Find research datasets worth reusing

opencc-by-4.0Mar 2023View details →

High-Resolution Water Surface Slopes from Multi-Mission Satellite Altimetry

1. Summary: This dataset contains water surface slopes (WSS) every kilometer along 11 Polish rivers derived from cross-calibrated multi-mission satellite altimetry (Schwatke et al. 2023a (in review). ). The approach to derive WSS is based on a weighted least-squares approach, which is described in detail in Schwatke et al. 2023b (in review). 2. Data Formats: This dataset is provided in netCDF and shapefile formats. Each netCDF file contains the data of a single river and parameters such as river chainage, WSS, WSS error, location, and nearest centerline information from the SWORD database (v1.1, Altenau et al., 2021). The shapefile consists of five files (.cpg, .dbf, .prj, .shp, .shx) containing the data of the 11 Polish rivers. The attributes are identical to the netCDF, but the river name has been added. 3. Attribute Description: The attributes of netCDFs and shapefiles are described in the following list: <ul> <li> river_chainage: The river chainage describes the distance from the river mouth to the location of each bin along the river (units: km) </li> <li> wss: Water surface slopes (WSS) at each bin along the river. WSS are set to NaN/NULL for unprocessed lakes/reservoirs or short river segments (units: mm/km). </li> <li> wss_error: Errors of WSS at each bin along the river. WSS errors are set to NaN/NULL for unprocessed lakes/reservoirs or short river segments (units: mm/km). </li> <li> longitude: Longitude of the 1 km bins along the river (units: degree). </li> <li> latitude: Latitude of the 1 km bins along the river (units: degree). </li> <li> centerline_id: Nearest centerline id extracted from the SWORD database (v1.1, Altenau et al., 2021). </li> <li> node_id: Node id from the SWORD database (v1.1, Altenau et al., 2021) for the corresponding centerline id. </li> <li> reach_id: Reach id from the SWORD database (v1.1, Altenau et al., 2021) for the corresponding centerline id. </li> <li> river_name: The name of the river is only available in the Shapefile. </li> </ul> 4. References: Schwatke C., Dettmering D., Passaro M., Hart-Davis M., Scherer D., Müller F. L., Bosch W., Seitz F.: OpenADB: DGFI-TUM`s Open Altimeter Database. Geoscience Data Journal, 2023a (in Review) Schwatke C., Halicki M., Scherer D.: Generation of high-resolution water surface slopes from multi-mission satellite altimetry. Water Resources Research, 2023b (in Review) Altenau E.H., Pavelsky T.M., Durand M.T., Yang X., Frasson R.P.d.M., Bendezu L.: SWOT River Database (SWORD) (Version v1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4917236">https://doi.org/10.5281/zenodo.4917236</a>, 2021

opencc-by-4.0Mar 2023View details →

All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal: high-resolution figures

High resolution versions of Figure 2 and Figure S3 for the corrigendum of the paper "All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal" by Warszawski et al. (2021) published in Environmental Research Letters.  Fig. 2: Spider plots for each of the 22 scenarios in the filtered ensemble (the corresponding model and scenario is printed above each plot), in order of increasing coverage, Vi . Note that the AIM/CGE2.1 TERL_15D_LowCarbonTransportPolicy scenario has coverage of Vi=1, despite E2050 lying below themedium upper bound due to how the two energy-sector levers are combined to calculate the coverage (see Supplementary material). Each lever has been normalised to the high upper bound (the bold black inner circle on each plot; the absolute value of the upper bound is printed below the lever label). The centre of each spider plot corresponds to the minimum value across the entire ensemble of 50 scenarios for each lever. The medium upper bounds are shown as a dashed polygon. The absolute value of the lever for the given scenario is also printed on the plot. The top row contains the two scenarios singled out in figure <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abfeec#erlabfeecf1">1</a>(c), which exceed the SR1.5 remaining carbon budget for staying below 1.5 °C with a 50% likelihood; these two scenarios also have the lowest coverage of all scenarios in the filtered ensemble. For a similar plot of the complete ensemble of 1.5 °C scenarios with no or low overshoot (50 scenarios), see the supplement. Fig. S3: Same as Fig. 2 in main text but for all 50 scenarios. Those scenarios shaded grey are categorised as ‘Below 1.5C’ in the SR1.5. All other scenarios fall into the ‘1.5C low overshoot’ category.

COLA-hires: High-resolution (0.5x0.625) regional carbon fluxes inferred from in-situ and OCO-2 data

This dataset contains high-resolution CO2 inversion estimate in North America, East Asia, and Europe at 0.5x0.625 resolution from 2015 to 2018 using the Carbon in Ocean-Land-Atmosphere (COLA) system. The in-situ observations obtained from NOAA obspack and the land-nadir/land-glint retrevials from OCO-2 are assimilated.

Accompanying Data for the Manuscript "There's more to life than O2: Simulating the detectability of a range of molecules for ground-based high-resolution spectroscopy of transiting terrestrial exoplanets"

This file contains results for all cases considered in the manuscript titled "There's more to life than O2: Simulating the detectability of a range of molecules for ground-based high-resolution spectroscopy of transiting terrestrial exoplanets"

CubaPrec1: A 48 years long term gridded daily precipitation dataset at very high-resolution for Cuba.

CubaPrec1 is a new high-resolution gridded dataset for daily precipitation across Cuba from 1961-2008. The dataset was built using the information from the data series of 630 stations from the network operated by the National Institute of Water Resources. The original station data series were quality controlled using a spatial coherence process of the data, and the missing values were estimated on each day and location independently. Using the filled data series, a grid of 3 × 3 km spatial resolution was constructed by estimating daily precipitation and their corresponding uncertainties at each grid box. This new product represents a precise spatiotemporal distribution of precipitation in Cuba and provides a useful baseline for future studies in hydrology, climatology, and meteorology.

Dataset used for "Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia"

General Info This dataset contains monthly output from two 20-year (1979-1998) variable-resolution (VR) CESM2 simulations (HMA_VR7a and HMA_VR7b). The coupled atmosphere-land simulations were run with a newly generated VR grid that has regional grid refinements up to 7 km over High Mountain Asia. The HMA_VR7b simulation was performed with an updated glacier-cover dataset (<a href="https://doi.org/10.5281/zenodo.7864689">https://doi.org/10.5281/zenodo.7864689</a>) and includes snow and glacier model modifications. Further, monthly output from a globally uniform 1-degree CESM simulation (NE30), used for evaluation of the HMA VR simulations, is also included. The monthly output have been used for analysis and discussion in the paper “Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia” that is currently under review in the Cryosphere Discussions, <a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>. Contact René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>)  Raw Data Raw monthly and daily unstructured HMA VR model output are available on request.   Dataset Contents <pre><code>NE30.tar HMA_VR7a.tar HMA_VR7b.tar </code></pre> These files contain atmosphere (CAM) and land (CLM) model output that are regridded to a 1-degree finite volume (0.9 x 1.25 degrees latitude/longitude) grid. The following variables are included: CLDLIQ, OMEGA, Q, STEND_CLUBB, SWCF, T, Z3, EFLX_LH_TOT, FGR, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSNO, FSM, FSR, H2OSNO, PCT_LANDUNIT, QICE_MELT, QSNOFRZ, RAIN, SNOW, and TSA.  <pre><code>SMB_HMA_VR7a.tar SMB_HMA_VR7b.tar</code></pre> These files contain unstructured SMB-related CLM model output (i.e., on the HMA VR grid). The following variables are included: PCT_LANDUNIT, QRUNOFF_ICE, QSNOFRZ_ICE, QSNOMELT_ICE, QSOIL_ICE, RAIN_ICE, and SNOW_ICE.

opencc-by-4.0Jan 2023View details →

CBRA: The first multi-annual (2016-2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with Super-resolution Segmentation from Sentinel-2 imagery

Large-scale and up-to-date maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, existing large-scale BRA datasets, such as those from Microsoft and Google, do not include China, hence there are no full-coverage maps of BRA in China. To this end, we produce the multi-annual China building rooftop area dataset (CBRA) with 2.5 m resolution from 2016-2021 Sentinel-2 images. The CBRA is the first full-coverage and multi-annual BRA data in China. The CBRA achieves good performance with the F1 score of 62.55% (+10.61% compared with the previous BRA data in China) based on 250,000 testing samples in urban areas, and the recall of 78.94% based on 30,000 testing samples in rural areas.  The CBRA is organized as GeoTIFF (.tif) raster file format with a single band and GCS_WGS_1984 coordinate system. The pixel values are 0 and 255, with 0 representing the background and 255 representing the building rooftop area. Furthermore, to facilitate the use of the data, the CBRA is split into 215 tiles of  spatial grid, named “CBRA_year_E/W**N/S**.tif”, where “year” is the sampling year, the “E/W**N/S**” is the latitude and longitude coordinates found in the upper left corner of the tile data.   Version 2.0: In version 1.0, there were empty raster images (because they didn't contain buildings). In version 2.0, these raster images were removed.

A high-resolution global land daily drought index dataset during 1979–2022

A global daily drought index dataset named as daily evapotranspiration deficit index (DEDI) is constructed using daily actual evapotranspiration and potential evapotranspiration data provided by European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The DEDI dataset has a spatial grid resolution of 0.25°×0.25° and covers global land areas for the period 1979 to 2022. The DEDI dataset can be a good index for assessing the dry and wet severity in terms of spatial patterns and temporal evolutions when compared to other available daily drought indices. Moreover, the DEDI dataset is also demonstrated to have advantages in detecting ecological or agricultural droughts. The DEDI dataset also appears reasonable and promising in facilitating drought monitoring and early warning from a daily perspective.This dataset accompanies the following publication: Zhang, X., Duan, J., Cherubini, F. et al. A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022. Sci Data 10, 824 (2023). https://doi.org/10.1038/s41597-023-02756-1

Data from Zeppenfeld et al. 2023: "Winter storm risk assessment in forests with high resolution gust speed data", Eur. J. For. Res.

Single-tree damage data from winter storm event "Lothar" 1999 in Baden-Wurttemberg, Germany. The data set includes the response (damage or no damage) and covariates for model parametrisation as described in Zeppenfeld et al. (2023).

INEMA: High resolution inventory of atmospheric emissions of Chile

Brief description This study presents the first high-resolution national inventory of anthropogenic emission for Chile (INEMA from spanish Inventario Nacional de EMisiones Antropogénicas). INEMA emission dataset considers emissions for  Vehicular, point sources (industrial, energy, and other sectors), residential, forest fires, and agricultural waste burning sectors estimated for 2015–2020 and spatially distributed onto a 0.01°x0.01° high-resolution grid. For all sectors, the pollutants included are CO2, NOx, SO2, CO, VOCs, NH3, PM10, and PM2.5. Also, CH4, N2O, and black carbon (BC) are included for transport, forest fires, agricultural waste burning, and residential sources. Emissions are classified on IPCC categories: <table> <tbody> <tr> <td>Sector</td> <td>IPCC codes</td> </tr> <tr> <td>Energy production</td> <td>1A1</td> </tr> <tr> <td>Industrial Energy consumption</td> <td>1A2</td> </tr> <tr> <td>On road transport energy consumption</td> <td>1A3b</td> </tr> <tr> <td>Comercial energy consumption</td> <td>1A4a</td> </tr> <tr> <td>residential firewood consumption</td> <td>1A4b</td> </tr> <tr> <td>Agriculture energy consumption</td> <td>1a4c</td> </tr> <tr> <td>Industrial processes</td> <td>2</td> </tr> <tr> <td>Agriculture waste burning</td> <td>3F</td> </tr> <tr> <td>Forest fires</td> <td>4A1b.iii</td> </tr> </tbody> </table> This work compiles new activity data and emissions factors and distributes them geographically based on census, Chile´s road network and CONAF information. To consult the main methodological considerations and results of the previous version of INEMA, review the article by Alamos et al.(2022). This inventory should contribute to the design of policies that seek to mitigate climate change and improve air quality by providing policy makers, stakeholders and scientists with qualified scientific spatial explicit emission information. Metadata Each .tar file contain netcdf (.nc) files for each pollutant of the sector and year of the .tar file. Netcdf  contains annual total emissions for the pollutant and year indicated per grid cell  The emission grid consists of Chilean territory in WGS84 projection (lon-lat) with a spatial resolution of 0.01 * 0.01  degrees (lon x lat). The extension boundaries of the grid are: [(-76-56.3), (-66,-17)] The unit in the .nc files is Gigagrames per year [Gg/year] The dataset is described in  Álamos, N., Hunneus, N., Opazo, M., Osses, M., Puja, S., Pantoja, N., Calvo, R., Denier Van Der Gon, H.A.C., Schueftan, A., Reyes, R., High-resolution inventory of atmospheric emissions from transport, industrial, energy, mining and residential activities in Chile. Earth System Science Data, 14(1), 361-379. 2022

Biometeorological Dataset for 'Novel algorithms for high resolution prediction of canopy evapotranspiration in grapevine'

A head trained Vitis vinifera L. cv. Zinfandel vine was grafted on St. George rootstock (V. rupestris) then planted in a 1.1 m3 plastic container filled with Yolo County, CA sourced sandy loam. To estimate evapotranspiration, we measured the wind speed, air temperature and relative humidity in vine canopies by mounting each vine with a suite of research grade sensors. We measured wind speed (units m ᐧ s-1) inside the vine canopy using a single needle anemometer (East 30 Sensors; Pullman, WA) that took instantaneous wind speed measurements every 10 seconds and recorded the average of the previous 12 instantaneous measurements for every 2-minute interval. We measured temperature (units oC) and relative humidity (units %) using HMP60L sensors (Campbell Scientific; Logan, UT) mounted both inside and outside of each vine canopy and recorded instantaneous measurements at each 2-minute interval. We filtered all biometeorological data using a 3-hour moving average to remove noise without causing any significant over or under-approximation of daily maxima and minima. We automated all data collection using two CR1000 data loggers (Campbell Scientific; Logan, UT), with 1 or 2 vines and associated sensors per logger, using custom CR1 programs.  A single 30W solar cell and 12V lead acid battery powered the entire vine-sensor system. This dataset represents all sensor data from a single vine, as measured in August 2020. Columns are named accordingly and include units. Please Note: The column named 'load_cell_kg' is not named accurately. The values given are in units of millivolts, and need to be translated from millivolts to kilograms. The 2020 calibration coefficient is 0.00330693663 millivolts per kilogram.

opencc-by-4.0Jun 2023View details →

High-resolution throughfall measurement design, Hainich, Germany, project AquaDiva

This dataset contains the sampling design for throughfall data used for the analysis published in Metzger et al. (2017) and Fischer et al. (2023). It gives spatially distributed throughfall measurement points and their forest structural properties. The measurement points are grouped into randomly distributed “kernel” points and “transect” points which are not part of the random design. The field site and sampling design are described in Metzger et al. (2017). The throughfall data is given in an associated published dataset (Metzger and Hildebrandt, 2023).

opencc-by-4.0Feb 2023View details →

ValEqt: A high-resolution Earthquake and Repeating earthquakes catalog of the 2017 Valparaiso sequence

The ValEqt earthquake catalog Description: Catalog of earthquakes detected near the 2017 Mw=6.9 Valparaiso (Chile) earthquake from 01/01/2016 to 01/01/2021. We also include a catalog of ValEqt's repeating earthquakes. Methods used to build this dataset are extensively described in this paper : Upcomming paper Doi Files: <ol> <li>ValEqt.txt : Earthquake catalog</li> <li>Repeater.txt : Repeating earthquake catalog</li> </ol>

Full-coverage high-resolution (Daily, 1-km) PM2.5 dataset in China (2000-present)

We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2022 in China using a random forest-based hindcast modeling method. Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The daily average data during 2000-2022 are released here and free for non-commercial use. If you want use our dataset, please cite the following publication.  The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively.    -He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. Journal of Environmental Management, 342, 118145.[<a href="https://doi.org/10.1016/j.jenvman.2023.118145">url</a>] -He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. Atmospheric Research, 281, 106481.[<a href="https://doi.org/10.1016/j.atmosres.2022.106481">url</a>] Full-coverage daily estimates spanning the years 2015 to Jun 2021 are archived here. These records, organized by month, are available for download in CSV format. For Jul-Dec 2021, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>. If you want more data (e.g.daily estimates before 2015), have any question, or further collaborate with us, please contact us via qqhe@whut.edu.cn. If you want to use monthly estimates from 2000 to 2022, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>. We also estimate other atmospheric data: For full-coverage, 1-km, AOD data in China, please go to <a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.

opencc-by-4.0Oct 2022View details →

Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets - training datasets

Sample datasets for the Case Studies section of the Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets web book (<a href="https://isprs-gis-sdg.readthedocs.io">https://isprs-gis-sdg.readthedocs.io</a>)

opencc-by-4.0Jun 2023View details →

Dataset of AI4ER MRes titled "Improving Urban Tree Management Using High-Resolution Satellite Data"

This repository contains the data used in the Master's thesis titled "Improving Urban Tree Management Using High-Resolution Satellite Data" by Andrés C. Zúñiga-González as part of the AI4ER MRes 1st year project at the University of Cambridge. The folders are split into large and small training and testing datasets. These folders contain the tiles (in png and tif formats) used in the models. In addition, it includes the crowns in ESRI Shapefile format for the training and testing datasets. Finally, it contains the best model from the project (named urban_trees_Cambridge_20230630.pth).

opencc-by-4.0Jul 2023View details →

High-resolution global map of closed-canopy coconut palm

The file ‘GlobalCoconutLayer_2020_v1-2.zip’ contains 878 raster tiles of 100x100 km in geotiff format. The raster files are the result of a convolutional neural network that classified Sentinel-1 and Sentinel-2 annual composites into a coconut palm layer for the year 2020. The images have a spatial resolution of 20 meters and contain two classes:       [0] Other land covers that are not coconut palm.      [1] Coconut palm. The file ‘GlobalCoconutLayer_2020_densityMap_1km_v1-2.zip’ contains the 20-meter coconut palm classification aggregated to 1 km. The value of each pixel represents the coconut palm area (in squared meters) within the 1-km pixel.  The file ‘Validation_points_GlobalCoconutLayer_2020_v1-2.shp’ includes the 10,200 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the class assigned by visual interpretation of sub-meter resolution images, and the attribute ‘predClass’, which reflects the predicted class by the convolutional neural network. The ‘predClass’ values are the same as the raster files:      [0] Other land covers that are not coconut palm.      [1] Coconut palm. The attribute ‘Class’ contains the following values:       [0] Land cover could not be determined because sub-meter resolution data was not available.      [1] Other land covers that are not coconut palm.      [2] Sparse coconut palm. Low density of coconut palms; between 1 and 4 coconut palms within the 20-meter pixel.      [3] Dense open-canopy coconut palm; more than 4 coconut palms within the 20-meter pixel but coconut trees do not reach the full canopy closure.       [4] Closed -canopy coconut palm; more than 4 coconut palms within the 20-meter pixel and coconut palms fully cover the ground.      [5] Palm species that are not coconut palm.   Changelog v1-2: - Pixels classified as class ‘coconut’ were reclassified to class ‘other’ in West Bengal.

opencc-by-4.0Jul 2023View details →

Dataset for the high-resolution NEMO4 model of Cumberland Bay, South Georgia

This data-set consist of the required model set up files to reproduce a NEMO4 high-resolution model of Cumberland Bay, South Georgia, for the years 2001 to 2010 as described in the thesis 'Oceanographic Variability in Cumberland Bay, South Georgia: Implications for Glacier Retreat and Fisheries Management' Joanna Zanker. Files required to reproduce the process tests detailed in the thesis are also provided with the relevant names and the scripts for running the individual-based model with the model flow fields parameterised for mackerel icefish.

opencc-by-4.0Feb 2022View details →

Supplemental material of "An annotated whole-genome multilocus sequence typing schema for scalable high resolution typing of Streptococcus pyogenes"

This supplemental material includes the genome assemblies, associated metadata and analysis results for five datasets used to define a publicly available annotated wgMLST schema for S. pyogenes and to evaluate its suitability for high resolution typing. A brief description for each file in the dataset is available in the included README file. Raw sequencing data and sample metadata for the 265 isolates included in Dataset1 have been deposited in the European Nucleotide Archive (ENA) under Project <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB49967?show=reads">PRJEB49967</a>. The wgMLST schema was created with <a href="https://github.com/B-UMMI/chewBBACA">chewBBACA</a> and is publicly available at <a href="https://chewbbaca.online/species/1/schemas/1">chewie-NS</a>, where a more detailed description of schema creation, annotation and curation can be found.