Find research datasets worth reusing

opencc-by-4.0Oct 2021View details →

Supplementary files: A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau

Supplement of "A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau". The supplementary files provide the outlines and parameters of the rock glaciers inventoried by InSAR-assist kinematic-based approach in the Daxue Shan, Southeast Tibet Plateau.  Based on the Sentinel-1A ascending SAR images acquired between 2015 and 2019, we derived a five-year-long LOS mean velocity map of the study area. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images.

SPAAM Summer School 2022: Introduction to Ancient Metagenomics - 4b Introduction to Genome Mapping

Teaching data for practical session: "4b Introduction to Genome Mapping" of the 2022 SPAAM Summer School: Introduction to Ancient Metagenomics (Aug. 1-5 2022). See: <a href="https://spaam-community.github.io/wss-summer-school/#/2022/">https://spaam-community.github.io/wss-summer-school/#/2022/</a> or <a href="https://doi.org/10.5281/zenodo.6976711">https://doi.org/10.5281/zenodo.6976711</a> for slides. Once downloaded, run: <pre><code>tar xvfz <session>.tar.gz</code></pre>  to decompress the data directory for the session.

opencc-by-4.0Apr 2022View details →

Figure 2. Mitotype tree and distribution maps for 98 in Integrative taxonomy reveals cryptic diversity in North American Lasius ants, and an overlooked introduced species

Figure 2. Mitotype tree and distribution maps for 98 DNA-barcodes belonging to 7 mitotypes of the ant Lasius niger (blue, n = 70) and 15 mitotypes of L. ponderosae sp. nov. (red, n = 28). The red dashed line delimits the expected natural range of L. ponderosae sp. nov.53 Maps have been created using the free R-package "ggmap" v3.0.0 (https://github.com/dkahle/ggmap) in R v4.1.1. Map tiles by Stamen Design, under CC BY 3.0.

opencc-by-4.0Jul 2022View details →

SEN12 Global Urban Mapping Dataset

The SEN12 Global Urban Mapping (SEN12_GUM) dataset consists of Sentinel-1 SAR (VV + VH band) and Sentinel-2 MSI (10 spectral bands) satellite images acquired over the same area for 96 training and validation sites and an additional 60 test sites covering unique geographies across the globe. The satellite imagery was acquired as part of the European Space Agency's Earth observation program Copernicus and was preprocessed in Google Earth Engine. Built-up area labels for the 30 training and validation sites located in the United States, Canada, and Australia were obtained from Microsoft's open-access building footprints. The other 66 training sites located outside of the United States, Canada, and Australia are unlabeled but can be used for semi-supervised learning. Labels obtained from the SpaceNet7 dataset are provided for all 60 test sites.

Mapping 10-m global impervious surface area (GISA-10m) using multi-source geospatial data

Artificial impervious surface area (ISA) documents human footprints. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA mapping at 10-m resolution is still lacking. To this end, we proposed a global ISA mapping method leveraging multi-source geospatial data. Based on the existing satellite-derived ISA maps and the crowdsourcing OpenStreetMap (OSM), 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. Combined with over 2.7 million Sentinel optical and radar images on the Google Earth Engine, we produced the 10 m global ISA dataset (GISA-10m). Based on the test samples that are independent to the training set, GISA-10m embraced an overall accuracy greater than 86%. In addition, the GISA-10m was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was demonstrated.

Annual carbon density and annual forest probability maps

The annual carbon density (file name is GBRF+year) shows aboveground woody biomass and uses boosted regression trees, which were trained with a static global benchmark map of carbon density of woody vegetation for 2018, using MODIS (MCD43A4 7 bands; NDII, EV12, MCD43A3 shortwave albedo) and STRM data. The carbon density was mapped at 1-ha spatial grid cells and the unit is MgC/ha. The forest probability maps (file name is LT_nbr_paramset01_fitted.+year) were created from annual MODIS data at the resolution of 500m x 500m. The forest maps take values ranging from 0 to1, and the probability shows the likeliness if a pixel belongs to the forest (1) or non-forest (0) class. The higher the value, the higher the probability that the pixel is forest land.

Endmember spectra and classified maps derived from CRISM targeted data at the south pole of Mars

Overview Current maps of compositional variation across south polar ice exposures on Mars do not resolve the meter-scales at which erosional processes are most active, ultimately limiting our understanding of how the deposits form and evolve and how they can be used to interpret long-term climate records. In this study, we use k-means clustering and random forest classification to identify and map a set of universal spectral endmembers across 167 high-resolution observations acquired during southern summer by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). The 21 endmembers show distinct combinations and strengths of key infrared absorption features reflecting diverse mixtures of CO2 ice, H2O ice, and dust. The resulting compositional framework can be used to characterize the nature of both seasonal CO2 frost and the residual ices it overlies across a variety of terrains.    Contents The repository contains three .zip files, which can be expanded to access the files described below: <ul> <li>classified_maps.zip <ul> <li>lookup_files <ul> <li>SP_CRISM_RF_ColorMap.clr : An ESRI-formatted color map file that can be used to apply the endmember color scheme to random forest-classified maps in ArcGIS (see Symbology settings).</li> <li>SP_CRISM_RF_ColorMap.txt : A text file that can be loaded into Python as a numpy array and used to generate a matplotlib color ramp. Row indices correspond to endmember numbers (see below) and columns are red, green, and blue values scaled from 0 to 1.</li> <li>SP_CRISM_RF_Endmember_Lookup.csv : A lookup table that can be used to cross-reference between endmember numbers (as stored in GeoTiffs or the indices of the colormaps above) and corresponding endmember names (C1, Dc2, etc.).</li> </ul> </li> <li>morphologic_reference <ul> <li>Contains GeoTiffs of the R1330 spectral parameter (reflectance at 1330 nm) for each processed CRISM observation. Filenames indicate the observation ID, Mars Year, and solar longitude (Ls) of acquisition ("Ls314-07" = Ls 314.07º). These images can be used as a reference for the surface morphology and albedo of each scene. Tie points used to georeference these images to other datasets can be applied to the random forest classification maps to properly align endmember mapping results.</li> </ul> </li> <li>random_forest_classification <ul> <li>Contains GeoTiffs of the random forest classification results for each processed CRISM observation. Filenames indicate the observation ID, Mars Year, and solar longitude (Ls) of acquisition ("Ls314-07" = Ls 314.07º). These images are not rendered to display colors consistent with the publication figures, but instead store the endmember classification for each pixel as a number from 0 to 21; use the contents of lookup_files to find the corresponding endmember name or render the image with the color scheme from the publication. To view rendered summary plots of each observation, see the contents of observation_info. To georeference these images to other datasets, use the corresponding morphologic reference (see above) to set tie points.</li> </ul> </li> </ul> </li> <li>observation_info.zip <ul> <li>footprint_shapefile <ul> <li>Contains the components of an ESRI shapefile that outlines the surface footprint/coverage of each processed CRISM observation. The attributes associated with each observation are the same as those in observation_lookup below. Note that the polygons extend slightly beyond the area shown in maps in random_forest_classification due to the inclusion of border pixels.</li> </ul> </li> <li>observation_lookup <ul> <li>SP_CRISM_Classified_Obs_Info.csv : Information on each processed CRISM observation; this is the same file as Table S1 in the publication. Includes the MY and Ls of acquisition and (where applicable) the figure panel where the observation appears in the publication. The location of each observation is indicated with Center Latitude/Longitude and the assigned Spatial Domain (see Figure 1 in the publication). The Observation ID can be used to locate the source Targeted Reduced Data Record (TRDRs, Version 3) on the Geosciences Node of the Planetary Data System. The spatial extent of each observation is provided in the footprint_shapefile described above. </li> </ul> </li> <li>summary_plots <ul> <li>Contains summary plots of the endmember map generated for each processed CRISM observation. Each plot notes the observation ID, Mars Year, and Ls and displays the morphologic reference map, random forest classification map, and a breakdown of the endmembers that are present. To access the maps rendered here, see the contents of classified_maps.zip.</li> <li>Also contains the full-resolution version of Figure S3 from the publication (All_Obs_Unprojected.png), which can be used to lookup observations of interest via small labels above each map. </li> </ul> </li> </ul> </li> <li>spectral_library.zip <ul> <li>Contains spectral libraries with the median spectrum of each endmember as presented in Figure 3 in the publication. A basic text file listing the wavelength (WVL) and normalized reflectance values for each endmember is included (SP_CRISM_EndmemberMedians.txt) as well as an ENVI-formatted spectral library (SP_CRISM_EndmemberMedians_ENVI.sli). Note that a subset of the 438 wavelengths sampled in the source CRISM data were removed around the longest and shortest wavelengths and the filter boundary to avoid error-prone bands, leaving these spectral libraries with 404 bands.</li> </ul> </li> </ul>

e-shape P5.6 EO based phytoplankton biomass for WFD reporting - samples of Chl-a and WFD classes maps of lakes Lauwersmeer and Saadjärv

The current data set is a sample of the data generated within the e-shape pilot 5.6 called "EO based phytoplankton biomass for WFD reporting" and consists of maps based on earth observation (EO) data from the Sentinel 2 satellite. There is a set of chlorophyll-a (Chl-a) maps, and a set maps of WFD ecological status of phytoplankton biomass, derived from the Chl-a maps by applying local WFD phytoplankton biomass thresholds. These maps can complement the monitoring for the Water Framework Directive (WFD). The lakes within this sample set are lakes Saadjärv (Estonia) and Lauwersmeer (the Netherlands). For more info please see the readme files which are provided with the data files.

Landsat-based maps of cropping practices in the irrigated drylands of the Aral Sea Basin (1987-2019)

Overview A set of maps revealing agricultural land use patterns in the irrigated drylands of the Amu Darya and Syr Darya basin between 1987 and 2019. The maps were produced using time series of 30m Landsat TM, ETM+ and OLI Collection 1 surface reflectance products and a Random Forest classification model trained with ~30,000 samples from the years 1987, 1998, 2008, and 2018. All processing steps were conducted in Google Earth Engine. The target classes of this product are "wet season cropping", "dry season cropping", "double cropping including fodder crops", and "non-cropland". Mapping was conducted across nine provinces in Uzbekistan, two in Turkmenistan, and three in Tajikistan, and post-processing was used to constrain the study area to regions which were irrigated in at least two years in the study period, areas below 2,000 m above sea level, and regions/years with a sufficient number of cloud-free Landsat images (n>6). Annual maps were aggregated temporally and spatially, resulting in different datasets described below. We advise map users to read the <a href="https://doi.org/10.1088/1748-9326/ac8daa">open access paper</a> and the associated supplementary materials for detailed insights. In case of further questions please contact the lead author of the work. Data This download contains three folders: <ul> <li>landuse_30m: Land use layers with 30m spatial resolution, representing the percentage (0-100%) of the three key land use types ("wet season cropping", "dry season cropping", "double cropping including fodder crops") within two time frames (1987-2000 and 2001-2019). Years with insufficient cloud-free Landsat observations were excluded from the calculation of percentages.</li> <li>landuse_3km: Land use metrics with 3km spatial resolution, representing - for each grid cell and year - the "percentage of cropland", "percentage of dry-season cultivation", and "cropping frequency". The layers in these datasets are ordered chronologically, with the layer 1 representing the year 1987, and layer 33 the year 2019. Gaps in the time series were filled using linear interpolation for subsequent trend calculation using <a href="https://github.com/morrowcj/remotePARTS/">remotePARTS</a>.</li> <li>mask: A mask with 30m spatial resolution constraining the study area to regions below 2,000 m above sea level and regions which were irrigated at least twice in the study period.</li> </ul> Map accuracy We conducted an area-adjusted accuracy assessment based on a stratified random sample (n = 2,784 per year), which yielded important insights regarding accuracies and error types. The median area-adjusted overall accuracy of the maps across the study period is 91.4%, but class-specific user´s and producer´s accuracies vary substantially. Users should consult the supplementary materials of the article for details on accuracies, confusion matrices, and the most important error types. Further resources The production of this map was made possible through the Landsat Program of the United States Geological Survey (USGS) and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. The code for preprocessing the Landsat time series is based on the Google Earth Engine Python API and made available at <a href="https://github.com/philipperufin/eepypr/">https://github.com/philipperufin/eepypr/</a>.

opencc-by-4.0Apr 2021View details →

Maps and R code from: Marginality indices for biodiversity conservation in forest trees

This dataset provides the raster map of marginality indices for eight European tree species as described in the article "Marginality indices for biodiversity conservation in forest tree". It also provided the R code to compute the marginality indices. The eight species are the following: <ul> <li>Abies alba Mill. (silver fir)</li> <li>Fagus sylvatica L. (European beech)</li> <li>Picea abies (L.) H.Karst. (Norway spruce) </li> <li>Pinus halepensis Mill. (Aleppo pine)</li> <li>Pinus nigra J.F.Arnold (black pine)</li> <li>Pinus pinaster Aiton (maritime pine) </li> <li>Pinus pinea L. (stone pine)</li> <li>Pinus sylvestris L. (Scots pine)</li> </ul> For each species, a 7z archive of a multi-layered image of the raster maps of the marginality indices is given. Each image has 12 layers: <ul> <li>Layer 1: distribution map of the species</li> <li>Layer 2: map of the probability of being marginal according to the Maxent model using eight marginality indices and countries as predictors</li> <li>Layer 3: map of the environmental marginality index, defined as the z-transform of the suitability of each location as predicted by a species distribution model using climatic variables as predictors</li> <li>Layer 4: map of the segmented distribution according to morphological spatial pattern analysis</li> <li>Layer 5: map of the area index</li> <li>Layer 6: map of the gravity index</li> <li>Layer 7: map of the centroid index</li> <li>Layer 8: map of the edge index</li> <li>Layer 9: map of the isolation index</li> <li>Layer 10: map of the second nearest core index</li> <li>Layer 11: map of the north/south index</li> <li>Layer 12: map of the east/west index</li> </ul>

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Using Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent kelp canopy after an ecological regime shift

Kelp forests are complex underwater habitats that form the foundation of many nearshore marine environments and provide valuable services for coastal communities. Despite their ecological and economic importance, increasingly severe stressors have resulted in declines in kelp abundance in many regions over the past few decades, including the North Coast of California, USA. Given the significant and sustained loss of kelp in this region, management intervention is likely a necessary tool to reset the ecosystem and geospatial data on kelp dynamics are needed to strategically implement restoration projects. Because canopy-forming kelp forests are distinguishable in aerial imagery, remote sensing is an important tool for documenting changes in canopy area and abundance to meet these data needs. We used small unoccupied aerial vehicles (UAVs) to survey emergent kelp canopy in priority sites along the North Coast in 2019 and 2020 to fill a key data gap for kelp restoration practitioners working at local scales. With over 4,300 hectares surveyed between 2019 and 2020, these surveys represent the two largest marine resource-focused UAV surveys conducted in California to our knowledge. We present remote sensing methods using UAVs and a repeatable workflow for conducting consistent surveys, creating orthomosaics, georeferencing data, classifying emergent kelp, and creating kelp canopy maps that can be used to assess trends in kelp canopy dynamics over space and time. We illustrate the impacts of spatial resolution on emergent kelp canopy classification between different sensors to help practitioners decide which data stream to select when asking restoration and management questions at varying spatial scales. Our results suggest that high spatial resolution data of emergent kelp canopy from UAVs have the potential to advance strategic kelp restoration and adaptive management.

opencc-zeroSep 2022View details →

Spectroscopic ellipsometry mapping of PAAO (AJ-6-03-31 sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide (PAAO). The sample was made by anodization of aluminum polycrystal in 0.3 mol/L oxalic acid at 40 V for 3 minutes and 42 seconds. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-6-03-31 Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method after being covered with hydrogenated amorphous diamond-like carbon and silver nanocomposite. The data can be found here: <a href="https://doi.org/10.5281/zenodo.7257252">https://doi.org/10.5281/zenodo.7257252</a>

Spectroscopic ellipsometry mapping of PAAO (AJ-7-03-31 sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide (PAAO). The sample was made by anodization of aluminum polycrystal in 0.3 mol/L oxalic acid at 40 V for 4 minutes and 35 seconds. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-7-03-31 Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method after being covered with hydrogenated amorphous diamond-like carbon and silver nanocomposite. The data can be found here: <a href="https://doi.org/10.5281/zenodo.7257907">https://doi.org/10.5281/zenodo.7257907</a>

opencc-by-4.0Sep 2022View details →

Mapping of Seaweed Aquaculture

In the Extraction results of seaweed aquaculture area.zip,we have placed the extraction results of the seaweed aquaculture area.They are raster data in tif format after geographic projection.In addition, we used the China Fisheries Statistical Yearbook data from 2016 to 2020 and placed it in the China Fisheries Statistical Yearbook From 2016 to 2020.zip.The spatial scope of the prohibited and restricted aquaculture areas of seaweed is presented at the end of  the Planning of Tidal Flats in Aquaculture Waters in Jiangsu Province (2020-2030).

Spectroscopic ellipsometry mapping of PAAO (AJ-8-03-31 sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide (PAAO). The sample was made by anodization of aluminum polycrystal in 0.3 mol/L oxalic acid at 40 V for 5 minutes and 4 seconds. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-8-03-31 Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method after being covered with hydrogenated amorphous diamond-like carbon and silver nanocomposite. The data can be found here: <a href="https://doi.org/10.5281/zenodo.7260186">https://doi.org/10.5281/zenodo.7260186</a>

Spectroscopic ellipsometry mapping of PAAO:Au (AJ-2-04-20-Au sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide covered with gold nanoparticles (PAAO:Au). The sample was made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for 3 minutes and 37 seconds. Then it was dip-coated in 60 nm diameter gold nanoparticles suspension. Extraction speed was 500 nm/s. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-2-04-20-Au Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method before being covered with gold nanoparticles. The data can be found here: https://doi.org/10.5281/zenodo.5718202

Spectroscopic ellipsometry mapping of PAAO:Au (AJ-1-04-20-Au sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide covered with gold nanoparticles (PAAO:Au). The sample was made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for 3 minutes and 16 seconds. Then it was dip-coated in 60 nm diameter gold nanoparticles suspension. Extraction speed was 500 nm/s. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-1-04-20-Au Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method before being covered with gold nanoparticles. The data can be found here: https://doi.org/10.5281/zenodo.5718025

opencc-by-4.0Sep 2022View details →

Generating Imperviousness Maps from Multispectral Sentinel-2 Satellite Imagery

This dataset contains a list of Sentinel-2 tiles covering Italy for the year 2017. For each tile, a corresponding ground truth GeoTIFF is present which contains a clip of the soil consumption provided by ISPRA (<a href="https://www.isprambiente.gov.it">https://www.isprambiente.gov.it</a>). Dataset can be used to train a Machine Learning model to extract imperviousness maps using Sentinel-2 satellite images. More details can be found reading the paper:  Giacco, G., Marrone, S., Langella, G., & Sansone, C. (2022). ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery. Future Internet, 14(10), 278.

opencc-by-4.0Sep 2022View details →

Data collection for article "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar"

This dataset contains supply, demand, and flow maps from nine ecosystem service models (as layer files in .tif format) underlying the publication "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar".