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.
423
datasets available to search
ShareScore release 0.9.0
Dataset results
423 results for “spatial use”
Code + simulated + publically accessable data for "Evaluating health facility access using Bayesian spatial models and location analysis methods"
<p># README</p> <p>These files contain r data objects and R files that represent the key details of the paper, "Evaluating health facility access using Bayesian spatial models and location analysis methods".</p> <p>The following datasources are available for simulation of some of the ideas in the paper.</p> <p>- dat_grid_sim: simulated data of the grid and grid cells<br> - dat_ohca_cv_sim: simulated data containing the cross validated test/training sets of OHCA data<br> - dat_ohca_sim: simulated OHCA event data<br> - dat_aed_sim: simulated AED location data<br> - dat_bldg_sim: simulated building location data<br> - dat_municipality_sim: simulated municipality information<br> - table_1: Table 1 information containing key demographic data</p> <p>These data were produced using the code in 01-create-sim-data.R, and one of the statistical models is demonstrated in 02-demo-inla-model.R</p> <p>In terms of the paper itself, the functions and code used in the manuscript are located in:</p> <p>* 01_tidy.Rmd - analysis code used to tidy up the data</p> <p>* 02_fit_fixed_all_cv.Rmd - analysis code used to place AEDs</p> <p>* 02_model.Rmd - analysis code used to fit the model in INLA</p> <p>* 03_manuscript.Rmd - Full code and text used to create the paper</p> <p>* 04_supp_materials.Rmd - full code and text used to create the supplementary materials</p> <p>The following files are a part of an R package "swatial" that was developed along with the paper. These files are:</p> <p>* DESCRIPTION</p> <p>* NAMESPACE</p> <p>* LICENSE</p> <p>* LICENSE.md</p> <p>* decay.R</p> <p>* spherical-distance.R</p> <p>* test-figure-data-matches.R</p> <p>* test-table-data-matches.R</p> <p>* testthat.R</p> <p>* tidy-inla.R</p> <p>* tidy-posterior-coefs.R</p> <p>* tidy-predictions.R</p> <p>* utils-pipe.R</p> <p>* All files that end in .Rd are documentation files for the functions.</p> <p>## Regarding data sources</p> <p>Census information for Ticino was transcribed from the Annual Statistical Report of Canton Ticino from years 2010 to 2015. This data was taken from their publicly accessible annual reports - for example: (https://www3.ti.ch/DFE/DR/USTAT/allegati/volume/ast_2015.pdf). The raw data was extracted from these annual reports, and placed into the file: "swiss_census_popn_2010_2015.xlsx". These data are put into analysis ready format in the file “01_tidy.Rmd”</p> <p>Housing and other relevant geospatial data can be accessed via http://map.housing-stat.ch/ and https://data.geo.admin.ch/. The maps of buildings from the REA (Register of Buildings and Dwellings) can be found here: https://map.geo.admin.ch/?zoom=11&bgLayer=ch.swisstopo.pixelkarte-grau&lang=en&topic=ech&layers=ch.bfs.gebaeude_wohnungs_register,ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill,ch.bfs.volkszaehlung-gebaeudestatistik_gebaeude,ch.bfs.volkszaehlung-gebaeudestatistik_wohnungen,ch.swisstopo.swissbuildings3d_1.metadata,ch.swisstopo.swissbuildings3d_2.metadata&E=2717616.28&N=1096597.25&catalogNodes=687,696&layers_timestamp=,,2016,2016,,&layers_visibility=true,false,false,false,false,false&layers_opacity=1,1,1,1,1,0.75</p> <p>For further enquiries on this data, contact the Swiss federal Office of Statistics at the details listed here: https://www.bfs.admin.ch/bfs/en/home/services/contact.html</p> <p>The shapefiles of the Comuni can be accessed here: https://www4.ti.ch/dfe/de/ucr/documentazione/download-file/?noMobile=1</p> <p>Data from the people living in the Municipalities in Ticino can be downloaded here: https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=dati.home&tema=33&id2=61&id3=65&c1=01&c2=02&c3=02</p> <p>## Future work</p> <p>In the future, these functions from the paper may be generalised and put into their own package. If that happens, this repository will be updated with a link to updated functions.</p>
Quantifying the spatial heterogeneity of forest conversion costs and how it relates to biodiversity, conservation and land use history
<b>Description: </b><p>Start and end dates of salvage logging activity at the SAFE Project experimental site</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/6"><b>Quantifying the spatial heterogeneity of forest conversion costs and how it relates to biodiversity, conservation and land use history</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Sime Darby (grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence na)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3266827">here</a></p><p><b>Files: </b>This dataset consists of 2 files: template_Symes.xlsx, SAFE_COUPE.zip</p><p><b>template_Symes.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Salvage logging records</b> (described in worksheet Data)</p><p>Description: Dates of earliest and latest known salvage logging activity in logging coupes</p><p>Number of fields: 10</p><p>Number of data rows: 187</p><p>Fields: </p><ul><li><b>CoupeNumber</b>: Coupe number (Field type: Location)</li><li><b>StartDateTrack</b>: Earliest date of logging activity recorded through GPS loggers on bulldozers (Field type: Date)</li><li><b>EndDateTrack</b>: Last date of logging activity recorded through GPS loggers on bulldozers (Field type: Date)</li><li><b>StartDateLocation</b>: Earliest date of logging activity recorded through 'Location' method (Field type: Date)</li><li><b>EndDateLocation</b>: Last date of logging activity recorded through 'Location' method (Field type: Date)</li><li><b>StartDateMeasurement</b>: Earliest date of logging activity recorded through 'Measurement' method (Field type: Date)</li><li><b>EndDateMeasurement</b>: Last date of logging activity recorded through 'Measurement' method (Field type: Date)</li><li><b>Contractor</b>: Name of the contractor responsible for the coupe (Field type: ID)</li><li><b>GlobalStart</b>: Earliest date of any recorded salvage logging activity (Field type: Date)</li><li><b>GlobalEnd</b>: Last date of any recorded salvage logging activity (Field type: Date)</li></ul></li></ol><p><b>SAFE_COUPE.zip</b></p><p>Description: SAFE coupe data</p><p><b>Date range: </b>2013-01-02 to 2015-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Data from: The spatial structure of phylogenetic and functional diversity in the United States and Canada: an example using the sedge family (Cyperaceae)
Systematically quantifying diversity across landscapes is necessary to understand how clade history and ecological heterogeneity contribute to the origin, distribution, and maintenance of biodiversity. Here, we chart the spatial structure of diversity among all species in the sedge family (Cyperaceae) throughout the USA and Canada. We first identify areas of remarkable species richness, phylogenetic diversity, and functional trait diversity, and highlight regions of conservation priority. We then test predictions about the spatial structure of this diversity based on the historical biogeography of the family. Incorporating a phylogeny, over 400,000 herbarium records, and a database of functional traits mined from online floras, we find that species richness and functional trait diversity peak in the Northeastern USA, while phylogenetic diversity peaks along the Gulf of Mexico. Floristic turnover among assemblages increases significantly with distance, but phylogenetic turnover is twice as rapid along latitudinal gradients as along longitudinal gradients. These patterns reflect the expected distribution of Cyperaceae, which originated in the tropics but radiated in temperate regions. We identify assemblages with an abundance of rare, range-restricted lineages, and assemblages composed of species generally lacking from diverse regions. We argue that both of these metrics are useful for developing targeted conservation strategies. We use the data generated here to establish future research priorities, including the testing of a series of hypotheses regarding the distribution of chromosome numbers, photosynthetic pathways, and resource partitioning in sedges.
Using machine learning to model nontraditional spatial dependence in occupancy data
<p>Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in occupancy data by using a correlated normally distributed site-level random effect, which might be incapable of modeling nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to model nontraditional spatial dependence, but these approaches do not account for observer errors such as false absences. By combining the flexibility of Bayesian hierarchal modeling and machine learning approaches, we present a general framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence as well as false absences. We demonstrate our framework using six synthetic occupancy data sets and two real data sets. Our results demonstrate how to model both traditional and nontraditional spatial dependence in occupancy data which enables a broader class of spatial occupancy models that can be used to improve predictive accuracy and model adequacy.</p>
Dataset : Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps
<ul> <li><strong>The three files in the dataset are:</strong></li> </ul> <p>1) Healthy Sheep Atria Fibre Orientation Dataset 300µm</p> <p>2) Heart Failure Sheep Atria Fibre Orientation Dataset 300µm</p> <p>3) Human Atria Fibre Orientation Dataset 330µm</p> <ul> <li><strong>Data is stored as numpy binary files. Given below is an example .py script to open the flat datasets:</strong></li> </ul> <p> import numpy as np<br> data = np.load("Human_330um.npy")</p> <ul> <li><strong>Volume and fibre orientation dataset stored in flat format as given below:</strong></li> </ul> <p> i, j, k, v1, v2, v3, ... repeated for each voxel </p> <p>where (i, j, k) are voxel coordinates and (v1, v2, v3) are vector components corresponding to fibre orientation within that voxel.</p>
Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization
<p>Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A. that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas or GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hotspots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat-abundance relationships, develop spatially-explicit predictions of abundance and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated high representation of this bird assemblage within the entire conservation network, but with a bias towards birds associated with moderately tall grasses relative to those associated with short or tall grasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serve as a complementary tool for conservation planning efforts globally.</p>
Data for: Spatial monitoring of flying insects over a Swedish lake using a CW lidar system
<p>Data for a field experiment on remote sensing of flying insects are supplied. A bistatic CW lidar system of the Scheimpflug type was employed, and echoes from flying insects over and close to a Swedish lake were recorded as read-out files from an array detector making observations along the emitted laser beam. The activated pixels of the detector could be converted into range data by triangulation. With a high detector read-out frequency, data on numerous insects could be obtained. Special emphasis was put on distinguishing between large and small insects (from the intensities of the recorded echoes) and the insect positions with regard to the shores of the lake. </p>
Data from: Direct generation of spatially entangled qudits using quantum nonlinear holography - variances
<p>Nonlinear holography shapes the amplitude and phase of generated new harmonics using nonlinear processes. Classical nonlinear holography influenced many fields in optics, from information storage, de-multiplexing of spatial information and all-optical control of accelerating beams. Here, we extend the concept of nonlinear holography to the quantum regime. We directly shape the spatial quantum correlations of entangled photon pairs in two-dimensional patterned nonlinear photonic crystals using spontaneous parametric down conversion, without any pump shaping. The generated signal-idler pair obeys a parity conservation law that is governed by the nonlinear crystal. Furthermore, the quantum states exhibit quantum correlations and violate the Clauser-Horne-Shimony-Holt inequality, thus enabling entanglement-based quantum key distribution. Our demonstration paves the way for controllable on-chip quantum optics schemes utilizing the high-dimensional spatial degree of freedom.</p>
Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data"
<p>Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data</p> <p>", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
Profiling the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics: datasets
<p>You can find here the datasets used in the publication: </p> <p><em><strong>Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong> </em></p> <p>This contents the raw Spatial Transcriptomics data, spot categorization made by pathologist, the results of the deconvolution and intermediary files required to run the analysis described in our manuscript and available in Github: </p> <p><a href="https://github.com/alberto-valdeolivas/ST_CRC_CMS">https://github.com/alberto-valdeolivas/ST_CRC_CMS</a></p> <p>In particular, you will find here several zip compressed files with the following content: </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Intermediary_FileObjects.zip?versionId=989cd48d-45f6-46b9-9f90-1927af392a7e">Intermediary_FileObjects.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some later scripts. </p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/IntermediaryFiles_ST_CRC_LiverMetastasis.zip">IntermediaryFiles_ST_CRC_LiverMetastasis.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some of the scripts dealing with the external CRC ST dataset used in our manuscript. </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Pathology_SpotAnnotations.zip?versionId=ce657a54-9fec-4633-9d89-31f1479b93b7">Pathology_SpotAnnotations.zip</a>: The categories assigned by the pathologists to all the spots across our set ST samples to a different anatomical category (tumor, stroma, non-neoplastic mucosa...) </p> <p>-<a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep1.zip?versionId=dbfaad0f-784b-44c9-91d1-713f063d64e3">SN048_A121573_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep2.zip?versionId=ae997080-ca69-44c3-86aa-65bc1d5ef120">SN048_A121573_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep1.zip?versionId=e453ed45-22d8-4d60-b7f3-4daa9212cc88">SN048_A416371_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep2.zip?versionId=be395926-eee8-4670-b355-125e72bf6281">SN048_A416371_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A551763_Rep1.zip?versionId=6b7fa01a-a0d9-43e7-8d1d-8c56cb422374">SN123_A551763_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A595688_Rep1.zip?versionId=f625a286-fbc7-48f6-a57d-7d0df67a0574">SN123_A595688_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A798015_Rep1.zip?versionId=3540f1e5-9cf4-412c-887c-b1d0cc4e03c5">SN123_A798015_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A938797_Rep1_X.zip?versionId=de59c354-fea2-4843-a5f9-5e7a8d863e51">SN123_A938797_Rep1_X.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A551763_Rep2.zip?versionId=9da50bec-8ba4-41b0-a29e-4fc778cf12b7">SN124_A551763_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A595688_Rep2.zip?versionId=29c3e99e-7db2-4c02-9004-dc9d8abf3c27">SN124_A595688_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A798015_Rep2.zip?versionId=a0cf2cca-f3c9-4c45-b311-1ddc81371e35">SN124_A798015_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A938797_Rep2.zip?versionId=e6e4e2bc-1593-4c0f-ac37-b00cc2fc1124">SN124_A938797_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep1.zip?versionId=ec31a69e-d0ce-4e4c-82dc-e7f2e617631a">SN84_A120838_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep2.zip?versionId=89c89532-7bb1-47e4-8900-b5c12a7c4ba0">SN84_A120838_Rep2.zip</a>: The output of Space Ranger, including processed count data matrices and histological images, for the ST data generated in this study</p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_BelgianCohort.zip">DeconvolutionResults_ST_CRC_BelgianCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_KoreanCohort.zip">DeconvolutionResults_ST_CRC_KoreanCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_LiverMetastasis.zip">DeconvolutionResults_ST_CRC_LiverMetastasis.zip</a>: These files contain the main results obtained when using the Cell2Location deconvolution approach in our samples (with two different references: Korean and Belgian cohorts) and in the external set of CRC ST samples (only Korean cohort)</p> <p> </p> <p>- We have also uploaded the whole slide images (WSI). These are the files with an ndpi extension: </p> <p><br><a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V10B01-048_new%20CRC_2021_02_16.ndpi?versionId=d5c8cbd3-40de-43da-8370-329def9e4f14">Visium Frozen_SN V10B01-048_new CRC_2021_02_16.ndp ...</a> (samples A121573_Rep1, A121573_Rep2, A416371_Rep1 and A416371_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-084.ndpi?versionId=6d91b1f9-56e9-45c3-a2e6-4714975678fb">Visium Frozen_SN V19S23-084.ndpi</a> (samples A120838_Rep1 and A120838_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-123.ndpi?versionId=c535482c-0a3c-4ba5-a056-f96796c366b0">Visium Frozen_SN V19S23-123.ndpi</a> (samples A551763_Rep1, A595688_Rep1, A798015_Rep1, A938797_Rep1) and <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-124.ndpi?versionId=49ce857c-47bb-4930-bb0c-09213e4acf28">Visium Frozen_SN V19S23-124.ndpi</a> (samples A551763_Rep2, A595688_Rep2, A798015_Rep2 and A938797_Rep2)</p> <p>- We have now included the fastq and Bam files for the different samples, excluding replicate 1 of the A938797 sample whose fastq files are missing: </p> <p><strong>IMPORTANT: Fastq files are in version 1, while bam files are in version 2 of the dashboards reported below: </strong></p> <ol> <li>Sample <a href="https://doi.org/10.5281/zenodo.13991781">S1_Cec</a> (A551763)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006187">S2_Col_R </a>(A595688)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13987002">S3_Col_R </a>(A416371) </li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13990328">S4_Col_Sig </a>(A120838)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13989699">S5_Rec </a>(A121573)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14008051">S6_Rec </a>(A938797)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006810">S7_Rec/Sig</a> (A798015)</li> </ol> <p> </p> <p> </p>
Data from: Monitoring temporal and spatial trends of illegal and legal fishing in Canada's marine conservation areas using vessel tracking datasets
<p>Expansion of marine conservation areas (CA) necessitates resource-efficient and achievable strategies for monitoring and evaluation of ongoing fishing activity at national levels. To demonstrate and explore such a strategy, we conducted the first extensive analysis of fishing activity within Canada's static, geographically defined marine CAs with fishing regulations (n = 264 areas). We used eight years of Automatic Identification System data to estimate fishing effort across three oceans and conducted temporal and spatial comparisons specific to each CA's regulations and enactment date. We addressed questions on CA effectiveness, fishing displacement, fishing the line behavior, and relationships between fishing activity and spatial CA attributes. We estimated 22,000 hours of fishing activity within CAs after enactments, 22% of which was identified as illegal. CA effectiveness appeared to be lowest for Atlantic CAs based on illegal fishing effort density within CAs. Fishing displacement and fishing the line was generally not apparent as buffer areas around CAs tended to already have higher fishing effort prior to enactments. CA effectiveness and responses to CAs varied considerably, as was visualized using timeseries plots and maps developed for each CA. Our evaluation of a nation's full suite of CAs provides managers with a foundation and approach for continued monitoring and reporting.</p>
Review data for: SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
<p>Climatology of snow water equivalent of Switzerland between winters 1962 and 2021. Obtained using quartile mapping between a model using data assimilation since 1998 and a model without data assimilation. This version of the dataset corresponds to the publication revision time. The publication has been submitted to GMD Copernicus journal as: <em>SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping</em></p>
Spatial separation of prey from livestock facilitates coexistence of a specialized large carnivore with human land use
<p><span>There is an increasing emphasis in</span><span> conservation strategies for large carnivores on facilitating their coexistence with humans.</span> <span>Justification for coexistence</span><span> strategies should be based on a quantitative assessment of currently remaining large carnivores in human-dominated landscapes. An essential part of a carnivore's coexistence strategy has to rely on its prey</span><span>. In this research, we studied snow leopards <em>Panthera uncia</em> whose habitat mainly comprises human-dominated, unprotected areas, to understand how a large carnivore and its primary prey, the bharal <em>Pseudois nayaur</em>, could coexist with human land use activities in a large proportion of its range. Using a combination of livestock census, camera trapping and wildlife surveys, across a broad gradient of </span><span>livestock grazing intensity in a 363,000 km<sup>2</sup> landscape on the Tibetan Plateau, </span><span>we found no evidence of livestock grazing impacts on snow leopard habitat use, bharal density and spatial distribution, even though livestock density was 13 times higher than bharal density.</span><span> Bharal were found to prefer utilizing more rugged habitats at higher elevations with lower grass forage conditions, whereas livestock dominated in flat valleys at lower elevations with higher productivity, especially during the resource-scarce season. These findings suggest that the spatial niche separation between bharal and livestock, together with snow leopards' specialized bharal diet, minimized conflicts and allowed snow leopards and bharal to coexist in landscapes dominated by livestock grazing. In recent years, reduced hunting and nomadic herder's lifestyle changes towards permanent residence may have further reinforced this spatial separation.</span><span> Our</span><span> results indicated that, </span><span>for developing conservation strategies for large carnivores, the niche of their prey in relation to human land-use is a key variable that needs to be evaluated.</span></p>
Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data
<p>This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling. This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling.</p>
Enhancing Spatial Navigation Using Non-Invasive Brain Stimulation
ClinicalTrials.gov study NCT01958437. IPD Sharing: NO. Countries: 1. Publications: 2.
Leveraging spatial patterns in precipitation forecasts using deep learning to support risk-averse flood management
Open the record for dataset details and reuse information.
Data from: Species-specific variation in germination rates contributes to spatial coexistence more than adult plant water use in four closely-related annual flowering plants
Open the record for dataset details and reuse information.
Exploring reversibility and contrasting patterns in temperature-size relationships across spatial and temporal scales using subfossil chironomids
Open the record for dataset details and reuse information.
A modeling framework for quantifying spatial recruitment dynamics using abundance estimation and sibship analysis: code and simulation study output
Open the record for dataset details and reuse information.
Data from: Ultra-fine scale spatially-integrated mapping of habitat and occupancy using structure-from-motion
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.