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542 results for “hardness”
Hard Layer
<p>This dataset comes from the first phase of MAIL methodology as described in D2.3. Here a top-down stepwise approach is followed, in which areas that are not Marginal Lands (MLs) are incrementally removed, based on thresholds of various marginality criteria and indicators. These results are an intermediate layer, named “ML HARD” including all potential MLs after the exclusion of all LULC types that are not fulfilling the definition of MLs.</p>
Entropy-Driven Crystallization of Hard Colloidal Mixtures of Polymers and Monomers
<p>Data archive corresponding to the publication "Entropy-Driven Crystallization of Hard Colloidal Mixtures of Polymers and Monomers " by O. Bouzid <em>et al</em>., Polymers <strong>16</strong>, 2311 (2024). </p> <p>Preprint available at: 10.20944/preprints202407.0786.v1</p> <p>Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor.</p> <p>All system configurations have been generated and successively analyzed by the Simu-D software.</p> <p> </p> <p>This research was funded by MICINN/FEDER (Ministerio de Ciencia, Innovación y Universidades, Fondo Europeo de Desarrollo Regional), grant number “PID2021-127533NB-I00”, by the scholarship program from the Algerian Ministry of Higher Education and Scientific Research and by UPM and Santander Bank, “Programa Propio UPM Santander”.</p>
Dataset for "Self-assembly of dodecagonal and octagonal quasicrystals in hard spheres on a plane"
<p>This dataset contains supplementary data for the publication:<br> <em>Self-assembly of dodecagonal and octagonal quasicrystals in hard spheres on a plane</em><br> E. Fayen, M. Impéror-Clerc, L. Filion, G. Foffi, and F. Smallenburg</p> <p> </p> <p><strong>Contents:</strong><br> The folder Data contains subfolders for each of the simulations performed for the construction of Fig. 3 of the main paper. Each folder name contains the size ratio q, the fraction of large particles x_L, and the packing fraction e in the file name. Note that the fraction of large particles x_L is related to the quantity x_S used in the paper via x_L = 1 - x_S.</p> <p>For simulations that were run for longer times, an additional folder with the same naming convention is included in the subfolder Long.</p> <p>Each simulation subfolder includes:</p> <p>- A coordinate file "last.sph" representing the final configuration of the simulation in plain text format. In this file, the first line specifies the number of particles, the second line the box size and non-additivity parameter Delta, and the remaining lines the coordinates of the particles. Each line containing coordinates consists of a letter indicating particle species (a or b), three spatial coordinates (with the z-coordinate always zero), and the particle radius. All lengths are given in units of the large-particle diameter.</p> <p>- An image of the final particle configuration "snapshot.png".</p> <p>- An image representing the associated scattering pattern, obtained by taking the Fourier transform of the particle coordinates and plotting the result as a function of the 2D wave vector on a logarithmic color scale.</p> <p> </p> <p>Additionally, the main folder contains a set of HTML files ("table_q*.html") that provide an overview of the snapshots and scattering patterns for each size ratio (specified in the file name). The HTML table for each size ratio uses the images from the "Data" and "Data/Long" subfolders as appropriate, and depends on the included "SAtable.css" and "SAtable.js" files. Within each table, clicking on one of the entries will enlarge the associated images.<br> <br> </p>
Dataset for "Reconfigurable Magnonic Crystals Based on Imprinted Magnetization Textures in Hard and Soft Dipolar-Coupled Bilayers"
<p>The dataset consist of the data of the numerical simulations used to prepare the figures for the manuscript: </p><p>Krzysztof Szulc, Silvia Tacchi, Aurelio Hierro-Rodríguez, Javier Díaz, Paweł Gruszecki, Piotr Graczyk, Carlos Quirós, Daniel Markó, José Ignacio Martín, María Vélez, David S. Schmool, Giovanni Carlotti, Maciej Krawczyk, and Luis Manuel Álvarez-Prado. <i>Reconfigurable Magnonic Crystals Based on Imprinted Magnetization Textures in Hard and Soft Dipolar-Coupled Bilayers</i>. ACS Nano <strong>2022</strong> <i>16</i> (9), 14168-14177.</p><p>Please read README.txt file to see the description of the data in the files.</p>
Review of redshift values of bright AGNs with hard spectra in 4LAC catalog v3
<p>Review of redshift values of bright AGNs with hard spectra in 4LAC catalog</p> <p>FITS table and description file (pdf)</p>
Dataset for the publication "Size matters: small biochar particles hardly disintegrate under cryo-stress"
<p>Corresponding dataset for the OA publication "Small biochar particles hardly disintegrate under cryo-stress" published in Geoderma</p>
Datasets of titrations of mineral water hardness monitored using SERS
<p>These datasets contains the spectral information used in a publication regarding the implementation of complexometric titrations of water hardness monitored using Surface Enhanced Raman Spectroscopy (SERS).</p> <p>The data consists of two csv files with semi-column separators.</p> <p><em>« MD150 to 155_Master_df_638_corrected_mod_v02.csv »</em> and <em>« MD150 to 155_Master_df_785_corrected_mod_v02.csv »</em> correspond to spectral data acquired under 638 and 785 nm irradiation respectively.</p> <p>The data are organised as row vectors.</p> <p>Each dataset has 432 rows corresponding to 3 water samples (Evian, Volvic, Contrex), 3 replicate titration series + 1 blank titration per batch of NPs, 2 batches of NPs and 18 titration steps per titration series: .</p> <p>The row vectors consist of a first sub-vector of spectral descriptors (identity of spectra, identity and composition of measurement samples and conditions of spectral acquisition), followed by a sub-vector of baseline-substracted spectral intensities.</p> <p>In both datasets, the first 14 columns consists of the spectral descriptors.</p> <p>In the <em>« MD150 to 155_Master_df_638_corrected_mod_v02.csv » </em>dataset, the spectra section consists of 1010 columns which names are the values of the Raman shifts at which the intensities have been recorded.</p> <p>In the <em>« MD150 to 155_Master_df_785_corrected_mod_v02.csv »</em> dataset, the spectra section consists of 1746 columns which names are the values of the Raman shifts at which the intensities have been recorded.</p>
Measure While Drilling (MWD) dataset with rock type labels for 15 Norwegian hard rock tunnels
<p>The dataset is presented in the paper: </p> <p><em>Building and analysing a labelled Measure While Drilling dataset from 15 hard rock tunnels in Norway</em>, by T.F. Hansen, Z. Liu, J. Torressen</p> <p>The paper has a preprint on SSRN: <a href="http://dx.doi.org/10.2139/ssrn.4729646" target="_blank" rel="noopener">http://dx.doi.org/10.2139/ssrn.4729646</a> and is under review in a peer-reviewed journal.</p> <p>The dataset is utilised in a machine learning analysis in the paper:</p> <p><em>Predicting rock type from MWD tunnel data using a reproducible ML-modelling process</em>, by T.F. Hansen, Z. Liu, J. Torressen</p> <p>The paper is published in the journal <em>Tunnelling and Underground Space Technology</em>: </p> <p><a href="https://doi.org/10.1016/j.tust.2024.105843">https://doi.org/10.1016/j.tust.2024.105843</a></p> <p> </p> <p><strong>Description of the dataset:</strong></p> <p>Measure While Drilling (MWD) is a technique in rock drilling, mainly used in drill and blast tunnelling, where data about the rock mass is registered by sensors while drilling. The extensive and geologically diversified dataset contains corresponding MWD-data and rock mass mappings for 5205 blasting rounds from 15 hard rock tunnels in Norway. MWD-data are presented as tabular data. 10 different rocktypes are the corresponding labels.</p> <p>Four files are given:</p> <ul> <li>A csv-file of the training dataset - with outliers removed</li> <li>A csv-file of the testing dataset (split train/test 0.75/0.25) - with outliers removed</li> <li>A csv-file with the full unsplitted dataset, cleaned and with outliers removed</li> <li>A csv-file with the raw dataset, before cleaning, processing and outlier removal</li> </ul> <p>The author gratefully acknowledge the tunnel software/hardware company Bever Control, which have facilitated data from the clients Bane NOR, Statens Vegvesen, Nye Veier, and the contractor AF-Gruppen.</p> <p> </p> <p><strong>NOTE:</strong> The dataset is only available for research, no commercial use.</p>
Data for "Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems"
<p>Contains instances of combinatorial optimizations problems (Sherrington-Kirkpatrick and MAX 2-SAT) as well as further results and plotting notebooks for the paper "Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems".</p>
Single-pulse hard x-ray holograms of an exploding water jet
<p>This h5 file contains the holograms of an exploding micro-fluidic jet recorded at the MID setup at EuXFEL. The holograms were recorded with single-pulse illumination of 17.8 keV hard x-rays.</p> <p>The file contains only one group (/frames/pixels) with 4499 frames recorded with the Andor Zyla 5.5 camera used in this experiment.</p> <p>The first 352 frames and frames 4233 to 4499 can be used as empty beam / reference frames. The frames in between are data frames. The microfluidic jet is put in the field of view and is pumped with an IR laser with pumping offset from 5 to -35 ns with respect to the arrival of the XFEL pulse. </p> <p>The data set has been published in:</p> <p>J. Hagemann, M. Vassholz, H. Hoeppe, M. Osterhoff, J. M. Rosselló, R. Mettin, F. Seiboth, A. Schropp, J. Möller, J. Hallmann, C. Kim, M. Scholz, U. Boesenberg, R. Schaffer, A. Zozulya, W. Lu, R. Shayduk, A. Madsen, C. G. Schroer, and T. Salditt, “Single-pulse phase-contrast imaging at free-electron lasers in the hard X-ray regime,” Journal of Synchrotron Radiation 28(1), 52–63 (2021).<br> </p> <p> </p>
Data Archive: Local and Global Order in Dense Packings of Semiflexible Polymers of Hard Spheres
<p>Data archive corresponding to the publication "Local and Global Order in Dense Packings of Semi-flexible 2 Polymers of Hard Spheres" by D. Martinez-Fernandez et al., Polymers 15, 551 (2023); DOI: https://doi.org/10.3390/polym15030551.</p> <p>Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor.</p> <p>All snapshots have been generated and successively analyzed by the Simu-D software.</p>
Polymorphism and Perfection in Crystallization of Hard Sphere Polymers
<p>Data archive corresponding to the publications "Polymorphism and Perfection in Crystallization of Hard Sphere Polymers" by M. Herranz et al., Polymers 14, 4435 (2022); DOI: https://doi.org/10.3390/polym14204435</p> <p>Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor.</p> <p>All snapshots have been generated and successively analyzed by the Simu-D software.</p>
Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations
<p>Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.</p> <p>For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.</p>
Backblaze Hard Drive Stats for 2018
<p>2018 Hard Drive Failure Rates: What 100,000+ Hard Drives Tell Us</p> <p>At the end of 2018 Backblaze was monitoring 104,954 hard drives used to store data. For our evaluation we remove from consideration those drives that were used for testing purposes and those drive models for which we did not have at least 45 drives (see why below). This leaves us with 104,778 hard drives. The table below covers what happened just in 2018.</p>
Fig. 3 in Water pH and hardness alter ATPases and oxidative stress in the gills and kidney of pacu (Piaractus mesopotamicus)
Fig. 3. Thiobarbituric acid reactive substances (TBARS) content (nmol TMP mg wet tissue-1) in a. gills and b. kidney of pacu (Piaractus mesopotamicus) juveniles under different water hardness and pH at different times. LWH = low water hardness (50 mg CaCO L-1); HWH = high water hardness (120 mg CaCO L-1). Data are presented as the means ± SEM (n = 3 3 9 fish treatment–1). Different uppercase letters indicate statistically differences between pH at the same hardness (P <0.05). Different lowercase letters indicate statistically differences between hardness at the same pH (P <0.05).
Fig. 2 in Water pH and hardness alter ATPases and oxidative stress in the gills and kidney of pacu (Piaractus mesopotamicus)
Fig. 2. Total antioxidant capacity against peroxyl radicals (ACAP) (relative area) in a. gills and b. kidney of pacu (Piaractus mesopotamicus) juveniles under different water hardness and pH at different times. LWH = low water hardness (50 mg CaCO L-1); HWH = high water hardness (120 mg CaCO L-1). Data are presented as the means ± SEM (n = 9 fish treatment–1). 3 3 Different uppercase letters indicate statistically differences between pH at the same hardness (P <0.05).
Distal prong of MA short, hardly longer than proximal one, straight (6a); TA fairly long and spiniform (6b) in A revision of the African wolf spider genus Amblyothele Simon
Distal prong of MA short, hardly longer than proximal one, straight (6a); TA fairly long and spiniform (6b)
Conjunctions between ICON-MIGHTI and 4 meteor radars, used in "Validation of ICON-MIGHTI thermospheric wind observations: 2. Greenline comparisons to meteor radars" by Harding et al. (2020, Submitted)
<pre>This dataset was used to generate the figures in the paper mentioned above and is being made available for the sake of reproducibility and future analysis. The primary variables are los_wind (the line of sight wind profiles observed by ICON-MIGHTI) and los_wind_r (the wind profiles observed by the meteor radar, interpolated in time and altitude to the MIGHTI sample, and projected onto the MIGHTI line of sight). Dimensions are "time" and "row" (which refers to the row of the MIGHTI CCD, roughly equivalent to altitude. Velocity units are m/s, distances are km, and lat/lon are in degrees. More information can be found in the paper.</pre>
Data and code for: High light alongside elevated pCO2 alleviates thermal depression of photosynthesis in a hard coral (Pocillopora acuta)
<p>Data and R scripts of analyses performed for the manuscript "<strong>High light alongside elevated pCO<sub>2</sub> alleviates thermal depression of photosynthesis in a hard coral (<em>Pocillopora acuta</em>)</strong>"</p>
Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel
<p>Raw data associated with a paper submission.<br> " Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel" submitted to Additive Manufacturing.</p> <p>Contained are all the raw images used in figures, as well as csv's of any data pltoted in graphs.</p> <p>Raw images captured during printing of various processing parameters<br> EBSD scans (.ctf) of all disucssed samples </p> <p>Wall definitions (EBSD compared to paper)<br> Wall 1 - Wall A1 300 W 2750 mm/s<br> Wall 2 - Wall D 500 W 2250 mm/s<br> Wall 3 - Wall B 300 W 2250 mm/s<br> Wall 4 - Wall C 500 W 2750 mm/s<br> Wall 5 - Wall A2 300 W 2750 mm/s</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.