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852 results for “grain”
Capillary-driven anisotropic abnormal sub-grain growth (AGG2D)
<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><strong>With an approximate total of 1TB the entire data set to upload is not practically feasible.<br> Instead, these data should be seriously requested from me directly.</strong></p> <p> </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>2DataAnalysis*<br> -All other archive Matlab analysis scripts and processed through TopologyTracer results.<br> Individual *.mat files to re-establish state upon Matlab exiting.</p>
Observations of groundwater fluctuations and surface moisture content on a medium-grained, planar beach (Sand Engine, the Netherlands)
<p>These data are groundwater and beach surface moisture values collected during the MegaPex campaign between October 11 and 20, 2014 at the Sand Engine, The Hague, the Netherlands by MSc students and staff of the Coastal Research Group at Utrecht University, the Netherlands. The data were obtained at 8 locations in a cross-shore array on the intertidal and upper beach. During the measurements the beach was planar (1:30) and the median grain size was 0.365 mm. The data are supplemented with bed profiles along the instrument array. For further information and meta-data, please consult the readme.txt and the header of the individual text files in the zip-file.</p>
Large-scale and fine-grained phenological stage annotation of herbarium specimens datasets
<p>This upload is constituted of four datasets of specimens from American herbaria covering different levels of information precision and different floras - from temperate to equatorial.</p> <p>Three of these datasets consist of selected specimens from herbaria located in different geographic and environmental regions. Each specimen of these three datasets was annotated with the following fields: family, genus, species name, fertile / non-fertile, presence / absence of flower(s), presence / absence of fruit(s). The resulting dataset was composed of 163,233 herbarium specimens belonging to 7,782 species, 1,906 genera, and 236 families. Specimens were annotated as “fertile” if any reproductive structures were present, such as sporangia (ferns), cones (gymnosperms), flowers, or fruits (angiosperms). Non-fertile specimens were those that lacked any reproductive structures.</p> <p>The fourth dataset consists of 20,371 herbarium specimens from 11 genera in the sunflower family (<em>Asteraceae</em>). The main difference in this dataset is that it is annotated with fine-grained phenophase scores rather than presence/absence attributes (see description below).</p> <p>Each of these datasets is described below:</p> <ul> <li> <p>NEVP: this dataset of New England vascular plant (NEVP) specimens was produced by members of the Consortium of Northeastern Herbaria. The dataset comprises 42,658 digitized specimens that belong to 1,375 species and come from several North American institutions. Most of the specimens in this dataset are from the north-temperate region of the northeastern United States.</p> </li> <li> <p>FSU: this dataset was produced by the Florida State University's Robert K. Godfrey Herbarium (FSU), a collection that focuses on northern Florida and the U.S. Southeast Coastal Plain, one of North America's biodiversity hotspots. This dataset contains 54,263 digitized herbarium specimen records that belong to 3,870 species, making it the taxonomically richest dataset in this study. Most species in this dataset grow under subtropical or warm temperate conditions in the southeastern region of the United States.</p> </li> <li> <p>CAY: this dataset comes from the IRD’s Herbarium of French Guiana (CAY). CAY is dedicated to the Guayana Shield flora, with a strong focus on tropical tree species. This dataset is composed of 66,312 herbarium specimens that belong to 3,024 species. All digitized specimens of this herbarium are accessible online. Most specimens were collected in the tropical rainforests of French Guiana, with the remaining specimens coming mostly from Suriname and Guyana.</p> </li> <li> <p>PHENO: this dataset includes 20,371 herbarium specimens of 139 species in the <em>Asteraceae</em> produced in a study of phenological trends in the U.S. Southeast Coastal Plain. The dataset is composed of specimen records from 57 herbaria. Each recorded specimen was annotated for quartile percentages (0, 25, 50, 75, or 100%) of (i) closed buds, (ii) buds transformed into flowers, and (iii) fruits. According to the distribution of these three categories for each specimen, a phenophase code was computed.</p> </li> </ul> <p> </p> <p><strong>Datasets format</strong></p> <p>These datasets are grouped in 3 tasks:</p> <ol> <li>fertility detection</li> <li>flowers and/or fruit detection</li> <li>phenophase classification</li> </ol> <p>The first 2 tasks are carried on the first 3 previous datasets and thus are based on the same set of images, unlike the third task which has its own disjoint set of images. This is why the dataset is presented into two separated files, one for each set of images.</p> <p><em>Fertility detection & flower/fruit detection</em></p> <p>These tasks are contained into the <em>herbarium_fertility_annotations.zip</em> archive. It consists of 3 files:</p> <ul> <li><em>metadata.csv</em>: general information about all the herbarium specimens for these tasks <ul> <li><em>id</em>: specimen identifier</li> <li><em>collection</em>: which of NEVP, FSU or CAY does the specimen come from</li> <li><em>herbarium</em>: institution of origin of the specimen, especially for NEVP collection</li> <li><em>clade</em>,<em> family</em>,<em> genus</em>,<em> species</em>: classification of the specimen</li> <li><em>URL</em>: URL of the scan</li> </ul> </li> <li><em>fertility_task.csv</em>: specific information regarding the fertility detection task <ul> <li><em>id</em>: specimen identifier</li> <li><em>is_fertile</em>: <em>True</em> if the specimen has an expression of fertility, <em>False</em> otherwise</li> <li><em>train_test_set</em>: which subset does the specimen belong to; possible values are: <em>train</em>, <em>random_test</em>, <em>species_test</em> and <em>herbarium_test</em></li> </ul> </li> <li><em>flower_fruit_task.csv</em>: specific information regarding the flower/fruit detection task <ul> <li><em>id</em>: specimen identifier, note that in this case not all the specimen described in <em>metadata.csv</em> are included in this task</li> <li><em>has_flower</em>: <em>True</em> if the specimen has at least one flower, <em>False</em> otherwise</li> <li><em>has_fruit</em>: <em>True</em> if the specimen has at least one fruit, <em>False</em> otherwise</li> <li><em>train_test_set</em>: which subset does the specimen belong to; possible values are: <em>train</em>, <em>random_test</em>, <em>species_test</em> and <em>herbarium_test</em></li> </ul> </li> </ul> <p><em>Phenophase classification</em></p> <p>These tasks are contained into the <em>herbarium_asteraceae_phenophase_annotations.zip</em> archive. It consists of a single file:</p> <ul> <li><em>annotations.csv</em>: <ul> <li><em>id</em>: specimen identifier</li> <li><em>URL</em>: URL of the scan</li> <li><em>genus</em>: genus of the specimen</li> <li><em>phenophase</em>: integer from 1 to 9 describing the phenophase of the specimen</li> <li><em>train_test_set</em>: which subset does the specimen belong to; possible values are: <em>train</em> and <em>test</em></li> </ul> </li> </ul> <p> </p> <p><strong>Additional ressources</strong></p> <p>More information can be found in the related paper:<br> <em>Lorieul, T., K. D. Pearson, E. R. Ellwood, H. Goëau, J.-F. Molino, P. W. Sweeney, J. M. Yost, J. Sachs, E. Mata-Montero, G. Nelson, P. S. Soltis, P. Bonnet, and A. Joly. 2019. Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Applications in Plant Sciences 7(3): e1233.</em></p> <p>For an example of usage of these datasets as well as a baseline, see: <a href="http://doi.org/10.5281/zenodo.2549996">http://doi.org/10.5281/zenodo.2549996</a></p> <p> </p>
Data For New Methods for Understanding and Controlling the Self-Assembly of Reacting Systems Using Coarse-Grained Molecular Dynamics
<p>Data necessary to reproduce results in the dissertation : Thomas, Stephen, "New Methods for Understanding and Controlling the Self-Assembly of Reacting Systems Using Coarse-Grained Molecular Dynamics" (2018). <em>Boise State University Theses and Dissertations</em>. 1448.</p> <p>10.18122/td/1448/boisestate</p>
Data Supporting The role of dopant segregation on the oxygen vacancy distribution and oxygen diffusion in CeO2 grain boundaries
<p>Data supporting the article "The role of dopant segregation on the oxygen vacancy distribution and oxygen diffusion in CeO<sub>2</sub> grain boundaries, accepted for publication in the Journal of Physics: Energy (<a href="https://doi.org/10.1088/2515-7655/ab28b5">doi.org/10.1088/2515-7655/ab28b5</a>). Data includes inputs for molecular dynamics simulations using the DL_POLY classic code.Simulations can be rerun from the inputs provide. </p> <p>Input data required for the generation of the grain boundary structures is included and uses the METADISE code. </p>
Experiments on Grain Size Segregation in Bedload Transport on a steep Slope
<p>This dataset is the basis of the publication:</p> <p>Frey, P., Lafaye de Micheaux, H., Bel, C., Maurin, R., Rorsman, K., Martin, T., Ducottet, C., 2020. Experiments on grain size segregation in bedload transport on a steep slope. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2019.103478.</p> <p>Experiments consisted in bedload of two-size spherical glass beads transported at equilibrium by a turbulent supercritical free surface water flow over a mobile bed. Two runs, one with a low rate of large black beads (S6), the other with a higher rate (S20), are considered. This dataset consists in:</p> <p>- temporal sequences of uncompressed tif images corresponding to figure 5 showing small particle concentration : 9 sequences for run S6 (BillesBaumerMicro) and 9 sequences for run S20 (BillesbaumerAmontSequence)</p> <p>- two ‘.mat’ file corresponding to runs S6 (trackData_Micro_S6.mat) and S20 (trackData_Amont_S20.mat) giving all the trajectories of all beads.</p> <p>Trajectories were obtained with a tracking algorithm developed by H. Lafaye de Micheaux et al. (2016,2018) building on Hergault et al. (2010). The code implementing the tracking algorithm is available on <a href="https://github.com/hugolafaye/BeadTracking">https://github.com/hugolafaye/BeadTracking</a>. The files contain the variable 'trackData' being a cell array of tracking matrices. There is one tracking matrix for each image of the sequence giving in particular the coordinate and velocity of each bead. Complete information on data format is given in the file readme.txt in the github BeadTracking package. Parameter files necessary to replicate our results from the images are also available on the BeadTracking package as well as on <a href="https://doi.org/10.5281/zenodo.3454628">https://doi.org/10.5281/zenodo.3454628</a> where a 1000-image ground truth is stored.</p> <p>Important note: Experimental images were grabbed with the flow from right to the left implying for instance negative values for the x-coordinate of velocities. To comply with a traditional convention, images and associated results in the publication are shown from left to the right.</p> <p> </p>
Research data supporting for "Characterization of recovery onset by subgrain and grain boundary migration in experimentally deformed polycrystalline olivine"
<p>Abstract: To apprehend plate tectonics and the dynamics of the lithosphere–asthenosphere boundary, composed principally of olivine, we need to understand the mechanisms that control plastic deformation of olivine in the relevant temperature domain. After more than 50 years of laboratory studies and investigations on natural rocks, the interplay of several key parameters (e.g. temperature, pressure, vacancy concentration, dislocation densities, grain size, strain rate) controlling polycrystalline olivine plasticity remains difficult to assess. Here, we study four olivine polycrystals, which have been deformed in axial compression under a confining pressure of 300MPa, at 1273 or 1473 K. Despite significant differences in mechanical properties (stress–strain curves), previous characterization by scanning (SEM) and transmission electron microscopy (TEM) did not reveal significant differences in dislocation microstructures which could explain these contrasted behaviours. We have undertaken automatic crystallographic orientation mapping (ACOM) analyses in TEM to increase the spatial resolution of characterization compared to previously obtained electron backscatter diffraction maps to further decipher the microstructures at nanoscale. With this novel technique applied to olivine, a noticeable difference in the onset of microstructural recovery has been identified between specimens deformed at 1273 and 1473 K. The microstructures of the olivine polycrystals deformed at 1473K exhibit numerous curved grain and subgrain boundaries, advocating for recovery by boundary migration. In contrast, the microstructures of the olivine polycrystals deformed at 1273K have significantly fewer subgrain boundaries and show more straight boundaries (i.e. closer to an equilibrium microstructure) than in the specimen deformed at 1473 K. Characterization by ACOM-TEM has permitted the identification of the onset of recovery, which is led by boundary migration even for very low macroscopic finite strains.</p> <p> </p>
Text-fig. 3. Schematic geological section of the Kristina Mine near Hrádek/N. (state in 1963–1964) – height/length ratio 3:1. Explanations: vertical hatching – lignite seam, seamlet; dotted – coarse-grained sand, pea-gravel; short lines – sandy clay; white – clay; black lines – clay ironstone concretions; black dots – individual fossiliferous horizons designated as (A) plastic clay from the upper part of the main xylitic seam (about 5 m under t of the seam, (B) clay and "Blätterkohle" from the uppermost part of the first seamlet (split off the Main Coal Seam), (C) slightly sandy brown clay under the uppermost part of the Main Coal Seam, (D) base of the sandy clay with large concretions of the clay ironstone above the Main Coal Seam, (E) sandy clay (incl. clay ironstone) supplying most of leaf material with cuticles (F) 1–2 cm thin silty lenticles or thin beds of the sandy clay with xylites and Eomastixia within peagravels and coarse-grained sands, (G) coarse-grained sands with clayish silts with Fagus, Ocotea, Pterocarya, Tectocarya, (H) brown sandy clay underlying the uppermost seamlet, (I) lignite clay, base of the uppermost seamlet (J) Glyptostrobus – "Blätterkohle", base of the uppermost seamlet (according to Holý 1975, modified). in A Review Of The Early Miocene Mastixioid Flora Of The Kristina Mine At Hrádek Nad Nisou In North Bohemia (The Czech Republic)
Text-fig. 3. Schematic geological section of the Kristina Mine near Hrádek/N. (state in 1963–1964) – height/length ratio 3:1. Explanations: vertical hatching – lignite seam, seamlet; dotted – coarse-grained sand, pea-gravel; short lines – sandy clay; white – clay; black lines – clay ironstone concretions; black dots – individual fossiliferous horizons designated as (A) plastic clay from the upper part of the main xylitic seam (about 5 m under t of the seam, (B) clay and "Blätterkohle" from the uppermost part of the first seamlet (split off the Main Coal Seam), (C) slightly sandy brown clay under the uppermost part of the Main Coal Seam, (D) base of the sandy clay with large concretions of the clay ironstone above the Main Coal Seam, (E) sandy clay (incl. clay ironstone) supplying most of leaf material with cuticles (F) 1–2 cm thin silty lenticles or thin beds of the sandy clay with xylites and Eomastixia within peagravels and coarse-grained sands, (G) coarse-grained sands with clayish silts with Fagus, Ocotea, Pterocarya, Tectocarya, (H) brown sandy clay underlying the uppermost seamlet, (I) lignite clay, base of the uppermost seamlet (J) Glyptostrobus – "Blätterkohle", base of the uppermost seamlet (according to Holý 1975, modified).
Text-fig. 6. Geological plan of Malo-Mikhaylovka. 1 – andesito-dacite; 2 – coarse-grained tuff; 3 – argillitic tuffite; 4 – tuffitic sandstone; 5 – lignite, coal clay; 6 – lenses of tuffitic conglomerate; 7 – acidic tuff; 8 – dacite; 9 – andesito-basalt; 10 – basalt; 11 – sandstone; 12 – andesite; 13 – break; 14 – inclination/direction of beds; 15 – plant-bearing levels; 16 – talus. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 6. Geological plan of Malo-Mikhaylovka. 1 – andesito-dacite; 2 – coarse-grained tuff; 3 – argillitic tuffite; 4 – tuffitic sandstone; 5 – lignite, coal clay; 6 – lenses of tuffitic conglomerate; 7 – acidic tuff; 8 – dacite; 9 – andesito-basalt; 10 – basalt; 11 – sandstone; 12 – andesite; 13 – break; 14 – inclination/direction of beds; 15 – plant-bearing levels; 16 – talus.
Text-fig. 3. Distribution of main types of volcanoes in the NearShore Volcanic Belt of Eastern Sikhote-Alin' (Eocene–Neogene). 1 – Central volcanoes (partly preserved); 2 – Central volcanoes (destructed); 3 – Shield and gentle sloping volcanoes with a dolerite or trachy-basaltic neck on the top; 4 – Lava and scoria cones; 5 – Pyroclastic, tuffaceous coarse- and fine-grained terrigenous sedimentary rocks, partly with plant-bearing levels; 6 – Eruption centers of plateau-basalts and the direction of lava flows; 7 – Main Late Cenozoic basaltic plateaus; 8 – Fumarol fields; 9 – Hot springs. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 3. Distribution of main types of volcanoes in the NearShore Volcanic Belt of Eastern Sikhote-Alin' (Eocene–Neogene). 1 – Central volcanoes (partly preserved); 2 – Central volcanoes (destructed); 3 – Shield and gentle sloping volcanoes with a dolerite or trachy-basaltic neck on the top; 4 – Lava and scoria cones; 5 – Pyroclastic, tuffaceous coarse- and fine-grained terrigenous sedimentary rocks, partly with plant-bearing levels; 6 – Eruption centers of plateau-basalts and the direction of lava flows; 7 – Main Late Cenozoic basaltic plateaus; 8 – Fumarol fields; 9 – Hot springs.
Text-fig. 12. Geological plan of the Velikaya Kema plant-bearing locality (4 km north of Velikaya Kema village). 1 – basalt with flaggy flows; 2 – massive basalt; 3 – andesite with flaggy flows; 4 – andesite-basalt; 5 – trachyte, 6 – felsite; 7 – agglomerate, basalt and andesite; 8 – tuff coarse-grained; 9 – conglomerate; 10 – thin layers of andesitic tuff; 11 – tuffite, tuffaceous argillite, diatomite; 12 – plant bearing levels. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 12. Geological plan of the Velikaya Kema plant-bearing locality (4 km north of Velikaya Kema village). 1 – basalt with flaggy flows; 2 – massive basalt; 3 – andesite with flaggy flows; 4 – andesite-basalt; 5 – trachyte, 6 – felsite; 7 – agglomerate, basalt and andesite; 8 – tuff coarse-grained; 9 – conglomerate; 10 – thin layers of andesitic tuff; 11 – tuffite, tuffaceous argillite, diatomite; 12 – plant bearing levels.
Figure 6 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 6. Total cross-validation AUC (CV-AUC) and spatial congruence AUC (SC- AUC) for a range of grain sizes.
Figure 4 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 4. Divergence between the uncorrelated and pruned models estimated through Parolo divergence index at 250-m resolution. As is shown, there was little divergence (0– 0.2) between models in most of the study area.
Figure 1 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 1. The location of the study area on a map of western Asia (right). Inset shows DEM of study area with polygons indicating the protected areas where populations of goitered gazelle occur.
Figure 5 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 5. The change in performance index (AUC, left) and habitat suitability area (right) of the output model with increasing extent size (open circles) and grain size (filled circles) from 250 to 3000 m.
Figure 3 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 3. Goitered gazelle distribution maps based on the uncorrelated model (left) and the pruned model (right) for the 250-m grid size.
Fig. 1 in Potential population growth of Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae) under six constant temperatures on grain sorghum (Sorghum bicolor L.)
Fig. 1. Age-specific survivorship (proportion of alive individuals per day) of Melanaphis sacchari under 6 constant temperatures on grain sorghum.
Fig. 2 in Potential population growth of Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae) under six constant temperatures on grain sorghum (Sorghum bicolor L.)
Fig. 2. Melanaphis sacchari constant temperature dependent development rate Lactin model estimation. Solid line representing the predicted values by Logan-Lactin model and dots represent the observed values.
FIGURE 8 in Angiosperm pollen grains from the Cuayuca Formation (Late Eocene to Early Oligocene), Puebla, Mexico
FIGURE 8. Paleopalynological assemblage of the Cuayuca Formation Mcy member and CONISS analysis. Sections were organized considering PAE analysis of Figure 7.
FIGURE 7 in Angiosperm pollen grains from the Cuayuca Formation (Late Eocene to Early Oligocene), Puebla, Mexico
FIGURE 7. PAE anlaysis between studied sections from the Cuayuca Formation Mcy member: second section, Izúcar de Matamoros (IzS); Tzompahuacan (Tzo); Lagunillas de Rayón (LagRay); Lagunillas (Lag); "B" section, Cuayuca (CyB); "F" section, Izúcar de Matamoros (IzF); "H" section, Izúcar de Matamoros (IzH); Principal section (CyPrincipal); "A" section (CyA).
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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.