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.
35
datasets available to search
ShareScore release 0.9.0
Dataset results
35 results for “field boundaries”
Oregon hydrologic area agricultural field boundaries and field level and hydrologic unit water-use data
Open the record for dataset details and reuse information.
PIV vector fields from: Boundary layer hydrodynamics of patchy biofilms
<p>This dataset contains the instantaneous velocity vector fields from PIV data taken over large acrylic plates fouled with diatomaceous biofilm of varying patchiness. </p> <p>See associated article, Boundary layer hydrodynamics of patchy biofilms, for methods description.</p> <p>Each zip folder contains data for one of the 4 non-uniform biofilms examined, PB-1 (patchy biofilm 1); PB-2 (patchy biofilm 2); SB-1 (sparse biofilm 1); SB-2 (sparse biofilm 2). For each biofilm, the corresponding folder contains 4000 statistically independent instantaneous velocity vector fields. Each vector field is saved in a .mat file, and the workspace variable that contains the data is called ‘vecfield’. </p> <p>Size calibration and water temperature are provided in the spreadsheet ‘experiment_metadata.xlsx’</p> <p> </p> <p>Column 1: X (streamwise distance [pixels]) </p> <p>Column 2: Y (wall-normal distance from bottom of frame [pixels])</p> <p>Column 3: U (streamwise velocity vector [pixels / 250 microseconds]) </p> <p>Column 4: V (vertical velocity vector [pixels / 250 microseconds]) </p> <p>Column 5: CHC (number of tracked particles. A value < 1 gives the location of the biofilm, which was masked out)</p> <p> </p>
Distribution. NW coastal Madagascar, restricted to the Sahamalaza Peninsula and the adjacent mainland; the biogeography of this area and the distribution pattern of the sympatric Blue-eyed Black Lemur (Eulemurflavifrons) make it likely that the boundaries of the range of the Sahamalaza Sportive Lemur are the Andranomalaza River in the N and the Maevarano Riverin the S. Field studies to determine the full extent of the distribution and that of neighboring Mittermeier's Sportive Lemur (L. mittermeiert) are underway. in Lepilemuridae
Distribution. NW coastal Madagascar, restricted to the Sahamalaza Peninsula and the adjacent mainland; the biogeography of this area and the distribution pattern of the sympatric Blue-eyed Black Lemur (Eulemurflavifrons) make it likely that the boundaries of the range of the Sahamalaza Sportive Lemur are the Andranomalaza River in the N and the Maevarano Riverin the S. Field studies to determine the full extent of the distribution and that of neighboring Mittermeier's Sportive Lemur (L. mittermeiert) are underway.
Data for "Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression"
<p>The dataset for the manuscript titled</p> <p>"Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression".</p> <blockquote> <p>Oh, G. and Austin, P. H.: Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-352, 2024.</p> </blockquote> <p> </p> <p>The dataset contains input parameters used for the SAM LES model run (Khairoutdinov and Randall, 2003) based on the CGILS S2 parameters (CGILS.zip) and the main raw output used for the manuscript (clusters.zip). The latter contains a list of Parquet files for the 12-hour model run, sampled every minute. The raw output consists of the coordinates of all cloudy cells in the model domain, which is used to construct the cloud size distribution as described in the manuscript.</p>
Density field data, VTK files and Jupyter notebook for dislocation embedding analysis (with periodic boundary condition)
<p><strong>pbc_dataset.zip</strong></p> <p>The density field data is only for 0 degrees of misorientation with loading directions along [100], [110], [111], [234]. </p> <p>Folder paths for low density and low resolution simulation are:</p> <ol> <li>0deg/dir100/2.5e+13/config3/10x10x10</li> <li>0deg/dir110/2.5e+13/config3/10x10x10</li> <li>0deg/dir111/2.5e+13/config3/10x10x10</li> <li>0deg/dir234/2.5e+13/config3/10x10x10</li> </ol> <p>Folder paths for high density and high resolutions are:</p> <ol> <li>0deg/dir100/1e+14/config1/20x20x20</li> <li>0deg/dir110/1e+14/config1/20x20x20</li> <li>0deg/dir111/1e+14/config1/20x20x20</li> <li>0deg/dir234/1e+14/config1/20x20x20</li> </ol> <p>Each set of simulation has 2000 density field data files. Total files : 16000</p> <p>Please ensure that above paths are entered in the Jupyter notebook script file.</p> <p> </p> <p><strong>vtk.zip</strong></p> <p>VTK files for each simulation. Each simulation has 2000 files. </p> <p><strong>Dislocation_embeddings_pbc.ipynb</strong></p> <p>Jupyter notebook to generate dislocation embeddings for uploaded dataset.</p> <p> </p>
Ukraine Field Boundary Delineation 2023
<h2><strong>Authors:</strong></h2> <div> <p>Yuval Sadeh<sup>a,b,*</sup>, <sup> </sup>Josef Wagner<sup>c</sup>, Shabarinath S. Nair<sup>c</sup>, Oleksandra Oliinyk<sup>b</sup>, Enguerran Belles<sup>c</sup>, Ayman Bibih<sup>c</sup>, Léna D'Harboullé<sup>c</sup>, Hamza Bendhamane<sup>c</sup>, Manav Gupta<sup>c</sup>,<sup> </sup>Inbal Becker-Reshef<sup>b,c</sup></p> <p><sup>a</sup>School of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.</p> <p><sup>b</sup>Department of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA</p> <p><sup>c</sup>ICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France</p> <p>* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)</p> </div> <div> </div> <h2><strong>Information:</strong></h2> <p>These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.</p>
Figure 5 in Testing concordance in species boundaries using acoustic, morphological, and molecular data in the field cricket genus Itaropsis (Orthoptera: Grylloidea, Gryllidae: Gryllinae)
Figure 5. Phylogeny of Itaropsis obtained from Bayesian analysis of combined morphological, mitochondrial, and nuclear data sets. Abbreviations as in Figure 4.
Figure 4 in Testing concordance in species boundaries using acoustic, morphological, and molecular data in the field cricket genus Itaropsis (Orthoptera: Grylloidea, Gryllidae: Gryllinae)
Figure 4. Phylogenetic trees of Itaropsis resulting from analysis of morphological and molecular data. A, cladogram obtained with morphological characters (one tree, length 120 steps, CI 41, RI 51); B, phylogenetic tree obtained from Bayesian analysis of mitochondrial data sets for 19 terminals; C, phylogenetic tree obtained from Bayesian analysis of nuclear data sets for 17 terminals. Abbreviations: B, Bombay–Bangalore cluster; K, Kadari cluster; V, Valparai cluster.
Figure 3 in Testing concordance in species boundaries using acoustic, morphological, and molecular data in the field cricket genus Itaropsis (Orthoptera: Grylloidea, Gryllidae: Gryllinae)
Figure 3. Dendrogram based on four song features: call duration, syllable duration, syllable period and dominant frequency for individuals of the five song types of Itaropsis. Closed circles, Valparai chirper; closed triangles, Kadari chirper; open triangles, Kadari triller; open squares, Bangalore triller; open circles, Bombay triller.
Figure 1 in Testing concordance in species boundaries using acoustic, morphological, and molecular data in the field cricket genus Itaropsis (Orthoptera: Grylloidea, Gryllidae: Gryllinae)
Figure 1. Map showing the four sampling sites for Itaropsis in peninsular India (black dots), together with the localities where the presence of Itaropsis has been acknowledged in the past (grey squares) and main biogeographical units (Palghat gap, Palk strait). Cricket picture courtesy of Ashok Kumar Mallik, CES, IISc, Bangalore, India.
Data from: A morphometric study of species boundaries of the wild potato Solanum series Conicibaccata: a replicated field trial in Andean Peru
Open the record for dataset details and reuse information.
Fig. 4 in Diversity of carabids (Coleoptera: Carabidae) within two Dutch cereal fields and their boundaries
Fig. 4. Activity-density of carabid beetles (log) plotted along species ranked from the most to least abundant. Closed square – field 1; open diamond – field 2.
Fig. 2 in Diversity of carabids (Coleoptera: Carabidae) within two Dutch cereal fields and their boundaries
Fig. 2. Increase in observed (species accumulation curves) and predicted (rarefaction curves) species richness in course of time. Closed diamond – species accumulation curve, field 1; open diamond – species accumulation, curve field 2; closed square – rarefaction curve, field 1; open square – rarefaction curve, field 2.
Fig. 3 in Diversity of carabids (Coleoptera: Carabidae) within two Dutch cereal fields and their boundaries
Fig. 3. Sample-based rarefaction curves of increase of species richness re-scaled to number of individuals. Closed square – field 1; open diamond – field 2.
Crop field boundary in SN v2
<p>This is the crop field boundary in Suining county, Jiangsu, China.</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.