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1,868 results for “Spatial Data”
Tutorial for the 2022 ACM SIGMOD Conference: Spatial Data Quality in the IoT Era: Management and Exploitation
<p>Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications.</p>
Pollinator data from: Pollinator movement activity influences genetic diversity and differentiation of spatially isolated populations of clonal forest herbs
<p>In agricultural landscapes, forest herbs live in small, spatially isolated forest patches. For their long-term survival, their populations depend on animals as genetic linkers that provide pollen- or seed-mediated gene flow among different forest patches. However, whether insect pollinators serve as genetic linkers among spatially isolated forest herb populations in agricultural landscapes remains to be shown. Here, we used population genetic methods to analyze: (A) the genetic diversity and genetic differentiation of populations of two common, slow-colonizing temperate forest herb species (<em>Polygonatum</em> <em>multiflorum</em> (L.) All. and <em>Anemone</em> <em>nemorosa</em> L.) in spatially isolated populations within three agricultural landscapes in Germany and Sweden and (B) the movement activity of their most relevant associated pollinator species, i.e., the bumblebee <em>Bombus</em> <em>pascuorum</em> (Scopoli, 1763) and the hoverfly <em>Melanostoma</em> <em>scalare</em> (Fabricus, 1794), respectively, which differ in their mobility. We tested whether the indicated pollinator movement activity affected the genetic diversity and genetic differentiation of the forest herb populations. Bumblebee movement indicators that solely indicated movement activity between the forest patches affected both genetic diversity and genetic differentiation of the associated forest herb <em>P</em>. <em>multiflorum</em> in a way that can be explained by pollen-mediated gene flow among the forest herb populations. In contrast, movement indicators reflecting the total movement activity at a forest patch (including within-forest patch movement activity) showed unexpected effects for both plant-pollinator pairs that might be explained by accelerated genetic drift due to enhanced sexual reproduction. Our integrated approach revealed that bumblebees serve as genetic linkers of associated forest herb populations, even if they are more than 2 km apart from each other. No such evidence was found for the forest-associated hoverfly species which showed significant genetic differentiation among forest patches itself. Our approach also indicated that a higher within-forest patch movement activity of both pollinator species might enhance sexual recruitment and thus diminishes the temporal buffer that clonal growth provides against habitat fragmentation effects.</p>
Data for "Global ocean pCO2 variation regimes: spatial patterns and the emergence of a hybrid regime"
<p>Model data for article "Global ocean pCO2 variation regimes: spatial patterns and the emergence of a hybrid regime".</p>
Data and code AEM article: Catching some air: A method to spatially quantify aerial triazole resistance in Aspergillus fumigatus
<h2>Name</h2> <p>Catching_some_air</p> <h2><a href="#description"></a>Description</h2> <p>This script project was written to visualise and analyse the data used in the manuscript: Catching some air: A method to spatially quantify aerial triazole resistance in <em>Aspergillus fumigatus</em>.</p> <p>In the R script we load and clean the data from the international air sampling pilot, analyse it, generate figures of the sampled regions, the CFU totals and resistance fractions. The genotyping and phenotyping data of isolated resistant strains.</p> <p>The following files are required to run this R script:</p> <ul> <li>RF_air_IP_cleaned.csv This fine contains total and resistance counts as well as metadata on samples from international air sampling pilot and includes the following variables:</li> </ul> <p>Sample ID: an arbitrary number given to the packages prior to them being handed out</p> <p> </p> <p>Country: Country in which sample was taken</p> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <p>City/Town: City/Town in which the sample was taken</p> <p>Start date: date on which the trap was deployed and the stickers exposed to the air</p> <p>End date: date on which the trap was taken down and the stickers were re-covered and no longer exposed to the air</p> <p>Total.ITR: A. fumigatus CFU count in the permissive layer of the itraconazole-treated plate</p> <p>Res.ITR: CFU count of colonies that had breached the surface of the itraconazole-treated layer after incubation and were visually (with the unaided eye) sporulating.</p> <p>RF.ITR: The itraconazole (~4 mg/L) resistance fraction = Res.ITR/Total.ITR</p> <p>Total.VOR: A. fumigatus CFU count in the permissive layer of the voriconazole-treated plate</p> <p>Res.VOR: CFU count of colonies that had breached the surface of the voriconazole-treated layer after incubation and were visually (with the unaided eye) sporulating.</p> <p>RF.VOR: The voriconazole (~2 mg/L) resistance fraction = Res.VOR/Total.VOR</p> <p>Total control: CFU count on the untreated growth control plate</p> <p>Date.Batch: The date on which proccessing of the sample was started. To be more specific, the date at which Flamingo medium was poured over the seals of the sample and incubation was started.</p> <p>Note: note on the sample based on either information given the participant or observations in the lab.</p> <p>Exclude: Binary to quickly filter out samples that were considered unsuitable for further analysis either because low or high CFU counts. See manuscript for rationale.</p> <p>Lat: Latitude at the centre of the sampled region, does not relate to sample-specific location.</p> <p>Long: Longitude at the centre of the sampled region, does not relate to sample specific location.</p> <ul> <li>Weather_data_IP_study_nov_dec_jan22_23.csv : contains raw weather data of the sampled regions during the sampling interval of the pilot downloaded from: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview" target="_blank" rel="nofollow noreferrer noopener">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview</a> (see link for full description of the data and the units). Contains the following variables:</li> </ul> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <p>Wind Nov : 10 m Wind speed (m/S) for the month november 2022 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>Wind Dec : 10 m Wind speed (m/S) for the month december 2022 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>Wind Jan : 10 m Wind speed (m/S) for the month januari 2023 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>UV Nov: UV radiation at the surface (J/m^2) for the month november 2022. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>UV Dec: UV radiation at the surface (J/m^2) for the month december 2022. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>UV Jan: UV radiation at the surface (J/m^2) for the month januari 2023. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>Temp Nov: Temperature (K) for the month november 2022. This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Temp Dec: Temperature (K) for the month december 2022. This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Temp Jan: Temperature (K) for the month januari 2023. This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Precipitation Nov: Total precipitation (m) for the month november 2022. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>Precipitation Dec: Total precipitation (m) for the month december 2022. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>Precipitation Jan: Total precipitation (m) for the month januari 2023. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>lat_rep: Latitude at the centre of the sampled region, does not relate to sample specific location.</p> <p>lon_rep: Longitude at the centre of the sampled region, does not relate to sample specific location.</p> <ul> <li>Genotyping_IP_cleaned.csv : contains the TR-type genotypes of the isolated resistant strains Contains the following variable:</li> </ul> <p>Order: Ordering variable included in the file to readily be able to order the isolates by the order in which they were isolated. Contains the following variables:</p> <p>Strain: Strain code with "I" for strains isolated from itraconazole and V for strains isolated from voriconazole followed by a number indicating the order in which they were isolated from the air sample plate.</p> <p>Air sample: The plate/air sample from which the isolate originates</p> <p>Triazole: The triazole treatment the resistant strain grew on can be ITRA (itraconazole) or VORI (voriconazole)</p> <p>Country: Country in which sample was taken</p> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <h2><a href="#project-status"></a>Project status</h2> <p>The manuscript has been published in AEM under the DOI: https://doi.org/10.1128/aem.00271-24</p>
Data set for the paper "Swimming ability of the Carybdea marsupialis (Cnidaria: Cubozoa: Carybdeidae): implications for its spatial distribution"
<p>This document provides data supporting the results of the scientific paper "Swimming ability of the Carybdea marsupialis (Cnidaria: Cubozoa: Carybdeidae): implications for its spatial distribution". It includes surface current data from the coast of Dénia (Spain) and swimming kinematic parameters.</p>
Data from: The basic-reproduction number of infectious diseases in spatially structured host populations
<p>The spatial structure of a host population has a profound effect on the dynamics of infectious diseases. The basic reproduction number, a central quantity in the study of epidemic dynamics, is affected by host clustering as well as host density. Several authors have developed methods to quantify the basic reproduction number in a spatially structured host population. The methods used and the expressions derived are however difficult to apply to real life spatial host structures. In this paper we introduce an explicit expression for the basic reproduction number using the O-ring statistic, developed in spatial statistics, that quantifies the host density as a function of the distance from a randomly selected host individual. The O-ring statistic is frequently used in the study of the ecology of spatially structured plant populations, being a convenient summary of the properties of a landscape by way of a single function. The connection we develop between spatial statistics and epidemic dynamics can be used to study the effect of host spatial pattern on the basic reproduction number of infectious diseases. As well as showing how explicit expressions for the basic reproduction number can be derived for landscapes with standard structures, our expression for the basic reproduction number is tested against a simulation model. The model structure in our simulation is motivated by the spread of a plant disease epidemic, although it is applicable more broadly. The agreement between our analytic expression for the basic reproduction number and the corresponding numeric quantity extracted from simulations is close to perfect across a wide range of landscape structures and model parameterisations, and including cases in which more than one species of host is at risk of infection.</p>
Data from: Three-dimensional infrared scanning: An enhanced approach for spatial registration of probes for neuroimaging
<p>Significance: Accurate spatial registration of probes (e.g., optodes and electrodes) for measurement of brain activity is a crucial aspect in many neuroimaging modalities. It may increase measurement precision and enable the transition from channel-based calculations to volumetric representations.</p> <p>Aim: This technical note evaluates the efficacy of a commercially available infrared three-dimensional (3D) scanner under actual experimental (or clinical) conditions and provides guidelines for its use.</p> <p>Method: We registered probe positions using an infrared 3D scanner and validated them against magnetic resonance imaging (MRI) scans on five volunteer participants.</p> <p>Results: Our analysis showed that with standard cap fixation, the average Euclidean distance of probe position among subjects could reach up to 43 mm, with an average distance of 15.25 mm [standard deviation (SD) = 8.0]. By contrast, the average distance between the infrared 3D scanner and the MRI-acquired positions was 5.69 mm (SD = 1.73), while the average difference between consecutive infrared 3D scans was 3.43 mm (SD = 1.62). The inter-optode distance, which was fixed at 30 mm, was measured as 29.28 mm (SD = 1.12) on the MRI and 29.43 mm (SD = 1.96) on infrared 3D scans. Our results demonstrate the high accuracy and reproducibility of the proposed spatial registration method, making it suitable for both functional near-infrared spectroscopy and electroencephalogram studies.</p> <p>Conclusions: The 3D infrared scanning technique for spatial registration of probes provides economic efficiency, simplicity, practicality, repeatability, and high accuracy, with potential benefits for a range of neuroimaging applications. We provide practical guidance on anonymization, labeling, and post-processing of acquired scans.</p>
Data from: The role of predation, forestry and productivity in moose harvest at different spatial levels of management units
<p>Management of ungulate populations to the desired density and/or demographic composition are challenged by contrasting aims of different stakeholders. For example, hunters may want to maximize hunting opportunities whereas commercial forest owners may want to minimize moose densities to mitigate browsing damage. In addition, the return of large predators such as wolves (<em>Canis lupus</em>) affects the possible harvest yield of ungulates and influences the population composition through their selection of specific age classes. The aim of this study was to gain a better understanding of factors related to the variation in moose (<em>Alces alces</em>) harvest. We used moose harvest statistics from the period 2012-2020, wolf annual monitoring data, annual brown bear (<em>Ursus arctos</em>) density, proportion of young forest per management unit, and proportion of agricultural land per management unit (index for productivity and roe deer (<em>Capreolus capreolus</em>) density) to explain variation in moose harvest across different management units at two spatial levels in two bordering countries, Sweden and Norway. The results showed variable responses in total harvest to changes in wolf territory density both at the regional and local management level. The proportion of young forest was correlated with both increased total harvest and proportion of calves. Increased proportion of agricultural land was linked to both increased total harvest and proportion of calves, likely due to that increased roe deer densities re-directed wolf predation from moose to roe deer, and an inverse relationship with brown bear density. Differences between countries may be due to differences in the management regime of moose, both in an historical and present perspective. Improved monitoring for individual hunting areas over time will be important for both the understanding of how different ungulate populations are affected by various factors and for the desired management of wildlife populations shared across borders.</p>
Representation learning for multi-modal spatially resolved transcriptomics data
<p>This folder contains the already unified input used for the models. The raw data is referenced here:</p> <ul> <li>LIBD Human DLPFC dataset is available at <a href="https://github.com/LieberInstitute/HumanPilot">https://github.com/LieberInstitute/HumanPilot</a> and <a href="http://research.libd.org/spatialLIBD" rel="nofollow">http://research.libd.org/spatialLIBD</a>;</li> <li>Human Breast Cancer - Zenodo <a href="https://doi.org/10.5281/zenodo.4739739" rel="nofollow">https://doi.org/10.5281/zenodo.4739739</a>,</li> <li>Human Liver Normal and Cancer - <a href="https://nanostring.com/products/cosmx-spatial-molecular-imager/human-liver-rna-ffpe-dataset/" rel="nofollow">https://nanostring.com/products/cosmx-spatial-molecular-imager/human-liver-rna-ffpe-dataset/</a>.</li> </ul>
Data from: Positive spatial and temporal density-dependence drive early reproductive economy-of-scale effects of masting in a European old-growth forest community
<p>Masting, the spatial synchronization of interannual variation in seed production, can enhance reproductive efficiency through positive density-dependent processes (DD) that result in economies of scale (EOS), such as decreased pollen limitation and predator satiation in years of high reproduction. While the general occurrence of such EOS effects has been documented for masting species, few studies simultaneously investigated how spatial and temporal variation in reproduction affects pollination and predation. Furthermore, it is unclear whether the same mechanisms apply to co-occurring species with different levels of conspecific density, pollen limitation, and seed defenses. Here, we use a long-term data set with high spatial resolution of seed production of European beech (<em>Fagus sylvatica</em>), Norway spruce (<em>Picea abies</em>), and silver fir (<em>Abies alba</em>) in a primeval montane forest to investigate the relationship between reproductive effort, pollination efficiency, and predispersal predation by insects. We found that, along the temporal axis, the proportion of sound (fertilized and unpredated) seeds correlated positively with annual seed production over the 14-year study period in all three species, most strongly in beech and only weakly in silver fir. Moreover, the results show that in beech, spatial seed density interacts with plot-wide annual seed rain to enhance DD effects on seed predation, suggesting additive effects of synchronous reproduction on fitness benefits.</p> <p>Synthesis: For both pollination and predispersal predation in beech and spruce, the strongest DD effects occur at low levels of reproduction and quickly reach asymptotes at higher levels, suggesting the presence of thresholds in different EOS mechanisms. As variability and synchrony in mast-seeding are expected to decline with climate change, EOS effects driven by DD may remain stable until the threshold is reached, at which sudden declines would result in devastating effects on the availability of viable seeds for germination and recruitment.</p>
Code and data for spatial and temporal magnitude clustering analysis
<p>Code used for performing spatial and temporal seismic magnitude clustering analysis. Includes documentation (README.txt) with steps on how to implement the code. The public datasets used for this study can be accessed at the following locations: </p> <ul> <li><strong>Southern California Catalog: </strong> <ul> <li>SCEDC (2013): Southern California Earthquake Center.<br> Caltech.Dataset. doi:<a href="https://dx.doi.org/10.7909/C3WD3xH1">10.7909/C3WD3xH1</a></li> </ul> </li> <li><strong>Northern California Catalog:</strong> <ul> <li>NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC.</li> </ul> </li> <li><strong>Mixed-mode Laboratory Catalog:</strong> <ul> <li>Lin, Qing, et al. "Opening and mixed mode fracture processes in a quasi-brittle material via digital imaging." <em>Engineering Fracture Mechanics</em> 131 (2014): 176-193.</li> </ul> </li> <li><strong>ETAS Code:</strong> <ul> <li>Leila Mizrahi, Shyam Nandan, Stefan Wiemer 2021;<br> Embracing Data Incompleteness for Better Earthquake Forecasting. (Section 3.1)<br> <em>Journal of Geophysical Research: Solid Earth</em>; doi: <a href="https://doi.org/10.1029/2021JB022379">https://doi.org/10.1029/2021JB022379</a></li> </ul> </li> </ul>
Processed CODEX Datasets from - Discovery and Generalization of Tissue Structures from Spatial Omics Data
<p>This entry provides access to processed CODEX data files of four studies analyzed in the article "Discovery and Generalization of Tissue Structures from Spatial Omics Data". Details of datasets can be found in the STAR Methods section of the article.</p> <p>For each dataset, a zip file containing multiple comma-separated values (CSV) files is included.</p> <p>Each region is assigned an unique identifier (e.g., DKD_kidney_001), and its related data files are:</p> <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified for all cells in this region.</li> <li>`{region_id}.scgp_annotations.csv`, a table containing two columns: "CELL_ID" and "SCGP". This table provides SCGP/SCGP-Extension annotations for all cells in this region.</li> </ul> <p>Code base for SCGP is also included in this entry. Please refer to <a href="https://gitlab.com/enable-medicine-public/scgp">https://gitlab.com/enable-medicine-public/scgp</a> for the latest codes, questions, and/or issues. Raw CODEX data and images will be accessible through links posted at the code base. Raw data will also be available from lead contact (A.E.T.) upon request.</p>
Increased spatial coupling of integrin and collagen IV in the immunoresistant clear-cell renal-cell carcinoma tumor microenvironment - Nanostring CosMx SMI Data
<p>Data export from Nanostring CosMx SMI, directly from Nanostring, in Seurat Object format for use in R. Clear cell renal cell carcinoma and papillary renal cell carcinoma were profiled before and after exposure to immunotherapy, with and without sarcomatoid features in clear cell tumors. Each tumor had a field of view in the stromal compartment and field of view in the tumor compartment.</p> <p>For appropriate clinical information associated with this study, please contact Dr. Brandon Manley.</p>
Data from: Spatial variation of soil CO2, CH4 and N2O fluxes across topographical positions in tropical forests of the Guiana Shield in Ecosystems
<p>Data from: Spatial variation of soil CO2, CH4 and N2O fluxes across topographical positions in tropical forests of the Guiana Shield in Ecosystems</p>
Envixlab/OpenMICE: OpenMICE: an open spatial and temporal data set of small mammals in south-central Italy based on owl pellet data
<p>Provided in support of the Data-paper: OpenMICE: an open spatial and temporal data set of small mammals in south-central Italy based on owl pellet data by Paniccia, C., M. Di Febbraro, L. Delucchi, R. Oliveto, M. Marchetti, and A. Loy. 2018. Ecology. <a href="https://github.com/Envixlab/OpenMICE/files/2273658/OpenMICE.sqlite.zip">OpenMICE.sqlite.zip</a></p>
Data used in manuscript Spatial modelling of local-scale biogenic and anthropogenic carbon dioxide emissions in Helsinki
<p>This data set includes data used to develop and evaluate carbon dioxide emission modelling component in the Surface Urban Energy and Water balance Scheme (SUEWS). The data files are:</p> <ol> <li>CO2_Model_Parameter_Fitting.zip contains m-files (Matlab) used to calculate parameters for photosynthesis modelling <ul> <li>F_pho_data.mat includes meteorological and EC data used to fit photosynthesis model parameters in Kumpula</li> <li>FitKumpulaData.m calculates the model parameters in Kumpula</li> <li>FitViikkiData.m calculates the model parameters in Viikki</li> <li>Other m-files needed by the above two codes</li> </ul> </li> <li>Data.zip contains measured data used to develop and evaluate SUEWS <ul> <li>KumpulaData2012.txt and TorniData2012.txt include eddy covariance data measured at the two sites in Helsinki</li> <li>SMEARIII_meteorology_2016MM_30.m meteorological data used to fit model parameters in Viikki street trees (see 00 ReadMe_SMEARIII_Meteorology.TXT for details)</li> <li>Viikki_SWC_2016.txt measured soil moisture from Viikki in 2016</li> <li>Kumpula_2016_HH_RLAI6_Output.out is SPP output used to fit model parameters in Viikki street trees</li> </ul> </li> <li>SUEWS_EC_Site_Model_runs: SUEWS input and output files for Kumpula and Torni model runs</li> <li>SpatialRun_input.zip: SUEWS input files for the spatial model run</li> <li>spatmatHel_final.mat: SUEWS output files for spatial model run in mat-format</li> </ol>
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>
Data repository - Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis
<p>Data associated with the manuscript entitled "Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis".</p> <p>Files:</p> <p>table_A_LCM_TPM_values.tsv: Gene expression levels of microdissected villus quintiles. First column is the ensemble gene id. Next 15 columns are the raw Kallisto TPM values for villus segments 1 (bottom) to 5 (top) for three different mice (a to c). Additional columns include the external gene name, description, and gene biotype.</p> <p>table_B_scRNAseq_UMI_counts.tsv: Raw UMI counts of cells that were utilized in this study. Analysis is based on raw data from the NCBI GEO datasets GSM2644349 and GSM2644350. Each of the columns represents a single cell, column headers are the corresponding cell barcodes and enable retrieval of tSNE coordinates from table_C_scRNAseq_tsne_coordinates_zones.tsv. Values represent raw UMI counts.</p> <p>table_C_scRNAseq_tsne_coordinates_zones.tsv: tSNE coordinates and reconstructed zones of cells that were utilized in this study. Tab separated text file. Analysis is based on raw data from the NCBI GEO datasets GSM2644349 and GSM2644350. Columns: cell_id: cell barcode, corresponds to column header of Table S2. Seurat tSNE coordinate 1 and tSNE coordinate 2. Last column is the inferred zone (Crypt, V1..V6).</p> <p>table_D_zonation_reconstruction.tsv: Zonation table of reconstructed scRNAseq data. Tab separated text file. Columns: Gene names: gene id, mean expression in each of the crypt zone and 6 villus zones, standard error of the means in the Crypt zone and 6 villus zones, p-value and q-value for the zonation profiles.</p> <p>raw_data.zip: The raw and intermediary data for runnning the scripts in <a href="https://github.com/aemoor/Code_spatial_reconstruction_enterocytes">https://github.com/aemoor/Code_spatial_reconstruction_enterocytes</a></p>
Data from: Hyperspectral imaging reveals differential carotenoid and chlorophyll temporal dynamics and spatial patterns in Scots pine under water stress
<p>Data and codes associated with the manuscript '<span>Hyperspectral imaging reveals differential carotenoid and chlorophyll temporal dynamics and spatial patterns in Scots pine under water stress</span>'. </p>
Characterizing the spatial correlation of coseismic slip distributions: A data driven Bayesian approach
<p>Slip models for the simulated case and the Illapel earthquake are provided. The zip file contains processed data, predictions, and uncertainty estimates for the Illapel event.</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)
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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.
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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
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