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.
645
datasets available to search
ShareScore release 0.9.0
Dataset results
645 results for “Spatial distributions”
Fig. 3 in Spatial and temporal distribution of fish eggs and larvae in a subtropical coastal lagoon, Santa Catarina State, Brazil
Fig. 3. Mean abundances · 100 m-3 (± sd) of fish eggs and larvae in Ibiraquera Lagoon, by month.
Spatial Distribution and Habitat Usage of <i>Coryphopterus personatus</i> and <i>C. hyalinus</i> in Turneffe Atoll, Belize
<p>Data used to develop 3D models of coral reefs using structure-from-motion photogrammetry. The data used to generate photogrammetry models are pictures of the reef from ~1 m above the substratum and coordinates of ground control points for each of twelve distinct ~20 m x 10 m reef areas. The photogrammetry workflow to create the digital models included photo alignment, followed by geometry building, and lastly texture building using Agisoft Pro. Additionally included are the orthomosaics and digital elevation models derived from the 3D models and training data used to build a classification algorithm to classify reef vs sand benthic types using the site orthomosaics. Finally, the location and sizes of mixed shoals of <em>Coryphopterus personatus</em> and <em>Coryphopterus hyalinus</em> are included. All data were collected from Turneffe Atoll (17.3638° N, 87.8581° W), Belize in January 2017. These data are used in conjunction to develop a habitat usage model to understand what features of coral reefs are correlated with the distribution of <em>C. personatus</em>/<em>hyalinus. </em>All code associated with the analysis can be found here: <a href="https://github.com/jdselwyn/Habitat_Usage">https://github.com/jdselwyn/Habitat_Usage</a>.</p>
Spatial distribution and its limiting environmental factors of native orchid species diversity in the Beipan River Basin of Guizhou Province, China
<p>Understanding the distribution of biodiversity and its determinants, particularly that of ecologically sensitive ones, has long been intriguing to the science community and will help formulate conservation strategies under future climate changes. To this end, we conducted extensive field surveys on the distribution of orchid flora in the Beipan River Basin in Guizhou Province, which is one of the biodiversity conservation priorities in China. The data we acquired, together with those published previously, were converted into orchid species richness for each of the 3km × 3km grid cells covering the study region. Redundancy analysis (RDA) and Geographically Weighted Regression (GWR) were then applied to determine which of the 30 environmental factors are potentially critical for the spatial distribution of orchid flora we have observed. Despite a moderate spatial extent, we found that the Beipan River Basin harbors about 249 native orchid species belonging to 74 genera, equivalent to 14.5% of orchid flora of China. Orchid species richness in this area follows a descending gradient from the southeast to the northwest, 70.41% of its variation among grid cells can be explained by environmental factors and spatial variables, and spatial variables accounted for 63.90% of the spatial variation of orchid distribution, indicating that spatial variables played a dominant role in the distribution of wild orchidaceae species richness. In addition, the main environmental driver is the mean temperature of the wettest quarter. Our study provides a good example for revealing the main drivers of orchid distribution characteristics, and has a certain reference value for the development of orchid conservation strategies.</p>
Data and code for: Functional traits mediate individualistic species-environment distributions at broad spatial scales while fine-scale species' associations remain unpredictable
<p>Ecological communities are structured by a diverse set of processes acting at different spatial scales. In plant communities, assembly processes like ecological sorting, limiting similarity, and stochastic events are all expected to influence plant distributions and co-occurrence patterns. We assembled a data set describing the distribution of 139 herbaceous plant species within and among 257 forest stands in Wisconsin (USA) to elucidate the spatial scales at which these assembly processes operate. Analyses of these data in conjunction with detailed information about environmental conditions, plant functional traits, and phylogenetic relationships provided new insights into the scale-dependent drivers of plant community assembly in temperate forest understories. Traits like leaf height, specific leaf area, and seed mass all influenced individualistic plant distributions along landscape-scale gradients in soil texture, soil fertility, light availability, and climate while phylogenetic relationships did not predict species-environment relationships. These findings point to the importance of trait-mediated ecological sorting in shaping individualistic plant distributions at broad spatial scales. Contrary to our expectations about the importance of limiting similarity at local scales, neither functionally similar nor phylogenetically related herbs segregated among microsites within forest stands. We hypothesize strong ecological sorting among forest stands coupled with stochastic fine-scale interactions among species appear deterministic, niche-based assembly processes at local scales.</p>
Data from: Two for the price of one: eDNA metabarcoding reveals temporal and spatial variability of mussel and fish co-distributions in Michigan riverine systems
<p>Freshwater mussels (family Unionidae) are among the world's most endangered taxa, with almost 75% of North American taxa classified as a species of concern, threatened, or endangered. Despite the critical importance of comprehensive distributional data for the conservation of unionids and fishes, these data are often lacking because of the labor and resources associated with traditional survey methods. During their larval stage, unionid mussels use various fish species as obligate hosts, making native fish species vital to unionid persistence and an understanding of host distribution similarly important. Here, we utilized an eDNA metabarcoding approach to evaluate patterns of co-distribution of unionid mussels and fishes along ~362 km of the densely sampled Grand River network as well as the outlets of 19 tributaries along the eastern shore of Lake Michigan, USA. We detected a total of 21 mussel and 40 fish taxa, with distinctive composition of both mussel and fish assemblages across tributaries and differences in fish taxa between sampling periods. Notably, we detected more mussel taxa within the Grand River watershed than at the outlets of all 20 rivers combined. Within the Grand River network, two fish taxa (<em>Pylodictus</em> <em>olivaris</em> and <em>Cyprinella</em>) were found more frequently in areas of high mussel diversity, and three fish taxa more frequently in areas of low mussel diversity (<em>Umbra</em>, Leuciscidae, and <em>Etheostoma</em>). There was little difference between eDNA detections of mussels from samples collected in June versus August, but we detected significantly more fish taxa in August compared to June. Taken together, our findings demonstrate the value of eDNA metabarcoding for evaluating co-distribution of ecologically connected taxa. The use of eDNA as a tool for determining distributions of mussels and their obligate hosts may facilitate conservation efforts for these imperiled taxa.</p>
Dataset for the manuscript: "Three-dimensional species distribution modeling reveals the realized spatial niche for coral recruitment on contemporary Caribbean reefs"
<p>Whether the three-dimensional (3D) structure of habitats influences and partition recruitment niches of corals is unknown. We developed a new method that combined Species Distribution Modeling and Structure from Motion to characterize and map the three-dimensional recruitment niches of two ecosystem engineers on Caribbean coral reefs, scleractinian corals and octocorals. </p> <p>In this repository, we include 48 3D models of small areas of the reef (i.e., within ~ 0.25 m<sup>2</sup> quadrats) reconstructed with Structure-from-Motion, as well as the geospatial data used to characterize and map the realized recruitment niche for scleractinian corals and octocorals on Caribbean coral reefs. We conducted the study at two shallow, fringing reefs off the south shore of St. John, US Virgin Islands, named Grootpan and Europa Bays (18° 18.360’N, 64° 43.140’W, and 18° 19.016’N, 64° 43.798’W, respectively). Within each 0.25 m<sup>2</sup> quadrat, we counted and marked all recruits (octocorals ≤ 5 cm height, and scleractinians ≤ 4 cm wide).</p> <p><em>DATASET DESCRIPTIONS:</em></p> <ul> <li><strong>"Quadname_data.zip":</strong> In each of this folders we included all the data calculated within a quadrat: <ul> <li>ASCII files (.txt).</li> <li>The annotated dense point cloud (.las) for each quadrat.</li> <li>The quadrat 3D model texture (.jpg).</li> <li>The quadrat 3D polygon mesh (.ply).</li> <li>The quadrat 2.5D Digital Elevation Model (i.e., DEM; .tif).</li> <li>Shape files with recruits local coordinates within each quadrat (.dbf, .prj, .shp, .shx).</li> </ul> </li> <li><strong>"datawide.rds": </strong>This is the file needed to run the analyses performed in Martínez-Quintana et al., 2023. This file is obtained after processing all the raw data calculated within each quadrat. All code associated with the workflow used to obtain the datawide.rds file and run the analyses performed in Martínez-Quintana et al., 2023 is available at <a href="https://github.com/AdamWilsonLab/meshSDM">github.com/AdamWilsonLab/meshSDM</a>.</li> </ul> <p><strong>IMPORTANT NOTES: </strong></p> <ul> <li>Quadrat names starting with the letters “eu” indicate the data were collected at Europa Bay, whereas those starting with the letters “ec” indicate that data were collected at Grootpan Bay (commonly named East Cabritte).</li> <li>Each ASCII file (quadname_ASCII_subsampled_X.txt) contains the slope and roughness of the quadrat calculated on the point cloud at 5, 10, 20, and 100 mm scales, and the smooth point cloud used to calculate the topographic exposure index (TEI) described in Martínez-Quintana et al., 2023. Calculations were performed and ASCII files were created with CloudCompare.</li> <li>Each dense point cloud, mesh, texture, and DEM were calculated with Agisoft Metashape.</li> <li>Agisoft Metashape allows the user to classify and annotate groups of points in the dense point cloud. However, the list of classes provided by the software corresponds to the standard list used for terrestrial LiDAR data; these classes cannot be renamed within the software. Thus, for the present study, we coded the automatic semantic classifications available in Metashape as follows: <ul> <li>Ground = Calcareous rock.</li> <li>Building = Igneous rock.</li> <li>High noise = Sand.</li> <li>Low vegetation = Adult Scleractinian corals.</li> <li>Medium vegetation = Adult Octocoral base.</li> <li>High vegetation = Sponge.</li> <li>Water = Octocoral recruit (named also ocr).</li> <li>Road Surface = Scleractinian recruit (named also scr).</li> <li>Unclassified = created points but never classified (excluded from the analyses).</li> <li>Low Point = noise (unreliable points).</li> <li>Transmission tower and Rail = Points outside the quadrat and excluded from the analysis.</li> </ul> </li> </ul>
Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data"
<p>Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data</p> <p>", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
Baseline concentrations, spatial distribution and origin of trace elements in marine surface sediments of the northern Antarctic Peninsula
<p>Supplementary data to article published in Marine Pollution Bulletin 187 (2023) 114501: https://doi.<br> org/10.1016/j.marpolbul.2022.114501</p>
Data set to: Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
<p>Data for the manuscript "Mapping a brain parasite: occurrence and spatial distribution in fish encephalon", doi: 10.1016/j.ijppaw.2023.03.004. Description of the distribution of metacercariae from the trematode species <em>Cardiocephaloides longicollis</em> in the brain of fish. </p>
Dataset of ``Plasma Distribution Solver: A Model for Field-Aligned Plasma Profiles Based on Spatial Variation of Velocity Distribution Functions"
<p>This dataset contains the plasma distribution data in the Jupiter–Io system, calculated from the Plasma Distribution Solver and used for figures in the paper “Plasma Distribution Solver: A model for field-aligned plasma profiles based on spatial variation of velocity distribution functions” by K. Saito et al. (2023).</p> <p> </p> <p>The contents of files ‘all_Case_1.csv’ and ‘all_Case_2.csv’ are as follows:</p> <ul> <li>Position along the magnetic field line (0 at the magnetic equator) [m] (column 1)</li> <li>Distance from the Jovian center [km] (column 2)</li> <li>Magnetic latitude [rad]([degree]) (column 3(4))</li> <li>Magnetic flux density [T] (column 5)</li> <li>The initial condition of electrostatic potential [V] (column 6)</li> <li>The result of electrostatic potential [V] (column 7)</li> <li>Number density profiles [m<sup>-3</sup>] (columns 8-17)</li> <li>Charge density profiles obtained from the integration of velocity distribution functions [C m<sup>-3</sup>] (column 18)</li> <li>Charge density profiles obtained from Poisson’s equation [C m<sup>-3</sup>] (column 19)</li> <li>Convergence value (column 20)</li> <li>Particle flux density [m<sup>-2</sup> s<sup>-1</sup>] (columns 21-30)</li> <li>Mean flow velocity parallel to the field line [m s<sup>-1</sup>] (columns 31-40)</li> <li>Plasma pressure perpendicular to the field line [Pa] (columns 41-50)</li> <li>Plasma pressure parallel to the field line [Pa] (columns 51-60)</li> <li>Plasma dynamic pressure [Pa] (columns 61-70)</li> <li>Perpendicular temperature [J] (columns 71-80)</li> <li>Parallel temperature [J] (columns 81-90)</li> <li>Alfvén speed considering the displacement current term in Ampère’s law [m s<sup>-1</sup>] (column 91)</li> <li>Alfvén speed per the speed of light (column 92)</li> <li>Ion inertial length using averaged mass [m] (column 93)</li> <li>Electron inertial length [m] (column 94)</li> <li>Ion Larmor radius using averaged mass [m] (column 95)</li> <li>Ion acoustic gyroradius using averaged mass [m] (column 96)</li> <li>Electron Larmor radius [m] (column 97)</li> <li>Current density [A m<sup>-2</sup>] (column 98)</li> </ul> <p>The Python codes ‘plot_all.py,’ ‘plot_plasma_beta_comparison.py,’ and ‘plot_Alfven_speed_comparison.py’ can plot Figures 5, 6, 7, and 9 of the paper using the above CSV files.</p> <p> </p> <p>The files ‘boundary_conditions_Case_1.csv’ and ‘boundary_conditions_Case_2.csv’ contain the boundary conditions for Cases 1 and 2.</p> <p> </p> <p>The zip files ‘probability_density_function_Case_1_H_Io.zip’ and ‘probability_density_function_Case_1_H_Jupiter_North.zip’ are zipped CSV files with the same name. The contents of these files are as follows:</p> <ul> <li>Magnetic latitude [degree] (column 1)</li> <li>Perpendicular velocity at the particle position [m s<sup>-1</sup>] (column 2)</li> <li>Parallel velocity at the particle position [m s<sup>-1</sup>] (column 3)</li> <li>Perpendicular velocity at the boundary [m s<sup>-1</sup>] (column 4)</li> <li>Parallel velocity at the boundary [m s<sup>-1</sup>] (column 5)</li> <li>Probability density function [s<sup>3</sup> m<sup>-3</sup>] (column 6)</li> <li>Differential flux per number density [cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> keV<sup>-1</sup>] (column 7)</li> </ul> <p>The Python code ‘plot_velocity_distribution_function.py’ can plot Figure 8 of the paper using this CSV file.</p>
Fig. 2 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis
Fig. 2. DCA analysis of the trapdays data for all of the sampling sites.
Fig. 1 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis
Fig. 1. PCA distribution of the sampling sites with full two-year catches.
Energy-water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperms species richness in China
Open the record for dataset details and reuse information.
Data from: Two for the price of one: eDNA metabarcoding reveals temporal and spatial variability of mussel and fish co-distributions in Michigan riverine systems
Open the record for dataset details and reuse information.
Habitat suitability modeling to predict the spatial distribution of cold-water coral communities affected by the Deepwater Horizon oil spill
Open the record for dataset details and reuse information.
Data from: Spatial and temporal distribution of ribosomes in single cells reveals aging differences between old and new daughters of Escherichia coli
Open the record for dataset details and reuse information.
Spatially explicit habitat selection: testing contagion and the ideal free distribution with culex mosquitoes
Open the record for dataset details and reuse information.
Soil dissolved organic carbon in terrestrial ecosystems: global budget, spatial distribution and controls
Open the record for dataset details and reuse information.
Spatial distribution pattern of mustelids in the eastern edge of the Qinghai-Tibet plateau
Open the record for dataset details and reuse information.
Environmental DNA reflects spatial distribution of a rare turtle in a lentic wetland assisted colonisation site
Open the record for dataset details and reuse information.
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.