Find research datasets worth reusing

opencc-by-4.0Feb 2023View details →

A near-field Head-Related Transfer Function (HRTF) data set of KEMAR with high distance resolution

A near-field Head-Related Transfer Function (HRTF) data set measured on a KEMAR head and torso simulator with high distance resolution and multiple elevations is presented ('KEMAR_NFHRIRmea_1cm.sofa'). HRTFs are measured at 83448 spatial points at distances ranging from 20 to 110 cm, elevations from -25° to 35°, and azimuths from 0° to 355°. The distance resolution of the HRTF data is 1 cm, higher than that of any existing public near-field HRTF databases. Therefore, the dataset enables further exploration of the distance dependence of near-field HRTFs, and is beneficial for applications of realistic and dynamic binaural rendering of nearby sound sources. An additional data set of simulated HRTFs with 1.5 cm distance resolution is also provided ('KEMAR_NFHRIRsim_1.5cm.sofa') for a direct comparison with the measured HRTFs or other purposes.

opencc-by-4.0Feb 2023View details →

Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China

Here presented the forcasted results of Potential Morbidity Risk Index (PMRI)  for the personal patients with allerigc rhinitis and the public health administrations, and these results are supplied to the published paper of "Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China".

opencc-by-4.0Nov 2022View details →

Dataset belonging to the paper "Atomic resolution observations of silver segregation in a [111] tilt grain boundary in copper"

This repository contains the raw data of the experimental (S)TEM imaging and the data corresponding to the simulations and theoretical calculations of the paper "Atomic resolution observations of silver segregation in a [111] tilt grain boundary in copper",. A pre-print version of the paper is available on arXiv: <a href="http://doi.org/10.48550/arXiv.2212.01180">http://doi.org/10.48550/arXiv.2212.01180</a> See the file README.md for a detailed description.

opencc-by-4.0Aug 2022View details →

Data from: high-resolution bioclimatic surfaces for southern Peru: an approach to climate reality for biological conservation

Climatic and bioclimatic surfaces were elaborated for southern Peru (Arequipa, Moquegua and Tacna). For the interpolations, meteorological information from in-situ stations, as well as orographic and geographic covariates were used. Statistical evaluations gave good results, showing some differences with other models also performed for the area. These data will contribute to a better understanding of the ecoclimatic requirements of the species in terms of ENMs and SDMs.

opencc-by-4.0Dec 2022View details →

Global maps of agricultural expansion potential at a 300 m resolution

Global maps of agricultural expansion potential at a 300 m resolution This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study. Abstract: The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments. Data description: We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively. Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*". The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions. Repository content: Full resolution layers: - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics  with <50% crops category at the spatial resolution of 10 arc-seconds. - "readme.txt" is the text file with the basic description and the metadata for the repository. Aggregated layers: This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below). File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif” - "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes. - "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. - "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops. Raster metadata: Driver: GTiff Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs Notes on use: Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.

dryad40/100

scRNA data from: Organization of the human Intestine at single cell resolution

The human adult intestinal system is a complex organ that is approximately 9 meters long and performs a variety of complex functions including digestion, nutrient absorption, and immune surveillance. We performed snRNA-seq on 8 regions of of the human intestine (duodenum, proximal-jejunum, mid-jejunum, ileum, ascending colon, transverse colon, descending colon, and sigmoid colon) from 9 donors (B001, B004, B005, B006, B008, B009, B010, B011, and B012). In the corresponding paper, we find cell compositions differ dramatically across regions of the intestine and demonstrate the complexity of epithelial subtypes. We map gene regulatory differences in these cells suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation, and organization in the human intestine, and serve as an important reference map for understanding human biology and disease.

opencc-zeroFeb 2023View details →

opencc-by-4.0Feb 2023View details →

High-resolution (5 m) surface water persistence map for 2021 in East-Africa

Raster surface water image highlighting the percentage of time in 2021 that there was water at a certain pixel in East Africa.  Script which classifies individual countries: <a href="https://code.earthengine.google.com/3f508773522979dfa62a75bda7750b5f?noload=true">https://code.earthengine.google.com/3f508773522979dfa62a75bda7750b5f?noload=true</a> Script which combines the individual maps and filters the end-result: <a href="https://code.earthengine.google.com/ed8ee1bbade0b51f3759565b30da37ab?noload=true">https://code.earthengine.google.com/ed8ee1bbade0b51f3759565b30da37ab?noload=true</a>

EUNIS habitat suitability maps at 100m resolution

Habitat suitability maps of 203 <a href="https://eunis.eea.europa.eu/">EUNIS</a> level 3 classes have been modeled at 100m resolution with <a href="https://biodiversityinformatics.amnh.org/open_source/maxent/">Maxent</a>. For the modeling plot observation from the <a href="http://euroveg.org/eva-database">European Vegetation Archive</a> have been used as observations for training and testing. As predictors various climate layers, soil layers, topographic layers and RS-enables EBVs have been used. Detailed information on the predictors can be found <a href="https://www.synbiosys.alterra.nl/nextgeoss/docs/Description_Abiotic_and_RSEBVs.pdf">here</a>. The 203 EUNIS habitat types comprise 8 groups according to the revised typology: <ul> <li>Salt marsh (MA)</li> <li>Coastal habitat (N)</li> <li>Wetland (Q)</li> <li>Grassland (R)</li> <li>Shrub (S)</li> <li>Forest (T)</li> <li>Sparsely vegetated habitat (U)</li> <li>Man-made habitat (V)</li> </ul>

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

Supplementary data for the manuscript entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" (JGR Atmospheres)

This dataset contains additional material to reproduce the simulation and some of the figures of the manuscipt entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" in the Journal of Geophysical Reasseach - Atmospheres.

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.

Active Region Magnetograms for Solar Flare Prediction: Reduced Resolution Dataset Images

This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.jq2bvq898.  These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO).  These data are the reduced resolution (224x224 pixels) images in .png format.

opencc-zeroApr 2023View details →

dryad40/100

Active region magnetograms for solar flare prediction: Reduced resolution dataset

In this dataset, we provide a comprehensive collection of magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset is a minimally processed, user configurable dataset of consistently sized images of solar active regions that can serve as a benchmark dataset for solar flare prediction research. This dataset consists of reduced resolution images (see usage notes below).

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"

Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1307 through 1505

This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n.  These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO).  These data are the full sized images (600x600 pixels) for active regions (ARs) 1307 through 1505 in .fits format.

Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1064 through 1306

SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation

The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA. The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively. The structure of the data is as follows: 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks. 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels. Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type. 1-Panels-Ortho     |----Train     |----Test     -----Validation 1-Panels-Mask     |----Train     |----Test     -----Validation 2-NoPanels-Ortho     |----Train     |----Test     -----Validation

Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1506 through 1707

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.