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.
628
datasets available to search
ShareScore release 0.9.0
Dataset results
628 results for “scattering”
Carbon x-ray Raman scattering mapping and spectroscopy of a fragment of Lepidodendron trunk from the Upper Carboniferous
<p>Carbon x-ray Raman scattering mapping and spectroscopy of a fragment of Lepidodendron trunk from the Upper Carboniferous (ca. 305 Mya) of Noyelles-lez-Lens, France</p>
Supporting data for "Quantifying the Strength of a Salt Bridge by Neutron Scattering and Molecular Dynamics"
<p>Supporting data for the following published paper: Mason, Jungwirth, Duboué-Dijon, 2019, JPhysChemLett, 10, 3254-3259</p> <p>Contains both data from neutron scattering measurements and input simulation files necessary for reproduction of the work.</p>
Dataset of electronic Raman scattering in rhombohedral graphite
<p>The dataset contains the raw data, used to generate the figures of our paper (Carbon 2024 and arxiv: 2401.17779).</p> <p>Each folder contains the raw data in text files as well as the python Jupyter notebooks used to analyse the data and generate the figures.</p> <p>To work with the data, open the Jupyter notebooks (*.ipynb files). The notebooks can be previewed by opening the html exported format of each notebook.</p>
Dataset for: In-operando microwave scattering-parameter calibrated measurement of a Josephson travelling wave parametric amplifier
<p>Dataset for manuscript "In-operando microwave scattering-parameter calibrated measurement of a Josephson travelling wave parametric amplifier", <a href="https://arxiv.org/abs/2406.03063">arXiv:2406.03063</a></p> <p>Containing the uncalibrated raw measurement data and the calibrated dataset after applying the 8-term error model.</p>
Mutually Beneficial Combination of Molecular Dynamics Computer Simulations and Scattering Experiments - DATA
<p>Specular reflectivities of the SoyPC bilayer stack measured at the vertical reflectometer MARIA at Heinz Maier-Leibnitz Zentrum (MLZ) in Garching, Germany.</p> <p>Offspecular reflectivity map (log scale) of the multilayer sample as a function of theangle of incidence (θi) and of the reflection angle (θi).</p> <p>Specular reflectivities of the Si/SiO<sub>2</sub>/DMPC/H2O at 4 different contrasts (H<sub>2</sub>O, D<sub>2</sub>O, SMW and 4MW)</p> <p>Small-angle neutron scattering of the unilamellar SoyPC</p>
Femtosecond electron diffuse scattering data of black phosphorus
<p>Femtosecond electron diffuse scattering data of black phosphorus measured at the Fritz Haber Institute in Berlin. The dataset contains an experiment at 100 K (measurement _1.h5) .</p>
Dataset and Code for Manuscript "Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification"
<p>Dataset of measurements for cell cycle analysis with description:</p> <ul> <li>ReadMe file with explanations on the data set and analysis</li> <li>exemplary Matlab script file for analysis</li> <li>binary data files conatining the pulse shapes in all channels</li> <li>FCS data files containing common flow cytometry parameters in each channel</li> </ul> <p>Data on unsorted HEK cells, HEK cells sorted for cell cycle phases, and unsorted Jurkat cell are included.</p>
Microwave Single Scattering Properties Database (Horizontally Aligned Aggregates of Dendrites)
<p>The database contains physical and microwave single scattering properties of horizontally aligned frozen hydrometeors as large as 11 cm in diameter. </p> <p>A description of the aggregation model used for particle generation can be found in:<br> Leinonen, J., and Szyrmer, W. (2015), Radar signatures of snowflake riming: A modeling study, <em>Earth and Space Science</em>, 2, 346– 358, doi:<a href="https://doi.org/10.1002/2015EA000102">10.1002/2015EA000102</a>.<br> The code used for particle generation is freely available at: <a href="https://github.com/jleinonen/aggregation">https://github.com/jleinonen/aggregation</a></p> <p>The scattering properties of particles were computed using discrete dipole approximation using ADDA software package (<a href="https://github.com/adda-team/adda">https://github.com/adda-team/adda</a>)</p> <p>Terminal velocity of snowflakes was computed using 4 hydrodynamical models that were implemented as a part of snowScat library (<a href="https://github.com/OPTIMICe-team/snowScatt">https://github.com/OPTIMICe-team/snowScatt</a>)</p> <p>Approximately one half of the snowflake structure files and one quarter of scattering properties (for X, Ku, Ka and W band) were generated for the publication of Leinonen and Szyrmer (2015). The remaining part of the dataset was generated using the ALICE High Performance Computing Facility at the University of Leicester.</p>
Single Scattering properties at W-band of ice populations
<p>The file contains coefficients of the polynomials that approximate radar observables at the W-band for a population of ice particles. </p> <p> </p> <p> </p>
Simulated data for "Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures"
<p>These are HDF5 files with the 15 simulated data sets which are used in the work "Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures", intended for use with the software MUMOTT.</p> <p> </p> <p>MUMOTT is <a href="https://pypi.org/project/mumott/">obtainable via PyPI</a>.</p>
Raindrop Particle Scattering Parameters Database
<p>The database contains non-spherical precipitation particle scattering parameters, including scattering phase functions and integral scattering characteristics, covering frequencies from 3 to 1000Ghz, which can be used for radar detection, microwave transmission and other fields.</p>
Data of the INFORMS Journal on Computing paper: Routing replenishment workers: The prize collecting traveling salesman problem in scattered storage warehouses
<p>In what follows, you will find data of the paper:<br> "Routing replenishment workers: The prize collecting traveling salesman problem in scattered storage warehouses" published in INFORMS Journal on Computing</p> <p>List of files:<br> - Computational_results_BB_NN_RW_CPLEX.xlsx: Excel file that gives all results<br> - instance_gen.cc: Instance generator<br> - instances.zip: compressed file of all instances that are sorted by Sections. It additionally includes the generator<br> - Makefile: Makefile for compiling/debugging, i.e., "make all" or "make debug" do the jobs<br> - MersenneTwister.h: needed by schedule_finder.cc<br> - results_Section_5_1.zip: compressed file of all output files of Section 5.1<br> - results_Section_5_2.zip: compressed file of all output files of Section 5.2<br> - results_Section_5_3.zip: compressed file of all output files of Section 5.3<br> - schedule_finder.cc: Main program containing the B&B, the S-shape, and Nearest Neighbor procedure (see details for customizing the parameters at the top of this file)<br> - valgrind_debug.txt: Only contains the used debug command</p> <p>instances/instance_gen.cc generates a problem instance in file problems.txt<br> The structure of the these problem files is the following:<br> /*<br> NE Total number of experiments given by the currently considered file<br> -2 Separator<br> EXPGRP Index of the current experiment group the current experiment belong to<br> N Number of vacant positions in the warehouse<br> M Number of requests to be stored by the tour<br> P Number of pickers to be scheduled in the warehouse<br> A Number of vertical aisles<br> B Number of horizontal (cross) aisles<br> L_A Length of each vertical aisle<br> L_B Length of each cross aisle<br> UF_VA Up-factor of each vertical aisle (A values)<br> DF_VA Down- factor of each vertical aisle (A values)<br> UF_CA Up-factor of each cross aisle (B values)<br> DF_CA Down- factor of each cross aisle (B values)<br> x_pos_vertical_aisle x-position of vertical aisle (A values)<br> y_pos_cross_aisle y-position of cross aisle (B values)<br> warehouse_graph values For each node of the warehouse graph all entries (15 each) are given (total_number_of_warehouse_graph_nodes*15)<br> FS << warehouse_graph[curr_node].free_position << " " << endl;<br> FS << warehouse_graph[curr_node].depot_node << " " << endl;<br> FS << warehouse_graph[curr_node].vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].succ_cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].succ_vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_cross_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].succ_cross_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].pred_vertical_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].succ_vertical_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].region << " " << endl;<br> FS << warehouse_graph[curr_node].x_position << " " << endl;<br> FS << warehouse_graph[curr_node].y_position << " " << endl;<br> shortest_path_distance For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the distance<br> shortest_path_length_including_start_and_end For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the number of visited nodes<br> shortest_path_visited_nodes For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the detailed path (length is respectively given by shortest_path_length_including_start_and_end)<br> dd_free_position For each free position and the depot (here with index N) the due date is transferred (N+1 values) (only relevant for the extended problem, is ignored here)<br> weight_of_free_position For each free position and the depot (here with index N) the weight is transferred (N+1 values) (only relevant for the extended problem, is ignored here)<br> capacity_of_free_position For each free position the storage capacity transferred (N values)<br> -2 Separator indicating the end of an instances<br> -3 Separator indicating the end of all experiments (i.e., indicating the end of the file)<br> */</p> <p>output files (results_Section_5_1.zip/results_Section_5_2.zip/results_Section_5_3.zip):<br> results_BB_NXXX_MYYY_A10_B05: Output file of applying B&B<br> results_RW_NXXX_MYYY_A10_B05: Output file of applying s-shape random walk<br> results_NN_NXXX_MYYY_A10_B05: Output file of applying nearest neighbor</p> <p>In these files you find all outputs of schedule_finder.cc. <br> Among others, you will find the generated tour schedules (for Experiment with index I) in the output files by searching the phrase: "Experiment I completed with result="<br> or for the next Experiment " completed with result="</p> <p>Example (results_BB_N030_M150_A10_B05.txt, experiment 0, the tardiness values are to be ignored, see comments in schedule_finder.cc)<br> Pos 0 depot node with index 80 Number of stored items 0 CT 0 No tardiness<br> Pos 1 position 27 Number of stored items 4 Current accumulated number of stored items 4 CT 163 DD 5629 No additional tardiness<br> Pos 2 position 28 Number of stored items 5 Current accumulated number of stored items 9 CT 399 DD 2962 No additional tardiness<br> Pos 3 position 29 Number of stored items 4 Current accumulated number of stored items 13 CT 670 DD 12631 No additional tardiness<br> Pos 4 position 26 Number of stored items 5 Current accumulated number of stored items 18 CT 840 DD 10142 No additional tardiness<br> Pos 5 position 23 Number of stored items 7 Current accumulated number of stored items 25 CT 1134 DD 6000 No additional tardiness<br> Pos 6 position 22 Number of stored items 4 Current accumulated number of stored items 29 CT 1170 DD 6396 No additional tardiness<br> Pos 7 position 16 Number of stored items 5 Current accumulated number of stored items 34 CT 1448 DD 8962 No additional tardiness<br> Pos 8 position 11 Number of stored items 10 Current accumulated number of stored items 44 CT 1674 DD 6336 No additional tardiness<br> Pos 9 position 0 Number of stored items 10 Current accumulated number of stored items 54 CT 2062 DD 1141 Additional tardiness 921<br> Pos 10 position 2 Number of stored items 9 Current accumulated number of stored items 63 CT 2201 DD 7742 No additional tardiness<br> Pos 11 position 4 Number of stored items 5 Current accumulated number of stored items 68 CT 2407 DD 4846 No additional tardiness<br> Pos 12 position 3 Number of stored items 5 Current accumulated number of stored items 73 CT 2717 DD 2316 Additional tardiness 401<br> Pos 13 position 1 Number of stored items 8 Current accumulated number of stored items 81 CT 2876 DD 9917 No additional tardiness<br> Pos 14 position 6 Number of stored items 3 Current accumulated number of stored items 84 CT 3073 DD 7299 No additional tardiness<br> Pos 15 position 5 Number of stored items 8 Current accumulated number of stored items 92 CT 3144 DD 6152 No additional tardiness<br> Pos 16 position 8 Number of stored items 6 Current accumulated number of stored items 98 CT 3337 DD 3705 No additional tardiness<br> Pos 17 position 12 Number of stored items 9 Current accumulated number of stored items 107 CT 3452 DD 7622 No additional tardiness<br> Pos 18 position 13 Number of stored items 4 Current accumulated number of stored items 111 CT 3522 DD 7833 No additional tardiness<br> Pos 19 position 14 Number of stored items 5 Current accumulated number of stored items 116 CT 3647 DD 9877 No additional tardiness<br> Pos 20 position 19 Number of stored items 1 Current accumulated number of stored items 117 CT 3922 DD 2905 Additional tardiness 1017<br> Pos 21 position 20 Number of stored items 5 Current accumulated number of stored items 122 CT 3923 DD 2538 Additional tardiness 1385<br> Pos 22 position 21 Number of stored items 7 Current accumulated number of stored items 129 CT 3976 DD 2769 Additional tardiness 1207<br> Pos 23 position 18 Number of stored items 10 Current accumulated number of stored items 139 CT 4173 DD 3552 Additional tardiness 621<br> Pos 24 position 25 Number of stored items 4 Current accumulated number of stored items 143 CT 4482 DD 11710 No additional tardiness<br> Pos 25 position 24 Number of stored items 7 Current accumulated number of stored items 150 CT 4509 DD 10599 No additional tardiness<br> Pos 26 visiting the node with index 80 Number of stored items 0 CT 4710 DD 10893 No additional tardiness<br> opt_makespan=4710 opt_total_tardiness=5552<br> TSP_procedure returned value 4710<br> Experiment 0 completed with result=3<br> BFS Branch&Bound report: Consumed time: 1</p> <p>Copied from schedule_finder.cc:<br> Note that the procedure used as a solution procedure in the paper is int TSP_procedure(struct bb_node *curr_bb_node, int version)</p> <p>It is called by BB_procedure() as a subroutine for computing a lower bound value of an extended problem<br> (for instance, this extended problem additionally covers due dates. Therefore, due dates are also part of the problem instances, but can be ignored)<br> Specifically, TSP_procedure(struct bb_node *curr_bb_node, int version) is called once by lb_computation()</p>
Output tomographic models for "The attenuation and scattering signature of fluids and tectonic interactions in Central-Southern Apennine."
<p>Output ASCII file for the seismic attenuation tomography in Central-Southern Apennines. The output format is the one from MuRAT software (De Siena et al., 2014). Q and Peak-Delay models in 1.5 Hz, 3 Hz and 6 Hz frequencies are reported as specificated by the files name. The output points of a grid with coordinates available in WGS84 degrees ("Degrees" suffix) or already projected in kilometric UTM coordinates ("UTM" suffix).</p> <p>All other information can be found in the main and supplementary text.</p>
small angle x-ray scattering from Zr-Cu-Ag metallic glass coatings
<p>small angle x-ray scattering from Zr-Cu-ag metallic glass coating to confirm whether they became amorphous or not. The coating is on PBT substrate. </p>
Earth-scattering likelihoods: Likelihood and p-value tables for reconstructing the local Dark Matter Density
<p>Tables of likelihoods, p-values and best-fits associated with the EarthScatterLikelihood code - <a href="https://github.com/bradkav/EarthScatterLikelihood">https://github.com/bradkav/EarthScatterLikelihood</a> - released alongside the paper "<em>Measuring the local Dark Matter density in the laboratory</em>" (<a href="https://arxiv.org/abs/2004.01621">arXiv:2004.01621</a>).</p> <p>Examples for how to load the files are given in 'EarthScatterLikelihood/plotting'. Simply extract the folders into 'EarthScatterLikelihood/results' in https://github.com/bradkav/EarthScatterLikelihood. </p>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite"
<p>Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.</p> <p>Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet. </p>
Scattering parameter (input port voltage reflection coefficient) of transmit network of NQR probehead from 40 MHz to 140 MHz
<p>Data to figure 7 in the related publication:</p> <p>Scattering parameter (input port voltage reflection coefficient) of transmit network of NQR probehead from 40 MHz to 140 MHz</p>
Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics"
<p>Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics". These data include experimental total scattering measurements and molecular dynamics simulations on the molten structure of FLiBe. </p>
Experimental data for "Exact inversion of partially coherent dynamical electron scattering for picometric structure retrieval"
Open the record for dataset details and reuse information.
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.