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Supplementary data to "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations"
<p>This dataset contains the data used to generate the results in the work "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations" [1].</p> <p>processed_data.zip contains the data used for the figures shown in [1], while raw_data.zip contains the original simulation results without further processing.</p> <p>To get an overview of the organization of the directories and a description of the data we recommend the README files in the top- and subdirectories.</p> <p>[1] C. Krämer et al., Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations, <a href="https://doi.org/10.48550/arXiv.2403.05223">10.48550/arXiv.2403.05223</a>, 2024</p>
Ground-state dataset "Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states"
<h1>2D QUANTUM EDWARDS-ANDERSON GROUND-STATE DATASET:</h1> <p>The dataset contains coupling and energy data for 50 instances of a 2D quantum Edwards-Anderson model at Gamma (transverse field) = 1.8, featuring N=LxL=100 spins on a square lattice of side-length L=10 with periodic boundary conditions. The couplings are sampled from a Gaussian distribution with zero mean and unit variance.<br>The dataset consists of two text files containing coupling values and the corresponding ground-state energies.</p> <h2>Coupling Data (`coup_dataset.txt`)</h2> <p>The file `coup_dataset.txt` contains fifty sets of coupling data. Each set consists of three columns representing the indices `i`, `j`, and the coupling value `J_ij`, respectively.<br>The spin indices range from 1 to 100, ordered progressively by rows. Each set of coupling data is separated by two empty lines.</p> <h2>Energy Data (eng_dataset.txt)</h2> <p>The file `eng_dataset.txt` contains fifty rows of energy data corresponding to the coupling sets in `coup_dataset.txt`. Each row contains two columns representing the energy value and its associated statistical error-bar, rounded to the fifth decimal digit.</p>
All-sky three-dimensional dust density and extinction Maps of the Milky Way out to 2.8 kpc
<p>Three-dimensional dust density maps are crucial for understanding the structure of the interstellar medium of the Milky Way and the processes that shape it. However, constructing these maps requires large datasets and the methods used to analyse them are computationally expensive and difficult to scale up. As a result it is has only recently become possible to map kiloparsec-scale regions of our Galaxy at parsec-scale grid sampling. We present all-sky three-dimensional dust density and extinction maps of the Milky Way out to 2.8~kpc in distance from the Sun using the fast and scalable Gaussian Process algorithm Dustribution. The sampling of the three-dimensional map is $l,b,d = 1^{\circ} \times1^{\circ} \times 1.7$~pc. The input extinction and distance catalogue contains 120 million stars with photometry and astrometry from Gaia DR2, 2MASS and AllWISE. This combines the strengths of optical and infrared data to probe deeper into the dusty regions of the Milky Way. We compare our maps with other published 3D dust maps. All maps quantitatively agree at the $0.001$~mag~pc$^{-1}$ scale with many qualitatively similar features, although each map also has its own features. We recover Galactic features previously identified in the literature. Moreover, we also see a large under-density that may correspond to an inter-arm or -spur gap towards the Galactic Centre.</p> <p> </p> <p>Here we provide the full dataset generated from Dustribution covering the full sky out to 2.8 kpc. We provide the 3D dust extinction and density files as well the l,b,d coordinate grid that the data is presented in. </p> <p> </p> <p>The data in each file is as follows:</p> <ul> <li>lbdGrid_l_bounds.pkl.npy: The boundaries of the grid cells of the merged map in l (degrees). Numpy array of size 881.</li> <li>lbdGrid_b_bounds.pkl.npy: The boundaries of the grid cells of the merged map in b (degrees). Numpy array of size 273.</li> <li>lbdGrid_d_bounds.pkl.npy: The boundaries of the grid cells of the merged map in d (pc). Numpy array of size 1644.</li> <li>Density_Median_lbd.pkl.npy: The median of 100 density samples of the merged map (mag pc^-1). Treat this as the estimate of the density. Numpy array of size (880, 272, 1644)</li> <li>Density_16P_lbd.pkl.npy: The 16th percentile of the density of the merged map (mag pc^-1). Treat median-16p as the estimate of the lower uncertainty on the density. Numpy array of size (880, 272, 1644)</li> <li>Density_84P_lbd.pkl.npy: The 84th percentile of the density of the merged map (mag pc^-1). Treat 84p-median as the estimate of the upper uncertainty on the density. Numpy array of size (880, 272, 1644) </li> <li>Ext_Median_lbd.pkl.npy: The median of the integral of 100 sample maps at each cell in the merged map (mag). Treat this as the estimate of the extinction in each cell. Numpy array of size (880, 272, 1644). </li> <li>Ext_16P_lbd.pkl.npy: The 16th percentile of the integral of 100 sample maps at each cell in the merged map (mag). Treat median-16p as the estimate of the lower uncertainty on the extinction in each cell. Numpy array of size (880, 272, 1644)</li> <li>Ext_84P_lbd.pkl.npy: The 84th percentile of the integral of 100 sample maps at each cell in the merged map (mag). Treat 84p-median as the estimate of the upper uncertainty on the extinction in each cell. Numpy array of size (880, 272, 1644)</li> <li>read_and_plot.ipynb: a short jupyter notebook demonstrating how to read in the data files, with some quick examples of plotting them.</li> </ul>
An innovative method of three-dimensional bone reconstruction of the anterior mandible with preserved dentition using an allogeneic bone block- 6-month follow-up- supplementary materials
<p>Table S1. The Shapiro-Wilk’s normality test (N=32 for each variable and each tooth) for the differences between before and after the treatment. W – test values, p – statistical significance.</p> <p>Table S2. Descriptive statistics of the studied bone growth parameters.</p> <p>Table S3 . Relative Treatment Effects for the studied teeth and the statistical significance of the differences between before and after treatment.</p> <p>Table S4. Spearman’s correlations of the bone growth parameters (averaged data for all teeth). N – sample size, R – correlation coefficient, t(N-2) – test value, p – statistical significance.</p> <p>Table S5. The differences in bone growth parameters (averaged data for all teeth) between sexes. N – sample size, U – test value, p – statistical significance.</p> <p>Table S6. The differences in bone growth parameters (averaged data for all teeth) between patients who underwent orthodontic treatment with a passive wire (Wire) and patients without ongoing treatment (Without wire). N – sample size, U – test value, p – statistical significance.</p> <p>Table S7. The differences in bone growth parameters (averaged data for all teeth) between patients with recessions (Yes) and patients without recession (No). N – sample size, U – test value, p – statistical significance.</p> <p>Table S8. The differences in bone growth parameters (averaged data for all teeth) between patients with thin biotype (Thin) and patients with thick biotype (Thick). N – sample size, U – test value, p – statistical significance.</p> <p>Table S9. The differences in bone growth parameters (averaged data for all teeth) between patients with excessive mentalis muscle tension (Yes) and patients without such the feature (No). N – sample size, U – test value, p – statistical significance.</p>
Characterizing multidimensional cellular physiological states with quantitative three-dimensional shape descriptors for cell membranes
<h3>Supplementary dataset and code for the article "<em>Characterizing Cellular Physiological States with Three-Dimensional Shape Descriptors for Cell Membranes</em>"</h3> <p>CShaper Dataset.zip: The 3D cell regions reused from the previously published article <a href="https://doi.org/10.1038/s41467-020-19863-x">https://doi.org/10.1038/s41467-020-19863-x</a>.</p> <p>Cell Shape Descriptors - Code & Data.zip: The code (exemplifed by embryo Sample04) and data (including embryo Sample04-Sample20) of 12 3D shape descriptors for all 3D cell regions in the <em>CShaper</em> dataset.</p> <p>GUI.zip: The user-friendly software <em>Shape Descriptor Tool</em> is a Graphical User Interface based on <em>Matlab</em> for calculating 12 shape descriptors for a 3D cell region (exemplified by embryo Sample20, time point 14, ABpl cell in the <em>CShaper</em> dataset). The instruction guidebook is included in the Supplementary Material of the article.</p>
Complete classification of six-dimensional iso-edge domains
<p>This dataset contains the complete list of the 55.083.357 iso-edge domains in dimension 6. <br>The data is stored in a netCDF-4 data file. <br>For each iso-edge domain it contains 63 canonicalized 6-dimensional coordinates that represent the iso-edge domain.<br>Furthermore, for each iso-edge domain it contains the number of neighbouring iso-edge domains.</p> <p>The netCDF dimensions and variables are as follows:</p> <pre><code>netcdf ctype_dim6 { dimensions: number_ctype = UNLIMITED ; // (55083357 currently) n = 6 ; n_vect = 63 ; variables: int Ctype(number_ctype, n_vect, n) ; Ctype:long_name = "Ctype canonicalized coordinates" ; Ctype:units = "nondimensional" ; int nb_adjacent(number_ctype) ; nb_adjacent:long_name = "number of adjacent Ctypes" ; nb_adjacent:units = "nondimensional" ; }</code></pre> <p>The data can be extracted to a human readable format using for example the <em>ncdump</em> utility. </p>
One-dimensional shallow flow solution data with uncertain topography
<p>Raw tabulated data generated by <a href="https://www.seamlesswave.com/">SEAMLESS-WAVE</a> stochastic models.</p>
Eulerian modelling of the three-dimensional distribution of seven popular microplastic types in the global ocean dataset
<p>Dataset for the paper "Eulerian modelling of the three-dimensional distribution of seven popular microplastic types in the global ocean" by A. S. Mountford and M. A. Morales Maqueda.</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_con.nc<a href="https://zenodo.org/api/files/c6c9cbac-d5db-452a-aade-fd852db07351/ORCA2_5d_00010101_00011231_ptrc_T_con.nc"> </a> - control experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_30m.nc - 30 m year<sup>-1</sup> piston velocity sensitivity experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_90m.nc - 90 m year<sup>-1</sup> piston velocity sensitivity experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_50.nc - neutrally buoyant sensitivity simulation (year 50)</p> <p>plastic_1_ts.nc - positively buoyant time series</p> <p>plastic_2_ts.nc - neutrally buoyant time series</p> <p>plastic_3_ts.nc - negatively buoyant time series</p> <p>plastic_input_ORCA2.nc - plastic input data file</p> <p>ORCA2_5d_00010101_00011231_grid_U.nc & ORCA2_5d_00010101_00011231_grid_V.nc - ocean velocity files</p>
Simulation data and source code for Hausdorff dimension measurements in two-dimensional quantum gravity
<p>This entry contains the source code and simulation data used as basis for the paper</p> <p>J. Barkley, T. Budd, "Precision measurements of Hausdorff dimensions in two-dimensional quantum gravity." Preprint <a href="https://arxiv.org/abs/1908.09469">arXiv:1908.09469</a> (2019)</p> <p>Both the source code and the data consist of two parts:</p> <ul> <li>Measurements of (dual) graph distances in various models of random planar maps.</li> <li>Measurements of discrete Liouville first passage percolation distances on a regular lattice with periodic boundary conditions.</li> </ul> <p>Instructions on compiling and running the simulation software are included with the source code (see README files). Descriptions of the simulation data formats accompany the data files (see README files again). For background on the simulation and data analysis we refer to the publication mentioned above.</p>
An Empirical Study of Word Embedding Dimensionality Reduction
<p>In order to analyze the impact on model quality while reducing the number of dimensions, strictly controlled trainings of word embedding are performed on Wikipedia corpora of 170 languages. The specially designed word embedding training tool makes use of processed corpus and intermediate results to accelerate the training, while keeping the consistency of negative sampling.</p> <p>Tests of semantic relatedness show that, except for some corpora of poor scale, the margin gain from extra dimensions significantly decreases above 200.</p>
Fine Three-Dimensional VHF Lightning Mapping Using Waveform Cross-Correlation TOA Method
<p>In a manuscript entitled “Fine Three-Dimensional VHF Lightning Mapping Using Waveform Cross-Correlation TOA Method”, the lightning data obtained by an improved VHF 3-D location system were analyzed. The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citation.</p>
Data for Three-dimensional X-line spreading in asymmetric magnetic reconnection
<p>The data and IDL scripts for all the figures of our 2019 JGR are organized in a self-explanatory way.</p>
Fig. 6 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 6. Detail of the calculation model: layer III of Miraflores I tracksite.
Aerosol products presented in "ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications"
<p>ALICENET output products on aerosol optical and physical properties and vertical layering presented in “Bellini, A., Diémoz, H., Di Liberto, L., Gobbi, G. P., Bracci, A., Pasqualini, F., and Barnaba, F.: Alicenet – An Italian network of Automated Lidar-Ceilometers for 4D aerosol monitoring: infrastructure, data processing, and applications, AMT, https://doi.org/10.5194/egusphere-2024-730, 2024”.</p> <p>The aod*.txt files include the following information:</p> <p>- date: date in UTC<br>- AOD_ALICENET: AOD as retrieved by ALICENET at 1064 nm<br>- AOD_AERONET/SKYNET: AOD measured by a co-located photometer from AERONET/SKYNET (level 2) at 1020 nm<br>- AE: Angstrom Exponent from AERONET/SKYNET (level 2)</p> <p>The contiunous.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- continuous_aerosol_layer: Continous Aerosol Layer heights as retrieved by ALICENET</p> <p>The mixed.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- mixed_aerosol_layer: Mixed Aerosol Layer heights as retrieved by ALICENET</p> <p>This work received partial financial support from the EC H2020 Project RI-URBANS (GA No 101036245), and benefited from work done within the Action PROBE (CA18235), supported by COST (European Cooperation in Science and Technology).</p>
Three-dimensional soil organic carbon density by logarithmic function and coefficient scaling in Yangtze River Delta, China
<h3>Three-dimensional soil organic carbon density (SOCD) dataset with 90-m resolution generated by Lin, S., Zhu, Q., Yin, B., Yang, G., Liao, K., Lai, X., Guo, C., 2025. Generating three-dimensional soil organic carbon density dataset by soil depth function and correction methods in Yangtze River Delta, China. Environmental Modelling & Software, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.envsoft.2025.106582" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.envsoft.2025.106582.</span></span></a></h3> <h3>Here, based on the best performance, the three-dimensional SOCD generated by LF corrected with coefficient scaling method were provided. The accurate SOCD maps with the spatial resolution of 90-m at any specific depth interval can be generated by our method. This dataset includes:</h3> <ul> <li>Spatial distribution map of parameter 1 (p1) of LF (LF_p1.tif)</li> <li>Spatial distribution map of parameter 2 (p2) of LF (LF_p2.tif)</li> <li>The calculation code and fitted functions of scaling coefficient a, k of LF (fitted_fx_scalingcoff.m)</li> <li>Readme.docx</li> </ul> <p>Note: the unit of SOCD is kg m-2; the spatial distribution maps provided by this dataset does not mask any water bodies.</p> <p><strong>How to use our dataset? Please refer to our article and Readme.docx for more details.</strong></p> <p> </p> <p> </p>
Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 4/4
<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> <p>Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118</p> <h3>Contents</h3> <p>Trajectories of positions and forces with corresponding input files of systems with 550 water molecules on 2D materials:</p> <ul> <li>MoS2/water</li> <li>MoS2/MoS2/water</li> <li>graphene/MoS2/water</li> <li>MoS2/graphene/water</li> <li>MoS2/hBN/water</li> <li>hBN/MoS2/water</li> <li>graphene/water</li> <li>hBN/water</li> </ul>
Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 3/4
<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> <p>Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118</p> <h3>Contents</h3> <p>Trajectories of positions and forces with corresponding input files of systems with 550 water molecules on 2D materials:</p> <ul> <li>graphene/graphene/water</li> <li>hBN/hBN/water</li> <li>graphene/hBN/water</li> <li>hBN/graphene/water</li> </ul>
Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 1/4
<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> <p>Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118</p> <h3>Contents</h3> <p>Trajectories of positions and forces with corresponding input files of systems with 120 water molecules confined between 2D materials:</p> <ul> <li>graphene/water/graphene</li> <li>hBN/water/hBN</li> <li>graphene/graphene/water/graphene/graphene</li> <li>hBN/hBN/water/hBN/hBN</li> <li>graphene/hBN/water/hBN/graphene</li> <li>hBN/graphene/water/graphene/hBN</li> </ul> <p> </p>
Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 2/4
<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> <p>Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118</p> <h3>Contents</h3> <p>Trajectories of positions and forces with corresponding input files of systems with 120 water molecules confined between 2D materials:</p> <ul> <li>MoS2/water/MoS2</li> <li>MoS2/MoS2/water/MoS2/MoS2</li> <li>graphene/MoS2/water/MoS2/graphene</li> <li>MoS2/graphene/water/graphene/MoS2</li> </ul>
Fisher zeroes and dynamical quantum phase transitions for two- and three-dimensional models
<p>Data for the publication <em>Fisher zeroes and dynamical quantum phase transitions for two- and three-dimensional models</em>. Included are data for return rates and Fisher zeroes. The names of the data files contain all metadata necessary for specifying the type of data, with a list of the paramters used. For more details on the models and paramters see the article.</p> <ul> <li>Firstly the three models are considered labelled by <em>Kit</em> for the 2D spinless px+ipy topological superconductor, <em>Sq</em> for the 2D spinfull topological superconductor, and <em>3D</em> for the 3D topological insulator.</li> <li>After <em>_N_</em> the system size is written, referring the number of sites along one direction (for finite size calcualtions only).</li> <li><em>RR</em> refers to the return rate. For <em>Kit</em> the columns are $t$, $l(t), $\dot{l}(t)$, $\ddot{l}(t)$. If only two columns exit then there is just $t$, $\dot{l}(t)$. For <em>Sq</em> the columns are $t$, $l(t), $\dot{l}(t)$, $|\lambda_0(t)|$. For <em>3D</em> the columns are $t$, $\dot{l}(t)$, $\ddot{l}(t)$.</li> <li><em>Density</em> refers to the density of Fisher zeroes along the real time axis, with the columns being simply time and density.</li> <li><em>Fisher</em> to Fisher zeroes in the complex plane with <em>kx</em>, etc meaning it is resolved along this momentum direction. The columns are the real and imaginary parts of the complex time argument.</li> <li><em>Critical_k</em> are the critical momenta.</li> <li><em>Critical_k_En</em> lists the energy for each critical momenta in the matching <em>Critical_k </em>file.</li> <li>For <em>Kit</em> the list of quench parameters are 100 times {$\mu_0$, $\Delta_0$, $\mu_1$, $\Delta_1$}.</li> <li>For <em>Sq</em> the list of quench parameters are 100 times {$\nu_0$, \nu_1$, $\mu_0$, $\Delta_0$, $\alpha_0$, $B_0$, $\mu_1$, $\Delta_1$, $\alpha_1$, $B_1$}.</li> <li>For <em>3D</em> the list of quench parameters are 100 times {$v_0$,$w_0$,$v_1$,$w_1$}.</li> <li>For <em>Fisher</em> data <em>branch</em> labels naturally the branch p.</li> <li><em>Zoom</em>, etc, label different zooms in differnet regions, as made explicit by the times inside the files.</li> </ul> <p>This work was supported by the National Science Centre (NCN, Poland) under the grant 2019/35/B/ST3/03625, by the German Research Council (DFG) via the Re- search Unit FOR 2316 and by the National Science and Engineering Resource Council (NSERC) of Canada via the Discovery Grant program.</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.