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6,999 results for “provinces”
Time series of carbon dioxide fluxes measured with eddy covariance for Danjiangkou Reservoir in Hubei Province, China during 2022-2024
This dataset contains half-hourly micrometeorological and eddy covariance flux measurements of carbon dioxide (CO₂) collected over the water surface of the Danjiangkou Reservoir in Hubei Province, China, from April 2022 to November 2024. The eddy covariance tower was installed at the deepest point of the reservoir, which serves as a critical water source for water supply and regional ecological functions in the middle reaches of the Yangtze River. Measurements were obtained using a LI-COR eddy covariance system (LI-COR Biosciences, Lincoln, NE, USA), and fluxes were calculated using EddyPro software (version 7.0.6). The dataset includes CO₂ and CH₄ fluxes as well as supporting micrometeorological, radiation, and water temperature measurements. All data were processed following established best practices for eddy covariance measurements, including comprehensive quality assurance and quality control procedures, which are fully documented and included with the dataset.
Copper mineralization at Carajás mineral province - Brazil: geological, structural, and geophysical data
<p>Gridded geological, structural, and geophysical data at the Carajás mineral province. A number of known Cu occurrences are provided. This dataset is suitable for experimenting with machine learning methods.</p>
Data on ground ice, organic carbon and soluble cations in tundra permafrost and active-layer soils near Lac de Gras in the Slave Geological Province, N.W.T., Canada
<p>Data and computer code for producing figures for the manuscript:</p> <p>Subedi, R., Kokelj, S. V., and Gruber, S.: Ground ice, organic carbon and soluble cations <br> in tundra permafrost soils and sediments near a Laurentide ice divide in the Slave <br> Geological Province, N.W.T., Canada. The Cryosphere, accepted for publication in October 2020. </p> <p>Discussion paper and final version: https://doi.org/10.5194/tc-2020-33</p> <p> </p> <p>==========================================================================================<br> CONTENT OF DIRECTORIES<br> ==========================================================================================<br> -– data [input data to produce plots]<br> |–– BoreholesMeta.csv<br> |–– brackets_photos_ice.csv<br> |–– brackets_photos_thawed.csv<br> |–– Lac_de_Gras_permafrost_20200612.csv<br> |–– NordicanaD<br> <br> |–– ds_000582159 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_TCR.csv<br> |–– Cored_Drill_TCR.csv_ReadMe.txt<br> <br> |–– ds_000582163 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_Logs.csv_ReadMe.txt<br> |–– Cored_Drill_Logs.csv</p> <p>–– plot [R scripts write plots into this subdirectory]</p> <p>–– src [R scripts to generate plots]<br> |–– Combined_Plots.R [produces Figures 3–6]<br> |–– Eskers.R [helper function called by Combined_Plots.R]<br> |–– Organics.R [helper function called by Combined_Plots.R]<br> |–– plot_boreholes_DD_single.R [produces Figures S3]<br> |–– plot_boreholes_DD.R [produces raw Figure S2 for further graphic processing]<br> |–– Till.R [helper function called by Combined_Plots.R]<br> |–– Valley.R [helper function called by Combined_Plots.R]</p> <p><br> ==========================================================================================<br> RUNNING SCRIPTS<br> ==========================================================================================</p> <p>Adjust the variable 'path' in these scrips, then run: <br> Combined_Plots.R<br> plot_boreholes_DD_single.R<br> plot_boreholes_DD.R </p> <p>Tested with R version 3.6.3 (2020-02-29) -- "Holding the Windsock"</p> <p> </p> <p>==========================================================================================<br> REFRERENCE<br> ==========================================================================================<br> Please note that the data contained in data/NordicanaD is published as Gruber et al. (2018)<br> and only included here for convenience. The full reference for the authoritative copy is: <br> <br> Gruber, S., Brown, N., Stewart-Jones, E., Karunaratne, K., Riddick, J., Peart, C., <br> Subedi, R., Kokelj, S. 2018. Drill logs, visible ice content and core photos from 2015 <br> surficial drilling in the Canadian Shield tundra near Lac de Gras, Northwest Territories, <br> Canada, v. 1.0 (2015-2015). Nordicana D38, doi: 10.5885/45558XD-EBDE74B80CE146C6. <br> http://www.cen.ulaval.ca/nordicanad/dpage.aspx?doi=45558XD-EBDE74B80CE146C6 </p>
Modelled gridded population estimates for the Kasaï-Oriental Province in the Democratic Republic of Congo (2024) version 4.2
<h2><strong>Content</strong></h2> <p>This repository contains the input data and scripts used to create the modeled gridded population estimates for Kasaï-Oriental Province in the Democratic Republic of Congo. It also includes the grid-cell posterior distributions and scripts to aggregate them within user-defined geographic boundaries.</p> <p> In particular, this repository contains two compressed files (.zip):</p> <p><strong>1. <code>population_estimates.zip</code></strong></p> <ul> <li>Includes raster files (<code>.tif</code>) with summaries of population count posterior predictions at the grid-cell level, specifically the mean, median, lower credible interval, and upper credible interval.</li> <li>Includes spatial files (<code>.gpkg</code>) with summaries of population count posterior predictions at the health-area and health-zone levels, specifically the mean, median, lower credible interval, and upper credible interval.</li> </ul> <p><strong>2. <code>population_model.zip</code></strong></p> <p>This directory comprises five subdirectories with scripts, input data, and output data necessary to replicate the population model:</p> <ul> <li><code><strong>01_model_stan</strong></code>: Contains the Stan model, input data, and an R script (<code>01_model_stan.R</code>) with a function to run the model.</li> <li><code><strong>02_model_run</strong></code>: Includes an R script (<code>02_model_run.R</code>) for running the model, along with output data.</li> <li><code><strong>03_model_evaluate</strong></code>: Features a Quarto report template (<code>03_model_evaluate.qmd</code>) and model evaluation summary files(.pdf).</li> <li><code><strong>04_predict_posterior</strong></code>: Provides R scripts (<code>04_predict_posterior.R</code> and <code>04_predict_run.R</code>) for generating predictions, along with input and output data, namely the posterior predictions files (.rds).</li> <li><code><strong>05_aggregate_posterior</strong></code>: Contains R scripts (<code>05_aggregate_posterior.R</code> and <code>05_aggregate_run.R</code>) and associated input and output data, namely the population count posterior summaries as presented in the file <code>population_estimates.zip</code> .</li> </ul> <p>The work was carried out in <code>R</code> (version 4.4.0), with the packages <code>tidyverse</code> (version 2.0.0), <code>terra</code> (version 1.7-78), <code>sf</code> (version 1.0-16), <code>furrr</code> (version 0.3.1), <code>doParallel</code> (version 1.0.17), <code>foreach</code> (version 1.5.2), <code>rstudioapi</code> (version 0.16.0), and <code>rstan</code> (version 2.32.6), on macOS Sequoia (version 15.1.1). While the scripts are designed to be portable, minor adjustments may be required for compatibility with other operating systems.</p> <h2><strong>Important</strong></h2> <p>This version includes changes in the STAN model <code>10h_survey_survey_covariate_building_random_effect_hierarchy_building_covariate_density_fixed_effect_hierarchy_density.stan</code>. Consequentely, all the files generated in the previous versions are now changed.</p> <p> </p> <p>For inquiries regarding the model and the data, please contact Gianluca Boo at gianluca.boo@soton.ac.uk.</p>
New maps of global geologic provinces and tectonic plates: global tectonics data and QGIS project file
<p>The global tectonics data compilation is a set of raster and vector data that are useful for investigating tectonics past and present. The datasets are useful on their own or can be used in GIS software, which includes the QGIS project file for convenience. The datasets include our new models for tectonic plate boundaries and deformation zones, geologic provinces and orogens. Additional datasets include earthquake and volcano locations, geochronology, topography, magnetics, gravity, and seismic velocity.</p> <p>The global tectonics collection is suitable for research and educational purposes.</p>
The soil province geodatabase of Italy, storing information of soil typological units and broad soil regions at the 1:1,000,000 and 1:10,000,000 scales
<p>The Soil Map of Italy at 1:1,000,000 scale, was the result of the work of Edoardo AC Costantini, Giovanni L'Abate, Roberto Barbetti, Maria Fantappié, Romina Lorenzetti, and Simona Magini affiliated to Research Centre for agrobiology and soil science (CREA-ABP), in collaboration with several regional institutions, universities and other research centers of the CREA - Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria. The map, was printed by S.EL.CA. of Florence. The map is an informative and educational work of general scientific interest, which updates the previous one edited by prof. Fiorenzo Mancini and collaborators in 1966 both in terms of knowledge and of the adopted methods. It was produced processing of all data within a geographical and soil geodatabase, collected by the CREA-ABP and other institutions collaborating in over ten years of work and using the latest international methods. The soil map shows the distribution of major soils in the country and constitutes a milestone in the process launched in 1999 as part of the project the Soil Map of Italy at a scale of 1: 250,000, funded by MIPAAF and implemented in collaboration with the regional institutions. Both broad soil regions and soil provinces (reference scale 1:10,000,000 and 1:1,000,000) are reported.</p> <p>Most small-scale soil maps report dominant typological units and allow only a partial appraisal of pedodiversity since territories with similar dominant soils can actually possess different pedodiversity. This is particularly true at the national scale, where a great wealth of soil information collected at more detailed scales is generalized.</p> <p>A methodology was set up, which aimed at preserving pedodiversity in upscaling soil maps by using geomatic techniques and the World Reference Base for soil resources (WRB). The main source of information was the soil system geodatabase of Italy, storing information of soil typological units and soilscapes at the 1:500,000 reference scale. Qualitative aggregation of soil taxa followed upscaling rules aimed at (i) maintaining the information about pedogenetic processes and (ii) grouping soilscapes showing recurrent patterns of soil forming processes. The upscaling methodology can be summarized in seven steps as follows: (1) soil forming processes selection, retrieved from soil typological units stored in the national database; (2) upscaling soil systems and creation of broad soil regions at 1:10,000,000 reference scale; (3) semantic upscaling of typological units to form taxa showing different soil forming processes; (4) ranking and associating soil forming processes; (5) geography upscaling of soil systems geometry to form polygons at 1:1,000,000 reference scale, called subregions; (6) ranking subregions according to their extension; (7) naming subregions by ranking the taxa according to the number of soil typological units.</p> <p>The soil subregion map reported 47 map unit and 148 taxa, belonging to 22 reference soil group of WRB and showing from one to four qualifiers. Each map unit had from 2 to 18 taxa, for a total of 317 occurrences. Thirty taxa had 3 or more occurrences, while the remaining took place in one or two subregions only.</p>
Prasat Cong Ang ប្រាសាទជើងអង or Cheung Ang (Kampong Cham Province), Cambodia. Doorframe.
<p>Prasat Cong Ang ប្រាសាទជើងអង or Cheung Ang (Kampong Cham Province), Cambodia. Doorframe showing attached pillar and inner jamb with part of inscription <a href="https://siddham.network/inscription/k99/">K.99</a>.</p>
30-m Spatial Resolution Bioclimatic Dataset of 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches
<p><strong>Brief Introduction of the Dataset</strong></p> <p>This bioclimatic dataset is the product of research article "Mapping 30-m Resolution Bioclimatic Variables During 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches." published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.</p> <p>The dataset contains 19 30-m resolution average bioclimatic variables during 1991-2020 Climate Normals for Hubei Province (108°21′42″—116°07′50″ E, 29°01′53″—33°6′47″ N), the core region of the Yangtze River middle reaches. The dataset was constructed by statistically downscaling the Climatic Research Unit (CRU) 1-km monthly climate variables (1440 in total), cablirating with ground observation data with 82 weather stations and aggregating based on the defination of 19 bioclimatic variables. The downscaling of four 1-km Climatic Research Unit monthly climate variables including monthly maximum, mean, minimum temperature and precipitation was firstly achieved by random forest model with 30-m resolution terrain and spatial data. Then the interpolation-based geographical differential analysis (GDA) was applied to improve the accuracy of downscaled products based on ground observation data. Finally, the bioclimatic variables were aggregated based on their definitions and averaged for the 30 years. The Yangtze River middle reaches is abundant of forestry, agriculture, biodiversity resources that requires finer bioclimatic data for better understands of these aspects. This dataset will provide higher spatial accuracy, more information and applicability in finer regional studies in the Yangtze River middle reaches.</p> <p> </p> <p><strong>Description of the 19 Bioclimatic Variables</strong></p> <p>The dataset contains 19 geotiff files in total. File names and the corresponding full name of bioclimatic variables are described as follows:</p> <p>Bio01 Mean annual air temperature (℃)<br>Bio02 Mean diurnal air temperature range (℃)<br>Bio03 Isothermality (%)<br>Bio04 Temperature seasonality (℃)<br>Bio05 Mean daily maximum air temperature of the warmest month (℃)<br>Bio06 Mean daily minimum air temperature of the coldest month (℃)<br>Bio07 Annual range of air temperature (℃)<br>Bio08 Mean daily mean air temperatures of the wettest quarter (℃)<br>Bio09 Mean daily mean air temperatures of the driest quarter (℃)<br>Bio10 Mean daily mean air temperatures of the warmest quarter (℃)<br>Bio11 Mean daily mean air temperatures of the coldest quarter (℃)<br>Bio12 Annual precipitation amount (mm)<br>Bio13 Precipitation amount of the wettest month (mm)<br>Bio14 Precipitation amount of the driest month (mm)<br>Bio15 Precipitation seasonality (%)<br>Bio16 Precipitation amount of the wettest quarter (mm)<br>Bio17 Precipitation amount of the driest quarter (mm)<br>Bio18 Precipitation amount of the warmest quarter (mm)<br>Bio19 Precipitation amount of the coldest quarter (mm)</p> <p> </p> <p><strong>Others</strong></p> <p>More information related to bioclimatic variables can be found on https://chelsa-climate.org/bioclim/</p>
High Speed Rail Seismic Observation in Baoding, Hebei Province of China
<p>H5 files includes all train events collected in the observation. Raw data in sac format is too big (600GB) to upload.</p> <p>To access all continuous data, please contact shiyxg@mail.iggcas.ac.cn/wenjc@pku.edu.cn/njy@pku.edu.cn</p> <p> </p> <table> <tbody> <tr> <td>File names</td> <td>Format</td> <td>date</td> </tr> <tr> <td>BSPK095*</td> <td>-100s-100s, dt=0.01</td> <td>0424-0504</td> </tr> <tr> <td>BSPKU87*</td> <td>-100s~100s, dt=0.01</td> <td>0510-0518</td> </tr> <tr> <td> <p>hsr_coor_stacked_201804*</p> </td> <td>stacked traces in different frequency bands</td> <td>0424-0504</td> </tr> <tr> <td> <p>hsr_coor_stacked_201805*</p> </td> <td>stacked traces in different frequency bands</td> <td>0510-0518</td> </tr> <tr> <td> <div>coor_all_201804_YNPK_CZ_158.npy</div> </td> <td> <p>ambient noise results at night, between 158 stations</p> <p>(S158_1804.txt)</p> </td> <td>0424-0504</td> </tr> <tr> <td> <div>coor_all_201805_YNPK_CZ_143.npy</div> </td> <td> <p>ambient noise results at night, between 143 stations</p> <p>(S143_18045txt)</p> </td> <td>0510-0518</td> </tr> <tr> <td> <div>1804_YNPK_CZ_f0.2_20_all_night.h5</div> </td> <td> <p>Continuous data at 13 nights of 4 stations for stability comparsion</p> </td> <td>0424-0504</td> </tr> <tr> <td> <div>H1.h5</div> </td> <td> <p>Correlation results of array in Baoding, 2023 March.</p> </td> <td>2023/0311-0328</td> </tr> </tbody> </table>
The soil province feature of Italy at the 1:1,000,000 scale
<p>Updated version of the feature Soil province of Italy at the 1:1,000,000 scale. The original geodatabase (version 1.0 - https://zenodo.org/record/7072306) is not replaced because collecting other features debribed by references. This version partially becomes observations raised by regional officiers of Regione Emilia Romagna and Regione Veneto: few map units have been splitted in order to better conform to regional features. This new map reports 51 map units.</p>
Crustal thickness and Vp/Vs database from the Amazonian Craton and adjacent provinces
<p>This database includes results of crustal thickness and Vp/Vs estimated from stations located in the Amazonian Craton and adjacent provinces.</p> <p>If you used this database, please cite:<br> <br> ALBUQUERQUE, D. F. et al. Crustal structure of the Amazonian Craton and adjacent provinces in Brazil. Journal of South American Earth Sciences, v. 79, p. 431–442, 2017. DOI: 10.1016/j.jsames.2017.08.019</p> <p> </p>
InSAR interferences and slip model related to the 2021 Maduo earthquake in Qinghai province, China
<p>The dataset includes the SAR unwrapped interferograms, and the slip model related to the 2021 Maduo earthquake on western Maduo County, Qinghai Province of China.</p> <p>SAR images:</p> <p>Sensor: Sentinel-1 A/B ascending and descending tracks interferograms, including the T099A, T026A, T172A, T106D, T004D and T033D.</p> <p>Time: 2021.05.13 - 2019.05.27, 6 radar phases images and 2 range offset images.</p> <p>Processing software: GAMMA</p> <p>Topographic data from the Shuttle Radar Topography Mission (SRTM) with a resolution of 1 arcsec were used to align the images and remove the topographic phase.</p> <p>First‐order tropospheric delays were mitigated by using the Generic Atmospheric Correction Online Service (GACOS)</p> <p>Silp Models:</p> <p>The model is generated through triangular dislocation inversion.</p> <p>The slip models is composed of two files:</p> <p> Main rupture: Slip_Maduo_Main.gmt</p> <p> Tip rupture: Slip_Maduo_Tip.gmt</p> <p>Format: GMT</p>
Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru
<p>Datasets with information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.</p> <p>Data processed from National Statistical System (INEI) publications.</p> <p>2025.</p>
Geospatial Analysis of Economic Development in kenya by Province
<p>This dataset presents both vector and raster data combinations for pm2.5, elevation, nightlight data, population density, area, and population that can be used to estimate the economic development of Kenya using distribution of banks as a proxy.</p>
CoastSeg: Shoreline data at 30-m spatial resolution for 2001 coastal provinces or regions of the world, in geoJSON format.
<p>Region: 2001 coastal provinces or regions of the world</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): province_files_bounds.json</p>
Mangrove Crab Sampling Data in Dongzhaigang National Nature Reserve, Haikou, Hainan Province, China
<p>This dataset contains the results of a study on mangrove crabs conducted in four seasons (Summer, SU; Autumn, AU; Winter, WI; Spring, SP) of 2020 and 2021. The samples were collected in the Dongzhaigang National Nature Reserve, Haikou, Hainan Province, China, at five sites: Sanjiang (SJ), Tashi (TS), Shanweitou (SWT), Luodou (LD), and Puqian (PQ). The primary focus is on crab species belonging to the superfamilies Ocypodoidea (ghost crabs), Grapsoidea (square crabs), and Portunoidea (swimming crabs).</p> <p>Sampling was conducted using net trapping, with three replicate plots set up for each habitat type at each site. Each plot was sampled continuously for three days. Vegetation information was recorded using dominant species as representatives, and water environmental information was collected using a WTW instrument. The parameters measured include total dissolved solids (TDS) (mg/L), dissolved oxygen (DO) (mg/L), salinity (SAL) (‰), water temperature (T) (℃), and pH. Finally, the longitude and latitude in the WGS84 coordinate system and Cartesian coordinates for each plot were recorded.</p> <p>The dataset fields are as follows:</p> <ul> <li>date: Date of sampling</li> <li>year: Year of sampling</li> <li>month: Month of sampling</li> <li>day: Day of sampling</li> <li>site: Sampling location, including TS, SJ, SWT, LD, PQ</li> <li>habitat: Habitat type, including tidal channels, tidal flats, and several vegetation types represented by mangrove trees such as Avicennia marina, Rhizophora stylosa, Bruguiera sexangular, Sonneratia apetala, and Ceriops tagal.</li> <li>plotname: Plot name</li> <li>species: Species name, as per the World Register of Marine Species (<a href="https://www.marinespecies.org/">https://www.marinespecies.org</a>)</li> <li>superfamily: Superfamily, as per the World Register of Marine Species (<a href="https://www.marinespecies.org/">https://www.marinespecies.org</a>)</li> <li>season: Season, including Summer (SU), Autumn (AU), Winter (WI), and Spring (SP)</li> <li>cname: Plot division by season, site, and habitat</li> <li>fullname: Plot division by season, site, habitat, and plot sequence number</li> <li>pname: Plot division by site, habitat, and plot sequence number</li> <li>TDS: Water total dissolved solids (mg/L)</li> <li>pH: Water pH</li> <li>DO: Water dissolved oxygen (mg/L)</li> <li>T: Water temperature (℃)</li> <li>SAL: Water salinity (‰)</li> <li>longitude: Longitude in WGS84 coordinate system</li> <li>latitude: Latitude in WGS84 coordinate system</li> <li>x: Cartesian coordinate x</li> <li>y: Cartesian coordinate y</li> </ul> <p>We thank Chengpu Jiang, Liangjun Wei and other colleagues for their assistance during the field samplings. Thanks also for the experimental conditions and sampling support provided by Hainan Dongzhaigang National Nature Reserve Authority.</p>
Supplementary data to "Destruction and regrowth of lithospheric mantle beneath large igneous provinces"
<p>Database files to accompany "<em>Destruction and regrowth of lithospheric mantle beneath large igneous provinces</em>", By <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">Stephenson et al. (2023)</a>. The article can be accessed by following <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">this permanent link</a>.</p> <p>The primary resources in this database are (i) estimates of melt equilibration pressure and temperature calculated using the scheme of <a href="https://github.com/fmcnab/meltPT">McNab & Ball (2023)</a>; (ii) a database of lithospheric thickness estimates beneath modern intraplate magmatic provinces using geochemical and seismological techniques; (iii) a database of the outlines and ages of large igneous provinces, substantially updated from <a href="http://https://doi.org/10.1029/93RG02508">Coffin & Eldholm (1994)</a>, and <a href="https://doi.org/10.5670/oceanog.2006.13">Coffin et al. (2006)</a>; (iv) a database of large igneous province eruption centres; and (v) a document of references used to build these databases. Files are numbered as in the Supplementary Information of <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">the paper.</a> Please see below for more details.</p> <ol> <li><strong>Data S1</strong>. A database of global geochemical compositions of mafic intraplate magmatic rocks compiled by <a href="http://doi.org/10.1038/s41467-021-22323-9">Ball et al (2021)</a>, and corresponding estimates of melt equilibration pressure and temperature P<sub>eq</sub> and T<sub>eq</sub>, respectively; <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">this study</a>). Note that authors should cite <a href="http://doi.org/10.1038/s41467-021-22323-9">Ball et al. (2021)</a> in reference to the global geochemical database. They should cite <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">Stephenson et al (2023)</a> in reference to the global equilibration pressure and temperature estimates, in which case they should also cite <a href="http://github.com/fmcnab/meltPT">McNab & Ball (2023)</a>, whose software was used to calculate P<sub>eq</sub> and T<sub>eq</sub>.</li> <li><strong>Data S2</strong>. A spreadsheet containing modern-day lithospheric thickness estimates beneath modern intraplate provinces. For complete references to geochemical analyses contained in this database, please see <a href="https://doi.org/10.1038/s41467-021-22323-9">Ball et al (2021)</a>. The database includes lithospheric thickness estimates obtained <ul> <li>by exploiting melt equilibration pressure and temperature <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">(this study)</a>;</li> <li>by inverse modelling of rare earth element compositions <a href="https://doi.org/10.1038/s41467-021-22323-9">(Ball et al., 2021)</a>; and</li> <li>from the lithospheric thickness model of<a href="https://doi.org/10.1038/s41561-020-0593-2"> Hoggard et al. (2020)</a>, which is based upon the tomographic model of <a href="https://doi.org/10.1093/gji/ggt095">Schaeffer & Lebedev (2013)</a>.</li> </ul> </li> <li><strong>Data S3</strong> & <strong>S4</strong>. A database containing outlines of magmatic provinces dating back to 750 Ma, including <ul> <li>a directory (Data_S3.zip) containing the unfiltered database shape files (lips.shp, lips.shx, lips.dbf, lips.cpg). This directory also contains the same data in a multisegment text file for plotting in the Generic Mapping Tools (polys_ID_age_unfiltered.dat) in which each polygon is separated by '>' where the header indicates polygon ID and time since eruption. And</li> <li>a database filtered for final magmatic event in a given location (Data_S4.dat), where each polygon header also contains '>' ID age polygon_area'. See <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">the paper</a> for methodological details.</li> </ul> </li> <li><strong>Data S5</strong>. A database of located LIP eruption centres.</li> <li><strong>Data S6</strong>. A pdf document of references. The document includes <ul> <li>references used to update locations and ages of the large igneous province database of <a href="http://doi.org/10.5670/oceanog.2006.13">Coffin et al. (2006)</a>;</li> <li>references for existing lithospheric thickness models used test our observed LAB depth as a function of time relationship; and</li> <li>references used to locate the eruption centres of mantle plumes (i.e. Data S5; <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">this study</a>).</li> </ul> </li> </ol>
Database of a study on long-term land cover changes in Thừa Thien Huế Province, Central Vietnam
<p>Data provided here forms the basis of a study which is published in the journal Land Use Policy (year 2023) under the title 'The nature of a ‘forest transition’ in Thừa Thien Huế Province, Central Vietnam – A study of land cover changes over five decades'. The database contains the image files of the maps (and associated legends) shown in the publication. Further data (currently under use for other related research) will eventually be added to the repository.</p>
Aggregated Eco Province data
<p>Aggregated Eco Province (AEP) data for each AEP between complexity 1 to 115.</p> <p>To accompany Sonnewald et al. "Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces".</p> <p>NOTE: A complexity >12 is recommended.</p>
Education statistics 1970 - 2012 province of Uusimaa, Finland
<p>Excel worksheet for project internal use.</p> <p>(http://tilastokeskus.fi/meta/til/kjarj.html TARGET=_blank) Kuvaus <br> (http://tilastokeskus.fi/til/kjarj/kas.html TARGET=_blank) Käsitteet</p> <p>määritelmät <br> (http://tilastokeskus.fi/til/kjarj/laa.html TARGET=_blank) <br> <br> Laatuseloste<br> <br> </p>
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.