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108 results for “spatial assessment”
Supplementary material 1 from: Nedkov S, Zhiyanski M, Dimitrov S, Borisova B, Popov A, Ihtimanski I, Yaneva R, Nikolov P, Bratanova-Doncheva S (2017) Mapping and assessment of urban ecosystem condition and services using integrated index of spatial structure. One Ecosystem 2: e14499. https://doi.org/10.3897/oneeco.2.e14499
Figure representing examples of catalogues used during the identification built and land cover types.
Supplementary material 2 from: Nedkov S, Zhiyanski M, Dimitrov S, Borisova B, Popov A, Ihtimanski I, Yaneva R, Nikolov P, Bratanova-Doncheva S (2017) Mapping and assessment of urban ecosystem condition and services using integrated index of spatial structure. One Ecosystem 2: e14499. https://doi.org/10.3897/oneeco.2.e14499
Table representing all combinations of the integrated index of spatial structute in urban ecosystems in Bulgaria
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>
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.
Data and code for "Assessing the spatial scale of synchrony in forest tree population dynamics"
<p>The data sets and code provided here facilitate reproduction of our results from this paper on synchrony of forest tree population dynamics. </p> <h3>Description of the data and file structure</h3> <p>The analyses in the paper were conducted at three scales, and each involves its own data files:</p> <ul> <li>Local scale: The relevant data files are named, e.g., "BCI1-7,L=250m,dbh=100mm.Rdata", where "BCI1-7" indicates the ForestGEO site name ("BCI") and census intervals (1 to 7 for BCI), "L=250m" indicates the quadrat size, and "dbh=100mm" indicates the diameter-at-breast height (DBH) threshold used. There are 12 such files (two ForestGEO plots--BCI and Pasoh--times three quadrat sizes times two DBH thresholds). Each file contains a single list "N_all", whose length is equal to the number of quadrats at the given grain. Each element in the list is a data frame containing mean census times (in days), tree species' population sizes and number of survivors across the two censuses for the corresponding quadrat.</li> <li>Regional scale: The relevant data files are "Marena_data,dbh=100mm,spp_anonymised.Rdata" and "Marena_data,dbh=100mm,spp_anonymised.Rdata". Each file contains three objects: "dists" is a matrix giving the distances between all pairs of sites; "N_all1" is a list with one element for each plot, and each element being a data frame with (anonymised) species ids in the first column and abundances in the remaining columns (column names give mean census dates in days); "S_all1" has a similar structure to "N_all1" except that the data give numbers of survivors from any given census to any subsequent census (column headings indicate the two census numbers).</li> <li>Global scale: The relevant data files are "global_data,dbh=10mm,spp_anonymised.Rdata" and "global_data,dbh=100mm,spp_anonymised.Rdata". The data in the files have the same structure as in the regional-scale files.</li> </ul>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Study of the Correlation Between Preoperative Precise Biometrics, Spatial Assessment and Postoperative Visual Quality in Cataract Patients
ClinicalTrials.gov study NCT04833491. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Spatial Frequency Domain Imaging (SFD) for Assessment of Diabetic Foot Ulcer Development and Healing
ClinicalTrials.gov study NCT03341559. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Assessment of Revascularization in Plantar Foot of Diabetic Patients Pre and Post Angioplasty Using Spatial Frequency Domain Imaging
ClinicalTrials.gov study NCT07097857. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Assessment of spatial discordance of primary and effective seed dispersal of European beech (Fagus sylvatica L.) by ecological and genetic methods
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Data from: Assessing the spatial ecology and resource use of a mobile and endangered species in an urbanized landscape using satellite telemetry and DNA faecal metabarcoding
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Data from: Optimizing the trade-off between spatial and genetic sampling efforts in patchy populations: towards a better assessment of functional connectivity using an individual-based sampling scheme
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The value of increased spatial resolution of pesticide usage data for assessing risk to endangered species: Data, notebooks, and results
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Data from: Detecting selection on temporal and spatial scales: a genomic time-series assessment of selective responses to devil facial tumor disease
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Data from: Genetic assessment of population structure and connectivity in the threatened Mediterranean coral Astroides calycularis (Scleractinia, Dendrophylliidae) at different spatial scales
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Data from: Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition
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Combining seascape connectivity with cumulative impact assessment in support of ecosystem-based marine spatial planning
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Data from: Improving species distribution models for stream networks by incorporating spatial autocorrelation in multi-sourced datasets: An assessment of Idaho giant salamander status and future risk
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Data from: Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system
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Data from: A spatially integrated framework for assessing socioecological drivers of carnivore decline
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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.