cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/kgu2nt
title: Protected Areas - Global Register of Introduced and Invasive Species - Kruger National Park, South Africa

description: The Protected Areas -Global Register of Introduced and Invasive Species (PA-GRIIS) presents validated checklists of introduced (alien) and invasive alien species at a site level. The primary intention of PA-GRIIS is to provide checklists of introduced (alien) and invasive species present on designated protected areas (such as National Parks, Ramsar sites- Wetlands of International importance, World Heritage Sites (natural and mixed), Marine protected areas etc.) and other areas of biodiversity significance (such as Important Plant Areas (IPA), Important Bird Areas (IBA) etc.), as well as buffer zones around these areas. Areas surrounding protected areas and other areas of high biodiversity significance can serve as an invasion source of alien and invasive species propagules. The establishment of buffer zones and the management of alien and invasive species within these areas is a key management strategy in the prevention of introduction and control/management of biological invasions. In the case of islands buffer zones. Checklists are living entities, especially for biological invasions given the growing nature of the problem. PA -GRIIS checklists are based on the GRIIS published methodology (Pagad et al 2018) and supported by the Integrated Publishing Tool of the Global Biodiversity Information Infrastructure (GBIF) that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness. Phase 1 of the PA -GRIIS project will present close to 100 exemplar checklists of introduced (alien) and invasive species in a selection of protected areas and other areas of biodiversity significance This annotated checklist is of introduced (alien) and invasive alien species known to occur in the nationally designated Kruger National Park, South Africa
DOI: 10.5281/zenodo.3732212
title: The Chlamydomonas reference genomes for visualisation in IGV

description: A repository for assembled reference genomes of the green model alga <em>Chlamydomonas reinhardtii</em> including files optimised for visualisation in IGV (Integrative Genomics Viewer, http://software.broadinstitute.org/software/igv/). Based on data from Merchant et al. (2007) The Chlamydomonas genome reveals the evolution of key animal and plant functions., <em>Science; </em>318 5848 245-50 and Gallaher et al (2017) High-throughput sequencing of the chloroplast and mitochondrion of <em>Chlamydomonas reinhardtii</em> to generate improved <em>de novo</em> assemblies, analyse expression patterns and transcript speciation, and evaluate diversity among laboratory strains and wild isolates., <em>The Plant Journal</em>. 2017; 93 545-565
DOI: 10.25934/00001371.0
title: Data Package for study.

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5281/zenodo.3732215
title: Simulated Seafloor Pressures for "The Ocean's Impact on Slow Slip Events"

description: These files in this archive contain the simulated seafloor pressures used in the study of Gomberg et al. (2020). These seafloor pressures were derived from a Regional Ocean Modeling System (ROMS) on the seafloor of the Hikurangi subduction zone off New Zealand [<em>Hadfield et al.</em>, 2007] at offshore sites deployed during the 10-month 2014-2015 Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip (HOBITSS) experiment [<em>Wallace et al.</em>, 2016]. Details of each file are described in the Readme.pdf file.These files in this archive contain the simulated seafloor pressures used in the study of Gomberg et al. (2020). These seafloor pressures were derived from a Regional Ocean Modeling System (ROMS) on the seafloor of the Hikurangi subduction zone off New Zealand [<em>Hadfield et al.</em>, 2007] at offshore sites deployed during the 10-month 2014-2015 Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip (HOBITSS) experiment [<em>Wallace et al.</em>, 2016]. Details of each file are described in the Readme.pdf file.
DOI: 10.5281/zenodo.3732216
title: Simulated Seafloor Pressures for "The Ocean's Impact on Slow Slip Events"

description: These files in this archive contain the simulated seafloor pressures used in the study of Gomberg et al. (2020). These seafloor pressures were derived from a Regional Ocean Modeling System (ROMS) on the seafloor of the Hikurangi subduction zone off New Zealand [<em>Hadfield et al.</em>, 2007] at offshore sites deployed during the 10-month 2014-2015 Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip (HOBITSS) experiment [<em>Wallace et al.</em>, 2016]. Details of each file are described in the Readme.pdf file.These files in this archive contain the simulated seafloor pressures used in the study of Gomberg et al. (2020). These seafloor pressures were derived from a Regional Ocean Modeling System (ROMS) on the seafloor of the Hikurangi subduction zone off New Zealand [<em>Hadfield et al.</em>, 2007] at offshore sites deployed during the 10-month 2014-2015 Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip (HOBITSS) experiment [<em>Wallace et al.</em>, 2016]. Details of each file are described in the Readme.pdf file.
DOI: 10.21227/xn63-he95
title: Aerial Lidar Dataset of an Indoor Stockpile Warehouse

description: Dataset of rosbags collected during autonomous drone flight inside a warehouse of stockpiles. PCD files created using reconstruction method proposed by article.Data still being move to IEEE. Until then, may be found on https://www.kaggle.com/reygago/aerial-lidar-dataset-of-an-stockpile-warehouse
DOI: 10.6084/m9.figshare.12044385.v3
title: Fig_test.xlsx

description: Test
DOI: 10.6084/m9.figshare.12044385.v4
title: Test.xlsx

description: Test
DOI: 10.6084/m9.figshare.12044385.v5
title: Test.xlsx

description: Test
DOI: 10.5281/zenodo.3732107
title: Repository: Will technology save the climate? The challenge of light-duty vehicle fleet electrification

description: This repository contains the raw data of the inputs and results presented in the paper "Will technology save the climate? The challenge of light-duty vehicle fleet electrification" published in Nature Climate Change (2020) by Alexandre Milovanoff, I. Daniel Posen, and Heather L. MacLean (Department of Civil & Mineral Engineering, University of Toronto).
DOI: 10.15468/ys3jqi
title: Global Register of Introduced and Invasive Species – Virgin Islands, British

description: The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level. The primary intention of GRIIS is to provide checklists for countries, islands and soon also for protected areas. Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness. Phase 1 of the project focused on developing validated and verified checklists of countries that are Party to the Convention on Biological Diversity (CBD). Phase 2 which is on-going, aims to achieve global coverage including non-party countries and all overseas territories of countries e.g. Netherlands, France and United Kingdom. Species belonging to all Kingdoms are covered as well as occurring in all environment/systems. Country/ Territory/ Island checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records as well as those under review are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF IPT This annotated checklist is of introduced (alien) and invasive alien species known to occur in British Virgin Islands, an Overseas territory of the United Kingdom
DOI: 10.7910/dvn/vhmfhk/6igaqe
title: FS_surveydesc_public.tab

description: Firm survey data used to generate descriptives
DOI: 10.7910/dvn/vhmfhk/zuae18
title: FS_descriptives_public.do

description: Code to generate firm survey descriptives
DOI: 10.15468/ytdnvt
title: Global Register of Introduced and Invasive Species – Turks and Caicos Islands

description: The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level. The primary intention of GRIIS is to provide checklists for countries, islands and soon also for protected areas. Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness. Phase 1 of the project focused on developing validated and verified checklists of countries that are Party to the Convention on Biological Diversity (CBD). Phase 2 which is on-going, aims to achieve global coverage including non-party countries and all overseas territories of countries e.g. Netherlands, France and United Kingdom. Species belonging to all Kingdoms are covered as well as occurring in all environment/systems. Country/ Territory/ Island checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records as well as those under review are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF IPT This annotated checklist is of introduced (alien) and invasive alien species known to occur in Turks and Caicos Islands, an Overseas territory of the United Kingdom.
DOI: 10.17632/zj9snmy57h.3
title: Data for: A hybrid analysis of LBSN data to early detect anomalies in crowd dynamics

description: This is a data set of 4,335,880 public posts published in Instagram, in the New York City area for 190 days.
DOI: 10.15468/tr9o5o
title: Global Register of Introduced and Invasive Species – British Indian Ocean Territory

description: The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level. The primary intention of GRIIS is to provide checklists for countries, islands and soon also for protected areas. Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness. Phase 1 of the project focused on developing validated and verified checklists of countries that are Party to the Convention on Biological Diversity (CBD). Phase 2 which is on-going, aims to achieve global coverage including non-party countries and all overseas territories of countries e.g. Netherlands, France and United Kingdom. Species belonging to all Kingdoms are covered as well as occurring in all environment/systems. Country/ Territory/ Island checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records as well as those under review are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF IPT This annotated checklist is of introduced (alien) and invasive alien species known to occur in British Indian Ocean Territory (BIOT), an Overseas territory of the United Kingdom.
DOI: 10.25345/c5411g
title: MassIVE MSV000085179 - Marine Sediment Actinobacterial Metabolomics Analysis - Low Res - Paired Omics
title: 9 Strains from the MAR4 clade of Streptomyces. Samples were generated from ethyl acetate extracts of whole cultures.
DOI: 10.5281/zenodo.3732245
title: What crowd density is most likely to induce spontaneous synchronization? Insights from a single-file crowd motion experiment

description: Data necessary to support the findings in the paper "What crowd density is most likely to induce spontaneous synchronization? Insights from a single-file crowd motion experiment"
DOI: 10.7910/dvn/u1z981/luacrf
title: 200328 Submitted JPSE Diversity and Ideology Appendix A Data for Figure 1.xlsx

description: % of All Faculty, Political Scientists, Social Scientists, and/or Psychologists/Social Psychologists surveyed self-identifying as Liberal, Left, or Democratic
DOI: 10.7910/dvn/u1z981/upoont
title: 200328 Submitted Hull JPSE Diversity and Ideology Appendix B Data for Figure 2.docx

description: % of APSA eJobs listings with term, February 16, 2019
DOI: 10.17632/22tftdjb7v.1
title: Data for: Changes in nursing students’ knowledge, attitudes and interest in caring for older adults: a longitudinal cohort study

description: Demographic data Palmore Facts on Aging Quiz Data Kogan Attitudes Towards Older People Scale Data
DOI: 10.17632/dr9hnpy8ys.1
title: Data for: Confidence intervals and sample size for estimating the prevalence of plastic debris in seabird nests

description: Data on the presence of anthropogenic debris in nests of seabirds from different locations worldwide.
DOI: 10.25345/c5098n
title: MassIVE MSV000085180 - Marine Sediment Actinobacterial Metabolomics Analysis - High Res - Paired Omics
title: 6 Strains isolated from various marine sediments. Samples were generated from ethyl acetate extracts of culture supernatants after filtering with a glass fiber filter.
DOI: 10.5281/zenodo.3732143
title: COVID-19 vaccine candidates: Prediction and validation of 174 novel SARS-CoV-2 epitopes

description: Datasets used in "COVID-19 vaccine candidates: Prediction and validation of 174 novel SARS-CoV-2 epitopes" SARS-CoV-2 peptides predicted to be binders to 11 HLA allotypes. Selected peptides have been measured in a stability assay revealing potential SARS-CoV-2 vaccine targets. File containing all predictions from tested tools. (Please note that not all tools use same measure.)
DOI: 10.5063/f1125r1w
title: Facilitating better outcomes: How positive species interactions can improve oyster reef restoration

description: This database provides the outputs from a literature review to review the literature associated with the structure and function of oyster reef ecosystems to identify key positive species interactions (i.e. those species interactions where at least one partner benefits and no partners are harmed) that, if systematically incorporated into restoration planning, have the potential to greatly increase restoration outcomes and success.We reviewed the scientific literature, by searching Web of Science and SCOPUS using the search string: TOPIC = ((oyster* OR ""oyster reef*"" OR ""oyster bed*"") AND (restor* OR creat* OR rehabilit* OR reconstruct* OR recover* OR benefit* OR construct* OR ""ecosystem service*"" OR ""ecosystem function*"" OR facilitat*))
DOI: 10.5281/zenodo.3732152
title: Graphs and Attributes used for the attribute-structure correlation pattern mining

description: ## SCPM: An implementation of an algorithm for structural correlation pattern mining. The structural correlation measures how a set of attributes induces dense subgraphs in an attributed graph. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Structural correlation pattern mining is useful to analyze how different attribute sets are correlated to dense subgraphs in several real-life attributed graphs. **Relevant Publications** * Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Structural correlation pattern mining for large graphs. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs (MLG '10). * Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Mining Attribute-structure Correlated Patterns in Large Attributed Graphs. In Proceedings of the VLDB Endowment (PVLDB '12). * Arlei Silva. Structural correlation pattern mining for large graphs. M.Sc Thesis, Computer Science Department, Universidade Federal de Minas Gerais, 2011. * Arlei Silva, Wagner Meira Jr. Structural correlation pattern mining for large graphs. Thesis and Dissertation Contest of the Brazilian Computer Society (CTD'12). <br> ## HOW TO cd to trunk and run make<br> see README in trunk <br> ## Datasets: ### Description: #### ATTRIBUTE FILE: Format: Lists the attributes of each vertex from the graph. &lt;VERTEX_ID&gt;,&lt;ATTRIBUTE_ID&gt;,&lt;ATTRIBUTE_ID&gt;...,&lt;ATTRIBUTE_ID&gt; Example: <br> 1,A,C <br> 2,A <br> 3,A,C,D <br> 4,A,D <br> 5,A,E <br> 6,A,B,C <br> 7,A,B,E <br> 8,A,B <br> 9,A,B <br> 10,A,B,D <br> 11,A,B #### GRAPH FILE: Format: Lists the neighbors of each vertex from the graph (adjacency list). Although the graph is undirected, each edge must be included in both directions. &lt;VERTEX_ID&gt;,&lt;NEIGHBOR_ID&gt;,&lt;NEIGHBOR_ID&gt;...,&lt;NEIGHBOR_ID&gt; <br> <br> Example: <br> 1,4 <br> 2,3 <br> 3,2,4,5,6,7 <br> 4,1,3,5,6 <br> 5,3,4,6 <br> 6,3,4,5,7,8,9,10 <br> 7,3,6,8,11 <br> 8,6,7,9,10,11 <br> 9,6,8,10,11 <br> 10,6,8,9,11 <br> 11,7,8,9,10 ### REAL DATASETS Lastfm: attributes: attrLastFm.csv.tar.bz2 network: graphLastFm.csv.tar.gz DBLP: attributes: newAttrDBLP.csv.tar.bz2 network: newGraphDBLP.csv.tar.bz2 CITESEER: attributes: attrCiteseer.csv.tar.bz2 network: graphCiteseer.csv.tar.bz2
description: {"references": ["Arlei Silva, Mohammed J. Zaki, and Wagner Meira Jr. Mining attribute-structure correlated patterns in large attributed graphs. PVLDB, 5(5):466\u2013477, 2012."]}
DOI: 10.5281/zenodo.3732153
title: Graphs and Attributes used for the attribute-structure correlation pattern mining

description: ## SCPM: An implementation of an algorithm for structural correlation pattern mining. The structural correlation measures how a set of attributes induces dense subgraphs in an attributed graph. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Structural correlation pattern mining is useful to analyze how different attribute sets are correlated to dense subgraphs in several real-life attributed graphs. **Relevant Publications** * Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Structural correlation pattern mining for large graphs. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs (MLG '10). * Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Mining Attribute-structure Correlated Patterns in Large Attributed Graphs. In Proceedings of the VLDB Endowment (PVLDB '12). * Arlei Silva. Structural correlation pattern mining for large graphs. M.Sc Thesis, Computer Science Department, Universidade Federal de Minas Gerais, 2011. * Arlei Silva, Wagner Meira Jr. Structural correlation pattern mining for large graphs. Thesis and Dissertation Contest of the Brazilian Computer Society (CTD'12). <br> ## HOW TO cd to trunk and run make<br> see README in trunk <br> ## Datasets: ### Description: #### ATTRIBUTE FILE: Format: Lists the attributes of each vertex from the graph. &lt;VERTEX_ID&gt;,&lt;ATTRIBUTE_ID&gt;,&lt;ATTRIBUTE_ID&gt;...,&lt;ATTRIBUTE_ID&gt; Example: <br> 1,A,C <br> 2,A <br> 3,A,C,D <br> 4,A,D <br> 5,A,E <br> 6,A,B,C <br> 7,A,B,E <br> 8,A,B <br> 9,A,B <br> 10,A,B,D <br> 11,A,B #### GRAPH FILE: Format: Lists the neighbors of each vertex from the graph (adjacency list). Although the graph is undirected, each edge must be included in both directions. &lt;VERTEX_ID&gt;,&lt;NEIGHBOR_ID&gt;,&lt;NEIGHBOR_ID&gt;...,&lt;NEIGHBOR_ID&gt; <br> <br> Example: <br> 1,4 <br> 2,3 <br> 3,2,4,5,6,7 <br> 4,1,3,5,6 <br> 5,3,4,6 <br> 6,3,4,5,7,8,9,10 <br> 7,3,6,8,11 <br> 8,6,7,9,10,11 <br> 9,6,8,10,11 <br> 10,6,8,9,11 <br> 11,7,8,9,10 ### REAL DATASETS Lastfm: attributes: attrLastFm.csv.tar.bz2 network: graphLastFm.csv.tar.gz DBLP: attributes: newAttrDBLP.csv.tar.bz2 network: newGraphDBLP.csv.tar.bz2 CITESEER: attributes: attrCiteseer.csv.tar.bz2 network: graphCiteseer.csv.tar.bz2
description: {"references": ["Arlei Silva, Mohammed J. Zaki, and Wagner Meira Jr. Mining attribute-structure correlated patterns in large attributed graphs. PVLDB, 5(5):466\u2013477, 2012."]}
DOI: 10.5281/zenodo.3732018
title: Wikidata Dump Nuclear

description: RDF dump of wikidata produced with wdumps. Nuclear disasters<br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.3732019
title: Wikidata Dump Nuclear

description: RDF dump of wikidata produced with wdumps. Nuclear disasters<br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.14469/hpc/7079
title: Michael 1,4 addition, G = TS2, C-C formation, wB97XD/Def2-TZVPP with cyano replacing formyl G = -494.464414 DG

description: Gaussian Calculation
DOI: 10.15468/ks9psh
title: Protected Areas - Global Register of Introduced and Invasive Species - Addo-Elephant National Park, South Africa

description: The Protected Areas -Global Register of Introduced and Invasive Species (PA-GRIIS) presents validated checklists of introduced (alien) and invasive alien species at a site level. The primary intention of PA-GRIIS is to provide checklists of introduced (alien) and invasive species present on designated protected areas (such as National Parks, Ramsar sites- Wetlands of International importance, World Heritage Sites (natural and mixed), Marine protected areas etc.) and other areas of biodiversity significance (such as Important Plant Areas (IPA), Important Bird Areas (IBA) etc.), as well as buffer zones around these areas. Areas surrounding protected areas and other areas of high biodiversity significance can serve as an invasion source of alien and invasive species propagules. The establishment of buffer zones and the management of alien and invasive species within these areas is a key management strategy in the prevention of introduction and control/management of biological invasions. In the case of islands buffer zones. Checklists are living entities, especially for biological invasions given the growing nature of the problem. PA -GRIIS checklists are based on the GRIIS published methodology (Pagad et al 2018) and supported by the Integrated Publishing Tool of the Global Biodiversity Information Infrastructure (GBIF) that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness. Phase 1 of the PA -GRIIS project will present close to 100 exemplar checklists of introduced (alien) and invasive species in a selection of protected areas and other areas of biodiversity significance This annotated checklist is of introduced (alien) and invasive alien species known to occur in the nationally designated Addo-Elephant National Park, South Africa