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1,868 results for “Spatial Data”
Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model
<p>Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially-varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic dataset, then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.</p>
Spatial distribution data of stomata at the areole level for eight Magnoliaceae species
<p>The dataset includes two .csv files of the spatial distribution data of stomata at the areole level for eight Magnoliaceae species: <span>"EightSpecies" and "OneSpecies" .csv files.</span><span> </span></p> <p><span>The "EightSpecies" .csv file saves the planar coordinates of the stomatal centres of eight Magnoliaceae species</span><span>. For each species, there are 41 to 60 leaves; </span><span>for each leaf, three lamina sections (1.2 mm × 0.9 mm) equidistantly spaced from the leaf left margin to the midrib along the leaf maximum width axis were selected. There are in total 1189 sections.</span></p> <p><span>The "OneSpecies" .csv file saves the planar coordinates of stomatal centres of 12 </span><span><em>Michelia cavaleriei</em> </span><span>var. <em>platypetala</em> leaves</span><span>. There are six layers from leaf apex to leaf petiole (represented by the numbers 1 to 6) and three positions from the left leaf margin to the midrib on each layer (represented by the numbers 1 to 3. In total, stomatal sections from 18 locations were sampled in 12 leaves (i.e. 12 replicates for different positions). There are in total 216 sections.</span></p>
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>
Data for "What it Takes to Get There: Spatial Cognition and Autonomous Indoor Robot Navigation"
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Learning tasks and result data for the 2024 IWERL@GECCO paper A Closer Look at Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning
<p>Learning tasks and result data for the paper Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning by David Pätzel, Richard Nordsieck and Jörg Hähner, presented at the International Workshop on Evolutionary Rule-based Machine Learning taking place as part of GECCO 2024.</p>
IMD New High Spatial Resolution (0.25X0.25 degree) Long Period (1901-2022) Daily Gridded Rainfall Data Set Over India: Matlab script for merging into single netcdf/mat file with datetime stamping
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Spatial data for 11 sensors in an apartment in Leuven, Belgium between March to May, 2021.
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Assessing and Correcting Neighborhood Socioeconomic Spatial Sampling Biases in Citizen Science Mosquito Data Collection
<p>Reporting data from the Mosquito Alert citizen science system, active catch basin surveillance, and mosquito trap surveillance used in "Assessing and Correcting Neighborhood Socioeconomic Spatial Sampling Biases in Citizen Science Mosquito Data Collection."</p> <p>The file named mosquito_alert_adult_bite_reports_Barcelona_2014_2023.Rds includes all adult mosquito and mosquito bite reports received from Barcelona Municipality from the start of the Mosqiuto Alert project in 2014 through the end of 2023. The file named mosquito_alert_validated_albopictus_reports_Barcelona_2014_23.Rds includes all expert-validated <em>Ae. albopictus </em>reports received from Barcelona Municipality during the same time period. The data is stored as RDS files and contain the following fields:</p> <ul> <li><strong>year </strong>- the year in which the report was made. Class = dbl.</li> <li><strong>date </strong>- the date om which the report was made. Class = date.</li> <li><strong>type </strong>- the report type, either adult mosquito ("adult") or mosquito breeding site ("site"). Class = chr.</li> <li><strong>lon</strong> - the longitude of the report location. Class = dbl.</li> <li><strong>lat</strong> - the latitude of the report location. Class = dbl.</li> <li><strong>validation_score</strong> - Entolab validation score. Either 1 (possible <em>Ae. albopictus</em>) or 2 (probable <em>Ae. albopictus</em>). This field is present only in the validated reports data. </li> </ul> <p>The file named active_catch_basin_drain_data.Rds includes information about all catch basin drains in Barcelona Municipality in which the Barcelona Public Health Agency (ASPB) detected mosquito activity as part of its continuous monitoring and control of mosquitoes from 2019 through 2023. The data is stored in an RDS file with the following fields:</p> <ul> <li><strong>any_reports </strong>- dummy variable indicating whether any Mosquito Alert adult mosquito or mosquito bite reports were sent through Mosquito Alert from within 200 m of the catch basin drain during the year in which the ASPB detected mosquito activity in hte catch basin drain. Class = lgl.</li> <li><strong>se_expected</strong> - sampling effort for the 0.025 degree lon/lat sampling cell in which the catch basin drain lies during the year in which the ASPB detected mosquito activity in the drain. This value is taken from the SE_expected variable in the sampling_effort_daily_cellres_025.csv.gz file available at https://zenodo.org/records/12602985. Sampling effort is estimated as the expected number of participants sending at least one report from the cell during the day in question given the the number of participants recorded in the cell that day and the amount of time elapsed since each one began participating in the project. Class = dbl.</li> <li><strong>p_singlehh</strong> - proportion of single-member households in the population of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>mean_age </strong>- mean age of the population of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>mean_rent_consumption_unit</strong> - mean income per consumption unit in the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>popd</strong> - population density of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>id_item </strong>- unique identifier given to the catch basin drain. Drain itentifiers appear multiple times in the data when the ASPB detected activity in the drain in multiple years. Class = dbl.</li> </ul> <p>The file named trap_data.Rds includes information on the adult mosquito trap surveillance analyzed in this article. The data is stored in an RDS file with the following fields:</p> <ul> <li><strong>females </strong>- number of Ae. albopictus females found in the trap. Class = dbl.</li> <li><strong>trap_name</strong> - unique identifier for the trap. Class = chr.</li> <li><strong>trapping_effort</strong> - number of days from when the trap was set to when it was checked. Class = dbl.</li> <li><strong>date</strong> - date on which the trap was checked. Class = date.</li> <li><strong>mean_tm30</strong> - mean temperature for the 30 days leading up to the date on which the trap was checked. Class = dbl.</li> <li><strong>mean_rent_consumption_unit </strong>- mean income per consumption unit for the census tract in which the trap was located. Class = dbl.</li> </ul>
Spatial data for precipitation, groundwater aesenic and salinity.
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Data for manuscript : Effect of spatial training on space-number mapping: A situated cognition account
<p><span>From an embodied perspective of cognition, sensorimotor mechanisms play a crucial role in abstract processing, such as the understanding of Arabic numerals. For instance, spatial cognition can influence number processing. These </span><span>spatial–numerical</span><span> associations (SNAs) have been thoroughly investigated since the pioneering </span><span>spatial–numerical association of response codes (SNARC) effect</span><span>, which demonstrates faster left/right responses to small/large numbers, respectively. While there </span><span>has been</span><span> no systematic assessment of SNAs on other planes in three-dimensional space in the literature, recent primary </span><span>evidence has</span><span> revealed that SNAs along </span><span>the transverse and sagittal planes </span><span>are</span><span> mutually exclusive </span><span>with respect</span><span> </span><span>to the required spatial reference frames used by the participant.</span><strong><span> </span></strong><span>Specifically, under </span><span>egocentric</span><span> spatial reference frames</span><span>,</span><span> SNAs have been observed only along the sagittal plane, </span><span>whereas</span><span> under</span><span> allocentric reference </span><span>frames, </span><span>the </span><span>reverse</span><span> pattern has been observed</span><span>,</span><span> with SNAs present exclusively along the transverse plane of the body. Given </span><span>this</span><span> empirical </span><span>evidence</span><span>, we have hypothesized that the subject's ability to switch spatial reference frames to match that of another person could significantly </span><span>influence</span><span> the occurrence of SNAs according to the processed plane. Therefore, this study has two aims. The first is to replicate </span><span>previous</span><span> seminal findings. The second is to investigate how referential </span><span>frame</span><span> switching (RFS) training can affect this organization. </span><span>While the results of</span><span> the two experiments reveal a general replication, more importantly, we find that RFS training enables </span><span>the development of</span><span> new situated cognition strategies </span><span>from</span><span> egocentric perspectives and </span><span>the generalization of</span><span> transverse SNAs to other spatial planes </span><span>from</span><span> allocentric perspectives.</span></p>
Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>The data have been reprojected to EU LAEA.</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>EU LAEA (EPSG: 3035)</p> <p>Spatial extent:<br>north: 6874000<br>south: -485000<br>west: 869000<br>east: 8712000</p> <p>Spatial resolution:<br>1000 m</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="https://doi.org/10.5281/zenodo.6146383">https://doi.org/10.5281/zenodo.6146383</a></p>
Deep Clustering Representation for Spatially Resolved Transcriptomics Data via Multi-view Variational Graph Auto-Encoders with Consensus Clustering
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Spatial Occurrence Data from The Ornithological Collection of The Emeritus Professor Kazi Zaker Husain Museum of the Department of Zoology, University of Dhaka, Dhaka
<p>Natural history collections are important as they provide significant information about biogeography and conservation. Occurrence data from such collections aid our understanding of species distribution, provide valuable information about historical geographic ranges and shed light on habitat loss. Here, we report such occurrence data collected from the avian collections of the Emeritus Professor Kazi Zaker Husain Museum of the Department of Zoology, University of Dhaka, Dhaka which holds Bangladesh's largest natural history collection. We restored the specimens with mild damage. As no register of collections was found, we examined bird skins to identify species and associated field slips attached to the tarsus for any available information. We extracted the date and the place of collection, the name of the collectors and identifiers where available. The data were digitized and details of all locations were geo-referenced. </p>
Data and figure production code for 'Hydrological cycle amplification imposes spatial pattern on climate change response of ocean pH and carbonate chemistry'
<p>Time mean data, and python code, used to create figures in 'Hydrological cycle amplification imposes spatial pattern on climate change response of ocean pH and carbonate chemistry', Biogeosciences, Hogikyan and Resplandy 2024</p>
Data for the manuscript "Spatially resolved uncertainties for machine learning potentials"
<p>This repository accompanies the manuscript "Spatially resolved uncertainties for machine learning potentials" by E. Heid, J. Schörghuber, R. Wanzenböck, and G. K. H. Madsen. The following files are available:</p> <ul> <li> <p><code>mc_experiment.ipynb</code> is a Jupyter notebook for the Monte Carlo experiment described in the study (artificial model with only variance as error source).</p> </li> </ul> <ul> <li> <p><code>aggregate_cut_relax.py</code> contains code to cut and relax boxes for the water active learning cycle.</p> </li> <li> <p><code>data_t1x.tar.gz</code> contains reaction pathways for 10,073 reactions from a subset of the Transition1x dataset, split into training, validation and test sets. The training and validation sets contain the indices 1, 2, 9, and 10 from a 10-image nudged-elastic band search (40k datapoints), while the test set contains indices 3-8 (60k datapoints). The test set is ordered according to the reaction and index, i.e. rxn1_index3, rxn1_index4, [...] rxn1_index8, rxn2_index3, [...].</p> </li> <li> <p><code>data_sto.tar.gz</code> contains surface reconstructions of SrTiO3, randomly split into a training and validation set, as well as a test set.</p> </li> <li> <p><code>data_h2o.tar.gz</code> contains:</p> <ul> <li> <p><code>full_db.extxyz</code>: The full dataset of 1.5k structures.</p> </li> <li> <p><code>iter00_train.extxyz</code> and <code>iter00_validation.extxyz</code>: The initial training and validation set for the active learning cycle.</p> </li> <li> <p>the subfolders in the folders <code>random</code>, and <code>uncertain</code>, and <code>atomic</code> contain the training and validation sets for the random and uncertainty-based (local or atomic) active learning loops.</p> </li> </ul> </li> </ul>
Spatial capture-recapture data of the Darwin's frog
<p>These data and code are intentended to serve as complementary information to allow the full reproducibility of the results presented in the study "Natal dispersal is facilitated by routine movements integrated along a straight path in the Darwin’s frog" submitted to Ecography.</p> <p>The first file titled "data and code used to run the sCJS.rar" contains capture-recapture data from three populations of the Darwin's frog (<em>Rhinoderma darwinii</em>) collected between March 2014 and December 2016. The age of the individuals, population of origin, and x- and y-coordinates of each capture are provided. The x- and y-coordinates are in meters. The sampling design follows the Pollock's robust design. The capture-history matrices (one for the x-coordinates and another for the y-coordinates) contain 48 columns representing twelve evenly distributed primary capture periods, each one composed of four consecutive days of capture (secondary capture ocassions). The time-period between primary capture periods is 3 months. However, note that we did not visited the populations during all these periods, therefore, some of collumns contain only NA's. The rows of the capture-history matrices represent the different individuals that were captured across the study. There are 370 rows, but for seven of these individuals we don't have spatial data (these individuals are removed in the code). Then we provide the R code used to run the sCJS of Schaub and Royle (2014). This code is almost identical to the provided by these authors, but a few modifications were made in order to fit our study design. Results of the model are also provided.</p> <p>The second file "Data required to run the simulation model.rar" contains the data required to run the movement path simulation model presented in the Supplementary material, Appendix 2 of the paper. Further details about these files and model can be found in the article.</p> <p>Any question can be adressed to andresvalenzuela.zoo@gmail.com</p> <p>Enjoy it!</p>
Supplementary material 1 from: Underwood E, Taylor K, Tucker G (2018) The use of biodiversity data in spatial planning and impact assessment in Europe. Research Ideas and Outcomes 4: e28045. https://doi.org/10.3897/rio.4.e28045
Biodiversity data sources at the EU level comprise the biodiversity and landuse-related EU reporting and monitoring programmes, and global or regional data portals which include data from European countries. Also gives an overview of spatial planning and data sources for biodiversity impact assessment in a selection of EU Member States: UK, Bulgaria, Netherlands, Germany, Sweden, Denmark and the Baltic Sea.
Input data and scripts for "Spatial conservation prioritization for the East Asian islands: a balanced representation of multi-taxon biogeography in a protected area network"
<p>This release contains the input files 'input_data.zip' for the spatial conservation prioritization analysis by Zonation software, which are conducted in Lehtomäki et al. Input data includes biodiversity features (species distribution maps from vascular plants, mammals, birds, reptiles, amphibians, and freshwater fishes), habitat condition map (human influence index), priority mask information (the categorized protected area distribution) and the Japanese prefecture polygons in GeoTiff format, and the list of species attributes for conservation weighting in CSV format. Note that endangered rare species have been excluded from this dataset, though they were reflected in the output files. The Zonation setting files and R scripts for pre- and post analyses are included in 'japan-zsetup-1.0.zip' and also placed at GitHub : https://github.com/cbig/japan-zsetup</p> <p>The output files (priority score maps and removal curves) from the original Zonation analyses are summarized in 'output_from_original.data.zip'</p>
Aggregated Spatial Data by province GPKG Format | Biodiversity Publication Bias Compromises Setting Conservation Priorities
<p>Result of running https://github.com/raffael-hickisch/provincer</p>
Data for "Temporal and Spatial Characteristics of Preliminary Breakdown Pulses in Intracloud Lightning Flashes"
<p>In the manuscript entitled "Temporal and Spatial Characteristics of Preliminary Breakdown Pulses in Intracloud Lightning Flashes", the corresponding data is provided in the following attachment. These files can be opened by Matlab 2016 (or later).</p>
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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.