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”
FIGURE 16 in First records of Leucania rawlinsi Adams and L. senescens M̂schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution
FIGURE 16. Distribution of the abundance of Leucania rawlinsi and L. senescens in different Brazilian biomes based on standardized collections.
FIGURE 14 in First records of Leucania rawlinsi Adams and L. senescens M̂schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution
FIGURE 14. Abundance of Leucania rawlinsi and L. senescens in Brazil based on standardized collections and data from entomological collections.
FIGURE 15 in First records of Leucania rawlinsi Adams and L. senescens M̂schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution
FIGURE 15. Relative abundance of Leucania rawlinsi and L. senescens based on standardized collections in Brazil. Abbreviations of states are in the Material and Methods section. MS–C: Chapadṳo do Sul, Mato Grosso do Sul; MS–M: Miranda, Mato Grosso do Sul; ES–A: Alegre; ES–D: Domingos Martins.
FIGURES 11–12 in First records of Leucania rawlinsi Adams and L. senescens M̂schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution
FIGURES 11–12. Female genitalia in ventral (left) and lateral view (right) of species of Leucania. 11, L. rawlinsi from Petrópolis, Rio de Janeiro, Brazil (DZ 31.118). 12, L. senescens from Chapadṳo do Sul, Mato Grosso do Sul, Brazil (EMBRAPA 4372). Scale bar: 1mm.
FIGURES 9–10 in First records of Leucania rawlinsi Adams and L. senescens M̂schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution
FIGURES 9–10. Male genitalia in posterior (left) and lateral view (right) and aedeagus of species of Leucania. 9, L. rawlinsi from Conquista d'Oeste, Mato Grosso, Brazil (DZ 40.187). 10, L. senescens from Parque Estadual de Campos do Jordṳo, Campos do Jordṳo, Sṳo Paulo, Brazil (MZUSP). The vesica is about 70% smaller than remaining genitalia. Scale bar: 1mm.
FIGURES 5–8 in First records of Leucania rawlinsi Adams and L. senescens M̂schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution
FIGURES 5–8. Anterior view of the head (upper) and dorsal view of the thorax (bottom) of species of Leucania. 5–6, L. rawlinsi, male from Estrada da Graciosa, Morretes, Paraná, Brazil (DD 357). 7–8, L. senescens, male from Parque Estadual de Campos do Jordṳo, Campos do Jordṳo, Sṳo Paulo, Brazil (MZUSP). Scale bar: 2mm.
Model data for " Topography Influence on Changes in Spatial Distribution of Eddy Kinetic Energy in the Southern Ocean"
<p>This dataset is for the paper " Topography influence on Changes in Spatial Distribution of Eddy Kinetic Energy in the Southern Ocean"</p>
Tracking climate vulnerability across spatial distribution and functional traits in Magnolia gentryi from the Peruvian tropical montane cloud forest
<p>Understanding the responses of tree species' functional traits to climate variability is essential for predicting the future of Tropical Montane Cloud Forest (TMCF) tree species through acclimation, especially in Andean montane environments where fog pockets act as moisture traps. We studied the distribution of <em>Magnolia gentryi</em> to measure its spatial arrangement and identify local hotspots, while also evaluating the extent to which climate-related factors are associated with its distribution. Finally, we analyzed variations in 13 functional traits of <em>M. gentryi</em> and the climate links to infer the shaping plant acclimate capacity. Our results show that Andean TMCF climatic factors constrain <em>M. gentryi</em> spatial distribution with significant patches or gaps, associated with high precipitation rates and mean minimum temperature. The functional traits of <em>M. gentryi</em> are constrained by Andean TMCF climatic factors, resulting in reduced within-species acclimation in functional traits associated with a hydric deficit. The association between functional traits and climate oscillation is crucial for understanding the growth conditions of relict-endemic species and is essential for conservation efforts. Changes in forest trait diversity and species composition occur because of fluctuations in hydraulic safety–efficiency gradients.</p>
F I G U R E 6 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 6 Euclidean distances moved by Tetrahymena individuals depending on densities in our entire dataset. Across all replicates of all landscapes (patches from different landscapes types highlighted by different symbols; see legend) we find positively densitydependent movement. The solid lines represent fits of the averaged linear mixed model (red: dendritic landscapes; blue: linear landscapes) and the shaded area shows 95% confidence intervals (see Table 4 for model selection results). [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 4 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 4 Comparison of variation in population densities between linear and dendritic networks at day 15 of the experiment. The solid line represents the difference between inter-quartile range (IQR) over median population densities of linear and dendritic landscapes. The distribution (grey) represents the distribution of the differences between IQR over median population densities of 200,000 random re-samplings for our data. As we theoretically expect the dendritic landscapes to be more variable we can perform a one-sided test which gives a probability of p =.047 of our observed difference between IQR to median ratios to be larger than zero. [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 3 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 3 Fit of theoretical expectations to the distribution of Tetrahymena population densities depending on network type (linear versus dendritic networks), network position (central versus inner versus outer nodes) for day 15. Violin plots show the overall distribution of the data, the white point gives the median, and the solid black line the 25% and 75% percentiles, respectively. Given the network structure (Figure 1) and the three replicates per landscape, distributions include N = 18 (9, 3) measurements for outer (inner, central) nodes of dendritic networks and N = 6 (6, 18) measurements for outer (inner, central) nodes of linear landscapes. Horizontal red and blue lines visualise fits of the theoretically expected distribution of population densities to data from the dendritic and linear networks assuming network specific dispersal rates (d) and carrying capacities (K). White squares show fits of the theoretically expected distribution of population densities assuming the same d and K values for both network types. Shaded areas, respectively, error bars, show 95% confidence intervals of the fits. [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 2 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 2 Distribution of Tetrahymena population densities depending on network type (linear versus dendritic networks), network position (central versus inner versus outer nodes) and time (days 0, 8 and 15). Violin plots show the overall distribution of the data, the white point gives the median, and the solid black line the 25% and 75% percentiles, respectively. Given the network structure (Figure 1) and the three replicates per landscape, distributions include N = 18 (9, 3) measurements for outer (inner, central) nodes of dendritic networks and N = 6 (6, 18) measurements for outer (inner, central) nodes of linear landscapes. Horizontal lines visualise back-transformed parameter estimates of the averaged linear mixed effects model and shaded areas show 95% confidence intervals (see Table 2 for model selection results). [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 1 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 1 Median population densities (in thousands of individuals) of Tetrahymena in corresponding dendritic (a) and linear (b) landscapes at the end of the experiment (day 15) and across the three replicate landscapes. In these landscapes, outer nodes are labelled "O," inner and central nodes are labelled "I" and "C", respectively. [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 5 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 5 Euclidean distances moved by Tetrahymena individuals depending on network type (linear versus dendritic networks), network position (central versus inner versus outer nodes) and time (days 0, 8 and 15). Violin plots show the overall distribution of the data, the white point gives the median, and the solid black line the 25% and 75% percentiles, respectively. Given the network structure (Figure 1) and the three replicates per landscape distributions include N = 18 (9, 3) measurements for outer (inner, central) nodes of dendritic networks and N = 6 (6, 18) measurements for outer (inner, central) nodes of linear landscapes. Horizontal lines visualise back-transformed parameter estimates of the averaged linear mixed effects model and shaded areas show 95% confidence intervals (see Table 3 for model selection results). [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 17 in First records of Leucania rawlinsi Adams and L. senescens M̂schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution
FIGURE 17. Leucania larvae collected in conventional and transgenic maize, Nortelândia, Mato Grosso state, Brazil.
Global distribution of primary and secondary vegetation at 1km spatial resolution
<p>This dataset provides the global spatial distribution of primary and secondary vegetation at approx. 1km spatial resolution (0.01°). It combines Hilda+ landuse/cover data by Winkler et al. (2020) and the global dataset on human influence by Riggio et al. (2020).</p> <p>The data consists of three NetCDF files: </p> <ul> <li>Primary vegetation</li> <li>Primary vegetation minimal use</li> <li>Secondary vegetation</li> </ul> <p>Primary vegetation is assigned where forests, unmanaged grass-/ shrubland or land with sparse vegetation according to the HILDA+ dataset (classes 44, 55, 66) are under full agreement of low human influence according to the global dataset on human influence.</p> <p>The same HILDA+ classes (44, 55, 66) with full agreement to be under very low human influence according to Riggion et al. (2020) are defined as primary vegetation minimal use.</p> <p>Secondary vegetation consists of forests, unmanaged grass-/ shrubland or land with sparse vegetation according to the Hilda+ dataset (classes 44, 55, 66) that are not classified as primary vegetation.</p> <p> </p> <p>Sources:</p> <p>Winkler, K., Fuchs, R., Rounsevell, M. D. A., Herold, M. (2020): HILDA+ Global Land Use Change between 1960 and 2019. PANGAEA. <a href="https://doi.org/10.1594/PANGAEA.921846">https://doi.org/10.1594/PANGAEA.921846</a> </p> <div> <div> <div> <div> <div> <div> <p>Riggio, J. et al. (2020): Global human influence maps reveal clear opportunities in conserving Earth’s remaining intact terrestrial ecosystems. Dryad. <a href="https://doi.org/10.25338/B80G7Z">https://doi.org/10.25338/B80G7Z</a></p> </div> </div> </div> </div> </div> </div> <p> </p> <p>This seperation of primary and secondary vegetation has been used e.g. in the following studies:</p> <p>Schneider et al. (2024): Effects of profit-driven cropland expansion and conservation policies. Nature Sustainability. <a href="https://doi.org/10.1038/s41893-024-01410-x">https://doi.org/10.1038/s41893-024-01410-x</a></p> <p><span>Piipponen et al. (2024): Protein and energy from grazing or crops - does livestock have a chance? Preprint: </span><a href="https://doi.org/10.21203/rs.3.rs-3392089/v1"><span>https://doi.org/10.21203/rs.3.rs-3392089/v1</span></a></p>
Dataset and code for A Multidimensional Error Verification Method for Weather Forecast Based on the Spatial Distribution Structural Similarity of Meteorological Elements
<p>The dataset includes rainfall observations and mesoscale numerical prediction data for 16 July 2019, 13 June 2020 and 2024 rainy season.<br>The code is the algorithm procedure of this paper. Among them, meiyutest.py is the rainfall scoring procedure for the Meiyu period, and 2019case is the forecast scoring and plotting procedure for two individual cases.</p>
Probing Spatial Variation in AFM-Measured Adhesion: Implication of Distributed Nanoscale Charge Heterogeneity
<p><span>The adhesion forces between a microsphere and a silica substrate were measured using AFM. NaCl solutions at concentrations of 0.001 M, 0.005 M, 0.01 M, 0.05 M, and 0.1 M were used sequentially to measure the adhesion forces. </span></p>
Data from: How do similarities in spatial distributions and interspecific associations affect the coexistence of Quercus species in the Baotianman National Nature Reserve, Henan, China
Congeneric species often have similar ecological characteristics and use similar resources. These similarities may make it easier for them to co-occur in a similar habitat but may also lead to strong competitions that limit their coexistence. Hence, how do similarities in congeneric species affect their coexistence exactly? This study mainly used spatial point pattern analysis in two 1 hm2 plots in the Baotianman National Nature Reserve, Henan, China, to compare the similarities in spatial distributions and interspecific associations of Quercus species. Results revealed that Quercus species were all aggregated under the complete spatial randomness null model, and aggregations were weaker under the heterogeneous Poisson process null model in each plot. The interspecific associations of Quercus species to non-Quercus species were very similar in Plot 1. However, they can be either positive or negative in different plots between the co-occurring Quercus species. The spatial distributions of congeneric species, interspecific associations with non-Quercus species, neighborhood richness around species, and species diversity were all different between the two plots. We found that congeneric species did have some similarities, and the closely related congeneric species can positive or negative associate with each other in different plots. The co-occurring congeneric species may have different survival strategies in different habitats. On one hand, competition among congenerics may lead to differentiation in resource utilization. On the other hand, their similar interspecific associations can strengthen their competitive ability and promote local exclusion to non-congeneric species to obtain more living space. Our results provide new knowledge for us to better understand the coexistence mechanisms of species.
Figure 5 in Spatial distribution of ichthyofauna in the northern Alboran Sea (western Mediterranean)
Figure 5. Non-metric multidimensional scaling (nMDS) analyses using fish abundance data (ind h−1). One- or two-letter labels stand for the sampled location. (a) Inner continental shelf (30–100 m); (b) outer continental shelf (100–200 m); (c) upper continental slope (200–500 m); (d) middle continental slope (500–800 m).
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.