cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14474136
title: FWT21Roster.csv

description: List of competitors in the 2021 Freeride World Tour
DOI: 10.6084/m9.figshare.14474136.v1
title: FWT21Roster.csv

description: List of competitors in the 2021 Freeride World Tour
DOI: 10.5066/p98okaof
title: Ecological Model Support for the Western Everglades Restoration Project (WERP) Round One of Four, 2018

description: Ecological models facilitate evaluation and assessment of alternative approaches to restore the Greater Everglades ecosystem. However, the provision of useful and accessible models is a challenge because there is often a disconnect between model output and its use by decision makers. Joint Ecosystem Modeling (JEM) meets this challenge by providing ecological model output tailored to management decisions. JEM is a partnership among federal and state agencies, universities, and other organizations. Ecological models (i.e., ecological planning tools) were developed and used by JEM during the Central Everglades Planning Project to evaluate potential effects to natural resources in the impacted areas. There is a desire by the planning agencies and bureaus involved in the Western Everglades Restoration Project (WERP) to use these same tools for WERP evaluations of alternative restoration plans. The models of particular interest to the WERP Ecological Subteam are: (1) Marl Prairie Habitat Suitability Index in conjunction with the (2) Cape Sable Seaside Sparrow Helper, (3) (native) Apple Snail population model (EverSnail), (4) Wading bird distribution and evaluation models (WADEM), (5) Small-sized freshwater fish density, and (6) American alligator habitat suitability index (HSI). This is round 1 (of 4) for the WERP.
DOI: 10.5066/p9d1ong7
title: Ecological Model Support for the Western Everglades Restoration Project (WERP) Round Two of Four, 2018

description: Ecological models facilitate evaluation and assessment of alternative approaches to restore the Greater Everglades ecosystem. However, the provision of useful and accessible models is a challenge because there is often a disconnect between model output and its use by decision makers. Joint Ecosystem Modeling (JEM) meets this challenge by providing ecological model output tailored to management decisions. JEM is a partnership among federal and state agencies, universities and other organizations. Ecological models (i.e., ecological planning tools) were developed and used by JEM during the Central Everglades Planning Project to evaluate potential effects to natural resources in the impacted areas. There is a desire by the planning agencies and bureaus involved in the Western Everglades Restoration Project (WERP) to use these same tools for WERP evaluations of alternative restoration plans. The models of particular interest to the WERP Ecological Subteam are: (1) Marl Prairie Habitat Suitability Index in conjunction with the (2) Cape Sable Seaside Sparrow Helper, (3) (native) Florida apple snail population model (EverSnail), (4) Wading bird distribution and evaluation models (WADEM), (5) Small-sized freshwater fish density, and (6) Alligator habitat suitability index (HSI; includes Breeding Potential metric). This is round 2 (of 4) in the WERP.
DOI: 10.25625/dqkf7k/x95t8m
title: Mixed species data.tab

description: :unav
DOI: 10.25625/dqkf7k
title: Replication Data for: Mixed support of the stress gradient hypothesis for explaining interspecific associations in African ungulates

description: The dataset contains sightings of large mammals in Manyara Ranch, northern Tanzania. Animal sightings were recorded along minor tracks from February to April 2015. Each cluster of animals was defined as individuals within 50 m of another individual. Clusterid is the running number of cluster observations, Obsdate and time are the observation date and time, perpdist the perpendicular distance between observer and the cluster, habitat denotes the habitat category (based on vegetation physiognomy), ImageDate is the date of the Modis dataset used to extract NDVI values for each cluster. These were binned into three classes (NDVIclass) according to the 0.33 and 0.67 percentiles of the NDVI values. The column insidelionhr indicates whether the cluster was inside (“yes”) or outside (“no”) the lion home range. The columns with the names of mammal species indicate the numbers of individuals of these species that were encountered. If only one species occurred in a cluster, this was denoted as “0” in the mixedgroup column; if more than one species occurred in a cluster, this was denoted as “1”.
DOI: 10.5281/zenodo.4716057
title: Datasets related to the VirAnimalOne project

description: <strong>VirAnimalOne</strong> is a project focused on large scale mining of publicly deposited genomic and transcriptomic datasets available from ENA/SRA and derived from pets, livestock and wild animal species: to identify unexpected coronavirus sequences; to mine the host animal genomes for potential variants that might confer resistance or susceptibility to SARS-CoV-2 and other coronaviruses known to infect both humans and animals; to phylogenetically and structurally evaluate host receptor conformations and infer potential animal susceptibility to coronavirus infections, with particular attention for SARS-Cov-2. Here, we report the pipeline, preliminary datasets and results produced in this project and related to the VirAnimalOne Technical Report: <strong>Technical_Report_VirAnimalOne_Pipeline. </strong>It reports and describes the bioinformatic pipeline used for the mining of the omics datasets. <strong>Tables S1-S6</strong>. Genome datasets used for mining activities. <strong>Table S7</strong>. List of host genes involved in coronavirus infection and used for mining polymorphisms in the host genome using the retrieved datasets. <strong>Table S8-S13</strong>. List of annotated variants in the selected host genes involved in coronavirus infection. <strong>Tables S14-S10</strong>. Viral sequences identified in the next-generation sequencing datasets generated from the selected domestic species. <strong>Warning</strong>: Please, check for updated versions as data can present some inaccuracies.
description: Fundings: EOSCsecretariat.eu has received funding from the European Union's Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.
DOI: 10.5281/zenodo.4709817
title: Datasets related to the VirAnimalOne project

description: <strong>VirAnimalOne</strong> is a project focused on large scale mining of publicly deposited genomic and transcriptomic datasets available from ENA/SRA and derived from pets, livestock and wild animal species: to identify unexpected coronavirus sequences; to mine the host animal genomes for potential variants that might confer resistance or susceptibility to SARS-CoV-2 and other coronaviruses known to infect both humans and animals; to phylogenetically and structurally evaluate host receptor conformations and infer potential animal susceptibility to coronavirus infections, with particular attention for SARS-Cov-2. Here, we report the pipeline, preliminary datasets and results produced in this project and related to the VirAnimalOne Technical Report: <strong>Technical_Report_VirAnimalOne_Pipeline. </strong>It reports and describes the bioinformatic pipeline used for the mining of the omics datasets. <strong>Tables S1-S6</strong>. Genome datasets used for mining activities. <strong>Table S7</strong>. List of host genes involved in coronavirus infection and used for mining polymorphisms in the host genome using the retrieved datasets. <strong>Table S8-S13</strong>. List of annotated variants in the selected host genes involved in coronavirus infection. <strong>Tables S14-S10</strong>. Viral sequences identified in the next-generation sequencing datasets generated from the selected domestic species. <strong>Warning</strong>: Please, check for updated versions as data can present some inaccuracies.
description: Fundings: EOSCsecretariat.eu has received funding from the European Union's Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.
DOI: 10.25810/47q1-ts57.7
title: Russian Newspaper Coverage of Climate Change or Global Warming, 2000-2021 - March 2021

description:         The Media and Climate Change Observatory Data monitors 120 sources (across newspapers, radio and TV) in 54 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.4715885
title: Discovery and classification of Twitter bots

description: Online Social Networks (OSN) are used by millions of users, daily. This user-base shares and discovers different opinions on popular topics. <br> Social influence of large groups may be influenced by user believes or be attracted the interest in particular news or products. A large number of users, gathered in a single group or number of followers, increases the probability to influence OSN users. <br> Botnets, collections of automated accounts controlled by a single agent, are a common mechanism for exerting<br> maximum influence. Botnets may be used to better infiltrate the social graph over time and create an illusion of community<br> behaviour, amplifying their message and increasing persuasion. This paper investigates Twitter botnets, their behavior, their interaction with user communities and their evolution over time.<br> We analyze a dense crawl of a subset of Twitter traffic, amounting to nearly all interactions by Greek-speaking Twitter users for a period<br> of 36 months. The collected users are labeled as botnets, based on long term and frequent content similarity events. We detect over a million events, where seemingly unrelated accounts tweeted nearly identical content, at almost the same time. We filter these concurrent content injection events and detect a set of 1,850 accounts that repeatedly exhibit this pattern of behavior, suggesting that they are fully or in part controlled and orchestrated by the same entity. We find botnets that appear for brief intervals and disappear, as well as botnets that evolve and grow, spanning the duration of our dataset. We analyze statistical differences between the bot accounts and human users, as well as the botnet interactions with the user communities and the Twitter trending topics.
DOI: 10.5281/zenodo.4715884
title: Discovery and classification of Twitter bots

description: Online Social Networks (OSN) are used by millions of users, daily. This user-base shares and discovers different opinions on popular topics. <br> Social influence of large groups may be influenced by user believes or be attracted the interest in particular news or products. A large number of users, gathered in a single group or number of followers, increases the probability to influence OSN users. <br> Botnets, collections of automated accounts controlled by a single agent, are a common mechanism for exerting<br> maximum influence. Botnets may be used to better infiltrate the social graph over time and create an illusion of community<br> behaviour, amplifying their message and increasing persuasion. This paper investigates Twitter botnets, their behavior, their interaction with user communities and their evolution over time.<br> We analyze a dense crawl of a subset of Twitter traffic, amounting to nearly all interactions by Greek-speaking Twitter users for a period<br> of 36 months. The collected users are labeled as botnets, based on long term and frequent content similarity events. We detect over a million events, where seemingly unrelated accounts tweeted nearly identical content, at almost the same time. We filter these concurrent content injection events and detect a set of 1,850 accounts that repeatedly exhibit this pattern of behavior, suggesting that they are fully or in part controlled and orchestrated by the same entity. We find botnets that appear for brief intervals and disappear, as well as botnets that evolve and grow, spanning the duration of our dataset. We analyze statistical differences between the bot accounts and human users, as well as the botnet interactions with the user communities and the Twitter trending topics.
DOI: 10.6084/m9.figshare.14474190.v1
title: Impacts of A Social-Norms Edutainment Program on Sexual Violence and Bystander Behavior among University Men in Vietnam: The GlobalConsent Randomized Clinical Trial

description: DATASET
DOI: 10.6084/m9.figshare.14474190
title: Impacts of A Social-Norms Edutainment Program on Sexual Violence and Bystander Behavior among University Men in Vietnam: The GlobalConsent Randomized Clinical Trial

description: DATASET
DOI: 10.5066/p9t01m5e
title: Ecological Model Support for the Western Everglades Restoration Project (WERP) Round Three of Four, 2019

description: Ecological models facilitate evaluation and assessment of alternative approaches to restore the Greater Everglades ecosystem. However, the provision of useful and accessible models is a challenge because there is often a disconnect between model output and its use by decision makers. Joint Ecosystem Modeling (JEM) meets this challenge by providing ecological model output tailored to management decisions. JEM is a partnership among federal and state agencies, universities, and other organizations. Ecological models (i.e., ecological planning tools) were developed and used by JEM during the Central Everglades Planning Project to evaluate potential effects to natural resources in the impacted areas. There is a desire by the planning agencies and bureaus involved in the Western Everglades Restoration Project (WERP) to use these same tools for WERP evaluations of alternative restoration plans. The models of particular interest to the WERP Ecological Subteam are: (1) Marl Prairie Habitat Suitability Index in conjunction with the (2) Cape Sable Seaside Sparrow Helper, (3) (native) Florida apple snail population model (EverSnail), (4) Wading bird distribution and evaluation models (WADEM), (5) Small-sized freshwater fish density, and (6) American alligator habitat suitability index (HSI). This is round 3 (of 4) for the WERP.
DOI: 10.5066/f700009n
title: Chesapeake Bay Land Change Model Version 3a Input and Output Data for October 2015 Run of Maryland Counties

description: This data requires 14.2 Gigabytes of disk space, uncompressed, and 425 megabytes compressed (Winzip .zip file). The CBLCMv3a.zip file contains four sets of 58 files representing image, tabular, and shapefile data used to drive one of four policy scenarios: ï¿½Policy Implementationï¿½ (POL), ï¿½Relaxed Zoningï¿½ (LAX), ï¿½Incentivized Urbanizationï¿½ (URB), and ï¿½Forest and Farmland Conservationï¿½ (CON). Each scenarioï¿½s input and output data is contained in its own folder. Each scenario is defined by its input files and a parameter specification table (ParmSpecs.txt). Every file has an associated XML Metadata file describing it in detail. For each scenario, there are eleven ERDAS images, six text tables, one text parameter specification file, and one point shapefile. The data was used as input and generated as output during a CBLCMV3a model run in October 2015.
DOI: 10.5066/p94jwx8w
title: Ecological Model Support for the Western Everglades Restoration Project (WERP) Round Four of Four, 2020

description: Ecological models facilitate evaluation and assessment of alternative approaches to restore the Greater Everglades ecosystem. However, the provision of useful and accessible models is a challenge because there is often a disconnect between model output and its use by decision makers. Joint Ecosystem Modeling (JEM) meets this challenge by providing ecological model output tailored to management decisions. JEM is a partnership among federal and state agencies, universities and other organizations. Ecological models (i.e., ecological planning tools) were developed and used by JEM during the Central Everglades Planning Project to evaluate potential effects to natural resources in the impacted areas. There is a desire by the planning agencies and bureaus involved in the Western Everglades Restoration Project (WERP) to use these same tools for WERP evaluations of alternative restoration plans. The models of particular interest to the WERP Ecological Subteam are: (1) Marl Prairie Habitat Suitability Index in conjunction with the (2) Cape Sable Seaside Sparrow Helper, (3) (native) Florida apple snail population model (EverSnail), (4) Wading bird distribution and evaluation models (WADEM), (5) Small-sized freshwater fish density, and (6) Alligator Habitat Suitability Index (HSI; includes Breeding Potential metric). This is round 4 (of 4) in the WERP.
DOI: 10.5281/zenodo.3948642
title: ODP Leg 190, Hole 1173A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 190: Deformation and Fluid Processes in the Nankai Trough Accretionary Prism
DOI: 10.5281/zenodo.3948641
title: ODP Leg 190, Hole 1173A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 190: Deformation and Fluid Processes in the Nankai Trough Accretionary Prism
DOI: 10.6084/m9.figshare.14474241
title: Script and classificatory and morphometric variables

description: Script and classificatory and morphometric variables of Nieto-Vilela et al, 2021<br>
DOI: 10.5281/zenodo.3948644
title: ODP Leg 191, Hole 1179D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 191: Northwest Pacific Seismic Observatory and Hammer Drill Test
DOI: 10.5281/zenodo.3948643
title: ODP Leg 191, Hole 1179D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 191: Northwest Pacific Seismic Observatory and Hammer Drill Test
DOI: 10.25747/dqk7-kt80
title: ENCKEP Evaluation module: code and case studies
DOI: 10.5281/zenodo.4716158
title: Data and Code for Atmospheric oxygen abundance, marine nutrient availability, and organic carbon fluxes to the seafloor

description: Code and Data for manuscript "<strong>Atmospheric oxygen abundance, marine nutrient availability, and organic carbon fluxes to the seafloor"</strong>
DOI: 10.25747/rp31-kf14
title: Sagres ship meteorological data 2020
DOI: 10.34740/kaggle/dsv/2151672
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.15468/hxxmsq
title: Two new species of Hyalojassini (Hemiptera: Cicadellidae: Iassinae) to the genera Comanopa Blocker and Gehundra Blocker, description of the male of G. sordida (Baker) and key to species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Domahovski, Alexandre C. (2021): Two new species of Hyalojassini (Hemiptera: Cicadellidae: Iassinae) to the genera Comanopa Blocker and Gehundra Blocker, description of the male of G. sordida (Baker) and key to species. European Journal of Taxonomy 746 (1): 112-129, DOI: https://doi.org/10.5852/ejt.2021.746.1323, URL: http://zoobank.org/c82e9877-eab4-41b1-a73a-0687c94d9472
DOI: 10.25810/37f9-1j65.36
title: Spanish Newspaper Coverage of Climate Change or Global Warming, 2000-2021 - March 2021

description:         The Media and Climate Change Observatory Data monitors 120 sources (across newspapers, radio and TV) in 54 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.17632/74k3532mcf.1
title: JAAD-D-20-02740 Supplemental Methods

description: JAAD-D-20-02740 Supplemental Methods
DOI: 10.5281/zenodo.4714702
title: Symbology layer files: Hierarchical geomorphological mapping in mountainous areas

description: Symbology layer files developed in ArcMap and ArcGIS Pro for the purpose of visualizing geomorphological codes using predefined color palettes. Supplemental material for: Hierarchical geomorphological mapping in mountainous areas, Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps in 2020, revisions made in 2021.These layer files will produce the complete geomorphological legend, even when all geomorphological units are not present in the dataset. When visualizing results, we recommend the following optimal scale ranges: 1:2,500 - 1:10,000 for Tier 3, 1:10,001 to 1:30,000 for Tier 2 and ≥ 1:30,001 for Tier 1.The complete set of layer files ("Geomorphological Map Vorarlberg - Tier 1", "Geomorphological Map Vorarlberg - Tier 2" and "Geomorphological Map Vorarlberg - Tier 3") are intended to visualize output of a model that creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail.
description: We also created an online storymap (https://arcg.is/Xjvy4) of all three tiers for both areas, which scales automatically as the user zooms in and out between scales to show how the data are dynamically visualized in a GIS.
DOI: 10.5281/zenodo.3948650
title: ODP Leg 193, Hole 1188B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 193: Anatomy of an Active Felsic-Hosted Hydrothermal System , Eastern Manus Basin