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852 results for “grain”
The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?
<p>This is a reproduction package for the paper "The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?" by Tazaki et al. (2021). In this repository, you will find the data files used to make figures in the paper. Source codes and scripts are included as well.</p>
The dataset of lamina structure in fine-grained rocks
<p>Fine-grained sedimentary rocks account for two-thirds of all sedimentary rocks, and contain multi-scale lamina structure. Lamina structure contain important information about paleoenvironmental reconstruction, climate change, depositional processes, hydrocarbon accumulation and even global carbon cycle. Fine-grained sedimentary rocks can be divided into massive, layered and laminated according to core-scale lamina observation. Thin section observation reveals that the mineral composition of individual lamina includes carbonate, silt, clay mineral, tuffaceous lamina and organic matter lamina in the micrometer-scales. However, for intervals without core data control, geophysical well logs are needed to predict the multi-scale lamina structure in fine-grained sedimentary rocks. High resolution image logs provide 5 mm vertical resolution images, however, they don’t highlight the lamina characteristics, therefore image logs are used to generate the slab image and button conductivity curves to facilitate the recognition of lamina structure up to 2.5 vertical resolution. This novel and multidisciplinary approach provides a powerful method for continuously identifying lamina structure with a 2.5 mm vertical resolution using well logs.</p>
Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022
<p>Provisional database: The data you have secured from the U.S. Geological Survey (USGS) database identified as <em>Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022</em> have not received USGS approval and as such are provisional and subject to revision. The data are released on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.</p> <p>Version 1 (January 2022) of the the Coastal Grain Size Portal (C-GRASP) database. This is a preliminary internal deliverable for the National Oceanography Partnership Program (NOPP) Task 1 / USGS Gesch team and project partners only.</p> <p>The primary purpose of this Provisional data release is to provide National Oceanography Partnership Program (NOPP) project partners with programmatic access to this preliminary version of the Coastal Grain Size Portal (C-GRASP) database for internal project use. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>This preliminary data release contains various files that list grain size information collated from secondary data already in the public domain, in the form of public datasets, or in published literature.</p> <p>Where possible, we have indicated the source, location, and sampling methods used to obtain these data. Where not possible to establish these facts, those fields have been left empty.</p> <p>More information on our methods, data sources, and data processing and analysis codes are found on our <a href="https://github.com/C-GRASP">github page </a></p> <p>The dataset consists of one zipped file, Source_Files.zip, and 4 comma separated value (csv) files</p> <ol> <li>dataset_10kmcoast.csv- This is all data that is found to be within 10km of the Natural Earth coastline polyline</li> <li>Data_EstimatedOnshore.csv- This is all the data from dataset_10kmcoast.csv that lies within the Natural Earth United States Polygon</li> <li>Data_VerifiedOnshore.csv- This is all data that was able to be verified onshore from either sampling method, note, or location type data</li> <li>Data_Post2012_VerifiedOnshore.csv- This is all the data from Data_VerifiedOnshore.csv that is after 2012</li> </ol> <p>The files each have the following fields (no data is blank):</p> <p>'ID': row ID integer</p> <p>'Sample_ID': identifier to raw data source</p> <p>'Sample_Type_Code': code of sample id</p> <p>'Project': raw datasource project identifier</p> <p>'dataset': raw dataset major identifier</p> <p>'Date': date, where specified, and to whatever precision that is specified</p> <p>'Location_Type': where specified, code indicating type of location information</p> <p>'latitude': latitude in decimal degrees</p> <p>'longitude': longitude in decimal degrees</p> <p>'Contact': where specified, raw data originator</p> <p>'num_orig_dists': number of unique grain size distributions</p> <p>'Measured_Distributions': number iof measured grain size distributions</p> <p>'Grainsize': grain size is sometimes reported without specification</p> <p>'Mean', mean grain size in mm</p> <p>'Median', median grain size in mm</p> <p>'Wentworth', wentworth name (one of ['Clay', 'CoarseSand', 'CoarseSilt', 'Cobble', 'FineSand', 'FineSilt', 'Granule', 'MediumSand', 'MediumSilt', 'Pebble', 'VeryCoarseSand', 'VeryFineSand', 'VeryFineSilt'])</p> <p>'Kurtosis', kurtosis value (non-dim)</p> <p>'Kurtosis_Class', kurtosis category</p> <p>'Skewness', skewness value (non-dim)</p> <p>'Skewness_Class', skewness category</p> <p>'Std', standard deviation of grain sizes </p> <p>'Sorting', sorting category</p> <p>'d5', grain size distribution 5th percentile</p> <p>'d10', grain size distribution 10th percentile</p> <p>'d16', grain size distribution 16th percentile</p> <p>'d25', grain size distribution 25th percentile</p> <p>'d30', grain size distribution 30th percentile</p> <p>'d50', grain size distribution 50th percentile</p> <p>'d65', grain size distribution 65th percentile</p> <p>'d75', grain size distribution 75th percentile</p> <p>'d84',grain size distribution 84th percentile</p> <p>'d90', grain size distribution 90th percentile</p> <p>'d95', grain size distribution 95th percentile</p> <p>'Notes': notes - these can be informative and substantial, do not disregard</p> <p> </p> <p>Source_Files.zip contains 11 comma separated value files, namely bicms.csv boem.csv clark.csv dbseabed.csv ecstdb.csv mass.csv mcfall.csv rossi.csv sandsnap.csv sbell.csv ussb.csv, which contain raw datasets that have been collated and extracted from their native formats into csv format</p>
Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models
<p>This data set contains the simulations and data analysis files used in the publication: "<em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models</em>", by D. Cortés-Ortuño, K. Fabian and L. V. de Groot.</p> <p>The data set includes:</p> <ul> <li>Scripts and output files from MERRILL simulations</li> <li>Jupyter notebooks with data analysis</li> <li>Figures</li> </ul> <p>A preprint of this work can be found in:</p> <p>David Cortés-Ortuño, Karl Fabian and Lennart V. de Groot. <em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models.</em> DOI: 10.1002/essoar.10510574.1. Earth and Space Science Open Archive. <a href="https://doi.org/10.1002/essoar.10510574.1">https://doi.org/10.1002/essoar.10510574.1</a></p> <p>The README file in this dataset (in markdown format) contains full details about the simulations. The dataset also contains pre-computed data files to calculate the inversions and produce the figures and analyze the inversion data without processing the vbox files.</p> <p>To cite this dataset you can use the following bibtex entry:</p> <pre><code>@Misc{Cortes2022, author = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.}, title = {{Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models}}, publisher = {Zenodo}, year = {2022}, doi = {10.5281/zenodo.6501818}, url = {https://doi.org/10.5281/zenodo.6501818}, } </code></pre> <p> </p>
FG-OVD: Fine-grained Open-Vocabulary Object Detection Benchmark Suite
<p>A collection of annotations for PACO images containing free-form fine-grained textual captions of objects, their parts, and their attributes. It also comprises several sets of negative captions that can be used to test and evaluate the fine-grained recognition ability of open-vocabulary models.</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 9)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>GBCurvApprx_175_to_5200.zip<br> Single-grain-boundary-contour-point-resolved curvature estimation tracking data for time step 175 to 5200 with 1 time step resolution and all grains</p> <p>CurvatureEvolution.zip,<br> CurvatureEvolutionAverage.zip<br> TopologyTracer analysis capillary driving force evolution with 100 time step resolution based on the<br> GBContour* data --- not the GBCurvApprx* data.</p> <p>CurvatureEvolutionSuccessful.zip<br> TopologyTracer analysis matrix of segment-length-averaged capillary migration speed for the surviving<br> grains at 1 time step temporal resolution based on the GBCurvApprx* data</p> <p>CurvatureEvolutionUnsuccessful.zip<br> TopologyTracer analysis matrix of segment-length-averaged capillary migration speed for the false<br> positive sub-grains, see paper for details, based on the GBCurvApprx* data</p> <p>UnbiasedGrowthMeasures.zip<br> TopologyTracer analysis temporal evolution of long-range characterisation with the xi metric, see paper for details</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 7)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_450_to_5296.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 450 to 5296 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 5)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_171_to_259.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 171 to 259 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 3)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_50_to_99.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 50 to 99 with 1 time step resolution</p> <p> </p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 2)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_1_to_49.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 1 to 49 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 4)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_100_to_170.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 100 to 170 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX3D)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>0Synthesis.zip --- Microstructure synthesis<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds and MicrostructureDiagnostics.uds meta data for the grains<br> -parameters.xml parameterization of microstructure synthesis<br> -Scripts and additional input referred to by parameters.xml</p> <p>1Coarsening.zip --- Simulation<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds meta data for the grains<br> -VoxelizedParameters.xml parameterization of coarsening simulation<br> -NrGrains&EnergyStatistics.dat descriptive stats simulation (Time step, Real time, number of grains, ---, grid size)<br> -Network.zip implicit 3d x,y,z unsigned int ID container detailing the microstructure</p> <p>Texture_Faces_1_to_1261.zip<br> Grain boundary network geometry and grain meta data tracking data</p> <p>Network.zip<br> Rendering images for evolution of the grain boundary network and volume properties</p> <p>2DataAnalysis --- Data analyses<br> -All other archive Matlab analysis scripts and TopologyTracer results<br> </p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 1)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>0Synthesis.zip --- Microstructure synthesis<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds and MicrostructureDiagnostics.uds meta data for the grains<br> -parameters.xml parameterization of microstructure synthesis<br> -Scripts and additional input referred to by parameters.xml</p> <p>1Coarsening.zip --- Simulation<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds meta data for the grains<br> -VoxelizedParameters.xml parameterization of coarsening simulation<br> -NrGrains&EnergyStatistics.dat descriptive stats simulation (Time step, Real time, number of grains, ---, grid size)<br> -Network.zip implicit 3d x,y,z unsigned int ID container detailing the microstructure</p> <p>1CoarseningTrackCurvature.zip<br> -Files of the re-run to the simulation along which capillary data were tracked on the fly in each time step</p> <p>GBContourPoints_100_to_5296.zip<br> -Grain boundary face network for time steps 100 to 5296 at 100 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 8)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>GBCurvApprx_1_to_174.zip<br> Single-grain-boundary-contour-point-resolved curvature estimation tracking data for time step 1 to 174 with 1 time step resolution and all grains</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 10)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>NetworkEvolutionPNG.zip<br> Rendered images of the evolving microstructure at very high resolution and reduced size FullHD</p> <p>PRX2DNetwork.gif and PRX2DNetwork.gifx<br> Video of the evolving microstructure and corresponding gif-x video generation file</p> <p>TrackingParallelRXEVO_Resolution_1.zip<br> TopologyTracer analysis of recrystallized volume fraction over time</p> <p>TrackingParallelSEE1002000.zip<br> TopologyTracer analysis of single-grain-resolved stored elastic energy driving forces segment length averaged</p> <p>TrackingParallelTrackingBK_Resolution_1.zip,<br> TrackingParallelTrackingBK_Resolution_100.zip<br> TopologyTracer analysis tracking backwards (BK) in time the evolution of the successful grains with different temporal resolution</p> <p>TrackingParallelTrackingFW_FID100.zip<br> TopologyTracer analysis tracking forward (FW) in time the metadata for all grains at time step 100</p> <p>TrackingParallelTrackingFW_Resolution_1.zip,<br> TrackingParallelTrackingFW_Resolution_20.zip,<br> TrackingParallelTrackingFW_Resolution_100.zip<br> TopologyTracer analysis tracking forward (FW) in time the evolution of all grains with different temporal resolution</p> <p>MPIEDevBranchPRX2D.zip<br> TopologyTracer analysis rendering of images and analysis scripts</p> <p>MPIEDevBranchPRX2DCapTrack.zip,<br> MPIEDevBranchPRX2DMajorRevPCA.zip,<br> MPIEDevBranchTrackSuccessful02.zip<br> TopologyTracer analysis of capillary tracking data in correlation with size and topology evolution and PCA</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 6)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_260_to_449.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 260 to 449 with 1 time step resolution</p>
Core log descriptions and sediment grain size data for Hurricane Ian sediment cores collected in Lee County, Florida, USA
<p>These data represent qualitative and quantitative measurements of sediment cores collected from various environments following the landfall of Hurricane Ian. These sediment cores were collected using pound coring techniques up to 2m into the subsurface to characterize the sedimentological signature of storm deposits resulting from Hurricane Ian. More details regarding these measurements and interpretations of storm deposits can be found in the folllowing manuscript:</p> <p>McCormick, W.M., Briggs, T.R., Hauptman, L.H., Wang, P., Morphologic and sedimentological signatures resulting from Hurricane Ian, southwest Florida, USA: Insight into intra-storm bidirectional sediment transport processes (In Review). </p>
Supplementary data: Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?
<p>Agronomic and quality data of organic wheat (<em>Triticum aestivum</em>), mean comparisons and supplementary figures related to the publication "Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?" by Grausgruber et al. (2024) published in the Journal of Cereal Science.</p>
Dataset for "Effects of weather and climate on fluctuations of grain prices in southwestern Bohemia, 1725–1824 CE"
<p><span>This deposit contains three .xlsx files.</span></p> <p><span>The file „01_prices_Sušice_1725-1824“ contains two sheets with mean annual prices (in Lower Austrian <em>měřice</em>) of four cereals (wheat, rye, barley and oats) in Sušice for the period 1725–1824. On the first sheet are compiled series, on the second detrended series (using high-pass filter).</span></p> <p><span>The file „02_monthly_prices_1725-1738“ contains four sheets with mean monthly prices (in Lower Austrian <em>měřice</em>) of four cereals in Sušice from July 1725 to July 1738 (July 1737 is missing). Sheets represent individual cereals – wheat, rye, barley and oats.</span></p> <p><span>The file „03_climate_char_1725-1824“ contains two sheets with mean seasonal (DJF, MAM and JJA) temperature, precipitation and scPDSI expressed in deviations relative to the 1961–1990 reference period. On the first sheet are original series, on the second detrended series (using high-pass filter). The original series are reconstructed temperatures for central Europe (Dobrovolný et al., 2010), reconstructed precipitation for the Czech Lands (Dobrovolný et al., 2015) and both of them were used for creation of scPDSI series (Brázdil et al., 2016).</span></p>
WikiChurches – A Fine-Grained Dataset of Architectural Styles with Real-World Challenges
<p>WikiChurches is a dataset for architectural style classification, consisting of 9,485 images of church buildings. Both images and style labels were sourced from Wikipedia. The dataset can serve as a benchmark for various research fields, as it combines numerous real-world challenges: fine-grained distinctions between classes based on subtle visual features, a comparatively small sample size, a highly imbalanced class distribution, a high variance of viewpoints, and a hierarchical organization of labels, where only some images are labeled at the most precise level. In addition, we provide 631 bounding box annotations of characteristic visual features for 139 churches from four major categories. These annotations can, for example, be useful for research on fine-grained classification, where additional expert knowledge about distinctive object parts is often available.</p> <p>Please refer to the README.md file for information about the different files contained in this dataset.</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.