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99 results for “High spatial resolution”
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Cascade project at North Temperate Lakes LTER - High-resolution spatial analysis of CASCADE lakes during experimental nutrient enrichment 2015 - 2016
This dataset contains high-resolution spatio-temporal water quality data from two experimental lakes during a whole-ecosystem experiment. Through gradual nutrient addition, we induced a cyanobacteria bloom in an experimental lake (Peter Lake) while leaving a nearby reference lake (Paul Lake) as a control. Peter and Paul Lakes (Gogebic county, MI USA), were sampled using the FLAMe platform (Crawford et al. 2015) multiple times during the summers of 2015 and 2016. In 2015 nutrient additions to Peter Lake began on 1 June, and ceased on 29 June, Paul Lake was left unmanipulated. In 2016 no nutrients were added to either lake. Measurements were taken using a YSI EXO2 probe and a Garmin echoMap 50s. Sensor- data were collected continuously at 1 Hz and linked via timestamp to create spatially explicit data for each lake. Crawford, J. T., L. C. Loken, N. J. Casson, C. Smith, A. G. Stone, and L. A. Winslow. 2015. High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology. Environmental Science & Technology 49:442–450.
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format
The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Cascade Project at North Temperate Lakes LTER – High-resolution Spatial Data for Whole Lake Experiments 2018 - 2019
Spatial measurements of water quality from Peter and Paul lakes in 2018 and 2019. In 2019, inorganic nitrogen and phosphorus were added to Peter Lake daily to cause an algal bloom while Paul Lake was an unmanipulated reference lake. In 2018, both lakes were sampled 1 time per week, while in 2019 lakes were sampled three times per week. Measurements were taken using the FLAMe sampling platform (Crawford et al. 2015, Environmental Science and Technology 49:442-450), which was driven in a grid pattern and recorded GPS coordinates and water measurements at 1Hz to create high resolution spatial maps.
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/344/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
The S&M-HSTPM2d5 dataset: High Spatial-Temporal Resolution PM 2.5 Measures in Multiple Cities Sensed by Static & Mobile Devices
<p>This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set up as one minute in Cangzhou, and three seconds in Foshan and Tianjin. For the specific detail of the setup, please refer to the Device_Setup_Description.txt file in this repository and the data descriptor paper.</p> <p>After the data collection process, the data cleaning process was performed to remove and adjust the abnormal and drifting data. The script of the data cleaning algorithm is provided in this repository. The data cleaning algorithm only adjusts or removes individual data points. The removal of the entire device's data was done after the data cleaning algorithm with empirical judgment and graphic visualization. For specific detail of the data cleaning process, please refer to the script (Data_cleaning_algorithm.ipynb) in this repository and the data descriptor paper.</p> <p>The dataset in this repository is the processed version. The raw dataset and removed devices are not included in this repository.</p> <p>The data is stored as a CSV file. Each CSV file which is named by the device ID represents the data that was collected by the corresponding device. Each CSV file has three types of data: timestamp as the China Standard Time (GMT+8), geographic location as latitude and longitude, and PM2.5 concentration with the unit of microgram per cubic meter. The CSV files are stored in either Static or Mobile folder which represents the devices' type. The Static and Mobile folder are stored in the corresponding city's folder.</p> <p>To access the dataset, any programming language that can access CSV files is appropriate. Users can also open the CSV file directly. The get_dataset.ipynb file in this repository also provides an option of accessing the dataset. To successfully execute ipynb file, Jupyter Notebook with Python 3.0 is required. The following python library is also required:</p> <p>get_dataset.ipynb:<br> 1. os library<br> 2. pandas library</p> <p>Data_cleaning_algorithm.ipynb:<br> 1. os library<br> 2. pandas library<br> 3. datetime library<br> 4. math library</p> <p>The instruction of installing the libraries above can be found online. After installing the Jupyter Notebook with Python 3.0 and the required libraries, users can try to open the ipynb file with Jupyter Notebook and follow the instruction inside the file. </p> <p>For questions or suggestions please e-mail Xinlei Chen <xinlei.chen@sv.cmu.edu></p>
Using RS-DAT to study continental-scale phenology at high-spatial resolution
This repository includes the notebooks employed to calculate and analyze a gridded phenological model, as computed from a set of daily meteorological variables.
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
High temporal and spatial resolution emission inventory for maritime shipping emissions on the North Sea and Baltic Sea (2015)
<p>A temporally and spatially highly resolved emission inventory for the North Sea and Baltic Sea for the year 2015, created with current emission factors and ship activity data. The emissions inventory is available as 396 csv files, one for each day in 2015 and December 2014, grouped as monthly archives. </p> <p><strong>Note that due to the underlying ship activity data and the geographic boundaries, the time index in the <em>Datetime </em>column in the <em>ship_emissions_YYYYMMDD.csv</em> files is not equidistant.</strong> For example, since vessels leave the geographic area and reenter later, no data is available for the time the vessel is not within the area.</p> <p>The underlying model source code is available on Github, with a release of the associated version on Zenodo: [](https://doi.org/10.5281/zenodo.6951672)</p> <p> </p>
High-resolution spatialization for estimation of precipitation in the Cordillera Blanca, Peru
<p>Supplementary materials for the article "High-resolution spatialization for estimation of precipitation in the Cordillera Blanca, Peru"</p>
HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020
<p>The monthly <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection (<strong>HiTIC-Monthly</strong>) includes <strong><em>near-surface air temperature</em> </strong>(SAT) and 11 commonly used<strong> <em>human-perceived temperature</em> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), simplified Wet Bulb Globe Temperature (sWBGT), Wet-Bulb Temperature (WBT), and Wind Chill Temperature (WCT). This dataset has a high spatial resolution of 1 km × 1 km and covers mainland China from January 2003 to December 2020. The overall R-square, root mean square error, and mean absolute error of the 12 thermal indices in the HiTIC dataset are 0.996, 0.693°C, and 0.512°C, respectively. It is stacked by year, and each stack is composed of 12 monthly images in the <strong>NetCDF format</strong>. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) for saving storage space, and need to be divided by 100 to get the values in degree Celcius when in use. The projection coordinate system of the dataset is Albers Equal Area Conic Projection. The naming rule and other detailed information can be found in “README.pdf”.</p> <p>If you have any questions when using the HiTIC-Monthly dataset, please feel free to contact Miss Hui Zhang via <a href="mailto:zhangh573@mail2.sysu.edu.cn">zhangh573@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:yqzhao@link.cuhk.edu.hk">zhaoyq66@mail.sysu.edu.cn</a>. More details on the procedure of producing the HiTIC-Monthly dataset and its accuracy assessment can be found in:</p> <p>Zhang, H., Luo, M., Zhao, Y., Lin, L., Ge, E., Yang, Y., Ning, G., Cong, J., Zeng, Z., Gui, K., Li, J., Chan, T. O., Li, X., Wu, S., Wang, P., and Wang, X.: HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020, Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, 2023.</p>
Global daily high spatial resolution XCO2 product during 2019-2021
<p>Validation results are in preparation. The full dataset will be uploaded after the acceptance of our paper.</p>
HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru
<p>The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.</p>
A High-Resolution Spatial Room Impulse Response Database
<p><strong>A High-Resolution Spatial Room Impulse Response Database:</strong></p> <p>A Database of various SRIRs measured in three rooms, for two receiver and 3 source positions each. The positions are shown in the floor plans. </p> <p><strong>Naming Convention:</strong></p> <p><strong>Receivers</strong>: SMA (DRIRs): spherical microphone array impulse responses on a 2702 sampling point Lebedev grid.</p> <p> KU100 (BRIRs): Neumann KU100 dummy head impulse responses on a 360 sampling point horizontal grid</p> <p> Omni_ir: Omnidirectional impulse responses measured with an Earthworks M30 microphone </p> <p><strong>Receiver positions:</strong> P1, P2 as indicated in the floor plans</p> <p><strong>Source positions:</strong> LSL: left speaker, LSR: right speaker, LSC: center speaker as indicated in the floor plans </p> <p><strong>Rooms: </strong>Audiolab, Classroom, Audimax</p> <p>_______________________________________________________________________________________</p> <p><strong>Contact:</strong><br> Tim Lübeck, Johannes M. Arend, and Christoph Pörschmann<br> TH Köln - University of Applied Sciences<br> Institute of Communications Engineering<br> Department of Acoustics and Audio Signal Processing<br> Betzdorfer Str. 2, D-50679 Cologne, Germany</p> <p><a href="https://www.th-koeln.de/personen/tim.luebeck/">https://www.th-koeln.de/personen/tim.luebeck/</a></p> <p><a href="https://www.th-koeln.de/personen/johannes.arend/">https://www.th-koeln.de/personen/johannes.arend/</a></p> <p><a href="http://www.th-koeln.de/personen/christoph.poerschmann/">https://www.th-koeln.de/personen/christoph.poerschmann/</a><br> <br> </p>
High spatial resolution dataset of grapevine yield components at the within-field level
<p>This dataset comprises a comprehensive mapping of vine yield at the plant scale over two vine fields located in the southern region of France. Both vine fields were planted with the Vitis vinifera : cv. Syrah. The first field (Field 1) occupies 0.8 ha and data were collected in 2022, while the second field (Field 2) has an area of 0.5 ha and data were collected in 2008. Throughout the growing season, information regarding unproductive vines, inflorescence number, and bunch weight was collected for both vine fields. For both fields, at the flowering stage, the location of each productive and unproductive vines (dead and missing vines) was georeferenced, and the number of inflorescences was manually counted for all productive vines. For Field 1, at harvest, all bunches of the field were manually weighed with an accuracy of ±1 gram and georeferenced precisely (one point per vine). For each vine, total yield (grams per vine) was then computed as as the sum of the weight of its bunches. For Field 2, at harvest, the total yield per vine was estimated based on the weighing of representative bunches obtained from several regularly spaced set of 5 vines. In addition to the yield data, two ancillary data, including soil apparent resistivity measurements and common vegetative index derived from remote sensed imagery, are provided for both vine fields. Overall, the dataset consists of 3644 vines, with 2151 being productive, along with a total count of 33354 inflorescences and 19635 manually weighed bunches at harvest.</p> <p>Raw data includes 9 shapefiles (.shp), one per data type and per field.</p> <ul> <li>“Field1_Dead_Missing_Vines.shp” (Figure 1.A) contains the location of missing and dead vine identified in Field 1;</li> <li>“Field1_Inflorescences.shp” and “Field2_Inflorescences.shp” (Figure 1.B and Figure 1.C) both contain the location and the number of inflorescences per vine counted during flowering;</li> <li>“Field1_Final_Yield.shp” and “Field2_ Final_Yield.shp” (Figure 1.D and Figure 1.E) both contain location and measured values of yield weight per vine at harvest. For Field 1, the list of the bunch weight is also available;</li> <li>“Field1_Soil_Resistivity.shp” and “Field2_ Soil_Resistivity.shp” (Figure 1.F and Figure 1.G) contain the electrical resistivity measurements of the soil on each field;</li> <li>“Field1_Vegetation_Index.shp” and “Field2_ Vegetation_Index.shp” (Figure 1.H and Figure 1.I) contain vegetation index values, NDVI (Normalized Difference Vegetation Index, without unit) for Field 1 and FCover (Fraction of vegetation Cover, in %) for Field 2.</li> </ul> <p>Aggregated data are composed of three csv files, two for Field 1 and one for Field 2. “Field1_Yield.csv” and “Field2_Yield.csv” aggregate all available yield data for each vine plant (either productive or other types). In both these csv files, each line represents a planted vine. Another file named "Field1_Bunches.csv" contains another representation of the data for Field 1. In this file, each row corresponds to a weighed bunch.</p> <p>A Data in Brief article is associated to this dataset.</p>
High spatial resolution dataset of downscaled LUH2 land use scenarios for Belgium (10 m and 100 m)
<p>This dataset comprises high-resolution land use data downscaled from LUH2 scenarios for Belgium at both 10 m and 100 m resolutions. These datasets were generated based on research conducted by Rashidi et al. in 2023 and published in the Land Journal. We employed the GLOBIO land allocation routine to downscale fractional land use data, originally at a 0.25° resolution (approximately 25 km), into discrete land use maps at 10 m and 100 m resolutions. This process utilized three distinct reference land cover maps: ESA WorldCover at 10 m resolution, ESA WorldCover upscaled to 100 m resolution, and CORINE land cover at 100 m resolution.</p> <p>During the downsizing process, we considered three SSP-RCP scenarios to model land use trends for both the present and the year 2050 on a national scale in Belgium. Key components of the model included regional land use demand, an assessment of grid cells' suitability for various land use types, and a reference land cover map. It's important to note that the classification system used in the reference maps differs from that of LUH2. To ensure comparability for land use simulations, we conducted a reclassification process following the methodologies outlined by Pérez-Hoyos et al. (2012), Dong et al. (2018), and Liao et al. (2020). This reclassification consolidated land use classes, except for water, into seven general categories: 1) urban, 2) cropland, 3) pasture, 4) forestry, 5) secondary vegetation, 6) undefined, and 7) natural.</p> <p>The raw data consists of three folders corresponding to the three reference maps, each containing four TIFF files (.tif), one for each scenario type.</p>
High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"
<p>High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"</p>
Supporting data for boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy
<p>This dataset includes the properties necessary to reproduce the analysis of water vapour statistics derived from imaging spectroscopy as in:</p> <p>Richardson et al. (2021a) DOI: 10.5194/amt-14-5555-2021<br> Richardson et al. (2021b) DOI: 10.5194/amt-2021-163 (pre-acceptance DOI, follow links to published version)</p> <p>Files include the retrieval emulator parameters, atmospheric profiles used in the emulator development, column-mean water vapour and cloud water both for the total column water vapour (TCWV) and "effective" TCWV, which accounts for the water vapour integrated along the direct solar path at a range of solar zenith angles, see Richardson 2021b, Eq. (7).</p>
Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
<p>Several types of all-sky viewing Fabry-Perot Interferometers (FPI) have been developed since the 1990s for ground-based remote sensing of thermospheric winds. The Scanning Doppler Imager (SDI) is one such instrument, which provides temporally simultaneous line-of-sight observations from hundreds of independent look directions per instrument exposure. A geographically distributed network of such instruments increases spatial coverage and, at many locations, also provides overlapping observations along multiple independent lines-of-sight. Together, these characteristics significantly increase the density and fidelity that is possible for reconstructed thermospheric vector wind fields, compared to a traditional narrow-field FPI, but at the cost of complexity and difficulty.<br><br></p> <p>Presently, we describe an application of inverse theory to reconstruct three-component vector thermospheric neutral wind fields using data from multiple SDI instruments. The salient features of the method used here are the ability to reconstruct three-component winds on a dense grid that is sampled regularly in latitude, longitude, and time, without assuming any a-priori underlying structure of the winds. This requires solving an inverse problem that does not in general yield a unique solution unless additional constraints are enforced. We describe this step, also known also as regularization, along with the strategy used to maximize the spatial resolution of the derived wind fields by automatically determining the minimum level of regularization that can produce stable inversions. We present example results obtained from applying this technique to one night of data from a network of SDIs in Alaska, and discuss the implications of these results for current understanding of thermospheric dynamics.</p>
GPR-BMP-SPI: High spatial resolution multi-scale SPI datasets over China from January 1984 to December 2020
<p>The datasets include standard precipitation index (SPI) at 1-month, 3-month, 6-month, 9-month and 12-month scales over the main terrestrial lands of China from January 1984 to December 2020. The SPI datasets were produced by blending the information from meteorological stations, and precipitation products, as well as topographical and geographical variables based on Gaussian process regression (GPR) models.</p> <p>The meteorological station data are from the China Meteorological Data Service Centre. Five precipitation products are used: (1) CHIRPS Daily: Climate Hazards Group InfraRed Precipitation With Station Data (Version 2.0 Final); (3) ERA5-Land Monthly Averaged by Hour of Day - ECMWF Climate Reanalysis; (3) FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System; (4) PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record; (5) TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces.</p> <p>The maps of the difference of the confidence intervals (the upper prediction limit minus the lower prediction limit) at a significance level of 95% are also provided to show the spatial uncertainty of every single SPI map.</p> <p>The drought events were counted during 1984-2020 at annual and seasonal scales. The variables related to the drought events are presented in “Drought_Event.zip”.</p> <p>Reference: He, Q., Wang, M., Liu, K., Li, B., & Jiang, Z. (2023). Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning. <em>Weather and Climate Extremes</em>, <em>40</em>, 100567.</p> <p> </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.