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.
19
datasets available to search
ShareScore release 0.9.0
Dataset results
19 results for “grain size distribution”
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.
Data from: Effects of grain size and niche breadth on species distribution modeling
Scale is a vital component to consider in ecological research, and spatial resolution or grain size is one of its key facets. Species distribution models (SDMs) are prime examples of ecological research in which grain size is an important component. Despite this, SDMs rarely explicitly examine the effects of varying the grain size of the predictors for species with different niche breadths. To investigate the effect of grain size and niche breadth on SDMs, we simulated four virtual species with different grain sizes/niche breadths using three environmental predictors (elevation, aspect, and percent forest) across two real landscapes of differing heterogeneity in predictor values. We aggregated these predictors to seven different grain sizes and modeled the distribution of each of our simulated species using MaxEnt and GLM techniques at each grain size. We examined model accuracy using the AUC statistic, Pearson's correlations of predicted suitability with the true suitability, and the binary area of presence determined from suitability above the maximum True Skill Statistic (TSS) threshold. Habitat specialists were more accurately modeled than generalist species, and the models constructed at the grain size from which a species was derived generally performed the best. The accuracy of models in the homogenous landscape deteriorated with increasing grain size to a greater degree than models in the heterogenous landscape. Variable effects on the model varied with grain size, with elevation increasing in importance as grain size increased while aspect lost importance. The area of predicted presence was drastically affected by grain size, with larger grain sizes over predicting this value by up to a factor of 14. Our results have implications for species distribution modeling and conservation planning, and we suggest more studies include analysis of grain size as part of their protocol.
Data for manuscript: An extrapolation algorithm for estimating river bed grain size distributions across basins
<p>Pebble counts collected and used for the analysis presented in the manuscript: An extrapolation agorithm for estimating river bed grain size distributions across drainage basins.</p>
Grain size distribution of Amazon river sediment samples collected over the period 2005-2008; and ADCP water velocity profiles collected on the major tributaries of the Amazon in Bolivia and Peru, 2007-2008
<p>This dataset contains two items:</p> <p>- The grain size distribution of river sediment samples collected along the Amazon River and its tributaries during four sampling campaigns performed in June 2005 (lower Amazon, Brazil), March 2006 (lower Amazon, Brazil), May 2007 (Upper Madeira, Bolivia), and April 2008 (Upper Solimões-Amazonas, Peru). [spreadsheet "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_data.xlsx"].</p> <p>- River water velocity profiles derived from Acoustic Doppler Current Profiler (ADCP) measurements performed on the major tributaries of the Amazon in May 2007 (Upper Madeira, Bolivia) and April 2008 (Upper Solimões-Amazonas, Peru) [folder "ADCP_dataset_Amazon_2007-2008_Bouchez_data"].</p> <p>The dataset description and the relevant references are provided in the text files "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_description.docx" and "ADCP_dataset_Amazon_2007-2008_Bouchez_description.docx" .</p> <p>These data were acquired thanks to the support of the French National Service for Observation "HYBAM" ("Hydrogeochemistry of the Amazon Basin"), part of the CNRS National Infrastructure "OZCAR" ("Critical Zone Observatories: Applications and Research").</p>
Grain Size Distribution of slickenlines from Plan de los Platanos and Big Piute Faults
<p>Grain Size Distribution (GSD) of Plan de Platanos (<strong>PP</strong>) and Big Piute (<strong>BP</strong>) Fault Slickenlines</p> <p>Grains were manually segmented from images of increasing magnifications obtained through optical and SEM microscopy. The size of the grains was then estimated from the grains' area by calculating the diameter (<strong>d<sub>equ</sub></strong>) of a circle of equivalent area.</p> <p><strong>Instruments:</strong></p> <ul> <li>ZEISS AX10 Petrographic microscope</li> <li>ZEISS Merlin HR-SEM (BSE)</li> </ul> <p><strong>PP</strong>---> sample <strong>PP_AU01a</strong>_--> Orientation <strong>XZ</strong>---->N=8617----->andesite</p> <p><strong>BP</strong>---> sample <strong>BP_07b</strong>--> Orientation <strong>YZ</strong>---->N=11425------>quartzite</p> <p><strong>Unit of length (1st colum): </strong>micro meters <strong>(µm</strong>)</p> <p>We use Tikoff et al (2019) naming convention for thin section orientation.</p> <p> </p> <p> </p> <p> </p> <p>Tikoff, B., Chatzaras, V., Newman, J., & Roberts, N. M. (2019). Big data in microstructure analysis: Building a universal orientation system for thin sections. <em>Journal of Structural Geology</em>, <em>125</em>, 226–234. https://doi.org/10.1016/j.jsg.2018.09.019</p>
Data from: Effects of grain size and niche breadth on species distribution modeling
Open the record for dataset details and reuse information.
Data from: The variation of grain size distribution in rock granular material in seepage process considering the mechanical-hydrological-chemical coupling effect: An experimental research
<p>As a common solid waste in geotechnical engineering, rock granular material should be properly treated and recycled. Rock granular material often coexists with water when it is used as the filling material in geotechnical engineering. Water flowing in rock granular materials is a complex progress with the mechanical-hydrological-chemical (MHC) coupling effect, i. e. the water scours in the gaps and spaces in the rock granular material structure, produces chemical reactions with rock grains, rock grains squeeze each other under the water pressure and compression leading re-breakage and producing secondary rock grains, the fine rock grains are migrated with water and rushed out. In this process, rock grain size distribution (GSD) changes, it affects the physical and mechanical characteristics of the rock granular materials, and even influences the seepage stability of the rock granular materials. To study the variation of GSD in the rock granular material considering the MHC coupling effect after the seepage process, seepage experiments of rock grain samples are carried out and analyzed in this paper. The result is expected to have a positive impact on further studies of the properties of the rock granular material.</p>
Data from: The variation of grain size distribution in rock granular material in seepage process considering the mechanical-hydrological-chemical coupling effect: An experimental research
Open the record for dataset details and reuse information.
Hydrodynamic-driven changes in the source and composition of sedimentary organic matter via grain size distribution in shallow lakes
<p>Sediment organic matter (SOM) differs in composition and function in different size fractions in relation to material and energy flows. The hydrodynamic effects on a lake's autochthonous (terrestrial plants) and allochthonous (microalgae, aquatic plants and bacteria) inputs and transformation, and the components of SOM are still far from clear. To elucidate the SOM composition as driven by the hydrodynamic conditions associated with sedimentary grain size, we performed an ecosystem-level investigation in the shallow lake Izunuma, Japan, measuring source-specific fatty acids and the current velocity. High organic matter concentrations occurred in the finer fractions. Bacteria were dominant in the <32 µm fraction, microalgae were mainly present in the <32 and 63-125 µm fractions, aquatic plants appeared to be evenly distributed in all size fractions and terrestrial plants mainly comprised the >125 µm fraction. Although the linear correlation between current velocity and sedimentary total organic carbon (TOC) was not statistically significant, the relationship between current velocity frequency and sedimentary TOC was significant. The highest Pearson coefficient occurred at a current velocity of 6 cm/s in sand. The relationship between allochthonous inputs and current velocity frequency was similar to that of TOC. Autochthonous organics had the greatest coefficient with two peaks at 4.5 and 8 cm/s. The threshold for sand resuspension varied with organic sources, while this phenomenon was absent in cohesive mud. Hydrodynamic forces affected the grain size of sediments and drove the gradient distribution of the SOM sources, which should be considered when managing freshwater lakes in light of future climate change.</p>
Figure 2 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 2. Jackknifing test of variable importance in the development of the uncorrelated model at 250-m resolution. Blue bars indicate the gain achieved when including that predictor only. Green gray bars show how much the total gain is diminished without the given predictor. Red bar indicate the gain achieved when including all predictors.
Impact of dam height and grain size distribution on breaching of non-cohesive dams due to overtopping
<div> <p>The National Inventory of Dams reports 74,400 earthen dams in the United States in 2021; of these dams, approximately 27% are considered at high or significant hazard risk, that is dam failure will cause widespread damage and loss of lives. The most frequent cause of dam failure is breaching caused by overtopping. Accurate predictions of breach evolution are thus crucial to determine flood hydrographs for the safety of communities and properties at risk. Laboratory experiments were conducted on non-cohesive, compacted embankments to understand the role of dam height and sediment grain size on breaching caused by overtopping. Dam heights varied from 10 cm to 45 cm. Model structures were built with fine sand or with a mixture of fine sand and silt. Experiments showed that increasing dam height increases peak discharge. The presence of silt in model embankment material, on the contrary, lowers peak discharge and makes failure longer. In sand dams, sediment deposition on the downstream face becomes important as dam height increases. This deposition reduces incision rate of the breach channel, delaying time to peak and rapid channel widening. In all embankments, breach evolution was gradual until erosion of the breach channel reached the reservoir. Throughout the experiment, bank failure caused gradual widening of the breach channel. In presence of silt, no sediment deposition occurred on the downstream face, and time to peak increased with dam height because upstream migrating erosional waves formed in the breach channel, and the time for these waves to reach the reservoir increased with dam height. Breach channel widening associated with bank failure is practically non-existent until peak discharge is reached, then sudden widening caused by major failures occurs. Overall, breach width decreases with an increase in silt content.</p> </div>
Impact of dam height and grain size distribution on breaching of non-cohesive dams due to overtopping
Open the record for dataset details and reuse information.
Hydrodynamic-driven changes in the source and composition of sedimentary organic matter via grain size distribution in shallow lakes
Open the record for dataset details and reuse information.
Delta-X: Sediment Core Grain Size Distribution, Wax Lake Delta, MRD, LA
This dataset provides grain size distribution measurements collected from sediment core samples on Mike Island in the Wax Lake Delta, Louisiana, as part of the Delta-X Spring campaign in March, 2021,and the Fall campaign during August, 2021. The data are for March 26 and 29, and August 18 and 24. Sediment cores were collected using a piston core, then volume-based grain size distribution for each sample was measured using a laser diffraction particle size analyzer. The data are provided in comma separated values (CSV) format.
Figure 7 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 7. Location of the protected area containing goitered gazelle in central Iran.
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.