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3,655 results for “Structural data”
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
Data supporting the publication "Many-body quantum sign structures as non-glassy Ising models"
<p>This repository contains all raw data that were used to draw conclusions and generate figures for the paper:</p> <p><strong>"Many-body quantum sign structures as non-glassy Ising models"</strong><br> by Westerhout, T., Katsnelson, M. I., & Bagrov, A. A.</p> <p><em>Abstract:</em> The non-trivial phase structure of the eigenstates of many-body quantum systems severely limits the applicability of quantum Monte Carlo, variational, and machine learning methods. Here, we study real-valued signful ground-state wave functions of frustrated quantum spin systems and, assuming that the tasks of finding wave function amplitudes and signs can be separated, show that the signs can be easily bootstrapped from the amplitudes. We map the problem of finding the sign structure to an auxiliary classical Ising model defined on a subset of the Hilbert space basis. We show that the Ising model does not exhibit significant frustrations even for highly frustrated parental quantum systems, and is solvable with a fully deterministic O(K log K)-time combinatorial algorithm (where K is the Ising model size). Given the ground state amplitudes, we reconstruct the signs of the ground states of several frustrated quantum models, thereby revealing the hidden simplicity of many-body sign structures.</p>
Data for "Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential"
<p><strong>PtNi_alloy_eam.db</strong> is the dataset (ase.db object) consisting of 55982 intially sampled Pt-Ni alloy structures with EAM energies and forces.</p> <p><strong>PtNi_alloy_dft.db</strong> is the dataset (ase.db object) consisting of the final 6828 resampled Pt-Ni alloy structures with DFT energies and forces calculated by VASP. This is the training set for the NNP, and could be very useful for fitting other machine learning models.</p> <p><strong>PtNi_nanoalloy_vertices_nnp.db</strong> is the dataset (ase.db object) consisting of all the vertices (stable structures) on the convex hulls obtained from NNP-based SCGA runs on 36 Pt-Ni nanoalloy systems. The energies are given by the NNP. Additional information such as mixing energy, motif and symmetry axis are also saved in the dataset and can be queried by the 'data' keyword. An xyz format trajectory of these stable structures is also uploaded.</p> <p>All the input files and scripts for hybrid MC-MD simulations, QBC resampling, DFT calculations, NNP training, NNP-based SCGA runs and convex hull analysis are provided in <strong>inputs_and_scripts.zip</strong>.</p>
Data set of article entitled: "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study"
<p>Data set of the immagies showed in figure 3 of the article entiteled "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study". All files contain the matrix of the dispersion relations of the two analysed Magnonic Crystals: the SAMPLE A and the SAMPLE B for different value of the interfacial Dzyaloshinskii-Moriya interaction (constant D). The first row is the set of values of k-vector, while the first column is the set of value of the frequencies. The other elements of the matrix are the values of the pixel related to the first row and first column. These elements are been obtanied by the Fast Fourier Transform in time and space of the micromagnetic simulations .</p>
Data and code for 'Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Turner, A., Heard, G., Hall, A., Wassens, S. (in review). Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk.</p> <p>The data are provided as a series of .csv files, R script and two zip folders of R packages (Surv_mod and VB_mod)</p> <p>1. <strong>Skeleto_dat_ready_Jan2021.csv</strong> Data from frog surveys conducted by Anna Turner</p> <p>2. <strong>Geoffs_data.csv</strong> Data from frog surveys conducted by Geoff Heard</p> <p>3. <strong>Environmental_variables_skeleto.csv</strong> Environmental data collected during surveys </p> <p>4. <strong>sk.dat_July21.csv</strong> Collated data from Anna and Geoff - created by 'Data_collation_for_analysis_2.R' ready for analysis</p> <p>5. <strong>Variables_that_are_highly_correlated_with_each_other_season_wide.csv</strong> Testing for correlation</p> <p>6. <strong>Model_structure_skeleto_2.csv </strong>creates model structure for analysis</p> <p>7. <strong>Model_selection_statistics_June_21.csv </strong>Output from model</p> <p>R code is provided seperately for each of the following components:</p> <p>1. <strong>Data_collation_for_analysis_2.R</strong> Collating data from Anna and Geoffs datasets</p> <p>2. <strong>Skeleto_analysis_5.R - </strong>First uses regression modelling to explore factors correlated with variation in age</p> <p> - Following Scheele et al. (2016) regression models with a poisson distribution</p> <p> - Use bayesian non-linear regression to fit the Von Bertalanffy growth model to size-at-age data</p> <p> - Plots male and female growth curves</p> <p> - Uses catch curve approach to estimate survival from best fitting regression model following Scroggie (2012) but with bayesian implementation</p>
Simulated genetic data in a hierarchical metapopulation structure
<p>The data are linked to a research article entitled: “<em>Interactions between microenvironment, selection and genetic architecture drive multiscale adaptation in a simulation experiment” </em>in<em> Journal of Evolutionary Biology</em> (see References).</p> <p>In this research on multiscale adaptation, we simulated a hierarchical metapopulation structure with four populations, two environments per population and three patches per environment, in a two-step procedure:</p> <ul> <li>an initialization step without selection, with eight combinations of mutation type, selfing rate and QTL number parameters (2 modes each); out of 200,000 simulated generations in each case, we chose one with appropriate characteristics as a starting point for the next step;</li> <li>a selection step with all possible combinations of the following parameters: environmental pattern (4 modes), environmental range (5 modes), selection intensity (4 modes), fecundity (3 modes).</li> </ul> <p>This resulted in 240 scenarios for each initialized metapopulation, i.e. 1,920 scenarios in total. Each scenario was replicated 10 times, i.e. 19,200 simulation runs.</p> <p>The archive includes all data needed to reproduce the simulations and analyses, or to re-use the simulated metapopulations for other analyses. It has the following structure (further detailed below):</p> <ol> <li><strong>NemoScripts directory </strong>contains the <em>Nemo </em>input files used to perform simulations for the initialization step and the selection step;</li> <li><strong>RScripts directory </strong>contains the <em>R</em> scripts to read the <em>Nemo </em>output files, compute synthetic variables(*), and produce the figures as they appear in the publication and supplementary material (*: long computations, therefore we also directly provide those synthetic variables in the Data directory);</li> <li><strong>Data directory </strong>contains the <em>Nemo </em>output files, the synthetic variables, and other data needed to reproduce the figures; this directory can be used as a working directory for the <em>R</em> scripts (recommended).</li> </ol> <p>Running the following command in a terminal <strong><em>tar –xzvf Archive_PC_SOM_IS_FL.tar</em></strong> will create a directory named <strong><em>Archive_PC_SOM_IS_FL</em></strong>, which detailed content is described in the <strong><em>README.pdf</em></strong> file.<br> Warning: the extracted archive is large (460Go, >40,000 files) and extraction may take some time.</p>
Data, plotting scripts, and figures for "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames" by Aaron J. Fillo, Peter E. Hamlington, and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
Experimental Seismic Data Obtained Using a 3D-Printed Model of the Los Angeles Basin Structure
<p>These data were obtained and analyzed by Park et al., (2022) "Seismic wave simulation using a 3D printed model of the Los Angeles Basin" (doi:10.1038/s41598-022-08732-w).</p> <p> </p>
Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
<p>This dataset contains the results of an experimental campaign, presented in the publication "Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures". The publication covers fibre optic measurements inside large-scale specimens subjected to external load. The specimens comprised reinforced concrete slabs, strengthened with reinforcement bars made from iron-based shape memory alloy.</p>
Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data - Datasets
<p>Includes raw and processed copies of the scRNA-seq datasets used for the paper: '<strong>How does data structure impact cell-cell similarity? Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data.'</strong></p> <p><strong>Real scRNA-seq.zip </strong>contains the Abundant (subset1) and Rare (subset 2) subsets generated to represent discretely structured datasets (sourced from<strong> </strong> Wegmann et al. 2019) and the continuously structured data (sourced from Popescu et al. 2019).</p> <p><strong>Simulated scRNA-seq.zip</strong> contains the Abundant, Moderately-Rare and Ultra-Rare subsets for discretely and continuously structured datasets. All data was simulated using the PROSSTT package in Python 3.8, as well as the dataset containing the labels to re-produce Figure 3 of the manuscript.</p> <p><strong>Results.zip </strong>contains the results for all datasets from the full analysis, in a pickled python dictionary. Code to read in and visualise results is available on the projects github</p> <p>The scripts for the dataset generation, processing and visualisation of results are available at <a href="https://github.com/Ebony-Watson/scProximitE">our github for the scProcimitE package</a>, and documentation is available <a href="https://ebony-watson.github.io/scProximitE/">here</a>.</p>
Data set for the manuscript 'Robustness Analysis of Metasurfaces: Perfect Structures are not always the Best'
<p>In this data set, there are 1 PDF, 3 m-files, and 3 zip files.</p> <p>The manuscript (<strong><em>Readme document<em>.</em>pdf</em></strong>) contains three sections: Quasi-analytical model (<em><strong>analytical_model_EnergyConservation.m</strong></em>), post-processing full-wave simulations (<strong><em>post_processing_from_COMSOL.m</em></strong>), and post-processing experimental data (<strong><em>post_processing_from_experiment.m</em></strong>). Each Matlab code is explained in this manuscript. Corresponding raw data (<em><strong>COMSOL simulation data for reflective metallic metasurfaces.zip</strong>,<strong> COMSOL simulation data for transmitive dielectric metasurfaces.zip</strong>, </em>and <strong><em>experimental data.zip</em></strong>) are attached. One can move the required m-file into the folder and run the m-file directly. In the COMSOL simulation data.zip file, one can find two COMSOL files, which retain the settings for simulation and extracting the required data. </p> <p>The Matlab codes are implemented with version R2018b.</p> <p>The COMSOL files are created with version COMSOL Multiphysics 5.6.</p> <p> </p>
Data used in the article: "Climate change impacts the vertical structure of marine ecosystem thermal ranges"
<p>This dataset is used in the manuscript "Climate change impacts the vertical structure of marine ecosystem thermal ranges" accepted in Nature Climate Change 2022.</p>
Multiset-trie data structure - datasets
<p>We propose a new data structure <em>multiset-trie</em> that is designed for storing and efficiently processing a set of multisets. Moreover, multiset-trie can operate on a set of sets without efficiency loss. The multiset-trie is a search tree with properties similar to those of a trie. It implements all standard search tree operations together with the multiset containment operations such as sub-multiset and super-multiset. Suppose we have a set of multisets <em>S</em> and a multiset <em>X</em>. The multiset containment operations retrieve multisets from <em>S</em> that are either sub-multisets or super-multisets of <em>X</em>. We present the mathematical analysis of a multiset-trie that gives the time complexity of the algorithms and the space complexity of the data structure. Further, the empirical analysis of the data structure is implemented in a series of experiments. The experiments illuminate the time complexity space of the multiset containment operations. For reproducability reasons we publish the datasets used in our experiments, in this repository.</p>
Raw data for the article "Size-Dependent Structural Alterations in Ag Nanoparticles During CO2 Electrolysis in a Gas-Fed Zero-Gap Electrolyzer"
<p>In the article "Size-Dependent Structural Alterations in Ag Nanoparticles During CO2 Electrolysis in a Gas-Fed Zero-Gap Electrolyzer" we described our investigation on the size-dependent degradation behavior of Ag NPs (10, 40, and 100 nm in size) on GDE during CO<sub>2</sub> electrolysis. Here we present the dataset the work was based on. For each figure in the article and the supporting information we provide a set of raw and unprocessed data.</p>
Supplementary data to `Do science maps from open access literature capture the overall topic structure of an academic field?`
<p>The dataset contains the 8,528 academic articles records related to Sustainable Food research sourced with the query `TS=("sustainab*" NEAR/2 "food*")` .</p> <p>They are the records present in the largest component of the citation network, as specified in the manuscript. </p> <p>The dataset was sourced from OpenAlex based on the original data used in the manuscript and it is composed of the following columns:</p> <table> <tbody> <tr> <td><em><strong>Column</strong></em></td> <td><em><strong>Description</strong></em></td> </tr> <tr> <td>Id</td> <td>OpenAlex ID</td> </tr> <tr> <td>DOI</td> <td>Document Object Identifier</td> </tr> <tr> <td>display_name</td> <td>The article title</td> </tr> <tr> <td>publication_year</td> <td>The publication year of the article</td> </tr> <tr> <td>open_access</td> <td>An object with details of the open access status of the article</td> </tr> </tbody> </table> <p>We choose the `.rdata` format for easy loading in R. Use the function `load()` to add the data frame to the enviroment. </p>
Raw data for crystal structure of the flavoprotein monooxygenase TrlE from Streptomyces cyaneofuscatus Soc7, PDB ID: 8RQH
<p>X-Ray raw data for crystal structure of the flavoprotein monooxygenase TrlE from Streptomyces cyaneofuscatus Soc7 (PDB ID: 8RQH). The data have been collected at the Swiss Light Source (2022-11-20) at the X06SA beamline at a wavelength of 1.00003A.</p>
Data for: Open Infrastructure Governance: Current structures, nomenclature, composition, and service trends, 2024 State of Open Infrastructure Report
<p>The purpose of the analysis based on these data was to<span> record information about community governance groups for open infrastructures, focused primarily on the individuals and institutions that serve in these groups. The data were summarized and reported in the “2024 State of Open Infrastructure Report” section “Open infrastructure governance: Current structures, nomenclature, composition, and trends.” The full report is available at <a href="The%20data%20were%20summarized%20and%20reported%20in%20the%20&ldquo;2024%20State%20of%20Open%20Infrastructure%20Report&rdquo;%20section%20&ldquo;Open%20infrastructure%20governance:%20Current%20structures,%20nomenclature,%20composition,%20and%20trends,&rdquo;%20available%20at%20https:/doi.org/10.5281/zenodo.10934089.">https://doi.org/10.5281/zenodo.10934089</a>.</span></p> <p><span>A readme is provided with the dataset with additional detail.</span></p>
X-Ray Diffraction data from Membrane transport protein AcrB, V612F mutant with bound minocycline, source of 9FHC structure
<p>Crystals were grown of the membrane transport protein AcrB, V612F mutant, with bound minocycline. </p> <p>X-ray diffraction data of this upload: 400 frames of 0.5° width were collected on 2007-04-30 at the X06SA beamline of Swiss Light Source at Paul-Scherrer-Institute (Switzerland).</p> <p>The data can be processed with XDS; XDS.INP is provided as part of the upload.</p> <p>The data are the basis of the PDB 9FHC structure.</p>
Supplement - Structure from Motion Raster Data
<p>We created orthorectified images and digital elevation models using Agisoft Metashape, a photogrammetric processing software application that uses SfM. We followed the workflow outlined in Bywater-Reyes and Pratt-Sitaula (2022). Once processed, orthorectified imagery and Digital Elevation Models (DEMs) were exported to ArcGIS Pro for additional analysis. Data collection metadata and postprocessing outcomes can be found in this repository. </p>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</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.