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1,868 results for “Spatial Data”
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization"
<p>Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization".<br>Preprint of the paper available at: <a href="https://arxiv.org/abs/2309.03308">https://arxiv.org/abs/2309.03308</a></p>
LAGOS-NE – Lake nutrient chemistry and geospatial data to measure spatial structure of ecosystem properties in a 17-state region of the U.S.
This dataset includes data for the lake water quality and geospatial variables that describe climate, hydrology, land use land cover, and lake characteristics that were used to study spatial structure in lake properties at the sub-continental scales (Lapierre et al. Quantifying spatial structure to improve understanding of the relationships between climate, landscape, and lake ecosystem properties, to be submitted to Ecology). All observations came from LAGOS-NELIMNO v. 1.054.1 and LAGOS-NEGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS-NE contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for the LAGOS-NELIMNO v. 1.054.1 dataset and were mostly generated by government agencies (state, federal, tribal) and universities. In this analysis, we compiled lake water quality data from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOS-NELIMNO v. 1.054.1 (2002-2011). We report the median total nitrogen, total phosphorus, secchi depth, and chlorophyll values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics including variables related to lake morphometry, climate, hydrology, atmospheric deposition, land use and land cover.
Data from publication: Castillioni, K., & Isbell, F. (2023). Early positive spatial selection effects of beta-diversity on ecosystem functioning. Landscape Ecology, 1-15.
Data from publication: Castillioni, K., & Isbell, F. (2023). Early positive spatial selection effects of beta-diversity on ecosystem functioning. Landscape Ecology, 1-15. Spatial beta-diversity may increase landscape productivity if there are positive spatial selection effects. Alternatively, dominant species in mixtures might not be the most productive species in monoculture leading to negative or neutral spatial selection effects. However, these hypotheses remain untested experimentally. Seedling survival can determine species establishment, influencing productivity later. To address this knowledge gap, we experimentally tested whether transplanted seedlings of dominant species optimally sort among habitat types (grassland dominated by Andropogon gerardii, savanna by Quercus macrocarpa, deciduous forest by Acer rubrum, coniferous forest by Pinus strobus, bog by Larix laricina), creating positive effects of landscape diversity on seedling survival and net biodiversity effects at Cedar Creek Ecosystem Science Reserve (CCESR) in Minnesota, USA. The study is named BetaDIV and consists of 100 plots (20 plots per habitat × 5 habitats). Each of the five habitats includes two true replicate monocultures for each of the five species and two true replicates for each of the five possible mixture compositions of four species (leaving each one out in turn to eventually explore the effect of species identity). Each plot is 1.5 by 1.5 m, with 12 seedlings planted 0.5 m apart in a 4 × 4 square grid, except in the plot corners. In the early June 2022, we tagged and planted all seedlings (i.e., bareroot seedlings for trees and plugs for the grass A. gerardii). Two weeks after the initial transplanting, we started tracking seedling survival (presented here) to investigate how seedlings responded to local habitat conditions. We conducted a seedling census for each of the 1200 tagged seedlings (12 seedlings per plot×100 plots), in early September 2022, which was two months at the end
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Slope data in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes landscape slope data. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Cascade Project at North Temperate Lakes LTER High Frequency Sonde Data from Spatial Dynamics Experiment 2018 - 2019
High-frequency continuous data for temperature, dissolved oxygen, pH, chlorophyll-a, and phycocyanin in Paul Peter lakes from mid-May to early September for the years 2018 and 2019. Inorganic nitrogen and phosphorus were added to Peter in 2019, while Paul Lake was an unfertilized reference.
Data on spatial distribution of tracers for optical sensing of stream surface flow
<p>Here, we present the numerical and field data used in the manuscript entitled <em>Spatial distribution of tracers for optical sensing of stream surface flow</em>. Numerical data were synthetically generated considering different values of seeding density and aggregation levels of tracers for image-velocimetry analyses. In total, 33,600 synthetic images were generated. Field data correspond with the Basento River case study located in southern Italy. The respective footage at 12 fps, pre-processed and stabilised frames, and reference velocity data are provided in this dataset.</p>
Supporting data for "Spatial Noise Correlations in a Si/SiGe Two-Qubit Device from Bell State Coherences"
<p>Datasets, analysis scripts and simulations for "Spatial Noise Correlations in a Si/SiGe Two-Qubit Device from Bell State Coherences".</p> <p>For more information and instructions, see READ ME.txt.</p> <p>For questions, contact Jelmer Boter (j.m.boter@tudelft.nl) or Lieven Vandersypen (l.m.k.vandersypen@tudelft.nl).</p>
The spatial landscape of lung pathology during COVID-19 progression - raw IMC data
<p>Recent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19). However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry9 targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.</p>
Predicting placenta transcriptional regulatory interactions based on spatial gene expression data and convolutional neural network
<p><strong>Aims:</strong> The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped.</p> <p><strong>Methods and Results:</strong> In this study, first-trimester chorionic villus and decidua tissues were collected. Next, we present a machine-learning system to infer gene interaction networks of the human placenta from immunofluorescence images of trophoblast specific transcription factors obtained by a high-resolution scanner.</p> <p><strong>Conclusions:</strong> The experimental results show that deep learning models reveal regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.</p>
Spatial Span and Matrix Reasoning data from the UW-Madison Learning and Transfer Lab
<p><strong>Matrices_SpatialSpan.csv</strong> includes one row for every mouse click for every trial for each participant's spatial span performance (for similar spatial span methods see Cochrane, Simmering, & Green, 2019, PLOS One). Participant IDs, trial numbers, the presence [f] or absence [n] of feedback, and task order (spatial span first or spatial span second) are included alongside by-click accuracy. Also included are each participants' average scores on a subset of items from the UCMRT (Pahor et al., 2019, Beh. Res. Meth) and from the matrices developed at Sandia National Laboratories (Matzen et al., 2010, Beh. Res. Meth.).</p> <p><strong>robustCor.R </strong>is R code implementing a test of bivariate correlation. Univariate Yeo-Johnson transformations are applied, then bootstrapped correlations coefficients are calculated. Point estimates, CI, and Bayes Factors are each returned.</p> <p>Data were collected and code was developed as part of A. Cochrane's dissertation work at the University of Wisconsin - Madison under the supervision of C. Shawn Green.</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: Spatial processes and evolutionary models: a critical review
Evolution is a fundamentally population level process in which variation, drift, and selection produce both temporal and spatial patterns of change. Statistical model fitting is now commonly used to estimate which kind of evolutionary process best explains patterns of change through time, using models like Brownian motion, stabilizing selection (Ornstein-Uhlenbeck), and directional selection on traits measured from stratigraphic sequences or on phylogenetic trees. But these models assume that the traits possessed by a species are homogeneous. Spatial processes such as dispersal, gene flow, and geographic range changes can produce patterns of trait evolution that do not fit the expectations of standard models, even when evolution at the local-population level is governed by drift or a typical OU model of selection. The basic properties of population level processes (variation, drift, selection, and population size) are reviewed and the relationship between their spatial and temporal dynamics is discussed. Typical evolutionary models used in palaeontology incorporate the temporal component of these dynamics, but not the spatial. Range expansions and contractions introduce rate variability into drift processes, range expansion under a drift model can drive directional change in trait evolution, and spatial selection gradients can create spatial variation in traits that can produce long-term directional trends and punctuation events depending on the balance between selection strength, gene flow, extirpation probability, and model of speciation. Using computational modelling that spatial processes can create evolutionary outcomes that depart from basic population-level notions from these standard macroevolutionary models.
Spatial Tournament Data on a Periodic Lattice Tournament Size 5 - MSc Dissertation
<p>A data set that contains the results of 1000 spatial games, for the iterated prisoner's dilemma. The spatial topology used is a periodic lattice network, and the tournament size set is 5.</p>
Spatial Tournament Data on a Periodic Lattice Tournament Size 50 - MSc Dissertation
<p>A data set that contains the results of 1000 spatial games, for the iterated prisoner's dilemma. The spatial topology used is a periodic lattice network, and the tournament size set is 50.</p>
Spatial Tournament Data on a Cyclic Tournament Size 5 - MSc Dissertation
<p>A data set that contains the results of 1000 spatial games, for the iterated prisoner's dilemma. The spatial topology used is a cyclic network, and the tournament size set is 5.</p>
Spatial Tournament Data on a Cyclic Tournament Size 50 - MSc Dissertation
<p>A data set that contains the results of 1000 spatial games, for the iterated prisoner's dilemma. The spatial topology used is a cyclic network, and the tournament size set is 50.</p>
Synthetic Smart Card Data for the Analysis of Temporal and Spatial Patterns
<p>This is a synthetic smart card data set that can be used to test pattern detection methods for the extraction of temporal and spatial data. The data set is tab seperated and based on a stylized travel pattern description for city of Utrecht in The Netherlands and is developed and used in Chapter 6 of the PhD Thesis of Paul Bouman. </p> <p>This dataset contains the following files:</p> <ul> <li>journeys.tsv : the actual data set of synthetic smart card data</li> <li>utrecht.xml : the activity pattern definition that was used to randomly generate the synthethic smart card data</li> <li>validate.ref : a file derived from the activity pattern definition that can be used for validation purposes. It specifies which activity types occur at each location in the smart card data set.</li> </ul>
IVMOOC 2017 - GloBI Data for Interactive Tableau Map of Spatial and Temporal Distribution of Interactions
<p>Global Biotic Interactions (GloBI, www.globalbioticinteractions.org) provides an infrastructure and data service that aggregates and archives known biotic interaction databases to provide easy access to species interaction data. This project explores the coverage of GloBI data against known taxonomic catalogues in order to <em>identify ‘gaps’ in knowledge of species interactions</em>. We examine the richness of GloBI’s datasets using itself as a frame of reference for comparison and explore interaction networks according to geographic regions over time. The resulting analysis and visualizations intend to provide insights that may help to enhance GloBI as a resource for research and education.</p> <p>Spatial and temporal biotic interactions data were used in the construction of an interactive Tableau map. The raw data (IVMOOC 2017 GloBI <em>Kingdom</em> Data Extracted 2017 04 17.csv) was extracted from the project-specific SQL database server. The raw data was clean and preprocessed (IVMOOC 2017 GloBI Cleaned Tableau Data.csv) for use in the Tableau map. Data cleaning and preprocessing steps are detailed in the companion paper.</p> <p>The <strong>interactive Tableau map</strong> can be found here: https://public.tableau.com/profile/publish/IVMOOC2017-GloBISpatialDistributionofInteractions/InteractionsMapTimeSeries#!/publish-confirm</p> <p>The<strong> companion paper</strong> can be found here: doi.org/10.5281/zenodo.814979</p> <p><strong>Complementary high resolution visualizations </strong>can be found here: doi.org/10.5281/zenodo.814922</p> <p><strong>Project-specific data </strong>can be found here: doi.org/10.5281/zenodo.804103 (SQL server database)</p>
ScienceDex guides
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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.