cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48690/1524198
title: LandScan Global 2002

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.48690/1524199
title: LandScan Global 2003

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.48690/1524200
title: LandScan Global 2004

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.48690/1524201
title: LandScan Global 2005

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.48690/1524202
title: LandScan Global 2006

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.48690/1524203
title: LandScan Global 2007

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.48690/1524204
title: LandScan Global 2008

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.48690/1524205
title: LandScan Global 2009

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.48690/1524215
title: LandScan Global 2012

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.48690/1524208
title: LandScan Global 2013

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.48690/1524209
title: LandScan Global 2014

description: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL's LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an 'ambient population' (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.
DOI: 10.3886/e114713v1-126220
title: Replication data for: Supply-Side Drug Policy in the Presence of Substitutes: Evidence from the Introduction of Abuse-Deterrent Opioids

description: Overdose deaths from prescription opioid pain relievers nearly quadrupled between 1999 and 2010. We study the consequences of one of the largest supply disruptions to date to abusable opioids—the introduction of an abuse-deterrent version of OxyContin in 2010. Supply-side interventions which limit access to opioids may have the unintended consequence of increasing use of substitute drugs, including heroin. Exploiting cross-state variation in OxyContin exposure, we find that states with the highest initial rates of OxyContin misuse experienced the largest increases in heroin deaths. Our results imply that the recent heroin epidemic is largely due to the reformulation of OxyContin.
DOI: 10.6084/m9.figshare.20751718
title: Frontiers_table2.docx

description: supplementary table2
DOI: 10.6084/m9.figshare.20751718.v1
title: Frontiers_table2.docx

description: supplementary table2
DOI: 10.34740/kaggle/dsv/4144075
title: Mahanagar Yatayat Dataset
DOI: 10.23645/epacomptox.20394501.v1
title: ToxValDB v9.1

description: This resource is a compilation of information sourced from many sites, databases and sources, including U.S. Federal and state sources and international bodies, which can save the user time by providing information in one location. The data are not reviewed by USEPA – the user must apply judgment in use of the information. You should consult the original scientific paper or data source if possible to ensure an understanding of the context of the data contained in the Dashboard. <br> Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25
DOI: 10.5281/zenodo.7038314
title: Reversals in temperature-precipitation correlations in the Northern Hemisphere extratropics during the Holocene

description: This repository consists of R code for calculating time-scale dependent correlation and hydrological sensitivity for an example record. The input data includes a custom R-function and example data, stored in machine-readable data format (.CSV). Also included are datasets for time-scale dependent correlation and hydrological sensitivity that were used for the analyses in Herzschuh et al.: Reversals in temperature-precipitation correlations in the Northern Hemisphere extratropics during the Holocene. Geophysical Research Letters, in review 2022.
DOI: 10.5281/zenodo.7038315
title: Reversals in temperature-precipitation correlations in the Northern Hemisphere extratropics during the Holocene

description: This repository consists of R code for calculating time-scale dependent correlation and hydrological sensitivity for an example record. The input data includes a custom R-function and example data, stored in machine-readable data format (.CSV). Also included are datasets for time-scale dependent correlation and hydrological sensitivity that were used for the analyses in Herzschuh et al.: Reversals in temperature-precipitation correlations in the Northern Hemisphere extratropics during the Holocene. Geophysical Research Letters, in review 2022.
DOI: 10.5281/zenodo.7038402
title: Reversals in temperature-precipitation correlations in the Northern Hemisphere extratropics during the Holocene

description: This repository consists of R code for calculating time-scale dependent correlation and hydrological sensitivity for an example record. The input data includes a custom R-function and example data, stored in machine-readable data format (.CSV). Also included are datasets for time-scale dependent correlation and hydrological sensitivity that were used for the analyses in Herzschuh et al.: Reversals in temperature-precipitation correlations in the Northern Hemisphere extratropics during the Holocene. Geophysical Research Letters, in review 2022.
DOI: 10.6084/m9.figshare.20751286
title: SBPH_genome

description: Supplementary data for Chromosome-level assembly, dosage compensation and sex-biased gene expression in the small brown planthopper, Laodelphax striatellus
DOI: 10.6084/m9.figshare.20751286.v1
title: SBPH_genome

description: Supplementary data for Chromosome-level assembly, dosage compensation and sex-biased gene expression in the small brown planthopper, Laodelphax striatellus
DOI: 10.5517/ccdc.csd.cc20x267
title: CCDC 1874018: Experimental Crystal Structure Determination

description: Related Article: Silvia Gutiérrez-Tarriño, José Gaona-Miguélez, Pascual Oña-Burgos|2021|Dalton Trans.|50|15370|doi:10.1039/D1DT02347K
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: 10.6084/m9.figshare.20751577
title: erythroxylum_rotundifolium_decontaminated_gx.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.20751577.v1
title: erythroxylum_rotundifolium_decontaminated_gx.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.19653993
title: Durations of Mitosis and Cytokinesis for Control & TTFields-Exposed HeLa and MCF-10A Cells

description: Data obtained in live-cell imaging experiments with HeLa cells exposed to TTFields using the Novocure inovitro live system. The data was analyzed to determine the durations of mitosis and cytokinesis in both unexposed and TTFields-exposed HeLa and MCF-10A cells.
DOI: 10.6084/m9.figshare.19653993.v5
title: Durations of Mitosis and Cytokinesis for Control & TTFields-Exposed HeLa and MCF-10A Cells

description: Data obtained in live-cell imaging experiments with HeLa cells exposed to TTFields using the Novocure inovitro live system. The data was analyzed to determine the durations of mitosis and cytokinesis in both unexposed and TTFields-exposed HeLa and MCF-10A cells.
DOI: 10.6084/m9.figshare.19653993.v4
title: Durations of Mitosis and Cytokinesis for Control & TTFields-Exposed HeLa and MCF-10A Cells

description: Data obtained in live-cell imaging experiments with HeLa cells exposed to TTFields using the Novocure inovitro live system. The data was analyzed to determine the durations of mitosis and cytokinesis in both unexposed and TTFields-exposed HeLa and MCF-10A cells.
DOI: 10.6084/m9.figshare.20751862.v1
title: Chart.json.tar.gz

description: This is the artifact for APR RTS submission.
DOI: 10.6084/m9.figshare.20751625
title: erythroxylum_tenue_decontaminated_gx.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.20751625.v1
title: erythroxylum_tenue_decontaminated_gx.fasta.gz

description: .......
DOI: 10.34740/kaggle/dsv/4144025
title: Fasting Experiment Data
DOI: 10.48420/20231061
title: Figure S6 source data and processing

description: Source data for static light scattering and the associated Unicode Origin Project file used to workup and present the data.
DOI: 10.48420/20231061.v1
title: Figure S6 source data and processing

description: Source data for static light scattering and the associated Unicode Origin Project file used to workup and present the data.