cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4322562
title: GeoVectors-Antarctica-location (v0.1)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains location-embeddings for the region "Antarctica" including the following countries: Antarctica <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4956951
title: GeoVectors-Antarctica-location (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains location-embeddings for the region "Antarctica" including the following countries: Antarctica <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4956954
title: GeoVectors-Asia-location (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains location-embeddings for the region "Asia" including the following countries: Afghanistan Armenia Azerbaijan Bangladesh Bhutan Cambodia China Gcc-States India Iran Iraq Israel-and-Palestine Jordan Kazakhstan Kyrgyzstan Laos Lebanon Malaysia-Singapore-Brunei Maldives Mongolia Myanmar Nepal North-Korea Pakistan Philippines South-Korea Sri-Lanka Syria Taiwan Tajikistan Thailand Turkmenistan Uzbekistan Vietnam Yemen <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4956950
title: GeoVectors-Antarctica-location (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains location-embeddings for the region "Antarctica" including the following countries: Antarctica <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4957746
title: GeoVectors-Germany-location (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains location-embeddings for the region "Germany" including the following countries: Germany <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4957745
title: GeoVectors-Germany-location (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains location-embeddings for the region "Germany" including the following countries: Germany <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.6084/m9.figshare.14252864
title: simulation_1600x

description: simulation_1600x
DOI: 10.6084/m9.figshare.14252864.v1
title: simulation_1600x

description: simulation_1600x
DOI: 10.5066/p90hiftx
title: Depth to the top of the Mesaverde Group -- National Assessment of Oil and Gas Project - Southwestern Wyoming Province (037)

description: This dataset shows depth contours to the top of the Mesaverde Group within the Southwestern Wyoming Province, southwestern Wyoming, northeastern Utah, and northwestern Colorado
DOI: 10.25345/c5s24v
title: MassIVE MSV000087647 - Phosphotyrosine peptide abundance in control and Cul5-deficient MCF10A cells
title: Phosphotyrosine peptide abundance in control and Cul5-deficient MCF10A cells
DOI: 10.5066/p9nt6rav
title: Unioned layer of coal resource calculation in the southern Piceance Basin, Colorado (ps*fing)

description: These are shapefiles and coverages of final unioned polygon coverages used to calculate coal resources of the Cameo/Fairfield coal group east of 107 deg 15' longitude, and the Cameo/Wheeler, South Canyon, and Coal Ridge coal zones west of 107 deg 15' longitude in the southern Piceance Basin, northwestern Colorado. Pscbfing was used for the area east of 107 deg 15' longitude; pscwfing was used for the Cameo/Wheeler coal zone; psscfing was used for the South Canyon coal zone; pscrfing was used for the Coal Ridge coal zone. Polygons that make up the final unioned polygons include counties, 7.5' quadrangles, townships, surface and coal ownership, reliability, and, overburden. The final unioned coverage for each coal zone, was clipped by the resource boundary polygon of each coal zone.
DOI: 10.6073/pasta/c2607b817bca35de3313a093bde00b24
title: Construction Costs of Carnivorous and Non-Carnivorous Plants at Harvard Forest 2006-2008

description: Leaf traits, including photosynthetic rates, leaf mass area, and leaf nutrient content covary in a coordinated way for a wide range of plant taxa. This covariation results from trade-offs between the costs of constructing plant tissues and the benefits accrued from photosynthesis. Carnivorous plants have been found to be outliers in the "universal spectrum of leaf traits" because they have very low photosynthetic rates for the amount of nitrogen in their leaves and traps. But no studies have measured simultaneously the actual construction costs of carnivorous traps and rates of photosynthesis to determine the amortization required to recover the investment (the "payback time") and thereby calculate the "marginal gain" of "investing" in carnivorous structures. The objective of this study was to measure construction costs (CCmass, grams of glucose required to build 1g of ash-free dry mass of tissue) and photosynthesis (Amass, nmol CO2 g-1 s-1) for traps, leaves, roots, and rhizomes of 15 carnivorous plant species with differing mechanisms of prey capture and consumption (pitfall traps, snap-traps, sticky pads) grown under greenhouse conditions. Payback time (h) was calculated as the quotient of CCmass and Amass after conversion to nmol of carbon per gram of ash-free dry mass. There were highly significant differences amongst species for CCmass of traps but there were no significant differences for CCmass amongst traps, roots and rhizomes. Mean (+- SD) CCmass for traps (1.14 +- 0.24 g glucose g-1) was significantly lower than the mean CCmass of leaves of 267 non-carnivorous plant species (1.47 +- 0.17 g glucose g-1). However, all 15 carnivorous plants examined in this study had low Amass and thus, the marginal gain of carnivory is small with a long payback time (524-1641 h). Our results of low CCmass for carnivorous traps is contrary to the oft-stated expectation of a high cost to construct elaborate carnivorous traps. Payback time integrates traits used to assess leaf scaling relationships with construction costs, and locates carnivorous plants at the "slow and tough" end of the universal spectrum of leaf traits.
DOI: 10.6073/pasta/e483b27291bfef711ab93d2591a86a86
title: Groundwater Levels on Hog Island, VA. 2007-2021

description: This dataset contains hourly measurements of groundwater levels on Hog Island, VA - off the Atlantic Coast of the Delmarva Peninsula. It continues measurements of groundwater levels that were initiated in by Frank Day in July 1990 (dataset VCR99066) and by Mark Brinson in October 1997 (dataset VCR05130). Refer to these datasets for previous measurements. The groundwater measurements use the same wells and locations, but differ in sensor technology and frequency of measurement. In August 2007, a system of wirelessly-connected data loggers designed by Thomas Williams was installed with help from Arthur Schwarzschild, Chris Buck, John Hayward and John Porter.
DOI: 10.6084/m9.figshare.14798739
title: Table 1.docx

description: <b>Tabela 1. Principais características metodológicas dos estudos incluídos</b>
DOI: 10.6084/m9.figshare.14798739.v2
title: Table 1.docx

description: <b>Tabela 1. Principais características metodológicas dos estudos incluídos</b>
DOI: 10.6084/m9.figshare.14798742.v2
title: Table 3.docx

description: <b>Tabela 1. Principais características metodológicas dos estudos incluídos</b>
DOI: 10.6084/m9.figshare.14798736
title: Table 2.docx

description: <b>Tabela 2. Características das intervenções e da população inscrita nos estudos incluídos</b>
DOI: 10.6084/m9.figshare.14798736.v3
title: Table 2.docx

description: <b>Tabela 2. Características das intervenções e da população inscrita nos estudos incluídos</b>
DOI: 10.6084/m9.figshare.14798742
title: Table 3.docx

description: <b>Tabela 1. Principais características metodológicas dos estudos incluídos</b>
DOI: 10.6084/m9.figshare.14798736.v2
title: Table 2.docx

description: <b>Tabela 2. Características das intervenções e da população inscrita nos estudos incluídos</b>
DOI: 10.6084/m9.figshare.14798739.v1
title: Table 1.docx

description: <b>Tabela 1. Principais características metodológicas dos estudos incluídos</b>
DOI: 10.6084/m9.figshare.14798742.v1
title: Table 3.docx

description: <b>Tabela 1. Principais características metodológicas dos estudos incluídos</b>
DOI: 10.5281/zenodo.4974880
title: Dataset for the paper "Ideology and identity in grammar: A diachronic-quantitative approach to language standardisation processes in Ancient Greek"

description: This is the dataset for the paper "Ideology and identity in grammar: A diachronic-quantitative approach to language standardisation processes in Ancient Greek"
DOI: 10.5281/zenodo.4974879
title: Dataset for the paper "Ideology and identity in grammar: A diachronic-quantitative approach to language standardisation processes in Ancient Greek"

description: This is the dataset for the paper "Ideology and identity in grammar: A diachronic-quantitative approach to language standardisation processes in Ancient Greek"
DOI: 10.5066/p9nbqlot
title: Lake Ontario Water Quality and Velocity Transect Data 2015-18

description: From May of 2015 through September of 2018, water-quality were collected at 178 locations and velocity data were collected along 7 transects from select tributaries, embayments, and nearshore lake locations along New York's Lake Ontario waterfront in support of the Great Lakes Restoration Initiative and the Lakewide Action Management Plan. These data include: depth, velocity magnitude, velocity direction, navigational data, ADCP temperature, and channel profile.
DOI: 10.6084/m9.figshare.14798985
title: Mixture Results

description: Results for the reconstructed mixture of beads object.<br>
DOI: 10.6084/m9.figshare.14798982
title: Bead Results

description: Results for the reconstructed bead object.<br>
DOI: 10.6084/m9.figshare.14798988
title: Phantom Results

description: Results for the reconstructed modified Shepp-Logan phantom object.<br>
DOI: 10.6084/m9.figshare.14798946.v1
title: Phantom Intensities

description: Intensity data for simulated modified Shepp-Logan phantom measurements. The appended number is the angle.<br>
DOI: 10.5066/p9hslfre
title: Tidal flow dynamics at Sutter and Steamboat Sloughs in the Sacramento-San Joaquin Delta, CA in 2014

description: The video shows a short animation of river flow dynamics (flow velocity vectors) over multiple tidal cycles.
DOI: 10.6084/m9.figshare.14798988.v1
title: Phantom Results

description: Results for the reconstructed modified Shepp-Logan phantom object.<br>
DOI: 10.6084/m9.figshare.14798946
title: Phantom Intensities

description: Intensity data for simulated modified Shepp-Logan phantom measurements. The appended number is the angle.<br>
DOI: 10.6084/m9.figshare.14798805
title: Mixture Intensities

description: Intensity data for simulated mixture of beads measurements. The appended number is the angle.
DOI: 10.5281/zenodo.4973523
title: Association of oligopaint FISH labeled super-enhancers and genes with polymerase clusters (raw data, genes)

description: This data set assesses the placement of different genomic regions relative to clusters formed by RNA polymerase II in zebrafish embryos. RNA polymerase was labeled by immunofluorescence, genomic regions by oligopaint DNA fluorescence in-situ hybridization (FISH). Microscopy images were acquired by instant-SIM microscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts. Raw image data for four genes: rnf19a, cdc25b, celf1, crsp7
description: This data set only contains raw data, no analysis scripts.
DOI: 10.5281/zenodo.4973522
title: Association of oligopaint FISH labeled super-enhancers and genes with polymerase clusters (raw data, genes)

description: This data set assesses the placement of different genomic regions relative to clusters formed by RNA polymerase II in zebrafish embryos. RNA polymerase was labeled by immunofluorescence, genomic regions by oligopaint DNA fluorescence in-situ hybridization (FISH). Microscopy images were acquired by instant-SIM microscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts. Raw image data for four genes: rnf19a, cdc25b, celf1, crsp7
description: This data set only contains raw data, no analysis scripts.
DOI: 10.5066/p9yyfhxg
title: National Assessment of Oil and Gas Project - Wyoming Thrust Belt Province (036) Assessment Units

description: The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
DOI: 10.5517/ccdc.csd.cc238pgk
title: CCDC 1944768: Experimental Crystal Structure Determination

description: Related Article: Ga-Young Cha, Hoje Chun, Do-Young Hong, Jaegyeom Kim, Kyung-Ho Cho, U-Hwang Lee, Jong-San Chang, Sam Gon Ryu, Hae Wan Lee, Seung-Joo Kim, Byungchan Han, Young Kyu Hwang|2020|J.Hazard.Mater.|398|122857|doi:10.1016/j.jhazmat.2020.122857
DOI: 10.5517/ccdc.csd.cc24mpl2
title: CCDC 1985134: Experimental Crystal Structure Determination

description: Related Article: Adrien T. Normand, E. Daiann Sosa Carrizo, Corentin Magnoux, Esteban Lobato, Hélène Cattey, Philippe Richard, Stéphane Brandès, Charles H. Devillers, Anthony Romieu, Pierre Le Gendre, Paul Fleurat-Lessard|2021|Chemical Science|12|253|doi:10.1039/D0SC04736H
DOI: 10.5517/ccdc.csd.cc24mpm3
title: CCDC 1985135: Experimental Crystal Structure Determination

description: Related Article: Adrien T. Normand, E. Daiann Sosa Carrizo, Corentin Magnoux, Esteban Lobato, Hélène Cattey, Philippe Richard, Stéphane Brandès, Charles H. Devillers, Anthony Romieu, Pierre Le Gendre, Paul Fleurat-Lessard|2021|Chemical Science|12|253|doi:10.1039/D0SC04736H