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13,117 results for “Maps”
ELABORATION OF THE ITALIAN PORTION OF THE GLOBAL SOIL ORGANIC CARBON MAP (GSOCMAP)
<p>The Global Soil Organic Carbon map (GSOCmap) published by the Food and Agriculture Organization<br> constitutes a baseline estimation of soil organic carbon stock (CS, ton ha–1) from 0 to 30 cm, on a grid at 30 arc-seconds<br> resolution (approximately 1 x 1 km). It has been produced for the Italian territory by the Italian Soil Partnership (ISP): a<br> national hub of institutions dealing with soils, either academic/research institutions, and regional soil services (RSS). The<br> RSS are the main soil data owners in Italy and play a central role in the elaboration of policies for soil management. The<br> RSS adhering to the ISP are: Calabria, Campania, Emilia Romagna, Friuli Venezia Giulia, Liguria, Lombardia, Marche,<br> Piemonte, Puglia, Sicilia, Toscana, and Veneto. A national soil database is maintained by the Consiglio per la Ricerca e<br> l'Analisi dell'Economia Agraria (CREA). The RSS contributed with soil data, with mean density of 1 point per 50 square<br> kilometres, selecting data analysed for soil organic carbon content (SOC, dag kg-1), which were representative and well<br> distributed for the following environmental covariates: land use, geomorphology, and climate. The data were selected inbetween<br> 1990 al 2013. This was necessary in order to exclude the effect of the new soil protection policies of the Rural<br> Development Programme 2014-2020. For the RSS not included in the ISP, the data were selected from the national soil<br> database. 6748 point data were finally selected. SOC values obtained with the Springer and Klee and flash combustion<br> elemental analyser methods were retained for elaborations, because the 2 methods, were found to give statistically<br> equivalent results. SOC values obtained with Walkey and Black method were, instead, corrected with an empirical factor<br> of 1.3. 2292 of the 6748 point data had also measured bulk density (BD, Mg m–3). Pedotransfer functions were calibrated<br> to estimate BD were measured BD were missing, with the following as auxiliary variables: land use, soil regions, texture,<br> and SOC. The carbon stock (CS, ton ha–1) was calculated by multiplying: 0.3 (m) * SOC (dag kg-1) * fine earth fraction (1 -<br> skeletal content expressed as daL m–3) * BD (Mg m–3). CS of the first 30 cm depth was calculated as depth-weighted<br> average. A spatial statistics method was used for the CS interpolation. The following auxiliary variables were used: soil<br> regions, soil subregions, Corine land cover 2006, lithology, soils affected by natural constrains (gleyic, histic, vertic,<br> coarse, shallow, arenic, sodic, and acid), sand content, silt content, 30-m aster-DEM, distance from coast, distance from<br> relieves, soil aridity index, annual mean precipitations, mean annual air temperature, soil inorganic carbon, and soil<br> depth. For the soil region of Po valley, the land units at 1:250,000 scale were also used. The interpolation method was a<br> general linear regression for the soil regions of Po valley, and a radial basis function for the remaining Italian territory.<br> The 6748 point data were divided, by spatial random sampling, into 10 subsets. Ten interpolations were produced, each<br> time leaving out 1/10 of the dataset. Average (fig. 1), standard deviation and confidence intervals of these 10<br> interpolations were calculated. Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) were respectively<br> 25.5 and 36.4 Mg/ha.</p> <p>A.85 Italy Map source: Country submission Point data Number of samples: 6748 Sampling period: 1990-2013 SOC analysis method: SOC values obtained with the Springer and Klee and ’flash combustion elemental analyser’ methods were retained for elaborations. Uncorrected values obtained by the Walkey and Black method were corrected with an empirical linear equation, based on previous studies and as recommended by the Italian official methods. BD analysis method: Undisturbed sampling, core method and pit method Mapping method Mapping method details: Neural Networks and GLM, according to soil region Validation statistics: Mean Error (ME) of the prediction is 1.688 Mg/ha, MAE 25.57 Mg/ha, Root Mean Squared Error (RMSE) is 36.24 Mg/ha. Contact Data Holder: Research centre for agriculture and environment Contact: CREA Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria edoardo.costantini@crea.gov.it</p>
Mapping of Collective Action Opportunities for Water Stewardship
<p>With this global mapping exercise, we attempted to identify catchments with stronger need and potential of Collective Action for Water Stewardship. On the “need” side we considered a selection of water and biodiversity risk layers from the <a href="https://riskfilter.org/">WWF Risk Filter Suite</a>. On the “potential” side we considered economic factors such as value of crop production, density of business facilities (assets), and potential for cross-industry collaboration. The result is a global map (or a shapefile) of collective action opportunities, highlighting 350 catchments, across 100 river basins and 7 regions of the world, where multiple NGOs and the private sector shall work together to accelerate collective action for water stewardship. See the interactive map <a href="https://panda.maps.arcgis.com/apps/mapviewer/index.html?webmap=8d5c4bf6c6ff46c1a5c00e0b62e0b821">here</a>.</p> <p>This repository hosts all outputs as well as the input data of the mapping exercise. For transparency, reproducibility, as well as for future enhancements, the code is publicly available at <a href="https://github.com/rafaexx/collective_action_opportunities">https://github.com/rafaexx/collective_action_opportunities</a></p> <p>Final outputs are basically the shapefiles in <strong>output/summarise_geo/shp/*</strong> as well as the list of basins in <strong>output/summarise_geo/3_prioritization/hybas6_list_basins_collective_action.xlsx</strong></p> <p>For more details see part III of the report <a href="https://wwfint.awsassets.panda.org/downloads/unpacking-collective-action-in-water-stewardship.pdf">Unpacking Collective Action in Water Stewardship: Shared Solutions for Shared Water Challenges</a>.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Datasets for Wohlfarth et al. (2023) An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer
<p>This document describes the datasets and modeling results presented and discussed in our full research article.<br> <br> Wohlfarth, K., Wöhler, C., Hiesinger, H., Helbert, J. 2023, An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer, Astronomy and Astrophysics, 672<br> <br> <a href="https://doi.org/10.1051/0004-6361/202245343">https://doi.org/10.1051/0004-6361/202245343</a><br> <br> We provide several visualization scripts that read and display the results for convenience. Access to the original MATLAB® code for the thermal model implementation is available upon request (<a href="mailto:kay.wohlfarth@tu-dortmund.de">kay.wohlfarth@tu-dortmund.de</a>).<br> <br> More info in Dataproducts.pdf</p>
Systematic map of literature on land use conflicts: Dataset
<p>This dataset compiles the international, peer-reviewed literature on land use conflicts that was published between 2005 and 2020. It lists 306 publications and provides for each of them information regarding the research methods, research aims, covered land use types, covered geographical region, subject area, definition of "land use conflict", and conceptual approach. It can be used to identify publications on specified topics or combinations of topics, such as literature on land use conflicts in Africa regarding the land use type pastoralism. The ROSES for systematic map reports file provides information of how the systematic map was compiled and the list of unobtainable and excluded publications lists those publications that appeared in the initial database search but were not included in the mapping, with reasons for exclusion. More information on the dataset's creation, a detailed analysis, and interpretation are provided in the corresponding journal article:</p> <p>Fienitz, Meike. 2023. "Taking stock of land use conflict research: A systematic map with special focus on conceptual approaches." <em>Society & Natural Resources</em>. http://dx.doi.org/10.1080/08941920.2023.2199380</p>
A subjective image quality assessment dataset of color graded inverse tone-mapped HDR images
<p>A subjective image quality assessment dataset that includes quality scores of HDR images generated by nine inverse tone mapping methods. The images in the dataset show a wide variety of artifacts commonly present in dynamic range expanded HDR images. Twelve image pairs comprised of an LDR image and its corresponding HDR version were used to conduct the subjective assessment study. These images contain scenes with a wide range of light conditions, representing challenging situations for dynamic range expansion methods.</p> <p>The image quality dataset includes subjective quality scores for 108 inverse HDR images obtained by the different dynamic range expansion methods, scaled in Just Objectionable Differences (JODs). In addition, it includes the raw data from pairwise comparisons obtained from subjective experimentation. The raw data is composed of 6480 trials collected from 15 human observers.</p> <p><strong>Files included</strong></p> <ul> <li>List of images used in our experiments (images.csv).</li> <li>LDR images used as input (ldr.zip).</li> <li>HDR images used as reference (hdr.zip).</li> <li>Inverse tone-mapped HDR images evaluated in our study (hdr_itmo.zip).</li> <li>Pairwise comparison results and JOD scores (subjective-scores.zip)</li> <li>The objective quality scores of the inverse tone-mapped HDR images, computed by each quality metric assessed (objective-scores.zip).</li> </ul> <p> </p>
A dataset of Earth Observation Data for Lithological Mapping using Machine Learning
<p><strong>Dataset Information</strong></p> <p>Machine Learning (ML) algorithms had successfully contributed in the creation of automated methods of recognizing patterns in high-dimensional data. Remote sensing data covers wide geographical areas and could be used to solve the problem of the demand of various in-situ data. Lithologicall mapping using remotely sensed data is one of the most challenging applications of ML algorithms. In the framework of the “AI for Geoapplications” project , ML and especially Deep Learning (DL) methodologies are investigated for the identification and characterization of the lithology based on remote sensing data in various pilot areas in Greece. In order to train and test the various ML algorithms, a dataset consisting of 30 ROIs selected mainly from low -vegetated areas, that cover 2% of the total area of Greece was created</p> <p><strong>Dataset Preprocessing</strong></p> <p>Dataset preprocessing was executed using a combination of SNAP, QGIS and ENVI tools.</p> <p>Preprocessing steps:</p> <p>Defining areas with the following properties:</p> <ul> <li> <p>Zero cloud and snow coverage</p> </li> <li> <p>No water bodies</p> </li> <li> <p>Minimum vegetation</p> </li> </ul> <p>For the Aster Images:</p> <ul> <li> <p>Subset on defined areas</p> </li> <li> <p>Mosaic images when needed</p> </li> <li> <p>Digitising clouds</p> </li> </ul> <p>For the Labels:</p> <ul> <li> <p>We got the Soil map from YPEN (<a href="https://ypen.gov.gr/">https://ypen.gov.gr/</a>)</p> </li> <li> <p>Subset on defined areas</p> </li> <li> <p>All categories are represented with good analogies</p> </li> <li> <p>Clip label files with digitised clouds</p> </li> <li> <p>Rasterize</p> </li> </ul> <p> </p> <p>For the Labels we have eighteen categories for the twenty-eight areas that we collected data. We use the following coding for the Labels of our <strong>Dataset</strong>:</p> <table> <tbody> <tr> <td> <p><strong>Alluvial deposits</strong></p> </td> <td> <p><strong>0</strong></p> </td> </tr> <tr> <td> <p><strong>Limestone colluvial deposits</strong></p> </td> <td> <p><strong>1</strong></p> </td> </tr> <tr> <td> <p><strong>Limestones</strong></p> </td> <td> <p><strong>2</strong></p> </td> </tr> <tr> <td> <p><strong>Schists</strong></p> </td> <td> <p><strong>3</strong></p> </td> </tr> <tr> <td> <p><strong>Quaternary sediments</strong></p> </td> <td> <p><strong>4</strong></p> </td> </tr> <tr> <td> <p><strong>Gneiss</strong></p> </td> <td> <p><strong>5</strong></p> </td> </tr> <tr> <td> <p><strong>Slope fan debris</strong></p> </td> <td> <p><strong>6</strong></p> </td> </tr> <tr> <td> <p><strong>Mixed flysch</strong></p> </td> <td> <p><strong>7</strong></p> </td> </tr> <tr> <td> <p><strong>Flysch shale and cherts</strong></p> </td> <td> <p><strong>8</strong></p> </td> </tr> <tr> <td> <p><strong>Dolomites</strong></p> </td> <td> <p><strong>9</strong></p> </td> </tr> <tr> <td> <p><strong>Granite</strong></p> </td> <td> <p><strong>10</strong></p> </td> </tr> <tr> <td> <p><strong>Sandstone flysch</strong></p> </td> <td> <p><strong>11</strong></p> </td> </tr> <tr> <td> <p><strong>Flysch colluvial deposits</strong></p> </td> <td> <p><strong>12</strong></p> </td> </tr> <tr> <td> <p><strong>Peridotite and Gabbro</strong></p> </td> <td> <p><strong>13</strong></p> </td> </tr> <tr> <td> <p><strong>River bed deposits</strong></p> </td> <td> <p><strong>14</strong></p> </td> </tr> <tr> <td> <p><strong>Gneiss colluvial deposits</strong></p> </td> <td> <p><strong>15</strong></p> </td> </tr> <tr> <td> <p><strong>Not available</strong></p> </td> <td> <p><strong>-100</strong></p> </td> </tr> <tr> <td> <p><strong>cloud coverage</strong></p> </td> <td> <p><strong>-999</strong></p> </td> </tr> </tbody> </table> <p>The following table lists the available <strong>areas </strong>and the <strong>categories </strong>that each contains<strong>: <a href="https://docs.google.com/spreadsheets/d/17q0L5Ltz7V4uBY9i6DhULJsJCtf7BOY1nbblB-hf3Pw/edit?usp=share_link">Lithology_Dataset</a> </strong></p> <p> </p> <p>For the <strong>Sentinel-2 images</strong>, we made the following process:</p> <ul> <li> <p><strong>Resampling 10m</strong></p> </li> <li> <p><strong>Subset on defined areas</strong></p> </li> </ul> <p>The Sentinel-2 map contains: Sentinel 2 false colour composite 11/8/4 with OSM background</p> <p>The Final step is the collocation of the previous into a datacube i.e a multidimensional array with 25 bands (datacube dimensions differentiate for every area) using the Aster image as base (15m spatial resolution). </p> <ul> <li> <p>Bands 1-14: Aster</p> </li> <li> <p>Bands 15-24: S2</p> </li> <li> <p>Band 25: Label</p> </li> </ul> <p>The code for preprocessing the dataset in order to be used for machine learning algorithms can be found in the following link: </p> <p><a href="https://github.com/georgegiannop/Lithology">https://github.com/georgegiannop/Lithology</a></p> <p><strong>Citation</strong></p> <p>If you use this dataset in your work, please cite our paper:</p> <p>Vernikos, I., Giannopoulos, G., Christopoulou, A., Begaj, A., Stefouli, M., Bratsolis, E., and Charou, E.: A dataset of Earth Observation Data for Lithological Mapping using Machine Learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17570, <a href="https://doi.org/10.5194/egusphere-egu23-17570">https://doi.org/10.5194/egusphere-egu23-17570</a>, 2023.</p> <p> </p> <p> </p>
Global Airborne Observatory: Mapped Infrastructure on Hawaii Island
<p>There are three mapping datasets showing infrastructure on Hawaii Island (Hawaii County) in the State of Hawaii. The datasets were created using a combination of a convolutional neural network (CNN) and gradient boosted trees (GBT) run on Global Airborne Observatory laser scanning and imaging spectroscopy data.. Full methodology is available in the following reference:</p> <p>Mason, R.E.; Vaughn, N.R.; Asner, G.P. Mapping Buildings across Heterogeneous Landscapes: Machine Learning and Deep Learning Applied to Multi-Modal Remote Sensing Data. <em>Remote Sensing </em><strong>2023</strong>, <em>15</em>, x. https://doi.org/10.3390/xxxxx</p>
Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps
<p><strong>Title:</strong></p> <p>Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps</p> <p><strong>Citation:</strong></p> <p>Seeger, K.; Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Zenodo, <a href="https://doi.org/10.5281/zenodo.7875965">https://doi.org/10.5281/zenodo.7875965</a>.</p> <p><strong>Supplement to:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2022-1425">https://doi.org/10.5194/egusphere-2022-1425</a>.</p> <p><strong>Abstract:</strong></p> <p>The local digital elevation model (DEM) of the Ayeyarwady Delta, referred to as AD-DEM, was generated based on elevation data of topographic maps at scale of 1:50,000 published in 2014 while source data was compiled between 2000 and 2004. Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate ~5100 elevation points (spot heights) and ~13600 elevation points extracted from contour data of the topographic maps. Elevation values higher than 10 m were excluded from interpolation and the SRTM water body mask created in 2000 was applied to the processed AD-DEM. The AD-DEM was transformed from its original vertical reference of local mean sea level at Kyaikkhami tide gauge to continuous mean sea level based on the mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) that we transposed to EGM96) in order to account for sea level variations along the Myanmar coast.</p> <p>The AD-DEM contains itself some uncertainty due to the lack of evenly distributed spot heights in areas of the upper delta, for which a separate shapefile is provided. However, we highlight to consider the AD-DEM as being the currently best available model against the background of the lacking possibility of ground truthing and being independent from satellite-based measurements.</p> <p>For further information on data processing, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.</p> <p>File name: ADDEM_Con250m_lesseq10_MDT_AD_MMR2000_masked_maskedSRTM.tif</p> <p>File format: GEOTIFF file</p> <p>Spatial reference: MMR2000_46N</p> <p>Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) transposed to EGM96</p> <p>Cell size: 750 × 750 m</p> <p>File name: DataPoorAreas_MMR2000.shp</p> <p>File format: ESRI Shapefile</p> <p>Spatial reference: MMR2000_46N</p>
Compton y-parameter map of thermal SZ effect from Planck PR4 data
<p>This dataset hosts the results and processing data from the paper "An improved Compton parameter map of thermal Sunyaev-Zeldovich effect from Planck PR4 data", <a href="https://doi.org/10.1093/mnras/stad3156">https://doi.org/10.1093/mnras/stad3156</a>. Please cite this paper, should you use this data.</p> <p>Contact: <a href="mailto:chandran@ifca.unican.es">chandran@ifca.unican.es</a></p> <p>UPDATED FITS HEADER.</p>
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach.
<p>The products can be vizualized at <a href="https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer">https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer</a></p> <p>- FORMS-H: Canopy height map of France at 10 m resolution. The units are in centimeter (10^-2 m).</p> <p>- FORMS-B: Above-ground biomass density map of France at 30 m resolution. The units are in Mg ha-1</p> <p>- FORMS-V: Wood volume density map of France at 30 m resolution. The units are in m3 ha-1</p> <p>Please refer to the paper <a href="https://doi.org/10.5194/essd-15-4927-2023">https://doi.org/10.5194/essd-15-4927-2023</a> for further details.</p>
Geologic Map of Ceres [Dawn Mission] - Global dataset based on the 15 individual quadrangle maps
<p><strong>Background:</strong> Between 2011 and 2018, the NASA Dawn spacecraft visited asteroid (4) Vesta and dwarf planet (1) Ceres to investigate the surfaces of both protoplanets through optical and hyperspectral imaging and their composition through gamma-ray and neutron spectroscopy from orbit.<br> For both Vesta and Ceres, a geologic mapping investigation was realized based on optical and hyperspectral data as well as a photogrammetrically derived digital terrain model. For the global mapping investigation, mappers employed Geographic Information System (GIS) software to map 15 quadrangles. The results were published as individual map sheets alongside research papers discussing the geologic evolution. The style of collaborative mapping to produce a consistent global view represented by individual quadrangle maps is comparably new despite abundantly available mapping experiences. Ongoing data acquisition during mapping created considerable challenges for the coordination and homogenization of mapping results.</p> <p>To handle this issue simultaniously to the active mission phase as best as possible a GIS-based environment was needed in order to conduct one homogenous dataset (w.r.t. geometrical and visual character) that represents one geologically-consistent map at the end. Therefore, the mapping team was supported by an predefined mapping template which was generated in the proprietary ArcGIS environment. The template contains different layers (called feature classes) for the different object/geomoetry types and contains predefined attribute values as well as cartographic symbols. The cartographic symbols follow international standards as far as possible. The colours for the geological units refering to established colour values used in geologic maps, e.g., standardized planetary maps generated by USGS, but considering individual needs and requests within the mapping team, too.<br> <br> The <strong>data product pubished here</strong> based on the mentioned GIS-based template and represents the merged global GIS-dataset of the 15 individually conducted geological maps of Ceres within the Dawn Mission. The detailed descriptions of all those scientific interpretions are published in the papers listed within the reference section. Based on team-internal decisions the dataset is provided within the properitary format of ESRIs ArcGIS environment. However, in order to use the data product also outside this software environment, single shapefiles with additional information about the symbology are also included. All available data are available within the compressed folder and the readme-file gives some informative remarks for the useage of the data</p> <p><strong>Additional remark: </strong>The data set provided here does not represent a holistic (in term of topological and scientifical) unification of the 15 individual mapping data as primarily geometric and content-related inconsistencies at quadrangle boundaries prohibited a unified compilation. On the one side, this is due to the fact that the the aim of the mapping project was not to produce a uniform global map, but rather to gain a first impression of the geology of Ceres and publish associated scientific papers. On the other side, that the geological mapping project ran parallel to the regular mission phase, and a finalizing review process for creating a global geological dataset wasn´t scheduled in the mission planning. This deficiency cannot be remedied simply by merging topological missmatches or changing the visualisation. Rather it will require ongoing and detailed scientific discussion of the interpretation results, which could be solved within an updating version of the global map.</p>
ΝEW ABC_ WP2 _ Task 2-1 Mapping the NEW ABC platform requirements_20230609_v1
<p>This data set consists of an analysis of users of existing platforms of other European projects, and the level of engagement they create, to inform the creation and successful use of NEW ABC project platform. This analysis will assess the number and typology of users who access these platforms and what kind of information and material is available to them. The data set will contain numerical and textual tabular data converted into digital format (survey made by online interviews with platform users), quantitative and qualitative data.</p> <p>Content of the files:</p> <ul> <li>file <strong><em>ΝEWABC_WP2_T2-1_Mapping the NEW ABC platform requirements_20230609_v1.csv </em></strong>contains a description of the data collected for each Horizon project relevant to the NEW ABC.</li> </ul> <p> </p> <ul> <li>file <strong>README_</strong><strong><em> ΝEWABC_WP2_T2-1_Mapping the NEW ABC platform requirements_20230609_v1.rtf</em></strong> contains basic information on the dataset uploaded.</li> </ul>
Dataset for 'Weld map tomography for determining local grain orientations from ultrasound'
<p>This dataset contains data files and Jupyter notebooks used to produce figures in the manuscript 'Weld map tomography for determining local grain orientations from ultrasound'.</p>
A mapping of keywords from published papers on alien squirrels to biological invasion research themes
<p><strong>Context</strong></p> <p>This dataset was used to produce the worldl and the graphs in the editorial to the research topic <a href="https://www.frontiersin.org/research-topics/29270/ecology-impact-and-management-of-squirrel-invasions"><em>Ecology, impact and management of squirrel invasions</em></a> (La Morgia et al. 2023).</p> <p><strong>Contents of the dataset</strong></p> <p>The dataset contains the keywords of papers since 2000 harvested with a Web of Science search (performed on 29/05/2023) using the advanced search string TS=(invasive squirrel) OR TI=(invasive squirrel) OR AB=(invasive squirrel). We screened the search results, excluding papers irrelevant to alien squirrels, for example, papers on computer science or physiology, medical or other aspects without any bearing to conservation science. To do this, we checked the abstract and keywords of the papers. Out of the 401 initial papers, after this first screening, we kept 217 in this dataset. The keywords of these papers were manually assigned to alien squirrel research topics by the authors of this dataset (using an own categorisation) and then mapped to the seven broad themes of invasive alien species research of <a href="https://doi.org/10.1007/s10530-023-03067-7">Stevenson et al. (2023)</a>: </p> <ol> <li>Ecosystems: topics which discuss a specific region, or biome, or focused on a particular species strongly associated with one ecosystem type;</li> <li>Monitoring: topics regarding all aspects of monitoring, including detection, identification, and distributional mapping;</li> <li>Management and decision-making: topics discussing the management and socio-political aspects of invasion science, such as prevention, control, and policy;</li> <li>Interactions: topics discussing the interactions with native species, or the effects of those interactions</li> <li>Assessing change: topics focused on studying and analysing temporal and ecological change;</li> <li>Traits: topics that explored the characteristics of alien squirrels;</li> <li>Invasion mechanisms: topics discussing dispersal pathways and drivers of spread.</li> </ol> <p><strong>Dataset description</strong></p> <p>Every row (N = 1275) in the comma-separated .csv represents one original keyword with reference to the paper in which that keyword appears and mapped to the research topics on invasive squirrels and the broad themes in invasion biology research. The .csv contains the following fields:</p> <ul> <li>ID: a unique ID assigned to the combination of an original keyword and the corresponding paper harvested from the WoS search</li> <li>original_keyword: the original keywords associated with the paper (WoS search)</li> <li>keyword_topic: categorization of original keywords into topics related to invasive squirrel research by La Morgia et al. (2023)</li> <li>mapped_category: mapping to one of the seven broad themes of invasive alien species research of <a href="https://doi.org/10.1007/s10530-023-03067-7">Stevenson et al. (2023)</a> as listed and described above</li> <li>authors: author(s) of the paper (WoS search)</li> <li>year: publication year of paper (WoS search)</li> <li>title: title of the paper (WoS search)</li> <li>journal: full journal name (WoS search)</li> <li>doi: full doi of the paper (WoS search)</li> </ul> <p><strong>Potential applications of the dataset</strong></p> <p>This dataset can be used to reproduce the graphs in La Morgia et al. (2023) or to perform more in-depth review or analysis of the literature on alien squirrel invasions. For more information and graph code, we refer to <a href="https://github.com/Vale-LaMo/squirrels">this GitHub repository</a>.</p>
Using Bidirected Graphs to Map Keywords
<p>This study attempts to demonstrate the significance of considering two-way relationships by proposing a keyword network formed using bidirected graphs and association rules to examine the two-way relationship of two or more keywords. A web application to visualize is accessible at <a href="http://www.coconut-libtool.com">www.coconut-libtool.com</a></p>
Dataset for "Mapping of centriolar proteins onto the post-embryonic lineage of C. elegans"
<p>This is the dataset with all spreadsheets, confocal and wide field microscopy images and related scripts, related to the publication " "Mapping of centriolar proteins onto the post-embryonic lineage of <em>C. elegans</em>". Refer to the Read me file for further information.</p>
Map Of Land Cover Agreement - MOLCA
<p>Map Of Land Cover Agreement (MOLCA) is generated by reusing existing datasets for High-Resolution Land Cover (HRLC), but only select portions with unanimous agreement among multiple datasets. We combined multiple HRLCs using intersection methods, retaining only the areas where all datasets agree on the land cover classes and disregarding areas of disagreement. The following HRLCs were used:</p> <ul> <li>FROM-GLC 2017 (<a href="http://data.ess.tsinghua.edu.cn/">[1]</a><a href="https://doi.org/10.1080/01431161.2012.748992">[2]</a><a href="https://doi.org/10.1016/j.scib.2017.03.011">[3]</a><a href="https://doi.org/10.1016/j.scib.2019.03.002">[4]</a>),</li> <li>GL30 2020 (<a href="http://www.globallandcover.com/">[5]</a><a href="https://doi.org/10.1016/j.isprsjprs.2014.09.002">[6]</a>),</li> <li>GHS BU S1NODSM 2016 (<a href="https://doi.org/10.1080/20964471.2017.1397899">[7]</a><a href="https://doi.org/10.3390/rs8040299">[8]</a>),</li> <li>WSF 2019 (<a href="https://geoservice.dlr.de/web/maps/eoc:wsf2019">[9]</a><a href="https://doi.org/10.6084/M9.FIGSHARE.C.4712852.V1">[10]</a><a href="https://doi.org/10.1553/giscience2021_01_s33">[11]</a>),</li> <li>GSW 2019 (<a href="https://global-surface-water.appspot.com/">[12]</a><a href="https://doi.org/10.1038/nature20584">[13]</a>),</li> <li>FNF 2018 (<a href="https://earth.jaxa.jp/en/data/2555/index.html">[14]</a><a href="https://doi.org/10.1016/j.rse.2014.04.014">[15]</a>),</li> <li>MapBiomas 2019 (<a href="https://doi.org/10.3390/rs12172735">[16]</a><a href="https://mapbiomas.org/en/accuracy-statistics?cama_set_language=en">[17]</a>),</li> <li>CCI Africa Prototype 2016 (<a href="https://2016africalandcover20m.esrin.esa.int/">[18]</a><a href="https://iiasa.dev.local/">[19]</a>),</li> <li>ESA DUE GlobPermafrost 2016 (<a href="https://doi.org/10.13140/RG.2.2.30661.76007">[20]</a><a href="https://doi.org/10.1594/PANGAEA.897916">[21]</a></li> </ul> <p> MOLCA contains around 117 billion 10-meter pixels (covering about 11.7 million square kilometers) distributed across a total area of 19 million square kilometers. It is available for 3 macro-regions in Siberia, Africa, and Amazon. The land cover classes represented in MOLCA are Bareland, Built-up, Cropland, Forest, Grassland, Shrubland, Water, Wetland, and Permanent ice and snow, covering the period between 2016 and 2020 The accuracy estimate for MOLCA indicates an Overall Accuracy (OA) of 96%. </p> <p>The repository contains:</p> <ul> <li><strong>MOLCA_<em>nnxxx</em>_v1.tif </strong>x 2075: 2075 MOLCA tiles (893 in Africa, 658 in Amazon, 524 in Siberia). Tiles of MOLCA follow the Sentinel-2 Level-1C product tiling grid, and consequently, the identifier of MOLCA tiles (<em>nnxxx</em>) is the same as the corresponding Sentinel-2 Level-1C tile identifier. </li> <li><strong>MOLCA_tiles_with_statistics.gpkg</strong>: Vector of MOLCA tile extents with statistics (number of pixels per class, total number of pixels, proportion of valid values) of each tile in the attribute table.</li> <li><strong>MOLCA_legend.csv</strong>: Class code and labels of MOLCA</li> </ul> <p> </p> <p>The creation of the MOLCA dataset was done in the project Climate Change Initiative Extension (CCI+) Phase 1 New Essential Climate Variables (NEW ECVS) High Resolution Land Cover ECV (HR_LandCover_cci) funded by the European Space Agency (ESA) known under the abbreviation CCI HRLC or CCI+ HRLC <a href="https://climate esa int/en/projects/high-resolution-land-cover/">[22]</a>.</p>
Multipad Agarose Plate (MAP): A Rapid and High-Throughput Approach for Antibiotic Susceptibility Testing
<p>The datasets used for the Multipad Agarose Plate (MAP) paper. Each experiment was labelled with BE followed by a number. </p> <p>The file <em>BE_condition_map.json</em> describes what was placed on each pad for the experiments. Attached here are JSON files with Pandas data frames that contain all segmentation information, along with debug videos showing how the segmentation aligns with the images. Contact us for access to the raw data.</p> <p>Datasets used for validation experiments:</p> <ul> <li>Leakage test: BE100, BE102</li> <li>Agarose concentration: BE103</li> <li>Illumination wavelength verification: BE138</li> <li>Seeding density verification: BE162</li> </ul> <p>Datasets used for AST:</p> <ul> <li>Chloramphenicol and Rifampicin: BE140, BE141, BE142, BE144, BE145</li> <li>Vancomycin, Ampicillin, Kanamycin: BE148, BE149</li> <li>Ciprofloxacin, Tetracycline, Carbenicillin, Mecillinam: BE150, BE151</li> </ul> <p>Broth microdilution data used for AST validation:</p> <ul> <li>BE139, BE143, BE160</li> </ul> <p>Some datasets also include data that was discarded. </p>
Climatological maps of sea ice variability for the Antarctic marginal ice zone
<p>Monthly climatologies of a statistical indicator of sea ice variability computed from daily maps of sea ice concentration (SIC) from satellites (Vichi, 2022). The sigma_SIA indicator is based on the standard deviation of SIC daily anomalies computed over<br> the monthly time scales, using a monthly climatology over the period 1989-2019 as the baseline. The analysis starts from year 1989 since before that period data were every second day.</p> <p><strong>Please use the most updated version of this dataset and cite the main DOI unless required</strong></p> <p>The following files have been derived from NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4 (<a href="https://doi.org/10.7265/efmz-2t65">https://doi.org/10.7265/efmz-2t65</a> and used in Vichi (2022). The code to produce them is available at the following repository <a href="https://github.com/mvichi/antarcticMIZ">https://github.com/mvichi/antarcticMIZ</a> (<a href="https://doi.org/10.25375/uct.21103363">DOI:10.25375/uct.21103363</a>) </p> <ol> <li><strong>NSIDC_cdr_clim_m_sigmaSIA.nc</strong>: Climatological monthly sigma_SIA (climatological 'pooled' monthly std of daily anomaly) from NOAA/NSIDC CDR V4 Southern Hemisphere Sea Ice Concentration</li> <li><strong>NSIDC_cdr_clim_m_maskMIZ.nc</strong>: Climatological mask of the MIZ extent using a sigma_SIA>0.1 criterion</li> <li><strong>NSIDC_cdr_clim_m_sigmaSIA_exceedance.nc</strong>: Climatological probability maps of exceeding sigma_SIA>0.1 and sigma_SIA>0.2</li> <li><strong>NSIDC_cdr_m_sigmaSIA.nc</strong>: Monthly sigma_SIA from NOAA/NSIDC CDR V4 Southern Hemisphere Sea Ice Concentration. Please note that file 1 above is not the climatology obtained from this file.</li> </ol> <p>NetCDF files 1-3 contain 12 monthly arrays with the climatological sigma_SIA computed over the period 1989-2019. Please note that the indicator in these file is in % of SIC, while in the original paper it is presented in fractional units.</p> <p>File 4 has 396 records for all the months in the analyzed period. The units are fractional as in Vichi (2022).</p> <p><strong>Reference</strong></p> <p>Vichi, M.: An indicator of sea ice variability for the Antarctic marginal ice zone, The Cryosphere, 16, 4087–4106, <a href="https://doi.org/10.5194/tc-16-4087-2022">https://doi.org/10.5194/tc-16-4087-2022</a>, 2022.</p>
Material stock map of CONUS - Great Plains
<p>Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks.</p> <p>This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.</p> <p><strong>Spatial extent</strong><br> This subdataset covers the <strong>Great Plains CONUS</strong>, i.e.</p> <ul> <li>KS</li> <li>ND</li> <li>NE</li> <li>OK</li> <li>SD</li> </ul> <p>For the remaining CONUS, see the <em>related identifiers</em>.</p> <p><strong>Temporal extent</strong><br> The map is representative for ca. 2018.</p> <p><strong>Data format</strong><br> The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p> <p>Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).</p> <p>Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.</p> <ul> <li>t at 10m x 10m</li> <li>kt at 100m x 100m</li> <li>Mt at 1km x 1km</li> <li>Gt at 10km x 10km</li> </ul> <p>For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. </p> <p>Additionally, the grand total mass per state is tabulated for each county in <em>mass_grand_total_t_10m2.tif.csv</em>. County FIPS code and the ID in this table can be related via <em>FIPS-dictionary_ENLOCALE.csv</em>.</p> <p><strong>Material layers</strong><br> Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see <em>related identifiers</em>):</p> <p>A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. <a href="https://doi.org/10.5281/zenodo.5045337.">Zenodo (2022), doi:10.5281/zenodo.5045337.</a></p> <p><strong>Further information</strong><br> For further information, please see the publication.<br> A web-visualization of this dataset is available here.<br> Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Publication</strong><br> D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. <em>In prep</em></p> <p><strong>Funding</strong><br> This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.</p> <p><strong>Acknowledgments</strong><br> We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.