cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6522070
title: Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots

description: <strong>Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots</strong> Fire regimes are areas having similar fire characteristics, and show the spatial pattern, frequency and intensity of fires that prevail in that area over long periods of time. Fire regimes are created and maintained by multivariate interactions between climate, vegetation/fuels, and ignitions. Like ecoregions, fire regimes indicate the extent and overlap of particular vegetative/fuel communities and climatic conditions, and are important for understanding, monitoring, predicting and managing fire. More than 83M MODIS “hotspot” thermal detections from 2002-2019 were grouped into 10km cells, and 21 derived variables describing fire characteristics of fire intensity, return frequency, and seasonality within each cell were developed and subjected to unsupervised Multivariate Geographic Clustering to produce world maps of Global Fire Regimes (GFRs), each having similar fire intensity and timing characteristics. Methodology behind these datasets are described in manuscript currently in review. <strong>W. W. Hargrove, Jitendra Kumar, Steven P. Norman, Forrest M. Hoffman (2022), "Empirical Characterization of Global Fire Regimes Show Shared Fire Relationships" 2022 (in review)</strong> This data collection includes: 1. Multivariate Geographic Clustering Global Fire Regimes at 3000, 1000, 500, 100, 50, 20, 10 levels of divisions in form of geospatial raster in IMG formats, and associated color tables. 2. Characteristics of GFRs 3. Location groups 4. Geospatial maps of global fire frequency modes, global seasonality strength, and 12 types of global fires. 5. PNG maps for all data products 6. Description and script for global date transform algorithm.
DOI: 10.5281/zenodo.6522071
title: Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots

description: <strong>Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots</strong> Fire regimes are areas having similar fire characteristics, and show the spatial pattern, frequency and intensity of fires that prevail in that area over long periods of time. Fire regimes are created and maintained by multivariate interactions between climate, vegetation/fuels, and ignitions. Like ecoregions, fire regimes indicate the extent and overlap of particular vegetative/fuel communities and climatic conditions, and are important for understanding, monitoring, predicting and managing fire. More than 83M MODIS “hotspot” thermal detections from 2002-2019 were grouped into 10km cells, and 21 derived variables describing fire characteristics of fire intensity, return frequency, and seasonality within each cell were developed and subjected to unsupervised Multivariate Geographic Clustering to produce world maps of Global Fire Regimes (GFRs), each having similar fire intensity and timing characteristics. Methodology behind these datasets are described in manuscript currently in review. <strong>W. W. Hargrove, Jitendra Kumar, Steven P. Norman, Forrest M. Hoffman (2022), "Empirical Characterization of Global Fire Regimes Show Shared Fire Relationships" 2022 (in review)</strong> This data collection includes: 1. Multivariate Geographic Clustering Global Fire Regimes at 3000, 1000, 500, 100, 50, 20, 10 levels of divisions in form of geospatial raster in IMG formats, and associated color tables. 2. Characteristics of GFRs 3. Location groups 4. Geospatial maps of global fire frequency modes, global seasonality strength, and 12 types of global fires. 5. PNG maps for all data products 6. Description and script for global date transform algorithm.
DOI: 10.5281/zenodo.6516721
title: UAS dataset for Crop Water Stress Index computation and Triangle Method applications

description: This dataset is related with a field campaigns carried out on a tangerine field located near Palermo (Sicily), within the Harmonious COST action CA16219 - Harmonization of UAS techniques for agricultural and natural ecosystems monitoring-, activities framework. The general aim was to acquire UAS (Unmanned Aerial Systems) remotely sensed imagery to detect the vegetation stress status. A multispectral camera (RIKOLA) and a thermal one (FLIR) were used. The images are already georeferenced with 3.7 cm pixel size. The dataset could be used to compute the Crop Water Stress Index and to compute the moisture status of the field by means of a combined use of the optical and thermal images. The dataset includes meteo data registered by a meteo station nearby the site.
DOI: 10.5281/zenodo.6516722
title: UAS dataset for Crop Water Stress Index computation and Triangle Method applications

description: This dataset is related with a field campaigns carried out on a tangerine field located near Palermo (Sicily), within the Harmonious COST action CA16219 - Harmonization of UAS techniques for agricultural and natural ecosystems monitoring-, activities framework. The general aim was to acquire UAS (Unmanned Aerial Systems) remotely sensed imagery to detect the vegetation stress status. A multispectral camera (RIKOLA) and a thermal one (FLIR) were used. The images are already georeferenced with 3.7 cm pixel size. The dataset could be used to compute the Crop Water Stress Index and to compute the moisture status of the field by means of a combined use of the optical and thermal images. The dataset includes meteo data registered by a meteo station nearby the site.
DOI: 10.5281/zenodo.6499615
title: Maps of the Sustainable Development Goal (SDG) indicator 15.3.1 with its sub-indicators for the entire Amazon River Basin

description: Maps of the SDG indicator 15.3.1 adopted by the United Nations Convention to Combat Desertification (UNCCD) together with its sub-indicators for the Amazon River Basin for the period 2001-2020. The sub-indicators are trajectory (or trend), state, and performance. The SDG indicator 15.3.1 was calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution and the 16-day MOD13Q1 NDVI and SoilGrids dataset were used as inputs. In addition, annualized maps of drought severity derived from SPI12, SPEI12, and scPDSI are added. A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution. Coding for the SDG indicator 15.3.1, trajectory, state, and performance. -32768 is ‘No data’ -1 is ‘Degraded’ 0 is ‘Stable’’ 1 is ‘Improvement’ Coding for the drought severity. From 0 (minimum drought severity) to 1 (maximum drought severity).
description: Funding: This work was supported by the CAPES 12/2020, Brazil, through Epidemias—Telemedicina e Análise de Dados Médicos, under the Grant/Award Number (23038.013745/2020-69 to H.A.B).
DOI: 10.5281/zenodo.6499616
title: Maps of the Sustainable Development Goal (SDG) indicator 15.3.1 with its sub-indicators for the entire Amazon River Basin

description: Maps of the SDG indicator 15.3.1 adopted by the United Nations Convention to Combat Desertification (UNCCD) together with its sub-indicators for the Amazon River Basin for the period 2001-2020. The sub-indicators are trajectory (or trend), state, and performance. The SDG indicator 15.3.1 was calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution and the 16-day MOD13Q1 NDVI and SoilGrids dataset were used as inputs. In addition, annualized maps of drought severity derived from SPI12, SPEI12, and scPDSI are added. A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution. Coding for the SDG indicator 15.3.1, trajectory, state, and performance. -32768 is ‘No data’ -1 is ‘Degraded’ 0 is ‘Stable’’ 1 is ‘Improvement’ Coding for the drought severity. From 0 (minimum drought severity) to 1 (maximum drought severity).
description: Funding: This work was supported by the CAPES 12/2020, Brazil, through Epidemias—Telemedicina e Análise de Dados Médicos, under the Grant/Award Number (23038.013745/2020-69 to H.A.B).
DOI: 10.5281/zenodo.5812385
title: Supplementary material for "Classification of Eurasian Watermilfoil (Myriophyllum spicatum) Using Drone-enabled Multispectral Imagery Analysis" paper

description: This material has the unsummarized versions of the water chemistry and light data collected for each site, by date. It also includes all error matrices for each classification. It also includes details of each classification site and result.
DOI: 10.5281/zenodo.5812386
title: Supplementary material for "Classification of Eurasian Watermilfoil (Myriophyllum spicatum) Using Drone-enabled Multispectral Imagery Analysis" paper

description: This material has the unsummarized versions of the water chemistry and light data collected for each site, by date. It also includes all error matrices for each classification. It also includes details of each classification site and result.
DOI: 10.5281/zenodo.5815487
title: S2-Agri - Pixel-Set

description: Dataset of satellite image time series for parcel-based crop type classification, in pixel set format. See related article for further details.
DOI: 10.5281/zenodo.5815488
title: S2-Agri - Pixel-Set

description: Dataset of satellite image time series for parcel-based crop type classification, in pixel set format. See related article for further details.
DOI: 10.5281/zenodo.5815523
title: S2-Agri - Patch

description: Dataset of satellite image time series for parcel-based crop type classification, in pixel patch format. See related article for further details.
DOI: 10.5281/zenodo.5815522
title: S2-Agri - Patch

description: Dataset of satellite image time series for parcel-based crop type classification, in pixel patch format. See related article for further details.
DOI: 10.5281/zenodo.6530186
title: Spatial Scaling Challenge - Additional Data. COST Action CA17134 SENSECO. Working Group 1

description: This dataset contains the data additional data provided for the <strong>SPATIAL SCALING CHALLENGE</strong> organized in the framework of the <strong>SENSECO COST Action CA17143</strong> “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (https://www.senseco.eu/), by the <strong>Working Group 1</strong><strong>.</strong> “Closing the scaling gap: from leaf measurements to satellite images” (https://www.senseco.eu/working-groups/wg1-scaling-gap/). The main dataset, documentation and scripts of the <strong>SPATIAL SCALING CHALLENGE</strong> must be downloaded from<strong>: https://doi.org/10.5281/zenodo.6451335</strong> This additional dataset includes half-hourly time series of down-welling incoming spectral irradiance (W m<sup>-2</sup> µm<sup>-1</sup>) in the visible and near-infrared domains as measured by a field spectroradiometer operating in a nearby ecosystem station. The inclusion of these data in the Spatial Scaling Challenge is at the discretion of the participants. The <strong>SPATIAL SCALING CHALLENGE</strong> aims at gathering the community’s expertise and knowledge to tackle the scaling problems posed by variables of different nature. These experiences will be summarized in a journal article where all the participants are invited to contribute. The exercise is internationally open. Ph.D. students, early career and senior researchers, spin-offs, and companies working in the field of remote sensing of vegetation ecophysiology are welcome to participate. <strong>STILL OPEN FOR PARTICIPATION! New deadline 31<sup>st</sup> of October 2022.</strong> Follow all the <strong>communications and updates</strong> of the <strong>SPATIAL SCALING CHALLENGE</strong> in the RG site: <strong>https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1</strong>.
DOI: 10.5281/zenodo.5877689
title: mDRONES4rivers-project: Portfolios of classification results, UAV and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany

description: Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. In this dataset, the following portfolios of classifications, UAS and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River are available for download: • Multispectral orthophotos produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: MS_ORTHO) • RGB-orthophotos and digital surface models produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PH_SR_ORTHO_DSM) • Multispectral orthophotos and Digital Surface Models produced with the aid of a gyrocopter (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PANX_ORTHO_DSM) • Classification results based on UAV- and a gyrocopter data (PDF, Detailed description of processing procedure for different classification levels; abbreviation: CLASSIF_PROD) • German translated version of all above mentioned product portfolios (PDF, abbreviation: product_portfolio_collection_ger)
description: This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Bjoern Baschek
DOI: 10.5281/zenodo.5877690
title: mDRONES4rivers-project: Portfolios of classification results, UAV and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany

description: Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. In this dataset, the following portfolios of classifications, UAS and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River are available for download: • Multispectral orthophotos produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: MS_ORTHO) • RGB-orthophotos and digital surface models produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PH_SR_ORTHO_DSM) • Multispectral orthophotos and Digital Surface Models produced with the aid of a gyrocopter (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PANX_ORTHO_DSM) • Classification results based on UAV- and a gyrocopter data (PDF, Detailed description of processing procedure for different classification levels; abbreviation: CLASSIF_PROD) • German translated version of all above mentioned product portfolios (PDF, abbreviation: product_portfolio_collection_ger)
description: This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Bjoern Baschek
DOI: 10.5281/zenodo.5877534
title: mDRONES4rivers-project: Classification results based on UAV data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany

description: Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download:<br> • Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units)<br> • Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands )<br> • Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types)<br> • associated reports (PDF; statistical and additional information on the classifiaction results and workflow)<br> The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided: <br> • Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands, 1x for classification of substrate types)<br> • Color Coding table for the visualization of the classifiaction units (.xlsx)
description: This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Bjoern Baschek
DOI: 10.5281/zenodo.5877535
title: mDRONES4rivers-project: Classification results based on UAV data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany

description: Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download:<br> • Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units)<br> • Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands )<br> • Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types)<br> • associated reports (PDF; statistical and additional information on the classifiaction results and workflow)<br> The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided: <br> • Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands, 1x for classification of substrate types)<br> • Color Coding table for the visualization of the classifiaction units (.xlsx)
description: This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Bjoern Baschek
DOI: 10.5281/zenodo.7559092
title: clarias_angolensis_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7559091
title: clarias_angolensis_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.5669967
title: As-bearing Minerals Raw XAS Data

description: This dataset includes raw XAS data collected in HERFD and transmission-detection mode for As-bearing reference minerals. This data was collected at 20-ID-C at the Advanced Photon Source at the Argonne National Laboratory.
description: This research used resources of the Advanced Photon Source, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Argonne National Laboratory, and was supported by the U.S. DOE under Contract No. DE-AC02-06CH11357, and its funding partners. We acknowledge the support of NSERC through a Strategic Partnership Grant for Networks (NSERC TERRE-NET, NETGP 479708-15) and a postgraduate doctoral scholarship to E.M.S.
DOI: 10.5281/zenodo.5669966
title: As-bearing Minerals Raw XAS Data

description: This dataset includes raw XAS data collected in HERFD and transmission-detection mode for As-bearing reference minerals. This data was collected at 20-ID-C at the Advanced Photon Source at the Argonne National Laboratory.
description: This research used resources of the Advanced Photon Source, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Argonne National Laboratory, and was supported by the U.S. DOE under Contract No. DE-AC02-06CH11357, and its funding partners. We acknowledge the support of NSERC through a Strategic Partnership Grant for Networks (NSERC TERRE-NET, NETGP 479708-15) and a postgraduate doctoral scholarship to E.M.S.