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.
852
datasets available to search
ShareScore release 0.9.0
Dataset results
852 results for “grain”
IODP Expedition 366 Bulk and grain density (MAD)
<p>Moisture and density (MAD) data were acquired on ~10 mL sediment or rock samples by measuring three out of four material parameters: wet (saturated) mass, wet volume, dry mass, and/or dry volume after 24 h drying in a convection oven at 105 degrees C. From the moisture and volume measurements, the following phase relationships are calculated: wet and dry water content, wet bulk density, dry bulk density, grain density, porosity, and void ratio. The combination of measurements is defined by the submethod chosen: A, B, C, or D. Wet (A, B, or C) and dry (A, B, C, or D) mass is determined using motion-compensated balances. Wet volume is determined either by helium pycnometry (A) or by the sample's geometric dimensions using calipers (A or D). Dry volume (C or D) is measured by helium pycnometry. Submethods A and B are not recommended by IODP. Submethod C is suitable for saturated materials such as fine-grained sediments. Submethod D is suitable for unsaturated porous material such as certain limestones and basalts.</p>
Supporting data: Grain-dependent responses of mammalian diversity to land-use and the implications for conservation set-aside
<p>Camera trap and live trap datasets underlying the analyses in an <em>Ecological Applications </em>paper (http://onlinelibrary.wiley.com/doi/10.1890/15-1363/abstract), provided in .csv format. Each row consists of a single trap night at a given location, with species in different columns. Old-growth forest, logged forest and oil palm plantation locations have the prefixes "Old", "Log" and "Palm", respectively. Values in each cell are the number of independent captures, as defined in the paper. </p>
Protein secondary-structure description with a coarse-grained model: code and datasets in ActivePapers format
<p>This file contains the supplementary material for the publication</p> <p><em>Protein secondary-structure description with a coarse-grained model</em><br /> by Gerald R. Kneller and K. Hinsen<br /> http://dx.doi.org/10.1107/S1399004715007191<br /> Acta Cryst. (2015). D<strong>71</strong>, 1411-1422</p> <p><strong>Datasets in this file</strong></p> <p>1) ScrewFit and ScrewFrame parameters for ideal secondary-structure elements</p> <p> Scripts:<br /> /code/import_ideal_structures<br /> /code/analyze_ideal_structures</p> <p>1.1) The PDB files generated with Chimera</p> <p> /data/ideal_structures/3-10.pdb<br /> /data/ideal_structures/alpha.pdb<br /> /data/ideal_structures/beta-antiparallel.pdb<br /> /data/ideal_structures/beta-parallel.pdb<br /> /data/ideal_structures/pi.pdb</p> <p>1.2) The corresponding MOSAIC datasets</p> <p> /data/ideal_structures/3-10<br /> /data/ideal_structures/alpha<br /> /data/ideal_structures/beta-antiparallel<br /> /data/ideal_structures/beta-parallel<br /> /data/ideal_structures/pi</p> <p>1.3) The ScrewFit parameters</p> <p> /data/ideal_structures/screwfit/3-10<br /> /data/ideal_structures/screwfit/alpha<br /> /data/ideal_structures/screwfit/beta-antiparallel<br /> /data/ideal_structures/screwfit/beta-parallel<br /> /data/ideal_structures/screwfit/pi</p> <p>1.4) The ScrewFrame parameters</p> <p> /data/ideal_structures/screwframe/3-10<br /> /data/ideal_structures/screwframe/alpha<br /> /data/ideal_structures/screwframe/beta-antiparallel<br /> /data/ideal_structures/screwframe/beta-parallel<br /> /data/ideal_structures/screwframe/pi</p> <p><br /> 2) Statistics for ScrewFit and ScrewFrame parameters computed<br /> for the ASTRAL SCOPe subset with less than 40% sequence identity.</p> <p> Scripts:<br /> /code/astral_analysis<br /> /code/fit_rho_distributions<br /> /code/plot_histograms</p> <p>2.1) The ASTRAL database (link to published ActivePaper)</p> <p> /data/astral_2.04</p> <p>2.2) The histograms for the ScrewFit and ScrewFrame parameters<br /> for the all-alpha and all-beta subsets</p> <p> /data/histograms/astral_alpha/screwfit<br /> /data/histograms/astral_alpha/screwframe</p> <p> /data/histograms/astral_beta/screwfit<br /> /data/histograms/astral_beta/screwframe</p> <p>2.3) The Gaussians fitted to the peaks in the distributions for rho</p> <p> /data/fitted_rho_distributions/screwfit<br /> /data/fitted_rho_distributions/screwframe</p> <p>2.4) Plots</p> <p> /documentation/delta.pdf<br /> /documentation/delta_q.pdf<br /> /documentation/delta_r.pdf<br /> /documentation/p.pdf<br /> /documentation/rho-detail.pdf<br /> /documentation/rho.pdf<br /> /documentation/sigma.pdf<br /> /documentation/tau.pdf</p> <p><br /> 3) Comparison of secondary-structure identification between ScrewFrame<br /> and DSSP.</p> <p> Script:<br /> /code/compare_secondary_structure_assignments<br /> /code/plot_histograms</p> <p>3.1) The histograms of the lengths of secondary-structure elements</p> <p> /data/histograms/secondary_structure/length-alpha-dssp<br /> /data/histograms/secondary_structure/length-alpha-screwframe<br /> /data/histograms/secondary_structure/length-beta-dssp<br /> /data/histograms/secondary_structure/length-beta-screwframe</p> <p>3.2) The 2D histograms of the number of residues inside identified<br /> secondary-structure elements</p> <p> /data/histograms/secondary_structure/n-alpha<br /> /data/histograms/secondary_structure/n-beta</p> <p>3.3) The distribution of rho inside alpha helices</p> <p> /data/histograms/secondary_structure/rho-alpha-dssp</p> <p>3.3) Plots</p> <p> /documentation/lengths-alpha.pdf<br /> /documentation/lengths-beta.pdf<br /> /documentation/n-alpha.pdf<br /> /documentation/n-beta.pdf<br /> /documentation/rho-alpha-dssp.pdf</p> <p><br /> 4) Illustration for myoglobin and VADC-1</p> <p> Scripts:<br /> /code/import_myoglobin_vdac<br /> /code/analyze_myoglobin<br /> /code/analyze_vdac<br /> /code/perturbation_analysis</p> <p>4.1) Imported structures in MOSAIC format:<br /> PDB code 1A6G for myoglobin<br /> PDB code 2K4T for VDAC-1</p> <p> /data/myoglobin<br /> /data/VDAC-1</p> <p>4.2) Plots showing rho and delta</p> <p> /documentation/rho-myoglobin.pdf<br /> /documentation/delta-myoglobin.pdf</p> <p>4.3) Tube models for visualization with Chimera</p> <p> /documentation/myoglobin-tube.bld<br /> /documentation/VDAC-1-tube.bld</p> <p>4.4) Sensitivity to perturbations in the coordinates</p> <p> /documentation/rho-perturbed-myoglobin.pdf<br /> /documentation/delta-perturbed-VDAC-1.pdf<br /> /documentation/rho-perturbed-myoglobin.pdf<br /> /documentation/delta-perturbed-VDAC-1.pdf<br /> /documentation/myoglobin-perturbation.pdf<br /> /documentation/VDAC-1-perturbation.pdf</p> <p>5) Analysis of CA-only structures in the PDB</p> <p> Scripts:<br /> /code/ca_analysis<br /> /code/import_calpha_structures<br /> /code/plot_histograms</p> <p>5.1) Imported CA-only structures in MOSAIC format</p> <p> /data/pdb_ca_only_structures</p> <p>5.2) Histograms for ScrewFrame parameters</p> <p> /data/histograms/ca_only_structures</p> <p>5.3) Plots</p> <p> /documentation/delta_ca.pdf<br /> /documentation/delta_q_ca.pdf<br /> /documentation/delta_r_ca.pdf<br /> /documentation/p_ca.pdf<br /> /documentation/rho_ca.pdf<br /> /documentation/sigma_ca.pdf<br /> /documentation/tau_ca.pdf</p> <p> </p>
3D image of an assembly of rice grains
<p>3D reconstruction of an assembly of rice grains. The image was acquired on the collaborative microtomography platform at Laboratoire Navier (http://navier.enpc.fr).</p> <p>Ordinary, long-grain rice is poured in a plastic container, 50mm in diameter. The average length of the grains is about 6.5 mm.</p> <p>The X-ray source is a Hamamatsu L10801 X-ray source (maximum voltage: 230V, maximum current: 1mA). Combined with the the Paxscan Varian 2520V flat-panel X-ray imager (1536x1920 pixels, pixel pitch 127µm), this setup leads to a voxel size of approx. 0.030mm. The specimen was scanned at 100kV and 300µA with an imager frame-rate of 6 images per second. To reduce noise, 12 radiographs were averaged to produce one projection; the total number of projections was 1440.</p> <p>3D reconstruction was carried out using standard tools developed by RX Solutions France. Contrast, resolution and signal-to-noise ratio were all excellent, so that the most basic reconstruction procedure resulted in very high quality 3D images.</p> <p>The dataset is provided as a set of 689 TIFF images, 1747×1751 pixels. It is used as illustrative material for a series of posts called “Orientation correlations among rice grains” on my blog (see related identifiers below).</p> <p> </p>
Going against the grain – Texture orientation affects direction of exploratory movement
<p>In haptic perception sensory signals depend on how we actively move our hands. For textures with periodically repeating grooves, movement direction can determine temporal cues to spatial frequency. Moving in line with texture orientation does not generate temporal cues. In contrast, moving orthog-onally to texture orientation maximizes the temporal frequency of stimulation, and thus optimizes temporal cues. Participants performed a spatial frequency discrimination task between stimuli of two types. The first type showed the de-scribed relationship between movement direction and temporal cues, the second stimulus type did not. We expected that when temporal cues can be optimized by moving in a certain direction, movements will be adjusted to this direction. However, movement adjustments were assumed to be based on sensory infor-mation, which accumulates over the exploration process. We analyzed 3 indi-vidual segments of the exploration process. As expected, participants only ad-justed movement directions in the final exploration segment and only for the stimulus type, in which movement direction influenced temporal cues. We con-clude that sensory signals on the texture orientation are used online during ex-ploration in order to adjust subsequent movements. Once sufficient sensory evi-dence on the texture orientation was accumulated, movements were directed to optimize temporal cues.</p> <p><strong>Lezkan</strong>, A. & <strong>Drewing</strong>, K. (2016). Going against the grain – Texture orientation affects direction of exploratory movement, part I. <em>Haptics: Perception, Devices, Control, and Applications</em> (pp. 430-440).</p> <p>The Zip file contains all data relative to the publication.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
The first 500-meter, long-term winter wheat grain protein content dataset for China from multi-source data
<p>In China, the demand for precise perception of wheat Grain Protein Content (GPC) has gained increased urgency, driven by the rising demands in the food consumption market and intensifying international market competition. However, due to the lack of extensive, prolonged high-resolution benchmark data, previous GPC studies have primarily focused on experimental fields, small geographic units, and limited temporal scopes. Additionally, the diversified geographical landscape in China introduces spatiotemporal heterogeneity and intricacy to the influence of wheat GPC, further amplifying the challenges of large-scale GPC estimation. To address this challenge and the data gap, the first 500-meter spatial resolution, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500) was created by integrating multi-source data from ERA5 and MODIS.</p>
A Novel Laboratory Technique for Measuring Grain Size Specific Transport Characteristics of Bed Load Pulses
<p>We present a novel, time-efficient and non-destructive laboratory technique to investigate grain size specific transport characteristics of bed load pulses. The method consists of a through-water, high-resolution image acquisition followed by the application of a supervised color classification algorithm (Gaussian Maximum Likelihood Classification). Quality assessment based on a confusion matrix approach and basic random sampling showed a high classification performance. By statistically analyzing the temporal and spatial color distribution of the experimental reach, characteristic parameters to describe the propagation behavior were determined. The analyzed bed load pulse consisted of five different grain size classes of dyed quartz sand and gravel, each having a unique color. The initial experimental bed was uni-colored and contained the same size fractions as the augmented pulse.</p>
Range expansion can promote the evolution of plastic generalism in coarse-grained landscapes
<p>Phenotypic plasticity is one way for organisms to deal with variable environments through generalism. However, plasticity is not found universally and its evolution may be constrained by costs and other limitations such as complexity: the need for multiple mutational steps before the adaptation is realized. Theory predicts that greater experienced heterogeneity, such as organisms may encounter when spatial heterogeneity is fine-grained relative to dispersal, should favor the evolution of a broader niche. Here we tested this prediction via simulation. We found that, contrary to classical predictions, coarse-grained landscapes can be the most favorable for the evolution of plasticity, but only when populations encountered those landscapes through range expansion. During these range expansions, coarse-grained landscapes select for each step in the complex mutational pathway to plastic generalism by blocking the dispersal of specialists. These circumstances provide ecological opportunities for innovative mutations that change the niche. Our results indicate a new mechanism by which range expansion and spatially structured landscapes interact to shape evolution, and reveal that the environments in which a complex adaptation has the highest fitness may not be the most favorable for its evolution.</p>
Data from: Promoting success in thin layer sediment placement: effects of sediment grain size and amendments on salt marsh plant growth and greenhouse gas exchange
<p>Thin layer sediment placement (TLP) is a method to mitigate factors resulting in loss of elevation and severe alteration of hydrology, such as sea level rise and anthropogenic modifications, and prolong the lifespan of drowning salt marshes. However, TLP success may vary due to plant stress associated with reductions in nutrient availability and hydrologic flushing or through the creation of acid sulfate soils. This study examined the influence of sediment grain size and soil amendments on plant growth, soil and porewater characteristics, and greenhouse gas exchange for three key US salt marsh plants: <em>Spartina alterniflora, Spartina patens, </em>and <em>Salicornia pacifica. </em>We found that bioavailable nitrogen concentrations (measured as extractable NH<sub>4</sub><sup>+</sup>-N) and porewater pH and salinity were found to have an inverse relationship with grain size, while soil redox was more reducing in finer sediments. This suggests that utilizing finer sediments in TLP projects will result in a more reduced environment with higher nutrient availability, while larger grain-sized sediments will be better flushed and oxidized. We further found that grain size had a significant effect on vegetation biomass allocation and rates of gas exchange, although these effects were species-specific. We found that soil amendments (biochar and compost) did not subsidize plant growth but were associated with increases in soil respiration and methane emissions. Biochar amendments were additionally ineffective in ameliorating acid sulfate conditions. This study uncovers complex interactions between sediment type and vegetation, emphasizing limitations of soil amendments. The findings aid restoration project managers in making informed decisions regarding sediment type, target vegetation, and soil amendments for successful TLP projects.</p>
Supporting Dataset (Tables S1-S7, Figure S1) for "Heavy-mineral grain counting: Counting techniques, error estimation, and the number of grains to be counted"
<p>This Dataset comprises Tables S1-S7 for the article <strong>"Heavy-mineral grain counting: Counting techniques, error estimation, and the number of grains to be counted"</strong> authored by Jan Schönig and submitted to J<em>ournal of Geophysical Research: Earth Surface</em>.</p> <p>Figure S1: Error of different counting methods in comparison with theory and without considering the finite population correction</p> <p>Table S1: Heavy-mineral dataset from semi-automated Raman analysis</p> <p>Table S2: Summary of heavy-mineral composition for individual samples in percent</p> <p>Table S3: Computed ribbon compositions for individual samples and ribbon sizes given in numbers of counts</p> <p>Table S4: Relative errors at 95 % quantile for consecutive ribbon counting simulations</p> <p>Table S5: Relative errors at 95 % quantile for maximum distance ribbon counting simulations</p> <p>Table S6: Relative errors at 95 % quantile for cluster counting simulations</p> <p>Table S7: Numerical solution for determining the number of counts required to discriminate the content of two mineral species in an aliquot</p>
CESNET-TLS22: A large dataset for fine-grained classification of TLS services
<p><strong>Please refer to the original article for further data description:</strong> Jan Luxemburk et al. Fine-grained TLS services classification with reject option, Computer Networks, 2023, 109467, ISSN 1389-1286, <a href="https://doi.org/10.1016/j.comnet.2022.109467">https://doi.org/10.1016/j.comnet.2022.109467</a></p> <p><strong>We recommend using the</strong> <strong>CESNET DataZoo python library, which facilitates the work with large network traffic datasets. </strong>More information about the DataZoo project can be found in the GitHub repository <a href="https://github.com/CESNET/cesnet-datazoo">https://github.com/CESNET/cesnet-datazoo</a>.</p> <p>The recent success and proliferation of machine learning and deep learning have provided powerful tools, which are also utilized for encrypted traffic analysis, classification, and threat detection. These methods, neural networks in particular, are often complex and require a huge corpus of training data. Moreover, because most of the network traffic is being encrypted, the traditional deep-packet-inspecting (DPI) solutions are becoming obsolete, and there is an urgent need for modern classification methods capable of analyzing encrypted traffic. These methods have to forgo the packet's opaque payload and focus on flow statistics and packet metadata sequences like packet sizes, directions, and inter-arrival times. The classification can be further extended with the task of "rejecting" unknown traffic, i.e., the traffic not seen during the training phase. This makes the problem more challenging, and neural networks offer superior performance for tackling this problem.<strong> When the factors of (1) the hardness of classification of encrypted traffic with unknown traffic detection and (2) the neural networks' inherent need for large datasets are combined, the requirement for a rich, large, and up-to-date dataset is even stronger.</strong></p> <p>Therefore, we created a large dataset spanning two weeks, consisting of 141 million network flows, and having 191 fine-grained service labels. The dataset is intended as a benchmark for the task of identification of services in encrypted traffic with the detection of unknown services.</p> <p><strong>Data capture</strong> The data was captured in the flow monitoring infrastructure of the <a href="https://www.cesnet.cz">CESNET2</a> network. The capturing was done for two weeks between 4.10.2021 and 17.10.2021. The following table provides per-week flow count, capture period, and uncompressed size:</p> <ul> <li><strong>W-2021-40</strong> <ul> <li>Uncompressed Size: 22 GB</li> <li>Capture Period: 4.10.2021 - 10.10.2021</li> <li>Flows: 73.2M</li> </ul> </li> <li><strong>W-2021-41</strong> <ul> <li>Uncompressed Size: 20 GB</li> <li>Capture Period: 11.10.2021 - 17.10.2021</li> <li>Flows: 68.5M</li> </ul> </li> <li><strong>CESNET-TLS22</strong> <ul> <li>Uncompressed Size: 42 GB</li> <li>Capture Period: 4.10.2021 - 17.10.2021</li> <li>Flows: 141.7M</li> </ul> </li> </ul> <p><strong>Dataset structure</strong> The dataset flows are delivered in compressed CSV files, which contain one flow per row. For each flow data file, there is a JSON file with the number of saved flows per service. There is also the <em>stats-week.json</em> file aggregating flow counts of a whole week and the <em>stats-dataset.json</em> file aggregating flow counts for the entire dataset. The mapping between services and service providers is provided in the <em>servicemap.csv</em> file, which also includes SNI domains used for ground truth labeling. The following table describes flow data fields in CSV files:</p> <ul> <li><strong>ID:</strong> Unique identifier</li> <li><strong>BYTES:</strong> Number of transmitted bytes from client to server</li> <li><strong>BYTES_REV:</strong> Number of transmitted bytes from server to client</li> <li><strong>PACKETS:</strong> Number of packets transmitted from client to server</li> <li><strong>PACKETS_REV:</strong> Number of packets transmitted from server to client</li> <li><strong>DURATION:</strong> Duration of the flow in seconds</li> <li><strong>PPI:</strong> Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]]</li> <li><strong>PPI_LEN:</strong> Number of packets in the PPI sequence</li> <li><strong>PPI_DURATION:</strong> Duration of the PPI sequence in seconds</li> <li><strong>PPI_ROUNDTRIPS:</strong> Number of roundtrips in the PPI sequence</li> <li><strong>APP:</strong> Web service label</li> <li><strong>CATEGORY:</strong> Service category</li> <li><strong>TCP_FLAGS:</strong> TCP flags sent from client to server</li> <li><strong>TCP_FLAGS_REV:</strong> TCP flags sent from server to client</li> <li><strong>FLAG_CWR:</strong> Presence of the CWR flag</li> <li><strong>FLAG_CWR_REV:</strong> Presence of the CWR flag in the reverse direction</li> <li><strong>FLAG_ECE:</strong> Presence of the ECE flag</li> <li><strong>FLAG_ECE_REV:</strong> Presence of the ECE flag in the reverse direction</li> <li><strong>FLAG_URG:</strong> Presence of the URG flag</li> <li><strong>FLAG_URG_REV:</strong> Presence of the URG flag in the reverse direction</li> <li><strong>FLAG_ACK:</strong> Presence of the ACK flag</li> <li><strong>FLAG_ACK_REV:</strong> Presence of the ACK flag in the reverse direction</li> <li><strong>FLAG_PSH:</strong> Presence of the PSH flag</li> <li><strong>FLAG_PSH_REV:</strong> Presence of the PSH flag in the reverse direction</li> <li><strong>FLAG_RST:</strong> Presence of the RST flag</li> <li><strong>FLAG_RST_REV:</strong> Presence of the RST flag in the reverse direction</li> <li><strong>FLAG_SYN:</strong> Presence of the SYN flag</li> <li><strong>FLAG_SYN_REV:</strong> Presence of the SYN flag in the reverse direction</li> <li><strong>FLAG_FIN:</strong> Presence of the FIN flag</li> <li><strong>FLAG_FIN_REV:</strong> Presence of the FIN flag in the reverse direction</li> </ul> <p><strong>Link to other CESNET datasets</strong></p> <ul> <li><a href="https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/">https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/</a></li> <li><a href="https://github.com/CESNET/cesnet-datazoo">https://github.com/CESNET/cesnet-datazoo</a></li> </ul> <p><strong>Please cite the original article:</strong></p> <blockquote> <p>@article{luxemburk_fine-grained-tls_2023, author = {Jan Luxemburk and Tomáš Čejka}, title = {Fine-grained TLS services classification with reject option}, journal = {Computer Networks}, volume = {220}, pages = {109467}, year = {2023}, issn = {1389-1286}, doi = {https://doi.org/10.1016/j.comnet.2022.109467}, url = {https://www.sciencedirect.com/science/article/pii/S1389128622005011} }</p> </blockquote>
Data for "Influence of variation in grain boundary parameters on the evolution of atomic structure and properties of [111] tilt grain boundaries in aluminum"
<p>This repository contains the raw data of experimental STEM images and of the simulations for the paper "Influence of variation in grain boundary parameters on the evolution of atomic structure and properties of [111] tilt boundaries in aluminum".</p>
Iron-rich grain-decorated Depressions on Surfaces of Lunar Impact Glasses
<p>The dataset includes the semi-quantitative values and EDS spectra of constitutions of iron-rich grain-decorated depressions on surfaces of Chang'E-5 impact glass particles. Semi-quantitative values were classified by ultra-thin sections, and EDS spectra were marked by sampling sites.</p>
Investigations on single and multi-grain optically stimulated luminescence (OSL) sensitivity and electron spin resonance (ESR) signals in quartz derived from sandstones: Insights on provenance of quartz in ancient depositional systems
<p><span>Trapped charge techniques of luminescence and electron spin resonance (ESR) are classic tools for dating Quaternary deposits. Over the past decade, these techniques have been routinely applied to investigate provenance and /or the sedimentary history of grains based on the different luminescence and ESR characteristics of quartz. Of these, optically stimulated luminescence (OSL) sensitivity is one of the most widely investigated parameter for luminescence-based provenance approach. A majority of studies on this parameter are based on evaluation of multi-grain OSL sensitivity of the samples. This is particularly concerning because single-grain quartz luminescence studies have shown that the luminescence signal of a multi-grain aliquot is contributed by less than ~1-10% of the total grains. Since the sole criteria for discrimination of sources based on luminescence sensitivity relies on its intensity, therefore the results based on multi-grain analysis will most likely be skewed depending on the proportion and ‘brightness’ of a few grains. This demands a need to evaluate the potential of single-grain quartz OSL sensitivity in provenance studies. In this study, we investigate single and multi-grain quartz OSL sensitivities from compositionally different sandstones with well-characterised sources based on U-Pb zircon ages. We further complement this analysis with characterisation of ESR centres commonly used in quartz provenance, namely E’<sub>1</sub> and [AlO<sub>4</sub>]<sup>0</sup> centres. Our study shows that single-grain quartz OSL sensitivity can help distinguish between sediments that have a predominant input from a single source as compared to those with contribution from multiple sources, which otherwise cannot be inferred from multi-grain studies. Moreover, our results on characterisation of quartz-based ESR intensity of E’<sub>1</sub> and saturated [AlO<sub>4</sub>]<sup>0</sup> centres successfully differentiates between sandstones and further complements the luminescence-based characterisation. </span></p>
Presolar Grain Database - Graphite
<p>The Presolar Grain Database (PGD) contains the vast majority of isotope data (published and unpublished) on presolar grains and was first released as a collection of spreadsheets in 2009. It has been a helpful tool used by many researchers in cosmochemistry and astrophysics. However, over the years, accumulated errors compromised major parts of the PGD. Here, we provide a fresh start, with the PGD for graphite grains rebuilt from the ground up.</p> <p>The PGD is provided here in two formats: (1) as Microsoft Excel (.xlsx) file, containing the main database as one large spreadsheet and additional information on extra spreadsheets, (2) as comma-separated ASCII (.csv) file containing the main database.</p> <p>The PGD is also available for silicon carbide (SiC) grains at the DOI <a title="10.5281/zenodo.8187219" href="../doi/10.5281/zenodo.8187219">https://zenodo.org/doi/10.5281/zenodo.8187219</a>.</p>
Dataset for paper entitled 'Quantification of strain fields and grain refinement in Ti-6Al-4V inter-pass rolled wire-arc AM by EBSD misorientation analysis'.
<p>Dataset for paper entitled 'Quantification of strain fields and grain refinement in Ti-6Al-4V inter-pass rolled wire-arc AM by EBSD misorientation analysis'. doi: https://doi.org/10.1016/j.matchar.2020.110673</p>
Rapid discovery of stable materials by coordinate-free coarse graining
<p>Prospective density function theory (DFT) calculations carried out on novel material structures predicted to be stable by the Wren model. In total 5,675 materials were selected for validation. Of these converged results were obtained for 4,721 structures. Calculations were conducting using the VASP DFT code with the settings and pseudopotentials selected such that the data is compatible with the Materials Project.</p> <p>A README file is included that describes how to load the data and the contents of the DataFrame.</p>
Data and code for: Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data
<p>UAV images used for SfM model generation and all images (both SI and OM), in which we measured grain sizes. The code used for image processing and uncertainty estimation of grain size distributions as python files and executable jupyter notebooks, where the latter also serve as documentation.</p>
ChRM and grain-size data of deep-sea sediments in the Central Philippine Sea
<p>The Philippine Sea is a typical region of eolian dust reposition and is located within the Western Pacific Warm Pool. Here, we use the paleo-magnetic stratigraphy and the grain-size distributions of Quaternary abyssal deposits in the Central Philippine Sea to investigate the factors controlling regional sedimentary and paleoenvironmental changes. Our principal results are as follows: (1) A reliable geochronologic framework for Quaternary sediments in the Central Philippine Sea is established. (2) An eastward expansion of the regional depocenter in the Middle Pleistocene is observed. (3) The mean grain size of the abyssal sediments is 7–8 μm, and there are only minor differences between the sites. Comparison of the geochronological framework with various paleoenvironmental events during the Mid-Pleistocene Transition shows that sedimentary processes can be correlated to a major transition in global climate which affected regions from the Asian interior to the tropical Pacific, and that changes in aeolian sedimentation are likely the predominant factor responsible. A derived grain-size proxy of the sedimentary dynamics and its comparison with various paleoenvironmental proxies show that the relative contributions are roughly estimated as 23%, 9%, and 68% for aeolian inputs, oceanic circulation, and the tropical Pacific zonal SST gradient, respectively in the studied region. The relative importance of tropical processes in abyssal sedimentary dynamics highlights the possibility of the long-term influence of (sub)mesoscale eddies in the upper ocean, via regional upwelling and unique submarine topography, on the deepest part (>5000 m) of the Central Philippine Sea, from meteorological to geological timescales.</p>
Additional files for manuscript titled 'The double round-robin population unravels the genetic architecture of grain size in barley'
<p>Additional file 1: Parental allele for barley orthologs of genes controlling grain size in rice</p> <p>Additional file 2: Cross-validation of quantitative trait loci (QTLs) detected for grain size characters in rice</p> <p>Additional file 3: Adjusted entry means of recombinant inbred lines of 45 HvDRR sub-populations</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.