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32 results for “2000-2020”
Rural population count at 1 km for 2000-2020 based on WorldPop and GHS-SMOD urbanization level
<p>Rural population count at 1 km grid in EPSG:4326 for 2000-2020 (annual). This is only an estimate of the rural population. This probably misses many rural areas, especially in the tropics. The maps were derived using two data sources:</p> <ol> <li><a href="https://hub.worldpop.org/geodata/listing?id=64">WorldPop population counts at 1 km</a>;</li> <li><a href="https://human-settlement.emergency.copernicus.eu/download.php?ds=smod">GHS-SMOD urbanization levels at 1 km</a>;</li> </ol> <p>Rural population is estimated using the following translation rules for GHS-SMOD (note: these are arbitrary rules based on the GHS-SMOD documentation):</p> <ul> <li>Class 30: “Urban Centre grid cell” = 0% rural</li> <li>Class 23: “Dense Urban Cluster grid cell” = 0.5% rural</li> <li>Class 22: “Semi-dense Urban Cluster grid cell” = 2% rural</li> <li>Class 21: “Suburban or per-urban grid cell” = 15% rural</li> <li>Class 13: “Rural cluster grid cell” = 95% rural</li> <li>Class 12: “Low Density Rural grid cell” = 100% rural</li> <li>Class 11: “Very low density rural grid cell” = 100% rural</li> </ul> <p>The nighttime images are based on: <a href="https://doi.org/10.5281/zenodo.7750174">https://doi.org/10.5281/zenodo.7750174</a></p> <ul> <li>Schiavina, Marcello; Melchiorri, Michele; Pesaresi, Martino (2023): GHS-SMOD R2023A - GHS settlement layers,<br>application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A,<br>multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi:<br>10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA PID: <a href="http://data.europa.eu/89h/a0df7a6f-49de-46ea-%209bde-563437a6e2ba">http://data.europa.eu/89h/a0df7a6f-49de-46ea-</a><br><a href="http://data.europa.eu/89h/a0df7a6f-49de-46ea-%209bde-563437a6e2ba">9bde-563437a6e2ba</a></li> </ul>
Global MODIS-based snow cover monthly long-term (2000-2012) at 500 m, and aggregated monthly values (2000-2020) at 1 km
<p>The Global monthly snow cover repository contains multiple products (based on the MODIS/Terra MOD10A2):</p> <ol> <li>Global snow cover monthly long-term (2000–2012) P90 and standard deviation derived from the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI snow cover weekly product</a>;</li> <li>Global snow cover monthly values P05, P50 and P95 for the period 2000–2020 derived using <a href="https://climate.esa.int/en/odp/#/project/snow">ESA snow cover fraction daily 1-km values</a>;</li> <li>Min and max geometric temperatures for the mid-month (dtm_temp.max_geom.*_m_1km_s0..0cm_xxxx_epsg4326_v1.tif);</li> </ol> <p>Quantiles (probability either 0.05, 0.5, 0.9 and/or 0.95) have been derived by matching dates in the filenames (daily or weekly values). After deriving quantiles, gaps were filled using temporal neighbors (e.g. missing values for year 2002 were filled using average of values between year 2001 and 2003). The gaps were especially large for months of November, December, January and February, northern Hemisphere. Important note: maps still contain some artifacts due to high reflections of white-sands e.g. Salar de Uyuni desert in Bolivia and similar. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/snow.cover"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>snow.cover = variable: snow cover fractions,</li> <li>esa.modis = data source ESA snow product,</li> <li>p.90 = upper 90% quantile,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2012 = time reference aggregated: from 2000 to 2012,</li> <li>v1 = version number: 1,</li> </ul>
Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.</p>
Soil pH in H2O [-] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: soil pH in H2O;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</p>
Soil clay content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: clay.tot = clay content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil sand content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: sand.tot = sand content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
DPMFA_EU_ENM_2000-2020: Dynamic Probabilistic Material Flows of Engineered Nanomaterials from 2000 to 2020 - Raw results
<p>This dataset is related to the following publication:</p> <p>Title: Dynamic probabilistic material flow analysis of engineered nanomaterials in European waste treatment systems</p> <p>Authors: Sana Rajkovic, Nikolaus A. Bornhöf<span>t</span>, Renata van der Weijden, Bernd Nowack, Véronique Adam</p> <p>Submitted to the journal Waste Management in September 2019.</p> <p>The files contain key values of probability distributions associated with the emissions of selected engineered nanomaterials to the environment.</p>
Journey North - Barn Swallow observations by volunteer community scientists across Central and North America (2000-2020)
This data package contains Barn Swallow migration data consisting of 3,836 total observational reports from 2000 - 2020 across North America and Central America. These data were collected by 1,320 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Barn Swallow Project is an ongoing study of Barn Swallow phenology conducted at broad spatial and temporal scales. Since 2000, community scientists have tracked first arrival dates of Barn Swallows (Hirundo rustica) in the United States. Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Barn Swallow Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Global leaf chlorophyll content (LCC) product from MODIS imagery (2000-2020)
<p>The spatial and temporal distribution of leaf chlorophyll content (LCC) is critical for understanding the capacity of vegetation photosynthesis. Here, a global 8-day leaf chlorophyll content (LCC) dataset at 500-m resolution was generated from MODIS data using a matrix system with two pairs of vegetation indices. </p> <p><strong>The following paper should be cited when using the data:</strong></p> <p>Xu, M., Liu, R., Chen, J.M., Liu, Y., Wolanin, A., Croft, H., He, L., Shang, R., Ju, W., Zhang, Y., He, Y., Wang, R., 2022. A 21-year time-series of global leaf chlorophyll content maps from MODIS imagery. IEEE Trans. Geosci. Remote Sens. <a href="https://doi.org/10.1109/TGRS.2022.3204185">https://doi.org/10.1109/TGRS.2022.3204185</a>.</p> <p>Detailed description of data organization can be found in the uploaded document "1Readme.docx".</p> <p>The whole dataset of MODIS LCC product is from 2000 to 2020. <strong>Due to the data volume limitation of Zenodo, currently the data from 2000-2010 can be downloaded through Google Drive sharing link:</strong></p> <p><a href="https://drive.google.com/drive/folders/11eXetjsAB_ZjFqGs8SXEWzd6byr_8LoW?usp=sharing">https://drive.google.com/drive/folders/11eXetjsAB_ZjFqGs8SXEWzd6byr_8LoW?usp=sharing</a></p> <p><strong>Related dataset: </strong>Mingzhu, X., Liu, R., Chen, J. M., Shang, R., & Liu, Y. (2022). Global leaf chlorophyll content product from MERIS imagery (GLOBMAP MERIS LCC) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10467919">https://doi.org/10.5281/zenodo.10467919</a></p> <p>For any other questions, please send email to Mingzhu Xu (<a href="mailto:xumzhu@gmail.com">xumzhu@gmail.com</a>).</p>
Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020
<p>Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".</p> <p> </p> <p>Please cite the original paper:</p> <p>Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework, 2000–2020.” <em>Applied Geography </em> 167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.</p> <p><br>WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).</p> <p>All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.</p> <p>Three WUI maps were generated using structures built on of before the years indicated below:</p> <p>2000 - "CA_WUI_2000.tif"</p> <p>2010 - "CA_WUI_2010.tif"</p> <p>2020 - "CA_WUI_2020.tif" </p> <p> </p> <p>Acknowledgments -</p> <p>We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.</p>
Database of non-communicable disease reports contributing to UN high-level meeting process (2000-2020)
<p>Database of the key non-communicable disease reports, policy papers, strategies and journal series published 2000-2020 that fed into the UN high-level meeting process</p>
SM2RAIN CHINA: 1 km rainfall estimation for CHINA region derived from SMCI Soil Moisture (2000-2020)
<p><strong>SM2RAIN-SMCI provides rainfall estimates for the whole CHINA at 1 km/daily spatial resolution </strong>by applying SM2RAIN algorithm (<em>Brocca et al., 2014; 2019) </em>to Soil Moisture data from SMCI Soil moisture (<a href="https://doi.org/10.5194/essd-14-5267-2022"><em>https://doi.org/10.5194/essd-14-5267-2022</em></a>) for the period from January 2000 to December 2020 (21 years). The SM2RAIN-SMCI rainfall dataset (in mm/day) is provided over a regular grid at 0.01-degree sampling. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The uncalibrated SM2RAIN version (<a href="https://doi.org/10.5194/hess-26-2481-2022">https://doi.org/10.5194/hess-26-2481-2022</a>) was adopted to obtain rainfall. Precipitation data from HRLT (<a href="https://doi.org/10.5194/essd-14-4793-2022">https://doi.org/10.5194/essd-14-4793-2022</a>) were adopted to obtain mean climatology.</p> <p>The rainfall dataset is provided in NetCDF format. To simplify data access, the CHINA region is divided in 101 subregion containing 540 (longitude) * 360 (latitude) pixels each.</p>
A dataset of thermokarst lake drainage events in northern Alaska (2000-2020)
<p>The catastrophic thermokarst lake drainage events occurring in the circumpolar Arctic permafrost region are referred to as the new Arctic hazard. To accurately detect this disturbance process, the authors performed a time series analysis of changes in the thermokarst lake region of northern Alaska since 2000 using all available Landsat TM, ETM+, and OLI continuous data on the Google Earth Engine platform. This dataset includes 90 thermokarst lake drainage events detected in northern Alaska during 2000-2020 and year-by-year water distribution maps based on AWEI. The thermokarst lake drainage events are published in Shapefile format, including information on the area, location, drainage proportion, and year of drainage for each drained lake.</p>
Urban Redevelopment by Census Tract in New York City (2000 -2020)
<p>The annual urban redevelopment map for NYC was produced using the classification method proposed in this experiment to highlight the spatial and temporal distribution of urban reconstructions. The time-series gentrification risk maps illustrate areas that have faced gentrification risk since 2000. The data was aggregated to the Census tract level for displaying a visually friendly result. The raw building-level data is also provided.</p>
Search results for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)
<p>This datasets is the result of the search for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)</p>
Generating Annual 4-m Land Cover Products over 28 Metropolises in China during 2000-2020
<p>A 4-m fine-grained land cover products over the 28 provincial capital cities in China (mainly in temperate monsoon climate and subtropical monsoon climate zone, and categorized into 7 classes, i.e., cropland, forest, grass, shrub, water, impervious surface, and bare land) is generated through the proposed model; A 4-m annual land cover products during 2000-2020 in Wuhan, China is generated through the proposed model.</p> <p>To validate the effectiveness of the derived products, three verification approaches are employed. 1) evaluation by the consistency with the contemporary products including 10-m Esri Land Cover, 10-m FROMGLC10, and 30-m GlobeLand30 ; 2) evaluation by the publicly certified 4-m annotation dataset GID; 3) evaluation by the third-party-created annotation samples. The validation results can be viewed in the manuscript, and the reproduction executation details can be viewed at Readme.txt.</p> <p>The products is in .tif format, and validaiton region is in ENVI/hdr format. Users need to install ENVI software or Arcgis to read the product.</p> <p>"open_source_DETNet_code_and_simple_demo.rar" is open source code and the corresponding intruction.</p>
Sample of Reanalysis Dead Fuel Moisture Content Dataset of California (2000-2020)
Open the record for dataset details and reuse information.
Aggregated database of forest soil chemical properties in the Czech Republic based on surveys from 2000-2020.
<p>The dataset includes data from forest soil surveys conducted in the period 2000–2020. It provides soil and site variables from 8,269 locations. Data are aggregated in three basic soil layers: upper organic soil horizon (FH, 6,876 locations), upper mineral layer 0–30 cm (M03, 8,051 locations) and deeper mineral soil layer (30–80 cm, M38, 2,260 locations).</p> <p>This dataset is linked to the following article published in Annals of Forest Science:</p> <p>Neudertová Hellebrandová, K., Fadrhonsová, V. & Šrámek, V. Aggregated database of forest soil chemical properties in the Czech Republic based on surveys from 2000 to 2020. Annals of Forest Science 81, 17 (2024). <a href="https://doi.org/10.1186/s13595-024-01225-y">https://doi.org/10.1186/s13595-024-01225-y</a></p>
Glacier inventory for the Greater Caucasus (2000-2020)
<p>Data from the new glacier inventory at two time periods (2000, 2020) covering the entire Greater Caucasus (Georgia, Russia, and Azerbaijan) is presented here. This work was conducted based on satellite imagery (Landsat, Sentinel, SPOT) and 30 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM).</p> <p>How to cite. Tielidze, L. G., Nosenko, G. A., Khromova, T. E., and Paul, F.: Strong acceleration of glacier area loss in the Greater Caucasus over the past two decades, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-312, in review, 2021.</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)
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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.
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.