txt<br> n FeTeSe_08_210604_0463_c_26_27_a_0_0_deconv.txt<br> o FeTeSe_08_210604_0463_deconv.txt<br> p 210622_FeTeSe08_2.3K_spectrum_27_24_qeff.txt</p> <p>Supplementary Fig. 5 a 220210_NbSe2_04_2.3K_spectrum_06_08_qeff.txt<br> b 210622_FeTeSe08_2.3K_spectrum_06_04_qeff.txt</p> <p>Supplementary Fig. 6 a FeSeTe_07_180716_0309_topo.txt<br> b FeSeTe_07_180716_0309_ring.txt<br> c FeTeSe_08_210604_0355_raw.txt<br> d 180809_FeSeTe7_ring_2.5MOhm_3K_map_04.txt<br> e 180907_FeSeTe7_Pbtip_10MOhm_3K_spectra_24.txt<br> f 180907_FeSeTe7_Pbtip_10MOhm_3K_spectra_24_qeff.txt<br> <br> Supplementary Fig. 7 qeff_vs_qpcontrib.py</p> <p>Supplementary Fig. 8 a FeTeSe_10_211111_didv_FB.txt<br> b FeTeSe_10_211111_didv_FB_ratio_sim.txt</p> <p>Supplementary Fig. 9 220810_NbSe2_06_2.3K_spectrum_01_19.txt</p> <p>Supplementary Fig. 10 a NbSe2_05_220503_dIdV_0017.txt<br> b 220510_NbSe2_05_2.3K_spectrum_01.txt</p>
Electric vehicle occupancy of charging points in the city of Paris
<p>Dataset collected by EDF R&D using the <em>Paris Data</em> open data platform, providing real-time occupancy of public charging points for electric vehicles in the city of Paris. This <strong>data.zip</strong> archive should be used at the root of the following gitlab repository (it replaces the empty data folder): <a href="https://gitlab.com/smarter-mobility-data-challenge/additional_materials">smarter-mobility-data-challenge/additional_materials</a>. V1 corresponds to the data provided for the Smarter Mobility Challenge, augmented with exogenous features such as weather and traffic. V2 corresponds to additional raw observations of occupancy data collected and accompanied by initial data processing.</p>
Data release for "Measurements of the muon-neutrino and muon-antineutrino-induced coherent charged pion production cross sections on Carbon-12 by the T2K experiment"
<p>The T2K experiment reports the measurement of the flux averaged charged current coherent pion production cross section for neutrino and anti-neutrino scattering from a Carbon nucleus. These results are at a mean (anti)neutrino energy of 0.85~GeV in a restricted final state kinematic phase space. The neutrino measurement is an update to a previous result with systematic uncertainties reduced by a half. The antineutrino measurement is the first measurement of this cross section to be made at these energies. We find that the neutrino and antineutrino cross sections are consistent, as expected from theory, and that both agree with the current theoretical models, the Rein-Sehgal and Berger-Sehgal models.</p> <p>The data release contains a summary of these results as well as neutrino and antineutrino flux histograms with which the reader can make their own flux averaged cross section calculation.</p> <p>The paper is published in <a href="https://doi.org/10.1103/PhysRevD.108.092009">Physical Review D</a> and is available on the <a href="https://arxiv.org/abs/2308.16606">arXiv:2308.16606 [hep-ex]</a>.</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.