Dataset Search

STEM images of kinks in α-In2Se3, DFT calculation of bending of α-In2Se3, PFM on as exfoliated and controllably bend α-In2Se3

This dataset includes the blue water intensity by sector (41 industries and service sectors) for provinces in China, economic and virtual water network flow for China in 2017, and the corresponding network properties for these two networks.

This dataset contains the historical and future (SSP3 and RCP7.0) CESM climate simulations used in the article "Large humidity effects on urban heat exposure and cooling challenges under climate change" (upcoming). Further details about these simulations can be found in the article. This dataset documents the monthly mean projections of air temperature, wet-bulb temperature, precipitation, relative humidity, and numerous other climatic variables for 2000-2009 (for the historical run) and for 2015-2100 (for the future projection under SSP3-RCP7). This dataset may be useful for urban planners, climate scientists, and decision-makers interested in changes in urban and rural climate under climate change.

This dataset consists of all the figure files that are part of the main text of the manuscript titled "Magnetic-field sensitive charge density waves in the superconductor UTe2". For detailed information on the individual files refer to the readme file.

This dataset contains all the code, notebooks, datasets used in the study conducted for the research publication titled "Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19 Data". Specifically, this package include the artifacts used to conduct spatial-temporal analysis with space time kernel density estimation (STKDE) using COVID-19 data, which should help readers to reproduce some of the analysis and learn about the methods that were conducted in the associated book chapter. ## What’s inside - A quick explanation of the components of the zip file * Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19.ipynb is a jupyter notebook for this project. It contains codes for preprocessing, space time kernel density estimation, postprocessing, and visualization. * data is a folder containing all data needed for the notebook * data/county.txt: US counties information and fip code from Natural Resources Conservation Service. * data/us-counties.txt: County-level COVID-19 data collected from New York Times COVID-19 github repository on August 9th, 2020. * data/covid_death.txt: COVID-19 death information derived after preprocessing step, preparing the input data for STKDE. Each record is if the following format (fips, spatial_x, spatial_y, date, number of death ). * data/stkdefinal.txt: result obtained by conducting STKDE. * wolfram_mathmatica is a folder for 3D visulization code. * wolfram_mathmatica/Visualization.nb: code for visulization of STKDE result via weolfram mathmatica. * img is a folder for figures. * img/above.png: result of 3-D visulization result, above view. * img/side.png: result of 3-D visulization, side view.

This data set contains all the map data used for "Quantifying transportation energy vulnerability and its spatial patterns in the United States". The multiple dimensions (i.e., exposure, sensitivity, adaptive capacity) of transportation energy vulnerability (TEV) at the census tract level in the United States, the changes in TEV with electric vehicles adoption, and the detailed data for Chicago, Los Angeles, and New York are in the dataset.

This dataset encompasses experimental results supporting the upcoming journal paper, "Laboratory-scale assessment of CO2 sealing potential for heterogeneous caprock", which investigates the sealing potential of heterogeneous caprock. The dataset includes the measurements and analyses conducted under controlled laboratory conditions, capturing sealing potential such as permeability and breakthrough pressure.

Example data for https://github.com/chenlabUIUC/UsiNet The data contains computer simulated and experimental tilting series (or sinograms) of gold nanoparticles. Two training data examples are provided: 1. simulated_data.zip 2. experimental_data.zip In each zip folder, we include an image_data.zip and a training_data.zip. The former is for viewing and only the latter is needed for model training. For more details, please refer to our GitHub repository.

This repository contains precipitation spectra from a Parsivel-2 disdrometer deployed at Lancaster High School, Lancaster, NY, as well as a MRR-2 radar deployed at the same site. The site was located at 42.9299° N, 78.6708° W. Parsivel data were converted to netCDF using the pyDSD python package. MRR-2 spectra are raw from the manufacturer's software. The Parsivel and MRR-2 data include periods collected during November 2022 as described in the paper.

These show the topography and relief of the Precambrian surface of the Cratonic Platform of the United States.

This dataset is about a topographic LIDAR survey (saved in “waxlake-lidar.img”) that was conducted over the Wax Lake delta, between longitudes −91.5848 to −91.292 degrees, and latitudes 29.3647 to 29.6466 degrees. Different from other elevation data, the positive value in the LIDAR data indicates land elevation, while the zero value implies riverbed without identifying specific water depth.

The data files are for the paper entitled: Melting of the charge density wave by generation of pairs of topological defects in UTe2 to be published in Nature Physics. The data was obtained on a 300 mK custom designed Unisoku scanning tunneling microscope using the Nanonis module. All the data files have been named based on the Figure numbers that they represent.

This data set includes the cochlear implant (CI) electrodograms recorded in 2 different acoustic conditions using acoustic head KEMAR. It is a part of a study intended to explore the effect of interaural asymmetry on interaural coherence after CI processing.

This dataset is the field measurements of water depth at the Wax Lake delta on the date 2012-12-01.

This datasets provide basis of our analysis in the paper - Potential Impacts on Ozone and Climate from a Proposed Fleet of Supersonic Aircraft. All datasets here can be categorized into emission data and model output data (WACCM). All the model simulations (background and perturbation) were run to steady-state and only the datasets used in analysis are archived here.

SCiLS MSI data files, images used in the figures and table contents for the tables found in the manuscript. The figures are labeled by figure and by their title on each figure set, including those found in the Supplementary Information. The tables are in an MS Excel sheet with the corresponding contents. The tables list the metabolites found in the images. To reduce the number of images in the manuscript, the tables complete the metabolite information not observed in the images. The images can be found using the SCiLS data files. A software license is needed to open these files. The SCiLS data files contains the processed MSI data for all obtained images. All files in the corresponding SCiLS data file must be present to open the individual data file. The feature list used for MSI analysis should be saved on the attached bookmark inside the SCiLS file so it should be available once the file is opened. SCiLS files can only be opened with the Bruker SCiLS software. If using an outdated version (before Version 13.01.17218), the files may not open or show poor quality.

Data for the publication of Magnetic Fields in the Pillars of Creation (Sarkar et al.). Contains the fits files and python scripts.

This dataset consists of all the files that are part of the manuscript titled "Evidence for a robust sign-changing s-wave order parameter in monolayer films of superconducting Fe(Se,Te)/Bi2Te3". For detailed information on the individual files refer to the readme file.

Supplemental data for the paper titled 'Environmental modulators of algae-bacteria interactions at scale'. Each of the excel workbooks corresponding to datasets 1, 2, and 3 contain a README sheet explaining the reported data. Dataset 4 comprising microscopy data contains a README text file describing the image files.

Trajectory data for Nature Nanotechnology manuscript "DNA double helix, a tiny electromotor" that demonstrates how an electric field applied along the helical axis of a DNA or RNA molecule will generate an electroosmotic flow that causes the duplex to spin about that axis, much like a turbine.

This is the complete dataset for the "Anomalous density fluctuations in a strange metal" Proceedings of the National Academy of Sciences publication (https://doi.org/10.1073/pnas.1721495115). This is an integration of the Zenodo dataset which includes raw M-EELS data. <b>METHODOLOGICAL INFORMATION</b> 1. Description of methods used for collection/generation of data: Data have been collected with a M-EELS instrument and according to the data acquisition protocol described in the original PNAS publication and in SciPost Phys. 3, 026 (2017) (doi: 10.21468/SciPostPhys.3.4.026) 2. Methods for processing the data: Raw data were collected with a channeltron-based M-EELS apparatus described in the reference PNAS publication and analyzed according to the procedure outlined both in the PNAS paper and in SciPost Phys. 3, 026 (2017) (doi: 10.21468/SciPostPhys.3.4.026). The raw M-EELS spectra at each momentum have been subject to minor data processing involving: (a) averaging of different acquisitions at the same conditions, (b) energy binning, (c) division of an effective Coulomb matrix element (which yields a structure factor S(q,\omega)), (d) antisymmetrization (which yields the imaginary chi) All these procedures are described in the PNAS paper. 3. Instrument- or software-specific information needed to interpret the data: These data are simple .txt or .dat files which can be read with any standard data analysis software, notably Python notebooks, MatLab, Origin, IgorPro, and others. We do not include scripts in order to provide maximum flexibility. 4. Relationship between files, if important: We divided in different folders raw data, structure factors and imaginary chi. <b>DATA-SPECIFIC INFORMATION</b> There are 8 folders within the Data_public_deposition_v1.zip. Each folder contain data needed to create the corresponding figure in the publication. <b>1. Fig1:</b> This folder contains 21 DAT files needed to plot the theory data in panels C and D, following this naming conventions: [chiA]or[chiB]or[Pi]_q_number.dat With chiA is the imaginary RPA charge susceptibility with a Coulomb interaction of electronically weakly coupled layers chiB is the imaginary RPA charge susceptibility with the usual 4\pi e^2/q^2 Coulomb interaction. Pi is the imaginary Lindhard polarizability. q is momentum in reciprocal lattice units Number is the numerical momentum value in reciprocal lattice units <b>2. Fig2:</b> Files needed to plot Fig. 2 of the PNAS paper. Contains 3 folders as listed below. The files in this folder are named following this convention: Bi2212_295K_(1,-1)_50eV_161107_q_number_2.16_avg.dat, 295K is the sample temperature (1,-1) is the momentum direction in reciprocal lattice units 50 eV is the incident e beam energy 161107 is the start date of the experiment in yymmdd format Q is the momentum Number is the momentum in reciprocal lattice units 2.16 is the energy range covered by the data in eV Avg identifies averaged data ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>3. Fig3:</b> Files needed to plot Fig. 3 of the PNAS paper. OP/ OD prefix identifies optimally doped or overdosed sample data, respectively. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>4. Fig4:</b> Files needed to plot Fig. 4 of the PNAS paper. The _fit_parameters.dat file contains the fit parameters extracted according to the fit procedure described in the manuscript and at all momenta. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>5. FigS1:</b> Files needed to plot Fig. S1 of the PNAS paper. There are 5 files in this folder. DAT files are M-EELS data following the prior naming convention, while the two .txt files are digitized data from N. Nücker, U. Eckern, J. Fink, and P. Müller, Long-Wavelength Collective Excitations of Charge Carriers in High-Tc Superconductors, Phys. Rev. B 44, 7155(R) (1991), and K. H. G. Schulte, The interplay of Spectroscopy and Correlated Materials, Ph.D. thesis, University of Groningen (2002). <b>6. FigS2:</b> Files needed to plot Fig. S2 of the PNAS paper. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>7. FigS3:</b> Files needed to plot Fig. S3 of the PNAS paper. There are 2 files in this folder: 20K_phi_0_q_0.dat: is a M-EELS raw intensity at zero momentum transfer on Bi2212 at 20 K 295K_phi_0_q_0.dat: is a M-EELS raw intensity at zero momentum transfer on Bi2212 at 295 K <b>8. FigS4:</b> Files needed to plot Fig. S4 of the PNAS paper. The _fit_parameters.dat file contains the fit parameters extracted according to the fit procedure described in the manuscript and at all momenta. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra

The data files are for the paper entitled: Long-lifetime spin excitations near domain walls in 1T-TaS2 to be published in PNAS. The data was obtained on a 300 mK custom designed Unisoku scanning tunneling microscope using the Nanonis module. All the data files have been named based on the Figure numbers that they represent.

This dataset consists of all the files and codes that are part of the manuscript (main text and supplement) titled "Spin-selective tunneling from nanowires of the candidate topological Kondo insulator SmB6". For detailed information on the individual files refer to the specific readme files.

This dataset contains best estimates of the particle size distribution and measurements of the radar reflectivity factor and total water content for instances where ground-based radar and airborne microphysical observations were considered collocated with each other.