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.
8,565
datasets available to search
ShareScore release 0.9.0
Dataset results
8,565 results for “characterization”
Fig. 1 in Molecular Characterization Of Lates Niloticus (Perciformes, Latidae) Populations From Three Nigerian Waterbodies Using Random Amplified Polymorphic Dna And Microsatellite Markers
Fig. 1. Map showing the sample locations of L. niloticus (Linnaeus, 1758). Population 1 — Kainji lake, Population 2 — River Benue, Makurdi and Population 3 — Ikere-Gorge reservoir, Iseyin, Oyo state.
Fig. 2 in Morphological Redescription And Molecular Characterization Of Dactylogyrus Labei (Monogenea, Dactylogyridae) From Catla Catla: A New Host Record In India
Fig. 2. Phylogenetic position of the present Dactylogyrus species based on 28S rDNA sequences. Distances were estimated using Kimura two-parameter model. The tree was constructed using the neighbor-joining method. The tree was identical to that obtained using maximum-parsimony and the numbers along branches represent bootstrap values given as above branch NJ and lower branch MP. Bootstrap support (> 50 % for 1,000 replicates) is shown at each node.
Fig. 1 in Morphological Redescription And Molecular Characterization Of Dactylogyrus Labei (Monogenea, Dactylogyridae) From Catla Catla: A New Host Record In India
Fig. 1. Dactylogyrus labei: a — сopulatory complex; b — egg; c — dorsal anchors and hooks I–VII; d — ventral bar; e — dorsal bar. Scale bar 40 μm.
Dataset of the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"
<p><br> #Dataset Package for the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"</p> <p><br> Description of the content:</p> <p><br> 1) "1_RQ-CPS-bugs-Taxonomy" folder contains all the main experimental data concerning the issues sampled and analyzed from all the Projects considered in the study,<br> including row-data on the taxonomy validtion steps.<br> <br> - Under "the sub-folder "1_Taxonomy-Raw-data" are reported the row-data concerning the taxonomy validtion steps </p> <p><br> 2) "2_Scripts" contains all scripts used to generate the issue data and sampled issue raw-data in the previous folders: </p> <p><br> - "setup.md" file in the folder describes how to set=up and run the script used for collecting and sampling the issues for the validation steps:<br> <br> - runJSONtoCSV.sh<br> - JSONtoCSV.py<br> - generateListOfAllSamples.py<br> - generateAllSamples.r<br> <br> Under "the sub-folder "1_Scripts/1_Data_Collection":<br> <br> <br> 3) "3_Final Taxonomy" folder contains the final Table representation (also reported in the previous folder) and main figures of the CPSs Bugs Taxonomy.</p>
Long read proteogenomics to characterize protein isoform diversity in human umbilical vein endothelial cells (HUVECs)
<p>Endothelial cells (ECs) comprise the lumenal lining of all blood vessels and are critical for the functioning of the cardiovascular system and their phenotypes can be modulated by protein isoforms. To characterize the isoform landscape within EC, we applied a long read proteogenomics approach to analyze human umbilical vein endothelial cells (HUVECs). Transcripts delineated from PacBio sequencing serve as the basis for a sample-specific protein database used for downstream MS analysis to infer protein isoform expression. We detected 53,836 transcript isoforms from 10,426 genes, with 22,195 of those transcripts being novel. Furthermore, the predominant isoform in HUVECs does not correspond with the accepted “reference isoform” 25% of the time, with vascular pathway-related genes among this group. We found 2,597 protein isoforms supported through unique peptides, with an additional 2,280 isoforms nominated upon incorporation of long-read transcript evidence. We characterized a novel alternative acceptor for endothelial-related gene <em>CDH5</em>, suggesting potential changes in its associated signaling pathways. Finally, we identified novel protein isoforms arising from a diversity of splicing mechanisms supported by uniquely mapped novel peptides. Our results represent a high resolution atlas of known and novel isoforms of potential relevance to endothelial phenotypes and function.</p>
Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots
<p><strong>Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots</strong></p> <p>Fire regimes are areas having similar fire characteristics, and show the spatial pattern, frequency and intensity of fires that prevail in that area over long periods of time. Fire regimes are created and maintained by multivariate interactions between climate, vegetation/fuels, and ignitions. Like ecoregions, fire regimes indicate the extent and overlap of particular vegetative/fuel communities and climatic conditions, and are important for understanding, monitoring, predicting and managing fire.</p> <p>More than 83M MODIS “hotspot” thermal detections from 2002-2019 were grouped into 10km cells, and 21 derived variables describing fire characteristics of fire intensity, return frequency, and seasonality within each cell were developed and subjected to unsupervised Multivariate Geographic Clustering to produce world maps of Global Fire Regimes (GFRs), each having similar fire intensity and timing characteristics.</p> <p>Methodology behind these datasets are described in manuscript currently in review.</p> <p><strong>W. W. Hargrove, Jitendra Kumar, Steven P. Norman, Forrest M. Hoffman (2022), "Empirical Characterization of Global Fire Regimes Show Shared Fire Relationships" 2022 (in review)</strong></p> <p>This data collection includes:</p> <p>1. Multivariate Geographic Clustering Global Fire Regimes at 3000, 1000, 500, 100, 50, 20, 10 levels of divisions in form of geospatial raster in IMG formats, and associated color tables.</p> <p>2. Characteristics of GFRs</p> <p>3. Location groups</p> <p>4. Geospatial maps of global fire frequency modes, global seasonality strength, and 12 types of global fires.</p> <p>5. PNG maps for all data products </p> <p>6. Description and script for global date transform algorithm.</p>
Data for Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments
<p>Input and output files from the manuscript analysis titled "Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments"</p>
Supporting data from: Characterizing changes in eastern U.S. pollution events in a warming world
<p>Risk assessments of air pollution impacts on human health and ecosystems would ideally consider a broad set of climate and emission scenarios, as well as natural internal climate variability. We analyze initial condition chemistry-climate ensembles to gauge the significance of greenhouse-gas-induced air pollution changes relative to internal climate variability, and consider response differences in two models. To quantify the effects of climate change on the frequency and duration of summertime regional-scale pollution episodes over the Eastern United States (EUS), we apply an Empirical Orthogonal Function (EOF) analysis to a 3-member GFDL-CM3 ensemble with prognostic ozone and aerosols and a 12-member NCAR-CESM1 ensemble with prognostic aerosols under a 21st century RCP8.5 scenario with air pollutant emissions frozen in 2005. Correlations between GFDL-CM3 principal components for ozone, PM<sub>2.5</sub> and temperature represent spatiotemporal relationships discerned previously from observational analysis. Over the Northeast region, both models simulate summertime surface temperature increases of over 4 C from 2006–2025 to 2081–2100 and PM2.5 of up to 1–4 μg m<sup>−3</sup>. The ensemble average decadal incidence of upper quartile Northeast PM<sub>2.5</sub> events lasting at least three days doubles in GFDL-CM3 and increases by ∼50% in CESM1. In other EUS regions, inter-model differences in PM<sub>2.5</sub> responses to climate change cannot be explained solely by internal climate variability. Our EOF-based approach anticipates future opportunities to data-mine initial condition chemistry-climate model ensembles for probabilistic assessments of changing regional-scale pollution and heat event frequency and duration, while obviating the need to bias-correct concentration-based thresholds separately in individual models.</p>
Characterization of the nuclear proteome of Chlamydomonas in response to salt stress
<p><strong>Supplementary Files and Figures for the manuscript </strong></p> <p><strong>"Characterization of the nuclear proteome of Chlamydomonas in response to salt stressCharacterization of the nuclear proteome of Chlamydomonas in response to salt stress"</strong></p>
Application of X‑ray Microcomputed Tomography for the Static and Dynamic Characterization of the Microstructure of Oleofoams
<p>Raw, greyscale image stacks collected during a X-Ray tomography analysis on cocoa butter-based oleofoams. The dataset is divided into three subsets: aeration, storage and heating, which contain samples that have been aerated for different amounts of time, samples that have been stored for 3 and 15 months at 20 °C, and finally samples that have been heated to the melting point of the stabilizing crystals, respectively. The dataset contains instructions and the scripts for ImageJ and MATLAB (as text files) to process and measure the bubble size distribution, and the thickness of the continous phase.</p>
Characterization data for the manuscript: "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF"
<p>This entry contains characterization data for the manuscript "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF", which we exported from the electronic lab notebook (ELN).</p> <p>To visualize the data in this dataset: <a href="https://www.cheminfo.org/flavor/zenodo/index.html?id=6620502">open entry</a></p>
In-depth characterization revealed polymer type and chemical content specific effects of microplastic on Dreissena bugensis
<p>The files contain datasets that were generated during laboratory-based real-time valvometry, and laser doppler anemometry measurements. The article was published in Journal of Hazardous Materials (accepted June 8, 2022).</p>
Molecular and Anatomical Characterization of Parabrachial Neurons and Their Axonal Projections
<p>The parabrachial nucleus (PBN) predominantly relays threatening signals and learned sensory cues that predict threats to forebrain regions. Viral-mediated expression of Cre-dependent effector genes in Cre-driver lines of mice has demonstrated that specific populations of PBN neurons are necessary and sufficient for learning about threats. However, these neurons account for only a fraction of the total population. To further our understanding of the complexity of the PBN neuronal populations, we used single-cell RNA-sequencing technologies, which revealed 21 clusters of neurons (19 glutamatergic, 2 GABAergic) in the PBN and neighboring regions. RNAscope HiPlex <em>in situ</em> hybridization located 12 of these clusters within subregions of the PBN. Viral expression of fluorescent proteins in 21 Cre-driver lines of mice was used to map their axonal projections throughout the brain; they constitute two pathways innervating distinct brain regions. These results are a resource for further interrogation of PBN functions.</p>
Characterizing protoclusters and protogroups at z∼2.5 using Lyman-α Tomography (data repo)
<p><strong>Data repo</strong> : Characterizing protoclusters and protogroups at z∼2.5 using Lyman-α Tomography :</p> <p>The generated data in <a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...930..109Q/abstract">Qezlou et. al. 2021</a> </p> <p>Guide on how to use the data :</p> <ul> <li>This data is used by this package (<a href="https://doi.org/10.5281/zenodo.6133235">Zenodo</a> or <a href="https://github.com/mahdiqezlou/LyTomo-Watershed">GitHub</a> ). </li> <li>Feel free to download only the relevant data you need. Refer to <a href="https://github.com/mahdiqezlou/LyTomo-Watershed/blob/main/CookBook.ipynb">this cook book</a> and all the other referred notebooks to see which data file you need to download for each task. </li> <li>There is a module in our package to download these files with python. See <a href="https://github.com/mahdiqezlou/LyTomo_Watershed#generated-data">this</a>.</li> </ul> <ul> <li>Below we provide a short description on each compressed file and the data inside it. </li> </ul> <p> </p> <ol> <li>./descendants/ : Containing data related to the z=0 descendants of the structures (aka. watersheds ) in mock-observed maps at z=2.5.</li> <li>./DM_Density_field/ : Holds the DM density field of the TNG snapshots at z= 2.3, 2=.245, 2.6</li> <li>./FGPA/ : Data generated for the noiseless FGPA map or used to test FGPA accuracy.</li> <li>./mock_maps_z*/ : 20 mock maps generated for each redshift snapshot</li> <li>./noiseless_maps/ : noiseless \delta_F maps for different redshifts</li> <li>./plotting_data/ : summary data used to make plotting easier</li> <li>./progenitor_maps/ : The density maps at z=2.5 for progenitor DM particles of all z=0 halos with M(z=0) > 10^13.5 M_\{odot}/h</li> <li>./progenitors/ : Summary data for progenitors. e.g cofm and a list of their z=0 halo mass</li> <li>./purenoise_maps/ : pure noise maps. Added noise on flat spectra (F = <F>) and then Wiener filtered. </li> <li>./spectra_z*/ : holding extracted spectra from TNG-Illustris snapshots. They are noise free and it will be added in the next step of the analysis. Please, refer to our <a href="https://github.com/mahdiqezlou/LyTomo_Watershed/blob/main/notebooks/CookBook.ipynb">cook book</a></li> <li>./watersheds_z*/ : The watersheds found in all mock maps and noiseless maps at 3 different redshifts. </li> </ol> <p> </p> <p>If you have any questions please feel free to reach mea via email : mahdi.qezlou@email.ucr.edu or raise and issue <a href="https://github.com/mahdiqezlou/LyTomo-Watershed/issues">on our code repository</a>:</p>
Characterizing Measures for the Assessment of Cluster Analysis and Community Detection
<p><strong>Description. </strong>The dataset is constituted of:</p> <ul> <li>`figs.zip`: an archive containing the plot files;</li> <li>`data&results.zip`: an archive containing the necessary data to perform our analysis, as well as result files.</li> </ul> <p>These are the resources used in the following articles:</p> <ol> <li>N. Arınık, V. Labatut and R. Figueiredo, "Characterizing measures for the assessment of cluster analysis and community detection", Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques (MARAMI), 2020. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02993542">hal-02993542</a>⟩</li> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Characterizing and comparing external measures for the assessment of cluster analysis and community detection,” <em>IEEE Access </em>9:20255–20276, 2021. DOI: <a href="http://doi.org/10.1109/access.2021.3054621">10.1109/access.2021.3054621</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-03124118">hal-03124118</a>⟩</li> </ol> <p><strong>Source code. </strong>The associated source code is available on GitHub: <a href="https://github.com/CompNet/ExtMeasEval">https://github.com/CompNet/ExtMeasEval</a></p> <p><strong>Citation. </strong>If you use these data, please cite the paper [2].</p> <p><br><code>@Article{Arinik2021,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Characterizing and Comparing External Measures for the Assessment of Cluster Analysis and Community Detection},</code><br><code> journal = {IEEE Access},</code><br><code> year = {2021},</code><br><code> volume = {9},</code><br><code> pages = {20255-20276},</code><br><code> doi = {10.1109/access.2021.3054621},</code><br><code>}</code></p>
[DATASET] Design and characterization of an RF applicator for in vitro tests of electromagnetic hyperthermia doi.org/10.3390/s22103610
<p>[DATASET] Design and characterization of an RF applicator for in vitro tests of electromagnetic hyperthermia <a href="https://doi.org/10.3390/s22103610">doi.org/10.3390/s22103610</a></p> <p>The data used in the paper are dived in separate folder for each published figure.</p>
Global characterization of the ocean's internal gravity wave vertical wavenumber spectrum from Argo float profiles
<p>Oceanic internal gravity wave energy levels E (m^2/s^2), vertical wavenumber spectral slopes s, and vertical wavenumber scale m* (1/m) estimated by fitting the Garrett Munk model vertical wavenumber shape function to strain spectra obtained from Argo float hydrographic profiles based on the finestructure method, as discussed in Pollmann (2020): "Global Characterization of the Ocean’s Internal Wave Spectrum" (<em>Journal of Physical Oceanography</em> 50.7: 1871-1891). The paper and hence this dataset are a contribution to the Collaborative Research Centre TRR181 ‘Energy Transfers in Atmosphere and Ocean’ funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Projektnummer 274762653. The hydrographic profiles used in this study were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System.</p> <p>Please cite Pollmann (2020) when using this dataset.</p> <p>This dataset includes:</p> <p>a) energy density (m^2/s^2) binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>b) vertical wavenumber spectral slopes binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>c) vertical wavenumber scale m* (1/m) binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>d) latitude and longitude, defined such that, e.g., E(10,10) represents energy levels in the bin bounded by lat(10), lat(11) as well as lon(10), lon(11)</p>
Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica: dataset
<p><strong>Abstract</strong>:<br> (from [1])</p> <blockquote> <p>The addition of fillers can significantly improve the mechanical behavior of polymers. The responsible mechanisms at the molecular level can be well assessed<br> by particle-based simulation techniques, such as molecular dynamics. However, the high computational cost of these simulations prevents the study of macroscopic<br> samples. Continuum-based approaches, particularly micromechanics, offer a more efficient alternative but require precise constitutive models for all<br> constituents, which are usually unavailable at these small length scales. In this contribution, we derive a molecular-dynamics-informed constitutive law by<br> employing a characterization strategy introduced in a previous publication. We choose silicon dioxide (silica) as an exemplary filler material used in polymer<br> composites and perform uniaxial and shear deformation tests with molecular dynamics. The material exhibits elastoplastic behavior with a pronounced anisotropy.<br> Based on the pseudo-experimental data, we calibrate an anisotropic elastic constitutive law and reproduce the material response for small strains accurately. <br> The study validates the characterization strategy that facilitates the calibration of constitutive laws from molecular dynamics simulations. Furthermore, the<br> obtained material model for coarse-grained silica forms the basis for future continuum-based investigations of polymer nanocomposites. In general, the presented<br> transition from a fine-scale particle model to a coarse and computationally efficient continuum description adds to the body of knowledge of molecular science<br> as well as the engineering community.<br> </p> </blockquote> <p><br> <strong>Contact</strong>:<br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><br> <strong>Software</strong>:<br> All simulations were performed with LAMMPS [3], version: 29 Oct 2020 / 20201029<br> Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11<br> Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p><strong>Installed packages:</strong><br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p><br> <strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context</strong>:<br> Data set supplementing journal paper:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.</p> <p><br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content</strong>:<br> The files to reproduce our simulations and their results are structured as follows:</p> <ul> <li>01_potentials<br> tabulated potentials calibrated via iterative Boltzmann inversion in [2] kindly provided by the Müller-Plathe group at Technische Universität Darmstadt <ul> <li>Angle_table<br> angular interactions</li> <li>Bond_table<br> bond interactions</li> <li>Nonbond_table<br> pair interactions</li> </ul> </li> <li>02_sample<br> Lammps data file (molecular style) of the investigated silica sample</li> <li>03_simulations<br> The condensed simulation directories with the naming convention given below are organized in the following subfolders: <ul> <li>01_time-proportional<br> time-proportional simulation data</li> <li>02_time-periodic<br> time-periodic simulation data</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li>lammps input file (*.in) of the specific simulation</li> <li>input.prm: input parameters of the specific simulation (read by the input file)</li> <li>meta.info: meta data of the specific simulation run</li> <li>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li>thermo_out.Dat: raw output</li> <li>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</li> <li>thermo_out_STD.Dat: standard deviation of raw output</li> </ul> </li> </ul> <p><br> <strong>Naming convention</strong>:<br> Silica-[deformation]-[direction]_[deformation function]-[deformation magnitude]_[deformation rate]<br> ● [deformation]: uniaxial tension (UT), simple shear (SS)<br> ● [direction]: deformation carried out in X/Y/Z (UT) or XY/XZ/YZ (SS)<br> ● [deformation function]: time-proportional (strain), time-periodic (strain_ampl)<br> ● [deformation magnitude]: maximum strain (time-proportional), strain amplitude (time-periodic); unitless<br> ● [deformation rate]: rate-[strain rate] (only time-proportional): 0.001/ns-0.1/ns</p> <p><br> <strong>Output quantities</strong> (columns of *.Dat files):<br> ● Step: time step<br> ● Time: time in fs<br> ● TotEng: total energy in kcal/mol<br> ● PotEng: potential energy in kcal/mol<br> ● KinEng: kinetic energy in kcal/mol<br> ● E_pair: pair energy in kcal/mol<br> ● E_bond: bond energy in kcal/mol<br> ● E_angle: angle energy in kcal/mol<br> ● E_dihed: dihedral energy in kcal/mol<br> ● Temp: temperature in K<br> ● Press: hydrostatic pressure in atm<br> ● Pxx: xx component of pressure tensor in atm<br> ● Pyy: yy component of pressure tensor in atm<br> ● Pzz: zz component of pressure tensor in atm<br> ● Pxy: xy component of pressure tensor in atm<br> ● Pxz: xz component of pressure tensor in atm<br> ● Pyz: yz component of pressure tensor in atm<br> ● Volume: volume of simulation box in (Angstroms)^3<br> ● Lx: box length in x direction in Angstroms<br> ● Ly: box length in y direction in Angstroms<br> ● Lz: box length in z direction in Angstroms<br> ● Density: density in g/(cm^3)<br> ● c_RG: radius of gyration in Angstroms<br> ● c_RG[1]: squared radius of gyration tensor (xx component) in (Angstroms)^2<br> ● c_RG[2]: squared radius of gyration tensor (yy component) in (Angstroms)^2<br> ● c_RG[3]: squared radius of gyration tensor (zz component) in (Angstroms)^2<br> ● c_RG[4]: squared radius of gyration tensor (xy component) in (Angstroms)^2<br> ● c_RG[5]: squared radius of gyration tensor (xz component) in (Angstroms)^2<br> ● c_RG[6]: squared radius of gyration tensor (yz component) in (Angstroms)^2<br> ● c_bondave[1]: bond energy averaged over all atoms in kcal/mol<br> ● c_bondave[2]: bond distance averaged over all atoms in Angstroms<br> ● c_bondave[3]: squared bond distance averaged over all atoms in (Angstroms)^2<br> ● c_angleave[1]: angle energy averaged over all atoms in kcal/mol<br> ● c_angleave[2]: angle averaged over all atoms degree<br> ● c_angleave[3]: cosine of angle (unitless)<br> ● c_angleave[4]: squared cosine of angle (unitless)<br> ● c_MSD[1]: mean squared displacement x-direction in (Angstroms)^2<br> ● c_MSD[2]: mean squared displacement y-direction in (Angstroms)^2<br> ● c_MSD[3]: mean squared displacement z-direction in (Angstroms)^2<br> ● c_MSD[4]: total mean squared displacement in (Angstroms)^2<br> ● c_COM[1]: x coordinate of center of mass in Angstroms<br> ● c_COM[2]: y coordinate of center of mass in Angstroms<br> ● c_COM[3]: z coordinate of center of mass in Angstroms<br> ● v_strain_xx: xx component of engineering strain tensor (unitless) <br> ● v_strain_yy: yy component of engineering strain tensor (unitless) <br> ● v_strain_zz: zz component of engineering strain tensor (unitless) <br> ● v_vMisesequivstress: von Mises equivalent stress in MPa<br> ● v_Cauchy_xx: xx component of stress tensor in MPa <br> ● v_Cauchy_yy: yy component of stress tensor in MPa<br> ● v_Cauchy_zz: zz component of stress tensor in MPa<br> ● v_Cauchy_xy: xy component of stress tensor in MPa<br> ● v_Cauchy_xz: xz component of stress tensor in MPa<br> ● v_Cauchy_yz: yz component of stress tensor in MPa<br> ● v_strain_xy: xy component of engineering strain tensor (unitless) <br> ● v_strain_xz: xz component of engineering strain tensor (unitless) <br> ● v_strain_yz: yz component of engineering strain tensor (unitless) </p> <p><strong>References</strong>:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.<br> [2] Ghanbari, A.; Ndoro, T. V. M.; Leroy, F.; Rahimi, M.; Böhm, M. C. & Müller-Plathe, F., “Interphase Structure in Silica-Polystyrene<br> Nanocomposites: A Coarse-Grained Molecular Dynamics Study”, Macromolecules, 2012, 45, 572-584.<br> [3] Plimpton, S., “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p> </p>
Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"
<p><strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong></p> <p><strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> </p> <p><em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis</p> <p> - "main-program-grid-search.py" file: Main program executing all experiments for the grid search analysis</p> <p><em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.</p>
Experimental characterization of heralded single photon source
<p>This set includes experimental measurements from the heralded single photon source. Characterisation of heralded single photon source; The experimental data consists of measurements of the heralding efficiency, overall count rates and source brightness.</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.