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285 results for “global study”
Higher orders for cosmological phase transitions: a global study in a Yukawa model
<p>Data used in the article with preprint title: <a href="https://arxiv.org/abs/2310.02308">Higher orders for cosmological phase transitions: a global study in a Yukawa model by Oliver Gould and Cheng Xie</a></p><p>Contains file: dataPublishedExport.csv, which consists of the variable used in, and evaluations from the global parameter scan. More details of the content are specified in README.txt.</p>
Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)
<p>Summary statistics for genome-wide association studies reported in:</p> <p>Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>. </p> <p><strong>Abstract</strong></p> <p>Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.</p> <p> </p> <p>Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.</p>
Collection of global datasets for the study of floods, droughts and their interactions with human societies
<p>This is a collection of 134 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to <a href="https://doi.org/10.1002/wat2.1424">Lindersson et al. (2020)</a> for further information about review methodology.</p> <p>The collection is a descriptive list, holding the following information for each dataset: </p> <ul> <li>Category<em> - as structured in Lindersson et al. (2020).</em></li> <li>Sub-category<em>- as structured in Lindersson et al. (2020).</em></li> <li>Abbreviation - <em>official or as specified in Lindersson et al. (2020).</em></li> <li>Title <em>- full title of dataset.</em></li> <li>Product(s)<em> - type of product(s) offered by the dataset.</em></li> <li>Period<em> - time period covered by the dataset, not defined for all datasets.</em></li> <li>Temporal resolution<em> - not defined for static datasets.</em></li> <li>Angular spatial resolution<em> - only defined for gridded datasets.</em></li> <li>Metric spatial resolution <em>- only defined for gridded datasets.</em></li> <li>Map scale</li> <li>Extent<em> - geographic coverage of dataset given in latitude limits.</em></li> <li>Description</li> <li>Creating institute(s)</li> <li>Data type<em> - raster, vector or tabular.</em></li> <li>File format</li> <li>Primary EO type<em> - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.</em></li> <li>Data sources<em> - lists the data sources behind the dataset, to the extent this is feasible.</em></li> <li>Data sources also in this table<em> - data sources that are also included as datasets in this collection.</em></li> <li>Intentionally compatible with<em> - defines other datasets in this collection that the dataset is intentinoally compatible with.</em></li> <li>Citation<em> - dataset reference or credit.</em></li> <li>Documentation <em>- dataset documentation.</em></li> <li>Web address<em> - dataset access link.</em></li> </ul> <p>NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.</p>
River network and hydro-geomorphological parameters at 1/12° resolution for global hydrological and climate studies
<p>Global scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and the increasing computing capacities, have allowed better performances of RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides flow direction of all grid cells. River networks may be derived at various spatial resolution by upscaling high resolution hydrography data.<br> This paper presents a new global scale river network at 1/12° derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the Hierarchical Dominant River Tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is here provided with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography).<br> The new river network and parameters are assessed by comparing the performances of two global scale simulations with the CTRIP model, one with the current spatial resolution (1/2°) and the other with the new spatial resolution (1/12°). It is shown that CTRIP at 1/12° overall outperforms CTRIP at 1/2°, demonstrating the added value of the spatial resolution increase.<br> The new river network and the consistent hydro-geomorphology parameters may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies.</p>
Location of ground stations, targets and spacecraft for Spire Global case study
<p>Datasets for case study in article:<br> "Data sink selection using consensus leadership: improving target connectivity for a spacecraft constellation"</p>
Data for study Conventional land-use intensification reduces species richness and increases production: A global meta-analysis
Most current research on land‐use intensification addresses its potential to either threaten biodiversity or to boost agricultural production. However, little is known about the simultaneous effects of intensification on biodiversity and yield. To determine the responses of species richness and yield to conventional intensification, this dataset was created and a global meta‐analysis on it was carried out, thus synthesizing 115 studies. The dataset consists of 449 cases that cover a variety of areas used for agricultural (crops, fodder) and silvicultural (wood) production. It was found that across all production systems and species groups, conventional intensification is successful in increasing yield (grand mean + 20.3%), but it also results in a loss of species richness (−8.9%). However, analysis of sub‐groups revealed inconsistent results. Within high‐intensity systems species losses were non‐significant but yield gains were substantial (+15.2%). Conventional intensification within medium intensity systems revealed the highest yield increase (+84.9%) and showed the largest loss in species richness (−22.9%). Production systems differed in their magnitude of richness response, with insignificant changes in silvicultural systems and substantial losses in crop systems (−21.2%). In addition, this meta‐analysis identifies a lack of studies that collect robust biodiversity (i.e. beyond species richness) and yield data at the same sites and that provide quantitative information on land‐use intensity. These findings suggest that, in many cases, conventional land‐use intensification drives a trade‐off between species richness and production. However, species richness losses were often not significantly different from zero, suggesting even conventional intensification can result in yield increases without coming at the expense of biodiversity loss. These results, which were published in a paper titled Conventional land‐use intensification reduces species richness and increa
Data for 'Future Transboundary Water Stress and Its Drivers Under Climate Change: A Global Study'
<p><strong>This dataset is a supplement to the following publication (please cite that when using the data):</strong></p> <p>Munia et al. 2020. Future transboundary water stress and its drivers under climate change: a global study. Earth’s future. <a href="https://doi.org/10.1029/2019EF001321">https://doi.org/10.1029/2019EF001321</a></p> <p> </p> <p><strong>Water stress category data</strong></p> <p>Dataset presents the water stress category in transboundary basins at sub-basin level for different scenarios (see article for details):</p> <ul> <li> <p>stress_category_Historical.gpkg: stress for years 1980 and 2010</p> </li> <li> <p>stress_category_SSP1‐RCP26.gpkg: stress for year 2050, SSP1‐RCP2.6 scenario</p> </li> <li> <p>stress_category_SSP1‐RCP45.gpkg: stress for year 2050, SSP1‐RCP4.5 scenario</p> </li> <li> <p>stress_category_SSP2‐RCP60.gpkg: stress for year 2050, SSP2‐RCP6.0 scenario</p> </li> <li> <p>stress_category_SSP3‐RCP60.gpkg: stress for year 2050, SSP3‐RCP6.0 scenario</p> </li> </ul> <p> </p> <p><strong>Dataset specifications:</strong></p> <p>Type: geopackage (gpkg)</p> <p>Spatial extent: -165, 141.5, -54.5, 70.5 (xmin, xmax, ymin, ymax)</p> <p>Temporal extent: see above</p> <p>Projection: long/lat WGS84 (EPSG:4326)</p> <p>Information: sub-basin name, country, stress level, stress category</p> <p>Unit: -</p> <p> </p>
A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1
<p>A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies. GSHA covers 21,568 watersheds from 13 agencies for as long as 43 years based on the discharge observations scraped from the web. GSHA includes yearly streamflow characteristics derived from daily discharge observations, daily meteorological variables (including precipitation, 2-m air temperature, long- and shortwave radiation, wind speed, actual and potential evapotranspiration (AET and PET)), daily or weekly water storage terms (4 layers of soil moisture, groundwater, and snow depth water equivalence), daily vegetation index (leaf area index (LAI)), yearly LULC characteristics (urban, cropland, and forest fraction), and yearly reservoir information (degree of regulation (DOR) and reservoir capacity). For each meteorological variable, multiple independent data sources are incorporated to provide uncertainty estimates. Static attributes like land physiography, soils, and geology are not additionally extracted, as similar efforts have been made by other researchers, so we directly matched our gauge locations to the HydroATLAS dataset by providing the river ID match table.</p> <p>For more details of GSHA, please refer to a companion research article submitted to ESSD.</p> <p>Please access the variables in version 1.0. Monthly streamflow indices files do not include Chinese basins.</p> <p>Citation: <strong> </strong>Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-256, in review, 2023.</p> <p> </p>
Coding frame and dataset for the study: Land-use governance: The interplay of social, market, and policy drivers – A global systematic review
<p>This file entails the coding frame and dataset used to conduct the systematic literature review "Land-use governance: The interplay of social, market, and policy drivers – A global systematic review".</p> <p>This study was first published as Chapter 2 of the PhD Dissertation "From soil to society - Rethinking governance for multifunctional land use and management" (Elsa L. Dingkuhn, 2025), and in a modified form in the journal Earth System Governance (Dingkuhn et al. 2025).</p> <p>The file consists of three sheets:<br>- Coding frame: Includes coding instructions and definitions used to extract and categorize the data.<br>- Variables: A list of dataset variables with explanations.<br>- List of included studies: The 81 studies from which the data was sourced.<br>- Data_List of observations: The dataset itself, consisting of 718 observations extracted from the included studies.</p>
Source data to create the figures of the study "Rising greenhouse gas emissions embodied in the global bioeconomy supply chain" using REX3 with new GHG extension including LULUCF
<p>This repository contains the source data to create the figures of the study <a href="https://doi.org/10.1038/s43247-025-02144-0">Rising greenhouse gas emissions embodied in the global bioeconomy supply chain</a> published in <em>Communications Earth & Environment</em>. The results were calculated with the REX3 database in Version 3.2 of this repository and the GHG extension and matlab codes in Version 3.4 of this repository.</p> <p>Figure 1, and 3–5 were created in Rstudio with the attached Rcode <em>Bioeconomy_GHG_sankeys.R</em></p> <p>Figure 2 was created in tableau with an <a href="https://public.tableau.com/app/profile/livia.cabernard/vizzes">interactive data visualizer</a> that allows to zoom into the global bioeconomy supply chain.</p>
A global phylogenomic study of the Thelypteridaceae
<p><span>The generic classification of the Thelypteridaceae has been the subject of much controversy. Proposed taxonomic systems have varied from recognizing more than 1000 species in the family within the single genus <i>Thelypteris</i>, to systems favoring upwards of 30 genera. Insights on intrafamilial relationships have been gained from recent phylogenetic studies, especially for the Neotropics; however, in the most recent classification, 10 of 30 recognized genera are either non-monophyletic or untested. In the present study, we sequenced 407 nuclear loci for 621 samples, representing all recognized genera and approximately half the known species diversity. Our phylogenomic results, coupled with morphological study, provide a foundation for a new generic classification. Our recently recognized monophyletic genera demonstrate greater geographic coherence than previous taxonomic concepts suggested. Additionally, our results demonstrate that certain morphological characters, such as frond division, are evolutionarily labile, and are thus inadequate for defining genera. </span></p>
Entrepreneurial Leadership toward Global Management of COVID-19, Is it always being used during Pandemic? A Bibliometric Study
<p>Entrepreneurial Leadership toward Global Management of COVID-19, Is it always being used during Pandemic? A Bibliometric Study entitled manuscript all of the metadata had taken from Scopus. </p>
Spatial patterns of extreme precipitation and their changes under ~2 °C global warming: A large-ensemble study of the western US: Data Release
<p>This dataset supports the analysis in Rupp et al. (2022). The dataset consists of 17,223 data files containing the water year (WY) maximum of the daily-averaged precipitation rate simulated with the HadRM3p regional climate model configured for the western United States. Each file contains the WY maxima across the model domain for a single WY, single model parameterization, and single set of initial conditions. Please refer to Hawkins et al. (2019) and Rupp et al. (2022) for a description of how the climate model data were generated.</p>
Code and data used in "A Tool for Air Pollution Scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies"
<p>Data and code for Tool for Air Pollution Scenarios (TAPS v1.0) as submitted to Geoscientific Model Development for publication. See the enclosed README and full user manual (https://github.com/watkin-mit/TAPS/wiki) for more information. </p>
Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021.
GLObal Building heights for Urban Studies (UT-GLOBUS)
<h1><strong>Important note: If you get a message that .zip archive is corrupt, please try updating WinRAR or <em>right-click the folder and</em> <em>select Extract All on Windows or use unzip command on Linux terminal</em>. If the issue persists, email: kamath.harsh@utexas.edu</strong></h1> <p> </p> <p><strong>Abstract</strong> </p> <p>We introduce GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for major cities worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse resolution urban canopy elevation data with a random forest model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had an RMSE of 9.1 meters, and mean building height within 1-km² grid cells had an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (~55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort at Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and ecological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.</p> <p><strong>Data</strong></p> <p>We are also supplying a vector file to represent the data coverage, and this file will receive updates as data for new city is added. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). These formats are compatible with the SUEWS and SOLWEIG models for the simulation of urban energy balance and thermal comfort. The vector files employ the Universal Transverse Mercator (UTM) projection. Both the vector and raster files are compatible with GIS platforms like QGIS and ArcGIS and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the BEP-BEM urban model in the urban WRF system in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These files can be directly incorporated into the WRF pre-processing system (WPS). The UT-GLOBUS UCPs are determined using a moving kernel with a size of 1 km2 and spacing of 300 meters in both the X and Y directions</p> <p><strong>Data coverage</strong></p> <p>The 'Coverage_xxxx.gpkg' files provide that geographical extents of cities that are included in our dataset.</p> <p><strong>How to find your city in the UT-GLOBUS dataset</strong></p> <p>Open the 'coverage' geopackage (.gpkg) files in QGIS or ArcGIS. Click on the city polygons and get the 'Label'/City name. Find a folder with the same 'Label'/City name. All the data for the periticular city will be in the folder.</p> <p><strong>How to run BEP-BEM model in WRF using UT-GLOBUS urban canopy parameters</strong></p> <div>Step 0: Before compiling WRF, go to 'dyn_em' folder and open 'module_initialize_real.F'.</div> <div>Change line 3121 (in version 4.5.2): </div> <div>From </div> <div>grid%HI_URB2D(i,k,j) = grid%URB_PARAM(i,k+117,j) </div> <div>To</div> <div>grid%HI_URB2D(i,k,j) = grid%URB_PARAM(i,k+117,j)*100.</div> <div> </div> <div>1. Change the name of the binary files 'ufrac' and 'urb_param' inside 'urb_fra' and 'GLOBUS_morph' folders, respectively to 00001-tile_x.00001-tile_y.</div> <div>Values for tile_x and tile_y can be found in the index file inside the 'urb_fra' and 'GLOBUS_morph' folders. Make sure to append zeros before tile_x and tile_y values to make 5 digits. </div> <div>Ex: tile_x = 260 and tile_y = 219; Then the binary files should be renamed as 00001-00260.00001-00209 </div> <div> </div> <div>2. Copy the 'urb_fra' and 'GLOBUS_morph' folders to WRF static data directory.</div> <div> </div> <div>3. Change the paths to 'URB_PARAM' and 'FRC_URB2D' variables inside GEOGRID.TBL file as follows:</div> <div> </div> <div>===============================</div> <div>name=URB_PARAM</div> <div> priority=1</div> <div> optional=yes</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> z_dim_name=num_urb_params</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/GLOBUS_morph/</div> <div> flag_in_output=FLAG_URB_PARAM</div> <div>===============================</div> <div>name=FRC_URB2D</div> <div> priority=1</div> <div> optional=yes</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/urb_fra/</div> <div> flag_in_output=FLAG_FRC_URB2D</div> <div>===============================</div> <div> </div> <div>4. Run geogrid.exe. If the domain covers the chosen city:</div> <div> -- 'FRC_URB2D' variable will show the urban fraction.</div> <div> -- 'URB_PARAM[91,:,:]' will show the plan area fraction.</div> <div> -- 'URB_PARAM[94,:,:]' will show the area averaged building heights.</div> <div> -- 'URB_PARAM[95,:,:]' will show the building surface to total area fraction.</div> <div> -- 'URB_PARAM[118-132,:,:]' will show the building height histograms with 5-meter bin size.</div> <div> </div> <div>5. If you see the data in 'FRC_URB2D' and 'URB_PARAM' variables after running the geogrid.exe, GLOBUS data is ingested in WPS and you can continue with ungrib and metgrid as usual.</div> <div> </div> <div>6. For running the model over the domain area which covers more that one city, UT-GLOBUS UCPs can be stitched together. For instance, if two cities are covered in the domain, step number 3 should be modified as follows:</div> <div> </div> <div>===============================</div> <div>name=URB_PARAM</div> <div> priority=1</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> z_dim_name=num_urb_params</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path=Your_WPS_static_data_folder/GLOBUS_morph_for_city-1/ </div> <div>flag_in_output=FLAG_URB_PARAM</div> <div>===============================</div> <div>name=FRC_URB2D</div> <div> priority=1</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/urb_fra_for_city-1/</div> <div> flag_in_output=FLAG_FRC_URB2D</div> <div>===============================</div> <div>name=URB_PARAM</div> <div> priority=2</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> z_dim_name=num_urb_params</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/GLOBUS_morph_for_city-2/</div> <div>===============================</div> <div>name=FRC_URB2D</div> <div> priority=2</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/urb_fra_for_city-2/</div> <div>===============================</div> <div> </div> <div><strong>References</strong></div> <div> <ol> <li>Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Liu, Z., Berner, J., Wang, W., Powers, J., Duda, M., Barker, D., Huang, X., 2021. A Description of the advanced research WRF model.</li> <li>Martilli, A., Clappier, A., Rotach, M.W., 2002. An urban surface exchange parameterisation for mesoscale models. Boundary Layer Meteorol 104, 261–304. https://doi.org/10.1023/A:1016099921195</li> <li>Sun, T., Grimmond, S., 2019. A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): Development, deployment and demonstration. Geosci Model Dev 12, 2781–2795. https://doi.org/10.5194/gmd-12-2781-2019</li> <li>Lindberg, F., Holmer, B., Thorsson, S., 2008. SOLWEIG 1.0 - Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. Int J Biometeorol 52, 697–713. https://doi.org/10.1007/s00484-008-0162-7</li> <li>Software: QGIS (https://www.qgis.org/en/site/)</li> </ol> </div>
Fig. 3 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 3. Prevalence of pathogens associated with D. nuttalli. If there was only one study included in a certain pathogen, the positive rate would be calculated by the positive number of ticks divided by the total number of detected ticks, and without the 95% confidence interval. If there were more studies, the positive rate and 95% confidence interval would be calculated by meta-analysis.
Fig. 2 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 2. Study design and data sources of the meta-analysis. A comprehensive meta-analysis was performed to evaluate D. nuttalli's potential threats based on detected pathogens and geographical distribution positions. The database of D. nuttalli was constructed from four sources, including field surveys, literature review, a reference book, and an online biodiversity database (Global Biodiversity Information Facility, GBIF, https://www.gbif.org).
Fig. 1 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 1. Relative pathogen abundance of four D. nuttalli samples and the phylogenomic analysis of four Rickettsia genomes. (A) Pathogen abundance at the family level. (B) Pathogen abundance at the genus level. (C) The phylogenetic tree of four Rickettsia assemblies. The phylogenetic tree of four Rickettsia assemblies (Rickettsia conorii subsp. raoultii str XinjiangF1, Rickettsia conorii subsp. raoultii str XinjiangF2, Rickettsia conorii subsp. raoultii str XinjiangF3, and Rickettsia conorii subsp. raoultii str XinjiangM1) was built with 28 other publicly available established or proposed Rickettsiales species. The tree was inferred by IQ-TREE based on 277 single-copy orthologs identified by OrthoFinder. Anaplasma phagocytophilum and Ehrlichia ruminantium were two outgroup species.
Fig. 5 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 5. Global potential distribution of D. nuttalli. The red area indicates greater possibilities of suitability for D. nuttalli, while the blue area is less likely to be suitable for D. nuttalli.
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.