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.
33
datasets available to search
ShareScore release 0.9.0
Dataset results
33 results for “spatial occurrence”
Predicted occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution
<p>The dataset contains predictions of occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution + all covariate layers used for modeling. Over seven million electronic health records (EHRs), among which 11,741 EHRs reported tick attachment, were used to evaluate climate, environmental and animal host factors affecting the risk of tick attachment in cats and dogs in Great Britain (GB). The tick presence/absence EHRs for dogs and cats were further overlaid with spatiotemporal time-series of climatic, vegetation, human influence, hydrological and terrain variables (slope, wetness index) to produce a spatiotemporal regression matrix; an Ensemble Machine Learning framework was used to fine-tune hyperparameters for Random Forest (classif.ranger), Gradient boosting (classif.xgboost) and GLM-net (classif.glmnet) algorithms, which were then used to produce a final ensemble meta-learner that predicts the probability of occurrence of ticks across GB with monthly intervals.</p> <ul> <li>gb1km_covariates.zip contains ALL covariate layers as GeoTIFFs (time-series) used for modeling ticks dynamics;</li> <li>data_1km_2014_M01.rds = contains all covariates for January 2014 prepared as SpatialGridDataFrame (R data object);</li> </ul> <p>Codes of files indicate e.g.:</p> <ul> <li>"monthly.tick.prob_savsnet.mar_p_1km_s_2014_2021" = monthly occurrence probability for January based on the training data from 2014 to 2021;</li> <li>"monthly.tick.prob_savsnet.oct_md_1km_s_20211001_20211031" = monthly prediction (model) error derived as the standard deviation from multiple base learners;</li> </ul> <p>The dataset is described in detail in the following publication:</p> <ul> <li>Arsevska, E., Hengl, T., Singelton, D. et al. (2023?) <strong>Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021</strong>. Submitted to Parasites & Vectors (in review).</li> </ul> <p>The model summary shows:</p> <pre><code>Call: stats::glm(formula = f, family = "binomial", data = getTaskData(.task, .subset), weights = .weights, model = FALSE) Deviance Residuals: Min 1Q Median 3Q Max -1.4749 -0.0557 -0.0471 -0.0430 3.7611 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.64495 0.02095 -364.957 < 2e-16 *** classif.ranger 4.95061 0.63615 7.782 7.13e-15 *** classif.xgboost 189.75543 5.53109 34.307 < 2e-16 *** classif.glmnet 140.24208 5.05375 27.750 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 170604 on 7303013 degrees of freedom Residual deviance: 162571 on 7303010 degrees of freedom AIC: 162579 Number of Fisher Scoring iterations: 9</code></pre> <p><em>Acknowledgements</em>: We are grateful to data providers in veterinary practice (VetSolutions, Teleos, CVS, and other practitioners). We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale Bioinformatics Facility, doi: <a href="https://entrepot.recherche.data.gouv.fr/dataverse/migale">10.15454/1.5572390655343293E12</a>) for providing computing resources. We are also grateful for<br> the help and support provided by <a href="https://www.liverpool.ac.uk/savsnet/">SAVSNET team members</a> Bethaney Brant, Susan Bolan and Steven Smyth.<br> This study was funded mainly by a grant from the <strong>Biotechnology and Biological Sciences Research Council</strong>,<br> BB/NO19547/1 and <strong>British Small Animal Veterinary Association</strong> (BSAVA). The research was partly funded by the National Institute for <strong>Health Research Health Protection Research Unit</strong> (NIHR HPRU) in Emerging and Zoonotic Infections at the <strong>University of Liverpool</strong> in partnership with <strong>Public Health England</strong> (PHE) and <strong>Liverpool School of Tropical Medicine</strong> (LSTM). This work has been partially funded by the <em>“Monitoring outbreak events for disease surveillance in a data science context"</em> (MOOD) project from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 874850 (<a href="https://mood-h2020.eu/">https://mood-h2020.eu/</a>). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.</p>
Spatial clustering of Neobuccinum eatoni occurrence data for potential distribution modeling
<p>The occurrence dataset for <em>Neobuccinum eatoni</em> was compiled through filtration process, starting with records from the Global Biodiversity Information Facility (GBIF) and supplemented by museum specimens and additional sources like SOMBASE, iBOL, NIWA, ANTABIF, and SCAR-AntOBIS. Further data were sourced from the National Museum of Natural History in Paris, the University of Vigo, and recent fieldwork in Antarctica, Heard Island, and Kerguelen Island. Records were meticulously screened to remove misidentified specimens, inaccurate locations, duplicates, and outdated entries, ensuring accuracy and relevance. To address spatial autocorrelation, clustering methods divided the data into distinct geographic clusters, producing a refined dataset used to model <em>N. eatoni</em>'s potential distribution with enhanced predictive reliability by reducing spatial autocorrelation effects.</p>
State of biodiversity documentation in the Philippines: Metadata gaps, taxonomic biases, and spatial biases in the DNA barcode data of animal and plant taxa in the context of species occurrence data
<p>These files can be categorized into three groups: (1) raw datasets obtained from public databases (i.e., GBIF, BOLD, and GenBank), (2) manually edited files needed for parsing and analysis, and (3) supplementary files for spatial analysis. All are used in the examination of gaps and biases present in Philippine biodiversity data, which can direct research on the taxa and spatial regions that need more sampling.</p>
Temporal trends in the spatial bias of species occurrence records
<p>Large-scale biodiversity databases have great potential for quantifying long-term trends of species, but they also bring many methodological challenges. Spatial bias of species occurrence records is well recognized. Yet, the dynamic nature of this spatial bias - how spatial bias has changed over time - has been largely overlooked. We examined the spatial sampling bias of species occurrence records within multiple biodiversity databases in Germany and tested whether spatial bias in relation to land cover or land use (urban and protected areas) has changed over time. We focused our analyses on urban and protected areas as these represent two well-known correlates of sampling bias in biodiversity datasets. We found that the proportion of annual records from urban areas has increased over time while the proportion of annual records within protected areas has not consistently changed. Using simulations, we examined the implications of this changing sampling bias for estimation of long-term trends of species' distributions. When assessing biodiversity change, our findings suggest that the effects of spatial bias depend on how it affects sampling of the underlying land-use change drivers affecting species. Oversampling of regions undergoing the greatest degree of change, for instance near human settlements, might lead to overestimation of the trends of specialist species. For robust estimation of the long-term trends in species' distributions, analyses using species occurrence records may need to consider not only spatial bias, but also changes in the strength of spatial bias through time.</p>
Рис. 4. Частота встречаемости водных брюхоногих моллюсков Калининградской области (%). Fig. 4. The occurrence frequency of gastropods of Kaliningrad Region (%). in Spatial distribution of gastropods (Mollusca: Gastropoda) from the Kaliningrad Region (Russia) water bodies
Рис. 4. Частота встречаемости водных брюхоногих моллюсков Калининградской области (%). Fig. 4. The occurrence frequency of gastropods of Kaliningrad Region (%).
Fig. 4 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 4. Transmission electron micrographs showing the tegument and capsule walls of metacercariae of Cardiocephaloides longicollis. A and F illustrate the capsule wall of monocyst and multicyst metacercariae; B and I represent diagrams of monocyst and multicyst metacercariae showing the location of the following TEM micrographs. C – E Longitudinal section through the capsule wall and tegument of a monocyst. G, H, J-M Longitudinal section through the inner capsule wall and tegument of a multicyst metacercaria. D, E, J-M Detail of necrotic material accumulated on the capsule wall surrounding the metacercaria. K, Detail of inner capsule walls merging together within a multicyst. CW, capsule wall; F, fibrocyte; Gx, glycocalyx; GxF, glycocalyx filaments; ICW, inner capsule wall; M, metacercaria; M1- M3 number of metacercaria in a multicyst; MA, macrophage; Mt, metacercarial tegument; N, nucleus; NC, necrotic cells. Head arrows indicate glycocalyx filaments, asterisks (*) outside of the cyst, (**) inside of the cyst, (***) inside of the cyst when encysted with more than one capsule wall. Scale bars: D, E = 1 μm; C, G, H, J, L, M = 5 μm; K = 10 μm.
Fig. 3 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 3. Occupation of the fish brain by Cardiocephaloides longicollis in fresh (A, B) and histological samples (C–F). Cardiocephaloides longicollis metacercariae within (A) the PGZ and (B) the medulla oblongata in experimentally-infected fish one month after infection. Asterisks indicate the position of metacercariae. Cardiocephaloides longicollis metacercariae are found at 6 dpi in the tectal ventricle (C), and as they grow (D, 21 dpi; E, 8 mpi; F, 15 mpi) they occupy larger part of the tectal ventricle, and also the PGZ. The representations of brains indicate the sections and positions (yellow square) where metacercariae have been found. Legend: TeO striped, cerebellum in dots and Mo squared. ICL, inferior cerebellar lobe; Mo, medulla oblongata; PGZ, periventricular gray zone of optic tectum; TeO, tectum opticum; TV, tectal ventricle. Scale bars: A = 300 μm; B = 450 μm; C–F = 200 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 1. Distribution of metacercariae of Cardiocephaoides longicollis in the different fish brain regions, i.e., olfactory bulbs (Olf-B), olfactory lobes (Olf-L), optic lobe region (Op-L), inferior and superior cerebellar lobes (ICL, SCL), medulla oblongata (Mo), and spinal cord (SC). Metacercarial distribution in different fish species sampling locations are provided. N, number of infected brains used for metacercarial distribution; P, prevalence (based on total number of fish, see Table 1); MI, mean intensity. Note that the number of metacercariae in the brain of fish from the marine pond is based only of half brain (see Materials and methods).
Fig. 2 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 2. Variation in the number of metacercariae of Cardiocephaloides longicollis encysted in different fish groups. Box plots represent the median number of metacercariae per brain region, upper and lower quartile (box) with maximum and minimum ranges (whiskers). Olfactory bulbs (Olf-B), olfactory lobes (Olf-L), optic lobe region (Op-L), inferior and superior cerebellar lobes (ICL, SCL), medulla oblongata (Mo), and spinal cord (SC). Y-axis is represented in logarithmic scale, and dots represent jittered raw data.
Data and analysis scripts for: Co-occurrence patterns at four spatial scales implicate reproductive processes in shaping community assembly in clovers
Open the record for dataset details and reuse information.
Temporal trends in the spatial bias of species occurrence records
Open the record for dataset details and reuse information.
Effects of phylogenetic distance, niche overlap and habitat alteration on spatial co-occurrence patterns in Neotropical bats and birds
Open the record for dataset details and reuse information.
Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA)
<p>Integrated IBD scRNASeq reference: Seurat object and cell type markers. </p><p>Molecular Cartography (Resolve) data data of human colon samples. 6 ulcerative colitis colon resections: 3 inflamed, 3 non-inflamed areas (4 patients in total). Segmented object (Seurat), and imaging data used for figures: B2-1 and C1-2 (healthy samples), C2-1 and C1-4 (inflamed samples) </p><p>Visium (10X) spatial transcriptomics data of human colon samples. 4 ulcerative colitis colon resections: 2 inflamed, 2 non-inflamed areas (3 patients in total). </p><p>Supplementary Table 1: positively co-occurring ligands and receptors in composition class 5.</p><p> </p><p> </p>
The global distribution of plants used by humans datasets: list of utilised species, occurrence data and model outputs at 10 arc-minutes spatial resolution
<p>Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders <em>raw_data</em> and <em>processed_data</em> containing respectively the list of utilised plant species modelled -<em>utilised_plants_species_list.csv</em>-, and their occurrence data -<em>occurrence_data.zip-</em> and predicted distribution -<em>species_proba_per_cell.rds-.</em></p> <p> </p> <ul> <li>The file <em>utilised_plants_species_list.csv</em> in the <em>raw_data</em> folder contains a<strong> </strong>list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:</li> </ul> <p><strong>plant_ID:<em> </em></strong>plant identifier number ranging from between 1-35687</p> <p><strong>binomial_acc_name:</strong> binomial accepted name of the plant species</p> <p><strong>author_acc_name</strong>: name of the author(s)</p> <p><strong>is_hybrid:</strong> logical TRUE or FALSE indicating whether the species is an hybrid or not.</p> <p><strong>AnimalFood:</strong> forage and fodder for vertebrate animals only.</p> <p><strong>EnvironmentalUses:</strong> examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water.</p> <p><strong>Fuels:</strong> charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials.</p> <p><strong>GeneSources:</strong> wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs.</p> <p><strong>HumanFood:</strong> food for humans only, including beverages and food additives.</p> <p><strong>InvertebrateFood:</strong> plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs.</p> <p><strong>Materials:</strong> woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products.</p> <p><strong>Medicines:</strong> both human and veterinary.</p> <p><strong>Poisons:</strong> plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides.</p> <p><strong>SocialsUses:</strong> plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance.</p> <p><strong>Totals:</strong> total number of uses recorded for a species</p> <p> </p> <ul> <li>The zipfile <em>occurrence_data.zip</em> in the <em>processed_data</em> folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to build species distribution models with the following 14 fields:</li> </ul> <p><strong>Species:</strong> the binomial accepted name of the species</p> <p><strong>Fullname:</strong> same as species</p> <p><strong>decimalLongitude:</strong> the geographic longitude of the occurrence records of the species in decimal degrees</p> <p><strong>decimalLatitude:</strong> the geographic latitude of the occurrence records of the species in decimal degrees</p> <p><strong>countryCode:</strong> a three-letter standard abbreviation for the country of the occurrence locality</p> <p><strong>coordinateUncertaintyinMeters</strong>: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location</p> <p><strong>year:</strong> year of the observation of the occurrence record of the species</p> <p><strong>individualCount:</strong> the number of individuals present at the time of the observation</p> <p><strong>gbifID:</strong> unique identifier number for the occurrence from the original database</p> <p><strong>basisOfRecords:</strong> the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen</p> <p><strong>institutionCode</strong>: the name of the institution or organization listed as the data publisher on GBIF</p> <p><strong>establishmentMeans:</strong> statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans</p> <p><strong>is_cultivated_observation:</strong> whether or not an organism is cultivated</p> <p><strong>sourceID:</strong> name of the source database</p> <p> </p> <ul> <li>The file <em>species_proba_per_cell.rds</em> in the <em>processed_data</em> folder is<em> a R Data Serialization </em>(RDS) file containing a data.table object with the following 3 fields:</li> </ul> <p><strong>plant_ID:</strong><em> </em>plant identifier number ranging from between 1-35687</p> <p><strong>proba:</strong> species occurrence probability</p> <p><strong>cell:</strong><em> </em>raster grid cell number between 1-2251762</p> <p>This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.</p>
Data from: Trait hierarchies are stronger than trait dissimilarities in structuring spatial co-occurrence patterns of common tree species in a subtropical forest
<p>1. The dissimilarity and hierarchy of trait values that characterize niche and fitness differences, respectively, have been increasingly applied to infer mechanisms driving community assembly and to explain species co-occurrence patterns. Here, we predict that limiting similarity should result in the spatial segregation of functionally similar species, while functionally similar species will be more likely to co-occur either due to environmental filtering or competitive exclusion of inferior competitors (hereafter hierarchical competition).</p> <p>2. We used a fully mapped 50-ha subtropical forest plot in southern China to explore how pairwise spatial associations between saplings and between adult trees were influenced by trait dissimilarity and hierarchy in order to gain insight into assembly mechanisms. We assessed pairwise spatial associations using two summary statistics of spatial point patterns at different spatial scales and compared the effects of trait dissimilarity and trait hierarchy of different functional traits on the interspecific spatial associations. These comparisons allow us to disentangle the effects of limiting similarity, environmental filtering and hierarchical competition on species co-occurrence.</p> <p>3. We found that trait dissimilarity was generally negatively related with interspecific spatial associations for both saplings and adult trees across spatial scales, meaning that species with similar trait values were more likely to co-occur and thus supporting environmental filtering or hierarchical competition. We further found that trait hierarchy outweighed trait dissimilarity in structuring pairwise spatial associations, suggesting that hierarchical competition played a more important role in structuring our forest community than environmental filtering across life stages.</p> <p>4. This study employed a novel method, by offering the integration of pairwise spatial association and trait dissimilarity as well as trait hierarchy, to disentangle the relative importance of multiple assembly mechanisms in structuring co-occurrence patterns, especially the mechanisms of environmental filtering and hierarchical competition, which lead to indistinguishable co-occurrence patterns. This study also reinforced the importance of trait hierarchy rather than trait dissimilarity in driving neighborhood competition.</p>
Potential map generated by the RF spatial model to define ideal zones for the occurrence of high density of giant trees in the Amazon
<p>The provided image is a theoretical map of giant tree density in the Amazon, generated from a spatial model based on the **Random Forest** algorithm. The map displays the spatial distribution of tree density, representing the number of trees taller than 60 meters per square kilometer (trees/km²). The model was developed using climatic, topographic, and soil variables to predict areas with higher concentrations of these giant trees.</p> <p>The areas are color-coded according to different density ranges, where:<br>- Lighter shades indicate lower tree density (≤ 5 trees/km²),<br>- Darker shades indicate higher density (up to 141 trees/km²).</p> <p>Biogeographic provinces within the Amazon biome, such as the **Guiana Shield**, **Xingu-Tapajós**, and **Roraima**, are highlighted, showing distinct density patterns across the Amazon region. This map is a valuable tool for understanding the spatial distribution of giant trees in the Amazon and plays a crucial role in conservation efforts and ecological monitoring in the region.</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>
Data from: Environmental and spatial effects on co-occurrence network size and taxonomic similarity in stream diatoms, insects, and fish
Open the record for dataset details and reuse information.
Data from: Fine scale spatial variability in the influence of environmental cycles on the occurrence of dolphins at coastal sites
Environmental cycles often influence the presence of animals, creating patterns at different temporal scales, which may mean that their effects overlap and/or interact. Interactions between diel and seasonal cycles have been reported to influence fish behaviour but little is known about such interactions in marine top predators. Here, we studied the combined effect of seasonal, tidal and diel cycles on the occurrence of bottlenose dolphins (Tursiops truncatus) within a Marine Protected Area in Scotland. Our analyses were based on echolocation detections from passive acoustic devices (CPODs) deployed at three coastal sites between 2010 and 2016. We described patterns of dolphins' occurrence using circular statistics and then used generalised additive mixed models to explore the relative importance of each cycle and any interactions between them. We found site-specific cyclical patterns of presence that remained constant across years. There was a highly significant interaction between seasonal and diel cycles at two sites around deep channels, where occurrence was diurnal in summer but became nocturnal in autumn. The study demonstrates the highly plastic behaviour of bottlenose dolphins and shows a previously unreported behaviour that has management implications for this and other marine protected areas.
The spatial analysis of biological interactions: morphological variation responding to the co-occurrence of competitors and resources
By sharing geographic space, species are forced to interact with one another and the contribution of this process to evolutionary and ecological patterns of individual species is not fully understood. At the same time, species turnover makes that species composition varies from one area to another, so the analysis of biological interaction cannot be uncoupled from the spatial context. This is particularly important for clades that show high degree of specialization such as hummingbirds, where any variation in biotic pressures might lead to changes in morphology. Here, we describe the influence of biological interactions on the morphology of Hylocharis leucotis by simultaneously considering potential competition and diet resources. We characterized the extent of local potential competition and local available floral resources by correlating two measurements of hummingbird diversity, floral resources and the size of morphological space of H. leucotis along its geographic distribution. We found that H. leucotis shows an important morphological variability across its range and two groups can be recognized. Surprisingly, morphological variation is not always linked to local hummingbird richness or the phylogenetic similarity of. Only in the southern part of its distribution, H. leucotis is morphologically more variable in those communities where it coexist with closely related hummingbird species. We also found that morphological variation in H. leucotis is independent from the availability of floral resources. Our results suggest that abiotic factors might be responsible for morphological differences across populations in Hylocharis leucotis being biological interactions of minor importance.
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.