cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4607229
title: Attributing European forest disturbances to storm and fire

description: This repository contains maps attributing each disturbance patch of the European Forest Disturbance Map (version 1.1.4) to bark beetle/wind, fire or other disturbances (mostly harvest). The dataset is based on methods described in following paper, but have been updated with new reference data covering now also bark beetle disturbances: Senf, C. and Seidl, R. (2021) Storm and fire disturbance in Europe: Distribution and trends. <strong>Global Change Biology</strong>. https://doi.org/10.1111/gcb.15679 To get the year of disturbance, please see the underlaying disturbance maps (version 1.1.4.; link given above). <strong>Map classes:</strong> NA = no disturbance<br> 1 = bark beetle or wind disturbances (both classes had to be grouped due to technical reasons)<br> 2 = fire disturbances<br> 3 = other disturbances, mostly harvest but might include salvage logging go small-scale natural disturbances and infrequent other natural agents (e.g., defoliation, avalanches, etc.) <strong>Reference system:</strong> The spatial reference system is EPSG 3035 (ETRS89 / LAEA Europe). <strong>Word of caution:</strong> Remote sensing-based maps, while fascinating to look at, contain errors. If you intent to use the map for your research, please carefully read the discussion on limitations in the paper accompanying the dataset. There will be many instances where the attribution (or even disturbance detection) is wrong. The maps are intended to give a broad, continental-scale overview on the distribution of disturbance agents.
DOI: 10.5281/zenodo.4607230
title: Attributing European forest disturbances to storm and fire

description: This repository contains maps attributing each disturbance patch of the European Forest Disturbance Map (version 1.0) to storm, fire or other disturbances. The exact details and methods are described in following paper: Senf, C. and Seidl, R. (2021) Storm and fire disturbance in Europe: Distribution and trends. <strong>Global Change Biology</strong>. https://doi.org/10.1111/gcb.15679 To get the year of disturbance, please see the underlaying disturbance maps (version 1.0; link given above). <strong>Map classes:</strong> 0 = no disturbance<br> 1 = other disturbances (not storm- or fire-related)<br> 2 = storm disturbances<br> 3 = fire disturbances <strong>Reference system:</strong> The spatial reference system is EPSG 3035 (ETRS89 / LAEA Europe). <strong>Word of caution:</strong> Remote sensing-based maps, while fascinating to look at, contain errors. If you intent to use the map for your research, please carefully read the discussion on limitations in the paper accompanying the dataset.
DOI: 10.5281/zenodo.7371009
title: Attributing European forest disturbances to storm and fire

description: This repository contains maps attributing each disturbance patch of the European Forest Disturbance Map (version 1.1.4) to storm, fire or other disturbances. The exact details and methods are described in following paper: Senf, C. and Seidl, R. (2021) Storm and fire disturbance in Europe: Distribution and trends. <strong>Global Change Biology</strong>. https://doi.org/10.1111/gcb.15679 To get the year of disturbance, please see the underlaying disturbance maps (version 1.1.4.; link given above). <strong>Map classes:</strong> 0 = no disturbance<br> 1 = other disturbances (not storm- or fire-related)<br> 2 = storm disturbances<br> 3 = fire disturbances <strong>Reference system:</strong> The spatial reference system is EPSG 3035 (ETRS89 / LAEA Europe). <strong>Word of caution:</strong> Remote sensing-based maps, while fascinating to look at, contain errors. If you intent to use the map for your research, please carefully read the discussion on limitations in the paper accompanying the dataset.
DOI: 10.5281/zenodo.8202241
title: Attributing European forest disturbances to storm and fire

description: This repository contains maps attributing each disturbance patch of the European Forest Disturbance Map (version 1.1.4) to bark beetle/wind, fire or other disturbances (mostly harvest). The dataset is based on methods described in following paper, but have been updated with new reference data covering now also bark beetle disturbances: Senf, C. and Seidl, R. (2021) Storm and fire disturbance in Europe: Distribution and trends. <strong>Global Change Biology</strong>. https://doi.org/10.1111/gcb.15679 To get the year of disturbance, please see the underlaying disturbance maps (version 1.1.4.; link given above). <strong>Map classes:</strong> NA = no disturbance<br> 1 = bark beetle or wind disturbances (both classes had to be grouped due to technical reasons)<br> 2 = fire disturbances<br> 3 = other disturbances, mostly harvest but might include salvage logging go small-scale natural disturbances and infrequent other natural agents (e.g., defoliation, avalanches, etc.) <strong>Reference system:</strong> The spatial reference system is EPSG 3035 (ETRS89 / LAEA Europe). <strong>Word of caution:</strong> Remote sensing-based maps, while fascinating to look at, contain errors. If you intent to use the map for your research, please carefully read the discussion on limitations in the paper accompanying the dataset. There will be many instances where the attribution (or even disturbance detection) is wrong. The maps are intended to give a broad, continental-scale overview on the distribution of disturbance agents.
DOI: 10.26050/wdcc/ar6.c6cmsnsu
title: IPCC DDC: SNU SAM0-UNICON model output prepared for CMIP6 CMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.SNU.SAM0-UNICON' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The SAM0-UNICON (SNU Atmosphere Model version 0 with Unified Convection Scheme) climate model, released in 2017, includes the following components: aerosol: MAM3, atmos: CAM5.3 with UNICON (1deg; 288 x 192 longitude/latitude; 30 levels; top level ~2 hPa), land: CLM4.0, ocean: POP2 (Displaced Pole; 320 x 384 longitude/latitude; 60 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Seoul National University, Seoul 08826, Republic of Korea (SNU) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
DOI: 10.5281/zenodo.8202363
title: Supplementary table 5.1

description: The table includes 192,744 branch point sequence positions predicted from the Susscrofa11.1 assembly. The table lists the gene name, strand, transcript, intron number, chromosome, intron start and end positions, branch point sequence (heptamer with the branch point at position 6), distance from the 3’ splice site, and prediction score from the BPP tool.
description: supplementary material for a doctorate thesis
DOI: 10.5281/zenodo.8202362
title: Supplementary table 5.1

description: The table includes 192,744 branch point sequence positions predicted from the Susscrofa11.1 assembly. The table lists the gene name, strand, transcript, intron number, chromosome, intron start and end positions, branch point sequence (heptamer with the branch point at position 6), distance from the 3’ splice site, and prediction score from the BPP tool.
description: supplementary material for a doctorate thesis
DOI: 10.34740/kaggle/dsv/6229662
title: AutoXGB
DOI: 10.6084/m9.figshare.23813544
title: wind disturbance pattern

description: This dataset includes 9 raster files and 1 vector file, involving wind disturbance pattern over the past ~110 years (1910-2021), and various topographic variables (such as elevation, slope, aspect) pattern, as well as the distribution of vegetation zone for the Changbai Mountain Nature Reserve, China. Here the wind disturbance pattern is derived from remote sensing images, together with machine-learning models constructed using topographic, vegetation and other variables. These files can be opened by ArcGIS.
DOI: 10.5281/zenodo.8202371
title: Understanding the driver, mechanism, and role of self-organized synchronization of human crowds

description: The data that support the findings in "Understanding the driver, mechanism, and role of self-organized synchronization of human crowds" Density-Flow.dat and Density-Velocity.dat are the raw data in Fig. 2.<br> Syn-flow.dat and Syn-density.dat are the raw data in Fig. 3.<br> Meandist.dat, Steplength.dat and Bufferdist.dat are the raw data in Fig. 4.<br> Syn-position and Syn-angles are the raw data in Fig. 5.
DOI: 10.5281/zenodo.8202370
title: Understanding the driver, mechanism, and role of self-organized synchronization of human crowds

description: The data that support the findings in "Understanding the driver, mechanism, and role of self-organized synchronization of human crowds" Density-Flow.dat and Density-Velocity.dat are the raw data in Fig. 2.<br> Syn-flow.dat and Syn-density.dat are the raw data in Fig. 3.<br> Meandist.dat, Steplength.dat and Bufferdist.dat are the raw data in Fig. 4.<br> Syn-position and Syn-angles are the raw data in Fig. 5.
DOI: 10.21954/ou.rd.23744085.v1
title: Supplementary data to PhD thesis "Forward limit sets of semigroups of substitutions and arithmetic progressions in automatic sequences"

description: Supplementary data to Ibai Aedo's PhD thesis entitled "Forward limit sets of semigroups of substitutions and arithmetic progressions in automatic sequences". <br> Each supplementary file contents a data set for a symbolic sequence considered in the thesis, preceded by an outline of the data given. The outline of the data in all files is also given within the accompanying readme file.
DOI: 10.21954/ou.rd.23744085
title: Supplementary data to PhD thesis "Forward limit sets of semigroups of substitutions and arithmetic progressions in automatic sequences"

description: Supplementary data to Ibai Aedo's PhD thesis entitled "Forward limit sets of semigroups of substitutions and arithmetic progressions in automatic sequences". <br> Each supplementary file contents a data set for a symbolic sequence considered in the thesis, preceded by an outline of the data given. The outline of the data in all files is also given within the accompanying readme file.
DOI: 10.5281/zenodo.7681472
title: Dataset of the publication: Large-scale Characterization of Java Streams

description: This repository contains all the data supporting the findings of the research article: <em>Large-scale Characterization of Java Streams</em>, including the list of all analyzed projects and their metadata. The repository contains two files as follows: 1. database.tar: This archive contains all the data that supports the findings reported in the manuscript entitled “Large-scale Characterization of Java Streams” which is under revision. The data is provided as a database created via PostgreSQL and can be restored to an installation of PostgreSQL (version 12 or later) by following the next procedure: A. Using pgAdmin (https://www.pgadmin.org/): - Enter to the "Restore Dialog" (https://www.pgadmin.org/docs/pgadmin4/development/restore_dialog.html). - Select as "Format" the "Custom or tar" option. - When selecting the "Filename", browse to the folder where this archive was decompressed and select the file "database.tar". 2. ListOfProjects.csv: This comma-separated value file contains the list of all analyzed projects and their metadata.
DOI: 10.5281/zenodo.7681471
title: Dataset of the publication: Large-scale Characterization of Java Streams

description: This repository contains all the data supporting the findings of the research article: <em>Large-scale Characterization of Java Streams</em>, including the list of all analyzed projects and their metadata. The repository contains two files as follows: 1. database.tar: This archive contains all the data that supports the findings reported in the manuscript entitled “Large-scale Characterization of Java Streams” which is under revision. The data is provided as a database created via PostgreSQL and can be restored to an installation of PostgreSQL (version 12 or later) by following the next procedure: A. Using pgAdmin (https://www.pgadmin.org/): - Enter to the "Restore Dialog" (https://www.pgadmin.org/docs/pgadmin4/development/restore_dialog.html). - Select as "Format" the "Custom or tar" option. - When selecting the "Filename", browse to the folder where this archive was decompressed and select the file "database.tar". 2. ListOfProjects.csv: This comma-separated value file contains the list of all analyzed projects and their metadata.
DOI: 10.5281/zenodo.7949597
title: Historical coral reef benthic cover for the Western Indian Ocean (1970-2008)

description: Benthic cover data (percent cover) for the Western Indian Ocean. Data have been extracted from published sources through a systematic literature review, and compiled with previously unpublished datasets. Data was collected prior to 2008 through quantitative coral reef surveys (e.g., Line or Point-Intercept-Transects, photo or visual quadrats) and visual estimates. Particular attention was given to compiling live hard coral cover and macro and turf algae cover data due to their importance as principal measures of coral reef health. Data are from 10 Western Indian Ocean countries and territories: Kenya, Seychelles, Tanzania, Mozambique, Comoros, Mayotte, Madagascar, Reunion, Mauritius and South Africa. Data were extracted from grey literature (technical reports, books, and book chapters), scientific journal papers (articles and reviews) and project reports. Data were generally reported as summarised mean cover values at a monitoring site level. Data aggregated at broader geographic scales was also included (e.g., “northern Kenya”), or other classes (e.g., “unspecified 9 sites (protected)”). Data found in more than one publication were identified and cross-referenced to the other sources. Locations were ordered hierarchically by Country, Sector, Site and Station. In some cases, the same site or station may have multiple entries for the same year because of surveys of different reef zones or depths, and this information is provided to enable a distinction to be made. The exact date or year of survey was not clear in a few publications, and this has been recorded in the <em>Year</em> column as either combined years, e.g. 1998/99, general time period, e.g. mid - 1990s, or if no information is available, as ‘n.d’.
DOI: 10.5281/zenodo.7949598
title: Historical coral reef benthic cover for the Western Indian Ocean (1970-2008)

description: Benthic cover data (percent cover) for the Western Indian Ocean. Data have been extracted from published sources through a systematic literature review, and compiled with previously unpublished datasets. Data was collected prior to 2008 through quantitative coral reef surveys (e.g., Line or Point-Intercept-Transects, photo or visual quadrats) and visual estimates. Particular attention was given to compiling live hard coral cover and macro and turf algae cover data due to their importance as principal measures of coral reef health. Data are from 10 Western Indian Ocean countries and territories: Kenya, Seychelles, Tanzania, Mozambique, Comoros, Mayotte, Madagascar, Reunion, Mauritius and South Africa. Data were extracted from grey literature (technical reports, books, and book chapters), scientific journal papers (articles and reviews) and project reports. Data were generally reported as summarised mean cover values at a monitoring site level. Data aggregated at broader geographic scales was also included (e.g., “northern Kenya”), or other classes (e.g., “unspecified 9 sites (protected)”). Data found in more than one publication were identified and cross-referenced to the other sources. Locations were ordered hierarchically by Country, Sector, Site and Station. In some cases, the same site or station may have multiple entries for the same year because of surveys of different reef zones or depths, and this information is provided to enable a distinction to be made. The exact date or year of survey was not clear in a few publications, and this has been recorded in the <em>Year</em> column as either combined years, e.g. 1998/99, general time period, e.g. mid - 1990s, or if no information is available, as ‘n.d’.
DOI: 10.5517/ccdc.csd.cc2c09b1
title: CCDC 2175032: Experimental Crystal Structure Determination

description: Related Article: Alejandra Gómez-Torres, Niki Mavragani, Alejandro Metta-Magaña, Muralee Murugesu, Skye Fortier|2022|Inorg.Chem.|61|16856|doi:10.1021/acs.inorgchem.2c02881
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.26050/wdcc/ar6.c6cmthcie
title: IPCC DDC: THU CIESM model output prepared for CMIP6 CMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.THU.CIESM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Community Integrated Earth System Model climate model, released in 2017, includes the following components: aerosol: MAM4, atmos: CIESM-AM (FV/FD; 288 x 192 longitude/latitude; 30 levels; top level 2.255 hPa), atmosChem: trop_mam4, land: CIESM-LM (modified CLM4.5), ocean: CIESM-OM (FD, SCCGrid Displaced Pole; 720 x 560 longitude/latitude; 46 levels; top grid cell 0-6 m), seaIce: CICE4. The model was run by the Department of Earth System Science, Tsinghua University, Beijing 100084, China (THU) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 50 km, seaIce: 50 km.
DOI: 10.5281/zenodo.8202412
title: Assessing the improvement of Tropical Cyclone representation in the IPSL model with increasing resolution -- Data

description: This folder contains the following data, used in the artcle "Assessing the improvement of Tropical Cyclone representation in the IPSL model with increasing resolution": * TC tracks in all the simulations, before and after the STJ filtering * Composite NetCDFs * Large scale variables used in the GPI, and the GPI itself, in ICO-VHR and ERA5
DOI: 10.5281/zenodo.8202411
title: Assessing the improvement of Tropical Cyclone representation in the IPSL model with increasing resolution -- Data

description: This folder contains the following data, used in the artcle "Assessing the improvement of Tropical Cyclone representation in the IPSL model with increasing resolution": * TC tracks in all the simulations, before and after the STJ filtering * Composite NetCDFs * Large scale variables used in the GPI, and the GPI itself, in ICO-VHR and ERA5
DOI: 10.34740/kaggle/dsv/6228991
title: Malaysia's Weather Data (1996-Present)
DOI: 10.25326/502
title: BIO-MAIDO

description: This dataset contains measurements from the BIO-MAÏDO campaign.
The campaign took place in Réunion Island from the 13th of March to the 4th of April 2019.
Five sites were instrumented along the slope of the MAÏDO (see Sites below for the details): Petite-France (PF-1), Domaine des Orchidées (DOS-2), Hôtel du Maïdo (HM-3), Piste Oméga (PO-4), Maïdo Observatory (MO-5). The objectives of the campaign were: (1) characterize the chemical and biological composition of air and cloud samples and identify main sources of gases and aerosols; (2) characterize the dynamics and the evolution of the boundary layer and the macro and micro-physical properties of clouds; (3) determine case studies for modelling work with CLEPS (Cloud Explicit Physico-chemical Scheme, 0D cloud chemistry model) and Meso-NH (3D model, http://mesonh.aero.obs-mip.fr/mesonh56).
DOI: 10.5281/zenodo.8202154
title: Experimental files for Dynamics of plasmas produced by a laser pulse, inside a dense gaseous target, formed in an ambient gas

description: Time development of plasmas produced by interaction of laser pulses, with a nitrogen gas, was investigated. The interaction took place inside a small portion of dense nitrogen gas injected temporarily into a chamber filled with the gas under low pressure. High-temperature plasmas produced directly by the laser pulse were a source of soft x rays and charged particles, ionizing and exciting the surrounding gas. In this way, low-temperature plasmas were produced. The formation of high-temperature plasmas was studied using soft x-ray spectroscopy and x-ray streak imaging. Low-temperature plasmas formed at various distances from the laser focus were investigated using an optical streak camera. The experimental data.zip file contains the experimental data from an X-ray spectrograph, X-ray streak camera and optical streak camera. Work supported by the National Science Centre, Poland, Grant Agreement No. UMO-2020/39/B/ST2/00509