Find research datasets worth reusing

opencc-by-4.0Jun 2021View details →

EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set

Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15" The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency. Update: EcoDes-DK15 v1.1.0 (4 Dec. 2021) Following the recommendations and feedback during the first round of peer-review, we updated the EcoDes-DK processing pipeline and EcoDes-DK15 data set. The key changes are: <ul> <li>New version of the source data optimised to contain only point data collected before the end of 2015. The source data for EcoDes-DK15 v1.0.0 unintentionally contained data from 2018. The new source data is documented <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/source_data/readme.md">here</a>.</li> <li>New "date_stamp_*" auxiliary variables that illustrate the survey dates for the vegetation points in each cell. See updated descriptor documentation <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/descriptors.md">here</a>.</li> <li>Re-scaling of "solar_radiation" variable to MJ per 100 m2 per year.</li> </ul> Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository: Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2021-222">https://doi.org/10.5194/essd-2021-222</a>, in review, 2021. <a href="https://github.com/jakobjassmann/ecodes-dk-lidar">https://github.com/jakobjassmann/ecodes-dk-lidar</a> Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example <a href="https://www.7-zip.org/">7z </a>on Windows, the archiving tool on macOS and bz2 on Linux).   A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 7 in the manuscript, can be found <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/manuscript/figure_7/EcoDes-DK15_teaser.zip">here</a>. Abstract (from manuscript) Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~94 GB, compressed 16.8 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond. Acknowledgements (from manuscript) We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set, Charles Davison for feedback regarding data use and handling, as well as Matthew Barbee and Zsófia Koma for sharing their insights on the source data merger and Zsófia’s script to generate summary statistics for the different versions of the DHM point clouds. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).

opencc-by-4.0Dec 2021View details →

Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning"

Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning". Predictors from the ERA5 reanalysis and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rda" format, to be read from R.

A closer look: High-resolution pore-scale simulations of solute transport and mixing through porous media columns

This dataset contains the results of fluid flow (Navier-Stokes) and solute transport (Advection-Diffusion) simulations within columns of granular media generated by virtual gravitational settling of spherical grains. The experiments comprise three media with different degrees of grain-size variability; a range of grain-Peclet numbers is explored. See the homonymous research paper by Sole-Mari et al. (2022, Water Resources Research) for more information. Grains.zip: Positions and radii of the spherical grains for each value of grain-size variability sigma (Matlab's .mat format). ResultsCoarse.zip: Coarse-scale data presented in the aforementioned WRR paper (Matlab's .mat format). Link to the full micro-scale dataset: (soon available) We thankfully acknowledge the computer resources at MareNostrum and the technical support provided by the Barcelona Supercomputing Center (AECT-2019-3-0014).

Ultra high-resolution biomechanics suggest that substructures within insect mechanosensors affect their sensitivity

Accessible data for the Manuscript "Ultra high-resolution biomechanics suggest that substructures within insect mechanosensors decisively affect their sensitivity".

Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v3.0)

Agricultural land resources – a global suitability evaluation (v3.0) Local climate, soil and topography determine the conditions under which agricultural crops are suitable for growth or not. The methodology uses a fuzzy logic approach that is described in Zabel et al. (2014). The approach is based on Liebig's law of the minimum. Accordingly, plant suitability is determined not by total available resources, but by the scarcest resource. The limiting factor depends on the local environmental conditions and the crop-specific requirements, that are taken from literature.  Determining Agricultural Suitability Agricultural suitability is calculated for each of 5 climate models (GFDL, HadGEM2, IPSL, MIROC and NorESM1) from the AR5 ISIMIP fast track protocol. Daily climate model data for temperature, precipitation and solar radiation are statistically downscaled to 30 arc seconds spatial resolution. A monthly bias-correction is applied using WorldClim data. The provided suitability data refers to the model median over the 5 climate simulations. Soil data is taken from the Harmonized World Soil Database (HWSD) v1.21. Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Soil depth is taken into account according to Pelletier et al. (2015). Topography data is applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the suitability of crops and is considered in this approach. Agricultural Suitability The agricultural suitability data is provided at a spatial resolution of 30 arc seconds (approximately 1 km2 at the equator). The dataset contains four time periods (1980-2009, 2010-2039, 2040-2069, 2070-2099) and two climate change scenarios (RCP2.6 and RCP 8.5). Agricultural suitability is provided for rainfed conditions and for irrigated conditions seperately. Additionally, we provide a dataset in which the current irrigation areas according to Maier et al. (2018) are applied. The suitability is provided for 23 food, feed, fibre, and 1st and 2nd generation bio-energy crops. An 'overall suitability' is provided for all crops that considers the most suitable crop on each pixel. Additionally, we provide a dataset excluding 2nd generation bioenergy crops (18-23) from the overall aggregation of crops. <table> <caption>Food, feed, fiber and first-generation bioenergy crops</caption> <tbody> <tr> <td>Barley</td> <td>Potato</td> <td>Sugarbeet</td> </tr> <tr> <td>Cassava</td> <td>Rapeseed</td> <td>Sugarcane</td> </tr> <tr> <td>Groundnut</td> <td>Rice</td> <td>Sunflower</td> </tr> <tr> <td>Maize</td> <td>Rye</td> <td>Summer wheat</td> </tr> <tr> <td>Millet</td> <td>Sorghum</td> <td>Winter wheat</td> </tr> <tr> <td>Oilpalm</td> <td>Soybean</td> <td> </td> </tr> </tbody> </table> <table> <caption> Second-generation bioenergy crops </caption> <tbody> <tr> <td>Jatropha</td> <td>Reed canary grass</td> </tr> <tr> <td>Miscanthus</td> <td>Eucalyptus</td> </tr> <tr> <td>Switchgrass</td> <td>Willow</td> </tr> </tbody> </table> Growing Season Adaptation The agricultural suitability considers the adaptation of the growing season. For each pixel and crop, the growing season is optimized throughout the year, taking the annual course of precipitation, temperature, and solar radiation as well as their interplay, into account. Most Suitable Crop The most suitable crop for each pixel is provided in the data. Please note that a value of 126 means that no crop suitable and 127 means that multiple crops have the same suitability. Further information Detailled information are available in the following publications: Zabel F, Putzenlechner B, Mauser W (2014) Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9(9): e107522. doi: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">10.1371/journal.pone.0107522</a> Cronin, J., Zabel, F., Dessens, O., Anandarajah, G. (2020): Land suitability for energy crops under scenarios of climate change and land-use. GCB Bioenergy, 12(8). doi: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcbb.12697">10.1111/gcbb.12697</a> Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data 9, 527. doi: <a href="https://doi.org/10.1038/s41597-022-01632-8">10.1038/s41597-022-01632-8</a> Meier, J., Zabel, F., Mauser, W. (2018): A global approach to estimate irrigated areas – a comparison between different data and statistics. Hydrol. Earth Syst. Sci., 22, 1119–1133, 2018. doi: <a href="https://hess.copernicus.org/articles/22/1119/2018/">10.5194/hess-22-1119-201</a> Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D. (2016), A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, J. Adv. Model. Earth Syst., 8, 41– 65, doi: <a href="https://doi.org/10.1002/2015MS000526">10.1002/2015MS000526</a>. Improvements in v3.0 Compared to the previous version (<a href="https://zenodo.org/record/3748350">v2.0</a>), this version (v3.0) uses updated input data for soil (HWSD v1.21) and high resolution irrigated areas (Maier et al. 2018), and additionally considers soil depth (Pelletier et al. 2016). Moreover, the suitability is calculated for an ensemble of 5 climate models, and is available for more crops, including a number of second generation bioenergy crops. Contact Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department of Geography, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)

High resolution 3D reconstruction of regenerating nerve within a chitosan conduit 7 days after injury and repair

Video S1: high resolution 3D reconstruction of 7 consecutive 50 µm thick sections labelled with Reca1 (red, endothelial cell marker) and S100β (green, Schwann cell marker).

High-resolution images of 1550 Ordovician to Silurian graptolite specimens for global correlation and shale gas exploration

A unique graptolite image dataset consists of  key graptolite species used for dating rocks, global correlation, and “gold caliper” for locating shale gas favourable exploration beds (FEBs) in China.  All images were taken from 1,550 carefully curated graptolite specimens, taxonomically belong to 113 graptolite species or subspecies. They were collected from the Ordovician to Silurian sediments of China and published in 1958-2020. These specimens are preserved as shale and were collected from 154 representative geological sections of China. All specimens are housed at the Nanjing Institute of Geology and Palaeontology (NIGP), Chinese Academy of Sciences (CAS). My working group spent over two years to complete photographing every specimen using a single-lens reflex camera Nikon D800E with Nikkor 60 mm macro-lens and Leica M125 and M205C microscopes equipped with Leica cameras. Every image is well focused and better shows the morphology of graptolite bodies. In total, we took 40,597 images, including 20,644 camera photos (each with a resolution of 4,912 × 7,360) and 19,953 microscope photos (each with a resolution of 2,720 × 2,048). Photos of low contrast or bad focus were removed from the whole collection. We only kept and selected the photos that show the visual morphology of every specimen and the diagnostic character of each graptolite species that the specimens represent. We selected one image for each specimen as the present final dataset, uploaded to and stored in our cloud server. We incorporated revision suggestions from distinguished palaeontologists to generate the ground-truth labels, providing a taxonomical authority of the dataset. The dataset potentially contributes to a range of scientific activities and provides 1) easy access to high-resolution images of 2951 specimens of 113 graptolite species for teaching and training in palaeontology and geologic survey; 2) Global bio-stratigraphic correlation using graptolites, especially with those bio-zone species; 3) A standard fossil specimen image dataset used in shale gas industry to improve exploration efficiency, and 4) The potential aid of developing image-based automated classification model. Every specimen has two photos, one is original, another shows specimen with a scale bar. Occasionally in some large image the scale bar is embedded and beside the fossil specimen. All in JPG format. Single JPG file ranges from 822 KB to 7.055 MB.  Total :10.4 GB.

A high-resolution gridded inventory of coal mine methane emissions for India and Australia

The dataset contains the high-resolution gridded coal mine methane emissions file (.csv) for India and Australia. The emissions are estimated for the year 2018 at a resolution of 0.1° × 0.1°. The emission unit is ton/grid/year.

opencc-by-4.0Mar 2022View details →

Dust extinction curves: Ferrara (1999) high resolution

These datasets are based on the models described in <a href="https://ui.adsabs.harvard.edu/abs/2018RNAAS...2..188B">Benson (2018)</a>, and are intended to closely match the models run by <a href="http://adsabs.harvard.edu/abs/1999ApJS..123..437F">Ferrara et al. (1999)</a> - they use the same dust grain properties and galactic geometry. However, they are tabulated using a much higher resolution grid of inclinations, optical depths, wavelengths, and morphologies than in <a href="http://adsabs.harvard.edu/abs/1999ApJS..123..437F">Ferrara et al. (1999)</a>. Dust Properties Dust grain albedos, scattering asymmetries, and opacities to extinction are taken from <a href="http://adsabs.harvard.edu/abs/1997ApJ...487..625G">Gordon et al. (1997)</a>, for either their Milky Way, “MW”, or Small Magellanic Cloud, “SMC”, models (as encoded in each file name), and assume Henyey-Greenstein scattering.   Stellar Geometry Galactic disks follow exponential profiles in both radial and vertical directions, with the vertical scale height equal to 0.0875 times the radial scale length. Spheroids follow spherical <a href="http://adsabs.harvard.edu/abs/1983MNRAS.202..995J">Jaffe (1983)</a> profiles. Note that spheroid radii in this work are listed as the scale radius, rs, while <a href="http://adsabs.harvard.edu/abs/1999ApJS..123..437F">Ferrara et al. (1999)</a> listed the corresponding effective radius, re=rs/1.16. Dust Geometry Dust is distributed in the disk, and follows exponential profiles in both radial and vertical directions. The vertical scale height is set to a multiple, hz, of the stellar disk scale height. The value of hz is encoded in each file name.

opencc-by-4.0Oct 2020View details →

Commodity Dataset | Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform

(Commodity data in raster format) Supplementary materials for “Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform” that had been published on Land MDPI (2020). doi:<a href="https://doi.org/10.3390/land9100377">10.3390/land9100377</a>  The data included: 1) Raster data of commodity maps (TIFF Compressed in ZIP) 2) READ ME for the dataset (DOCX) 3) Legend for raster data in ArcGIS Format (LYR)

opencc-by-4.0Jun 2019View details →

High resolution maps of climatological parameters for analyzing the impacts of climatic changes on Swiss forests

Assessing the impacts of climatic changes on forests requires the analysis of actual climatology within the forested area. In mountainous areas, climatological indices vary markedly with the micro-relief, i.e. with altitude, slope, and aspect. Consequently, when modelling potential shifts of altitudinal belts in mountainous areas due to climatic changes, maps with a high spatial resolution of the underlying climatological indices are fundamental. Here we present a set of maps of climatological indices with a spatial resolution of 25 by 25 m. The presented dataset consists of maps of the following parameters: average daily temperature high and low in January, April, July, and October as well as of the year; seasonal and annual thermal continentality; first and last freezing day; frost-free vegetation period; relative air humidity; solar radiation; and foehn conditions. The parameters represented in the maps have been selected in a knowledge engineering approach. The maps show the climatology of the periods 1961-1990 and 1981-2010. The data can be used for statistical analyses of forest climatology, for developing tree distribution models, and for assessing the impacts of climatic changes on Swiss forests.

opencc-by-4.0Apr 2022View details →

A 21st century high-resolution glacier and ice sheet fractional area dataset: Code and Data

This resource contains code and input data to develop a high-resolution (0.1°) gridded global glacier and ice sheet fractional area dataset, which is also available in this resource. The dataset is developed from Randolph Glacier Inventory v6.0 shapefiles and supplementary shapefiles for the Antarctic and Greenland ice sheets. The approach is adapted from Li et al., (2021; <a href="https://doi.org/10.1017/jog.2021.28">https://doi.org/10.1017/jog.2021.28</a>). The dataset provides estimates of the fraction (0 to 1) of land cover that is glaciated in each 0.1° x 0.1° grid cell. The dataset was designed for use in the SPEAR model (<a href="https://www.gfdl.noaa.gov/spear/">https://www.gfdl.noaa.gov/spear/</a>) from the NOAA Geophysical Fluid Dynamics Laboratory (GFDL), but may be useful for other applications as well. This work is documented in a NOAA Technical Memorandum (citation information to follow).

Millstätter See seismic and core data for the publication "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)"

This dataset comprises the core data and the 3.5 kHz seismic data of Millstätter See, a lake in the Eastern European Alps, Austria. Together with a bathymetric dataset (10.5281/zenodo.5875923) and a core/seismic dataset from Wörthersee (10.5281/zenodo.5875576), this is the basis for the publication Daxer et al. "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)". 28 core sections (individual short cores or sections of long cores - see MillstaetterSee_core_data.xlsx for information) were analysed with a multi-sensor core logger (MSCL) and photographed with a smartcube camera image scanner and an ITRAX core scanner. The generated data are available in the folders MSCL.zip and Photos.zip. Some core sections were also analysed with a Malvern Mastersizer 3000 and/or CT scanning. The generated data are provided in the folders Grain Size.zip and CT data MI17-04.zip (as .dcm files). The seismic profiles are provided as .SGY files (Seismic Pinger Data.zip).

Woerthersee seismic and core data for the publication "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)"

This dataset comprises the core data and the 3.5 kHz seismic data of Wörthersee, a lake in the Eastern European Alps, Austria. Together with a dataset from Millstättersee (core and seismic data: 10.5281/zenodo.5875911; bathymetric data: 10.5281/zenodo.5875923), this is the basis for the publication Daxer et al. "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)". 24 short cores were analysed with a multi-sensor core logger (MSCL) and photographed with a smartcube camera image scanner and an ITRAX core scanner. The generated data are available in the folders MSCL.zip and Photos.zip. Some core sections were also analysed with a Malvern Mastersizer 3000. The generated grain-size data are provided in the folder Grain Size.zip. The seismic profiles are provided as .SGY files (Seismic Pinger Data.zip).

opencc-by-4.0Apr 2022View details →

SROADEX: Dataset for binary recognition and semantic segmentation of road surface areas from high resolution Aerial Orthoimages Covering Approximately 8,650 km2 of the Spanish Territory Tagged with Road Information

The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the axes of the different types of roads (urban, interurban and rural). This cartography has been obtained from different Spanish official sources (National Geographic Institute and autonomic cartographic agencies) that we have revised and edited in a meticulous and systematic way to verify that the roads are represented on the cartography according to the orthoimages, available on January 1, 2021 in the download center of the National Center of Geographic Information (CNIG), on 16 rectangular areas (28,5 km * 18,5 km) of the Spanish territory (insular and peninsular). The dataset consists of  777599 images in png format of 256x256 pixels, organized in folders for the different trainings, separating those corresponding to training, testing and validation. The structure of the data is as follows: 1-Road-Ortho and 1-Road-Mask contain the images and ground true for training the semantic segmentation networks. 1-Road-Ortho and 2-NoRoad-Ortho contain aerial images containing or not containing vials, for the training of binary tessellation networks identifying tessellations with vials. Moreover, in each folder the structure is the same: train, test, validation containing 90%, 5% and 5% of the total images and masks of each type. 1-Road-Ortho     |----Train     |----Test     -----Validation 1-Road-Mask     |----Train     |----Test     -----Validation 2-NoRoad-Ortho     |----Train     |----Test     -----Validation

opencc-by-4.0Jun 2022View details →

High resolution global mass coral bleaching dataset Version 2.0

This two-part database is version 2.0 of the global mass coral bleaching database presented in <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175490">Donner et al. (2017)</a>. The updated database is documented in <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281719">Virgen-Urcelay and Donner (2023)</a>.The first part is a spreadsheet listing raw bleaching observations from 1963 to 2017, developed through outreach to the coral reef research and monitoring community and from observations available in the literature. The spreadsheet in .xlsx format lists each individual bleaching observation and a database legend (see "README").The second part is the annual probability of bleaching occurrence in a given year between 1985 and 2017 at 0.05° X 0.05° latitude-longitude resolution for all warmwater reef cells, developed via spatial modelling. Probabilities were not estimated for years in which there were no reports or the modeled semi-variograms failed to converge due to the low number of bleaching observations that year. The annual gridded maps are provided in .geotiff format within the zip file.Please consult the manuscript and the authors before employing this data.

Dataset of very-high-resolution satellite RGB images to train deep learning models to detect and segment high-mountain juniper shrubs in Sierra Nevada (Spain)

This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to perform instance segmentation of Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 810 images (.jpg) of size 224x224 pixels. We also provide partitioning of the data into Train (567 images), Test (162 images), and Validation (81 images) subsets. Their annotations are provided in three different .json files following the COCO annotation format.

Dataset of very-high-resolution satellite RGB images to train deep learning models to recognize high-mountain juniper shrubs from Sierra Nevada (Spain)

This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to recognize Juniperus communis L. and Juniperus sabina L. shrubs.  All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 2000 images (.jpg) of size 512x512 pixels partitioned into two classes: Shrubs and NoShrubs. We also provide partitioning of the data into Train (1800 images), Test (100 images), and Validation (100 images) subsets.

A high-resolution GPM IMERG precipitation dataset for China

The database of the paper: An attention mechanism based convolutional network for satellite precipitation downscaling over China

HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020

The monthly High spatial resolution human Thermal Index Collection (HiTIC-Monthly) includes near-surface air temperature (SAT) and 11 commonly used human-perceived temperature indices: indoor Apparent Temperature (ATin), outdoor shaded Apparent Temperature (ATout), 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 NetCDF format. 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”. 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: 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.