Find research datasets worth reusing

Benchmark map of deforestation for Amazonia

Title: Benchmark map of deforestation for Amazonia Contact: Celso H. L. Silva-Junior (celsohlsj@gmail.com) Data: Old-growth forest deforestation Coverage: Amazonia Period: 1986 to onwards (according to new MapBiomas project collections) Spatial resolution: 30-meters Temporal resolution: Annual Coordinate reference system: Geographic Coordinate System with Datum WGS-84 File format: The zip file contains nine tiles in compressed TIFF format. The pixel values represent the year of deforestation. Code: <a href="https://github.com/celsohlsj/amazonia_deforestation">https://github.com/celsohlsj/amazonia_deforestation</a> Dataset usage: It is free to use, but if you use this dataset in your work, please cite the repository and our paper correctly. We also welcome users to invite us for collaboration. Associated publication: Silveira, M.V. F., Silva-Junior, C.H.L., Anderson, L.O., Aragão, L.E.O.C. Amazon fires in the 21st century: the year of 2020 in evidence. Global Ecology and Biogeography (2022). https://doi.org/10.1111/geb.13577 For this dataset, please use the following references: <ul> <li>Silveira, M.V. F., et al. Amazon fires in the 21st century: the year of 2020 in evidence. Global Ecology and Biogeography (2022). doi: 10.1111/geb.13577</li> <li>Silva-Junior, C. H. L. . (2022). Benchmark maps of deforestation for Amazonia [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6808579</li> </ul>

TIME4CS WP4 Mapping of citizen science training resources

This dataset was compiled as part of the TIME4CS project, WP4, and lists identified citizen science training resources, as of July 2022. The <a href="https://eu-citizen.science/">EU-citizen.science</a> platform provided the basis for mapping CS training in Europe, as the team behind the platform has put considerable effort into compiling, and encouraging the CS community to contribute, CS training resources. Additionally, training courses were identified based on the case studies in WP1, as most universities do not list their courses on the EU-citizen.science platform.

High-resolution maps of material stock and population in Germany from 1985 to 2018

Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed. This dataset features detailed maps of material stock and population for Germany on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data. Temporal extent The data contains annual maps from 1985 to 2018. Data format and units Per German federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files. All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018. The dataset features <ul> <li>population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)</li> <li>mass (in tons) of … <ul> <li>total material stock <ul> <li>… material stock in buildings <ul> <li>… in commercial and industrial buildings</li> <li>… in multi-family residential buildings</li> <li>… in single-family residential buildings</li> <li>… in high-rise buildings</li> <li>… in lightweight buildings</li> </ul> </li> <li>… material stock in road infrastructure</li> <li>… material stock in rail infrastructure</li> <li>… material stock in other infrastructure</li> </ul> </li> </ul> </li> </ul> Material stock in high-rise and lightweight buildings is not featured in the corresponding publication due to its overall negligible amount. It is, however, included here for completeness. Further information For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Corresponding publication Schug, F., Frantz, D., Wiedenhofer, D., Virág, D., Haberl, H., van der Linden, S., Hostert, P. (in rev.): High-resolution mapping of 33 years of material stock and population growth in Germany. Journal of Industrial Ecology Funding This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

References and Metadata for Electric vehicles' consumer behaviours: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011)

The bibliography and metadata used for the analysis published in the Journal of Business Research - Electric vehicles' consumer behaviors: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011).

A map of active cropland and short-term fallows across Northern Mozambique derived from PlanetScope data

Overview A map of smallholder-dominated landscapes covering the provinces Niassa, Zambezia, Cabo Delgado, and Nampula in Northern Mozambique. The map includes active cropland and short-term fallows as separate classes, as well as five land cover classes (herbaceous vegetation, open woodlands, closed woodlands, non-vegetated land, water). The map is based on PlanetScope mosaics and consequently comes at 4.77m spatial resolution. The download contains the following files: <ul> <li>ps_lc_nmoz.tif / .qml: land cover map and associated QGIS style file</li> <li>ps_lc_nmoz_probmargins.tif / .qml: probability margins and associated QGIS style file</li> <li>training.gpkg: training samples with class labels</li> <li>LICENSE.pdf: NICFI data program user license</li> </ul> Map accuracy We conducted an area-adjusted accuracy assessment based on a stratified random sample, which yielded important insights regarding accuracies and error types. The area-adjusted overall accuracy of the map is 88.9%, but users should be aware of the most important error types: <ul> <li>Active cropland were overestimated, whereas local topographical depressions with moist soils, and regions with exposed soils/rocks and sparse vegetation cover were found to be falsely classified.</li> <li>Short-term fallows were underestimated, particularly in regions with high growth rates and extensive land management, such as parts of the northern and north-eastern study region.</li> </ul> Further resources The production of this map was made possible through the <a href="https://www.planet.com/nicfi/">NICFI data program</a>, providing the PlanetScope mosaics and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. As such, the use of the map falls under the <a href="https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf">NICFI data program license agreement</a> included in the download. The code for preprocessing the PlanetScope mosaics 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>. We advise map users to read the <a href="https://eartharxiv.org/repository/view/3174/">preprint</a> or the <a href="https://doi.org/10.1016/j.jag.2022.102937">open access paper</a> for detailed insights. In case of questions please consult these resources or contact the lead author of the work.

Code and data: Slush limits of the western flank of the Greenland Ice Sheet, mapped from MODIS, 2000 - 2021

Code and output of the slush limit detection for the western flank of the Greenland Ice Sheet, as described in Machguth, H., A. Tedstone and E. Mattea (2022), Daily Variations in Western Greenland Slush Limits, 2000 to 2021, mapped from MODIS, Journal of Glaciology, https://doi.org/10.1017/jog.2022.65.

opencc-by-4.0Nov 2021View details →

Data used in ECLIPSER methods paper and GTEx snRNA-seq cross-tissue reference map analysis

The tables were used in the papers: Rouhana*, Wang* et al., ECLIPSER: identifying causal cell types and genes for complex traits through single cell enrichment of e/sQTL-mapped genes in GWAS loci, bioRxiv 2021, doi: https://doi.org/10.1101/2021.11.24.469720; and Eraslan et al., Single-nucleus cross-tissue molecular reference maps to decipher disease gene function, bioRxiv 2021, doi: https://doi.org/10.1101/2021.07.19.452954. '<a href="https://zenodo.org/api/files/1f8d48d0-6bf7-4bec-b6ef-5c7a9ead8079/GTEx_v8_HG38_all_variants.tsv.gz">GTEx_v8_HG38_all_variants.tsv.gz</a>' is an input file for running GWASvar2gene on GTEx v8 eQTLs and sQTLs, and all other files are input files for ECLIPSER.

opencc-by-4.0Aug 2022View details →

Nanoscale mapping of point defect concentrations with 4D-STEM

The following 4D-STEM data sets were collected on the ThemIS and TitanX scanning transmission electron microscopes located at the National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.  1• Au_thermal_beforeHT_17C.dm4, Au_thermal_HT_800C.dm4, Au_thermal_HT_1000C.dm4 and Au_thermal_afterHT_17C.dm4 are datasets from 4D-STEM measurements conducted in situ on an FEI ThemIS image corrected microscope at 300 kV during a thermal cycling experiment. These digital micrograph (.dm4) files were collected at 17 C before heat treatment, 800 C during heat treatment, 1000 C during heat treatment, and 17 C after heat treatment, respectively. Nano-diffraction data was collected using a Gatan K2-IS (2k x 2k) detector at 400 frames per second. Each dataset contains a set of electron diffraction patterns taken at each scan position with a ~ 1 nm probe step size. Approximately 80 x 80 scan positions were recorded from each region with a dwell time of 0.0025 seconds per frame. A custom 40µm patterned “bullseye” circular probe forming aperture was used to enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 3.20 milli-radians, spot size of 8, and diffraction pixel size of 0.16 Å-1 was used in micro-probe lens configuration. The data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Data processing were performed using strain mapping scripts provided in the open source py4DSTEM software package. Au_thermal_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set. 2• Al_irradiated.dm4 is a dataset from 4D-STEM measurement conducted in situ on an FEI TitanX microscope equipped to do high-angle STEM tomography and operating at 300 kV. Nano-diffraction data was acquired using a Gatan Orius 830 (2k x 2k) detector capable of collecting 30 frames per second. Each dataset contains a stack of convergent beam electron diffraction (CBED) patterns taken at each scan position with maximum resolution equivalent to 1.6 nm probe size. Approximately 50 x 50 frame scan regions were recorded with a dwell time of 0.01 seconds per frame. A custom 70 µm patterned “bullseye” circular C2 aperture was used to greatly enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 2.7 milli-radians, spot size 10, and camera length 195 mm was used in micro-probe lens configuration.  With a measured screen current of 300 pA in this configuration, the total sum of electrons incident in a region of the sample, commonly known as the fluence (total dose), was determined at 67,100 electronsÅ-2 per 4D-STEM scan. The 4D-STEM data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Al_irradiated_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.

Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset

Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames 1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points 2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin 3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin The relevant code is available at:  OGM prediction: https://github.com/TempleRAIL/SOGMP OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch

opencc-by-4.0Nov 2014View details →

Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data

Overview: The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°).  The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MAMin; total area, 6.5 × 106 km2), mean annual maximum (MAMax; 12.1 × 106 km2), and long-term maximum (LTMax; 17.3 × 106 km2). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. Projection: WGS84 (EPSG:4326) Geographic extent: <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> Spatial resolution: 15 arc-second (500m at equator) Legend (for discrete pixel values): <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MAMin)</li> <li>2 = Mean Annual Maximum (MAMax)</li> <li>3 = Long Term Maximum (LTMax)</li> </ul>

opencc-by-4.0Jan 2021View details →

Baseline map of 137Cs inventories in reference soil sites at the continental scales of South America

This dataset contains the baseline map of 137Cs inventories in reference soil sites (Bq m-2, decay-corrected to 2020) estimated by Partial Least Square Regression (PLSR) with a spatial resolution of 2 km at the continental scale of South America, as well as the prediction uncertainties of the baseline map (coefficient of variation, %). Details information regarding this dataset can be found in the original publication: Mapping the spatial distribution of global 137Cs fallout in soils of South America as a baseline for Earth Science studies, Earth-Science Reviews, Volume 214, 2021, 103542, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2021.103542.

Annual report on surveillance for avian influenza in poultry and wild birds in Member States of the European Union in 2021 – monthly maps

Here you can find the monthly maps referred to in the 'Annual report on surveillance for avian influenza in poultry and wild birds in Member States of the European Union in 2021' by EFSA. Figure legend for all figures: Monthly observations and samples from wild birds on the EFSA list of target species for 2021 by NUTS3 region. The green colour scale represents the number of wild bird observations from the target species, as per data provided by the EuroBirdPortal project. The black dots represent the number of wild bird samples from target species tested within the countries' avian influenza passive surveillance programmes. Wild bird samples reported at NUTS2 level are not shown on these maps.

Story Map of the AI Ethics Lab of the Austrian Institute of Technology (AIT)

The Co-Change Lab at AIT, the Austrian Institute of Technology, focuses on addressing the promises and challenges associated with research work on and the application of machine learning and artificial intelligence. An interdisciplinary team of social and data scientists is working on AI ethics.

opencc-by-4.0Aug 2006View details →

Global map of elastic thickness on Venus

This map is Figure 14 from  Anderson, F. S., and S. E. Smrekar (2006), Global mapping of crustal and lithospheric thickness on Venus, J. Geophys. Res., 111, E08006, doi:10.1029/2004JE002395.  The location labels have been removed.  Estimates of elastic thickness have an error of ±10 to 15 km. Caveats in Anderson and Smrekar (2006) should be carefully understood prior to use. No value of elastic thickness was obtained in areas in white.

Data for "Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes" by Shao et al.

Data set for manuscript of “Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes” by Shao et al. submitted to Journal of Geophysical Research-atmosphere

opencc-by-sa-4.0Oct 2020View details →

Map of locations in bylines from al-Ḥaqāʾiq

Geographic distribution and relative frequencies of locations mentioned in bylines. This update contains more maps with different regions and much improved titles and legends. In addition, unknown locations (or NA values) were mapped to a location in the Black Sea.

opencc-by-sa-4.0Oct 2020View details →

Map of locations in bylines from al-Ḥasnāʾ

Geographic distribution and relative frequencies of locations mentioned in bylines. This update contains more maps with different regions and much improved titles and legends. In addition, unknown locations (or NA values) were mapped to a location in the Black Sea.

opencc-by-sa-4.0Oct 2020View details →

Map of locations in bylines from al-Muqtabas

opencc-by-sa-4.0Oct 2022View details →

Krefeld_Maps_Mingei

Documentation material from the Silk pilot of the Mingei project