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.
110
datasets available to search
ShareScore release 0.9.0
Dataset results
110 results for “Spatially explicit”
2007 Environmental Protection Agency (EPA) National Lakes Assessment dataset plus derived data and additional spatially explicit ancillary environmental data.
Lake water quality is known to be affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology conceptual framework using a random forest algorithm on large, national-scale, spatially explicit dataset, the United States Environmental Protection Agency 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity).
MCR LTER: Coral Reef: Turbinaria CHN from a spatially explicit sampling campaign in lagoons of Moorea, French Polynesia
Nutrients are important for ecosystem structure and community dynamics. To quantify time-integrated patterns of nutrient regimes in tropical lagoon ecosystems, concentrations of nitrogen, hydrogen, and carbon were measured from tissues of the macroalga Turbinaria ornata collected in lagoons around Moorea, French Polynesia in 2016, 2017, and annually starting in 2019. These sampling periods initially corresponded with distinct seasonal shifts in rainfall and wave forcing and later focused on the rainy season. Results showed that N enrichment was highest nearshore in fringing reef habitats, as well as in bays and at reef passes.
Global spatially explicit critical nitrogen surpluses and critical nitrogen inputs, and their exceedances
<p>Data files belonging to manuscript:</p> <p>Schulte-Uebbing, LF, AHW Beusen, AF Bouwman & W de Vries (2022): From planetary to regional boundaries for agricultural nitrogen pollution. <strong>Nature</strong>, Vol 610 (7932), https://doi.org/10.1038/s41586-022-05158-2</p> <p>* Input datafiles: Contains complete set of input files used in the calculations of global, spatially explicit critical nitrogen surpluses and critical nitrogen inputs. All input files are output from the IMAGE-GNM model. For further information on IMAGE-GNM, see: Beusen, A. H. W., Van Beek, L. P. H., Bouwman, A. F., Mogollón, J. M., & Middelburg, J. J. (2015). Coupling global models for hydrology and nutrient loading to simulate nitrogen and phosphorus retention in surface water - Description of IMAGE-GNM and analysis of performance. Geoscientific Model Development, 8(12), 4045–4067. https://doi.org/10.5194/gmd-8-4045-2015</p> <p>* Output datafiles: Selection of output datafiles, supporting results presented in the paper. </p> <p>For more information, see file "README.xlsx".</p>
Data and code from: Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras.
<p>Detection data from American black bears and code used in "Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras" published in the Journal of Applied Ecology (Evans & Rittenhouse 2018). Unmarked detection data was collected using remote cameras in northwest Connecticut in 2014, and individual detection data was determined from unique genotypes obtained from non-invasive hair snares constructed at camera sampling locations.</p> <p>EN14.rds contains detection data as an R list:</p> <p>$y (num): J (sites) x K (occasions) matrix containing detection counts</p> <p>$X (int): 2 x J matrix of site coordinates</p> <p>$xlims (num): bounding x-coordinates</p> <p>$ylims (num): bounding y-coordinates</p> <p>$M (int): upper bound for super population of individuals</p> <p>$nTraps (int): number of sampling sites (J)</p> <p>$nReps (int): number of MCMC interations</p> <p>$forest (num): vector of site-specific covariates</p> <p>$mark (int): K x I matrix storing site numbers at which individual (i) was detected on occasion k</p> <p>FullModel.R provides functions used to fit constant density models to unmarked detections incorporating covariates of detection probability.</p> <p>partialID.R provides functions and code used to estimate density from mixtures of marked and unmarked detection data</p> <p>VariableDensity.R provides functions and code used to fit variable density models to unmarked detection data incorporating spatial covariates of density.</p> <p> </p>
Setting files from: Spatially explicit paleogenomic simulations support cohabitation with limited admixture between Bronze Age Central European populations.
<p><strong>Simulated Data and Custom Scripts</strong></p> <p>This dataset release permits to simulate the expansion of populations from the Pontic Steppes to Central Europe with the version of SPLATCHE3 which is included. There are 2 main zipped folders: i) the one called "SPLATCHE3executableAndSettings" contains a "ReadMe.txt" file that contains all required information to make the simulations: the resulting ".prop" file contains proportions of genomic ancestry of the P2 layer, ancestry from P1 layer is equal to 1-(proportion from P2) ; ii) the other one called "HowToMakeFigure2" contains the R script and the tables necessary to reproduce Figure 2. See Rio J, Quilodrán CS & Currat M., Communications Biology (2021), for background.</p> <p><strong>Acknowledgments</strong></p> <p>This project was financially supported by the Swiss National Research Foundation grants n° 31003A_182577 to MC and n° P400PB_183930 to CQ, as well as the IGE3 Student Salary Award to JR.</p>
Simulated spatially-explicit above ground biomass of forests (larch) in the vicinity of the Ilirney lake system region, Chukotka, Russia
<p>The model LAVESI (Kruse et al. 2016) was updated (Kruse 2023) and forced with historical and future climate forcing for 3 simulation repeats. The data set contains simulated larch above ground biomass (AGB, in kg m<sup>-2</sup>) for the three climate forcings RCP 2.6, 4.5 and 8.5 and each complemented with a hypothetical cooling scenario from year 2300 CE onwards. The data provided is from years 2020, 2050, 2100 and proceeding in 100-year steps until 3000 CE.</p> <p>Format: Geotiff; projection UTM58N and 30x30 m tiles; extent: 640008.2, 649998.2, 7475006, 7494716 m (xmin, xmax, ymin, ymax)</p>
Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways
<p>This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).</p>
A method for generating coherent spatially explicit maps of seasonal palaeoclimates from site-based reconstructions
<p>Reconstruction of climate anomalies in southern Europe for the Last Glacial Maximum (LGM, ca 21,000 years ago), made by combining pollen based reconstructions (from Bartlein et al. 2011) and averaged outputs of LGM simulations from the 3rd round of the Palaeoclimate Model Intercomparison Project (PMIP, Braconnot et al. 2011), under a variational data assimilation technique. Reconstructions made using this technique are designed to be used for data-model comparison, specifically against the results of PMIP4. The dataset consists of 6 variables: moisture index (the ratio precipitation and equilibrium evapotranspiration), mean annual precipitation (mm), mean annual temperature (degrees C), mean temperature of the coldest month (degrees C), mean temperature of the warmest month (degrees C), growing degree days above 5 degrees C (day degrees C). The standard deviation of these variables is also given.</p>
Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
<ol> <li>Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition.</li> <li>We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement.</li> <li>The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing.</li> <li>Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.</li> </ol>
Data from: Complementary strengths of spatially-explicit and multi-species distribution models
<p><span><span><span><span><span><span><span><span><span><span><span> Species distribution models (SDMs) project the outcome of community assembly processes - dispersal, the abiotic environment, and biotic interactions - onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock (<i>Tsuga canadensis</i>L.) and five additional co-occurring overstory tree species in 35,569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span> Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species-environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.</span></span></span></span></span></span></span></span></span></span></p>
Data from: Developing spatially explicit and stochastic measures of ecological departure
<p>Background: Ecological departure is a metric applied to mapped ecological systems measuring dissimilarity between the distributions of observed and expected proportions of non-stochastic reference vegetation classes within an area.</p> <p>Aims: We created spatially explicit measures of ecological departure incorporating stochasticity for each ecological system and all ecological systems from a central Nevada USA landscape.</p> <p>Methods: Spatially explicit ecological departures were estimated from a radius from each pixel governed by a distance-decay function within a moving window. Variability was introduced by simulating replicate climate time series for each spatial reference condition and calculating departure per replicate.</p> <p>Key results: Single system spatial ecological departure was highly and extensively departed, except for one area of low-elevation groundwater-dependent systems. Variance of spatial ecological departure was extensively low, except in areas of lower ecological departure, despite vegetation differences among replicates. The multiple-system ecological departure exhibited lower ecological departure.</p> <p>Conclusions: Spatial ecological departure was warranted for efficient land management as results were concordant between non-spatial and spatial metrics; however, rapid coding languages will be required.</p>
Dataset - Spatially explicit linkages between redox potential cycles and soil moisture fluctuations
<p>This repository holds data collected during three lysimeter experiments where laboratory column scale lysimeters have been subjected to different wetting/drainage cycles. The lysimeters have been filled with forest soil in the Lausanne forest characterised by the SwissMEX project </p> <p>The following dataset contains soil redox potential and soil moisture measurements that have been continuously monitored in time for different depths. Pore water samples of specific dissolved chemicals have been collected at the end of each cycle. </p> <p> </p> <p>Specifically, this dataset is composed by the following files:</p> <ul> <li> "Lysimeter_configuration.png" illustrates the lysimeter used and the sensors scheme adopted.</li> <li>"METADATA.txt" contains specific information about each recorded variable and data point collected throughout the three experiments SM-B, SM-I1 and 2.</li> <li>Pore water analysis data</li> <li>Soil moisture and tension data</li> <li>Soil redox potential data</li> </ul> <p>We thank Pascal Froidevaux for providing the lysimeter used for SM-B experiment. The authors acknowledge key funding provided by the Swiss National Science Foundation through its grant number CRSII5 186422.</p> <p> </p> <p> </p>
spectre: An R package to estimate spatially-explicit community composition using sparse data
<p>An understanding of how biodiversity is distributed across space is key to much of ecology and conservation. Many predictive modelling approaches have been developed to estimate the distribution of biodiversity over various spatial scales. Community modelling techniques may offer many benefits over single-species modelling. However, techniques capable of estimating precise species makeups of communities are highly data intensive and thus often limited in their applicability. Here we present an R package, spectre, which can predict regional community composition at a fine spatial resolution using only sparsely sampled biological data. The package can predict the presence and absence of all species in an area, both known and unknown, at the sample site scale. Underlying the spectre package is a min-conflicts optimisation algorithm that predicts species' presences and absences throughout an area using estimates of α-, β-, and γ-diversity. We demonstrate the utility of the spectre package using a spatially-explicit simulated ecosystem to assess the accuracy of the package's results. spectre offers a simple-to-use tool with which to accurately predict community compositions across varying scales, facilitating further research and knowledge acquisition into this fundamental aspect of ecology.</p>
Spatially explicit regions of different suitability for Sentinel-2A and 2B Level-1C data.
<p>Spatially explicit regions of different suitability for Sentinel-2A and 2B Level-1C data. High suitability means a combination of high coverage and low average cloud cover. The suitability of data at a specific location is decreasing with either adecrease of number of scenes or an increase of average cloud cover.</p> <p>See here for more information: https://www.tandfonline.com/doi/full/10.1080/17538947.2019.1572799</p>
Satellite derived floodplains (GFD ) combined with spatially- explicit socio-economic data and DFO impacts
<p>These data is linked to the paper </p> <p><strong>Limited progress in global reduction of vulnerability to flood impacts over the past two decades</strong></p> <p><strong>Inga J. Sauer¹ ², Benedikt Mester¹ ³, Katja Frieler¹, Sandra Zimmermann1, Jacob Schewe¹, and Christian Otto¹</strong></p> <p><strong>¹ Potsdam Institute for Climate Impact Research, Potsdam, Germany </strong></p> <p><strong>² Institute for Environmental Decisions, ETH Zurich, Zurich, Switzerland</strong></p> <p><strong>³ Institute of Environmental Science and Geography, Potsdam University, Potsdam, Germany</strong></p> <p><span>DOI: 10.1038/s43247-024-01401-y</span></p> <p>It combines the satellite observed flooded areas from the <strong>global flood database (GFD) </strong>with socio-economic and infrastructure data and the socio-economic impacts recorded in the <strong>Global Active Archive of Large Flood Events provided by the Dartmouth Flood Observatory (DFO).</strong></p>
Output raster datasets from an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the output results for a spatial water yield model applied to an 11 county area in the state of Florida panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in this datasets associated publication. All data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p> <p>The output raster datasets contained here are water yield estimate informed with 2018 pine basal area, binary depth to water table, and average aridity index input rasters. These rasters have 10m spatial resolution, the raster extent covers 11 counties in the panhandle of Florida, the units are in centimeters of water yield per year. The water yield outputs consist of ten rasters representing the current water yield using the mean aridity raster, the water yield expected from the three pine tree thinning scenarios: 7m/hectare ba, 11 m/hectare, and 18 m/hectare, taken from the mean aridity index. Then rasters representing the water yield expected from the three thinning scenarios under maximum, and minimum aridity indexes.</p> <p> </p> <p>These ten outputs are listed here:</p> <p>"wy_current_mean" Based on 2018 BA conditions; Mean ARID</p> <p>"wy_18_mean" BA reduced to 18m2ha-1; Mean ARID</p> <p>"wy_11_mean" BA reduced to 11m2ha-1; Mean ARID</p> <p>"wy_7_mean" BA reduced to 7m2ha-1; Mean ARID</p> <p>"wy_ 18_max" BA reduced to 18m2ha-1; Maximum ARID</p> <p>"wy_ 11_max" BA reduced to 11m2ha-1; Maximum ARID</p> <p>"wy_7_max" BA reduced to 7m2ha-1; Maximum ARID</p> <p>"wy_ 18_min" BA reduced to 18m2ha-1; Minimum ARID</p> <p>"wy_ 11_min" BA reduced to 11m2ha-1; Minimum ARID</p> <p>"wy_ 7_min" BA reduced to 7m2ha-1; Minimum ARID</p> <p> </p> <p>Water yield was estimated for 2018 using the following datasets to inform the model in the Current Water Yield Calculation tool:</p> <ul> <li>Leaf area index modeled from a 2018 pine species basal area raster,</li> <li>Depth to water table data provided by Florida Geological Survey and reclassified as a binary raster,</li> <li>Average aridity index raster generated with precipitation data from PRISM Climate Group and MODIS PET data.</li> </ul> <p>For detailed information on how the above inputs were developed, please see the associated publication:</p> <p>Vernon, J., St. Peter, J., Crandall, C., Awowale, O.E., Medley, P., Drake, J., & Ibeanusi, V. (2023). Spatial application of southern pine water yield for prioritizing forest management activities. ISPRS International Journal of Geo-Information, 12(2), 34. <a href="https://doi.org/10.3390/ijgi12020034">https://doi.org/10.3390/ijgi12020034</a> </p>
Input raster datasets for an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the inputs for a spatial water yield model applied to an 11 county area in the state of Florida's panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in the associated publication. The five input datasets required for this water yield analysis are: 1) a model of pine species basal area, named "ARSA_PineBA_10m" 2) a binary depth to water table raster named "DTW_cm_binary2" , and 3) three spatial aridity index raster dataset named "Aridity_Min", "Aridity_Max" and "Aridity_Mean", created from potential evapotranspiration, and precipitation raster datasets. The min max and mean codifiers relate to the range of aridity values found in our dataset of 7 year temporal range, from MODIS PET and PRISM percipitation yearly data. All input and output data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p>
Data and code: Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management
<ol> <li><span>Sustainable management of living resources must reconcile biodiversity conservation and socioeconomic viability of human activities. In the case of fisheries, sustainable management design is made challenging by the complex spatiotemporal interactions between fish and fisheries.</span></li> <li><span>We develop a comprehensive metapopulation framework integrating data on species life-history traits, connectivity and habitat distribution to identify priority areas for fishing regulation and assess how management impacts are spatially distributed. We trial this approach on European hake fisheries in the north-western Mediterranean, where we assess area-based management scenarios in terms of stock status and fishery productivity to prioritize areas for protection. </span></li> <li><span>Model simulations show that local fishery closures have the potential to enhance both spawning stock biomass and landings on a regional scale compared to a status quo scenario, but that improving protection is easier than increasing productivity. Moreover, the interaction between metapopulation dynamics and the redistribution of fishing effort following local closures implies that benefits and drawbacks are heterogeneously distributed in space, the former being concentrated in the proximity of the protected site. </span></li> <li><span>A network analysis shows that priority areas for protection are those with the highest connectivity (as expressed by network metrics) if the objective is to improve the spawning stock, while no significant relationship emerges between connectivity and potential for increased landings.</span></li> <li> <span><em>Synthesis and applications</em> – </span><span>Our framework provides a tool for 1) assessing area-based management measures aimed at improving fisheries outcomes in terms of both conservation and socioeconomic viability and 2) describing the spatial distribution of costs and benefits, which can help guide effective management and gain stakeholder support. Adult dispersal remains the main source of uncertainty that needs to be investigated to effectively apply our model to fisheries regulation.</span> </li> </ol>
Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity
<p>Gridded global potentially affected fraction of species (PAF) in 2050 and 2100 per kg GHG for 3 RCPs (2.6, 4.5,8.5) averaged over all species groups. </p> <p>Full method description is available in the article: <a href="https://www.sciencedirect.com/science/article/pii/S092134492300294X">Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity - ScienceDirect</a></p>
Data from: Developing spatially explicit and stochastic measures of ecological departure
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.