cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.oyzjvt
title: Occurrence Download

description: A dataset containing 506 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tropaeolaceae",
 "Country is Chile"
 ]
}

 The dataset includes 506 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006773-151016162008034/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.te3uny
title: Occurrence Download

description: A dataset containing 6960187 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((166.35938 -23.72505,158.8711 -39.19821,162.98439 -58.05928,178.73437 -58.05928,188.8594 -51.59617,196.73437 -44.86522,172.37109 -23.42696,166.35938 -23.72505))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6960187 records from 158 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066648-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.0hzcn9
title: Occurrence Download

description: A dataset containing 2202 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panthera onca (Linnaeus, 1758)"
 ]
}

 The dataset includes 2202 records from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006773-160526112335914/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pjbv9f
title: Occurrence Download

description: A dataset containing 2794 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Bilans des saisons de capture de l'Association Départementale des Piégeurs Agréés de Gironde - Saison 2015/2016 de piégeage en Gironde"
 ]
}

 The dataset includes 2794 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018043-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7jj4tu
title: Occurrence Download

description: A dataset containing 209814665 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Arthropoda"
 ]
}

 The dataset includes 209814665 records from 27871 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048896-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.23522010.v1
title: Supplementary Material for: Pronounced State-Level Disparities in Prescription of Cannabinoids to Medicaid Patients

description: <b><i>Introduction:</i></b> Dronabinol is approved in the USA for chemotherapy-induced nausea as well as vomiting and HIV-induced anorexia, while cannabidiol is primarily approved for childhood epileptic disorders Lennox-Gastaut and Dravet syndrome. The use pattern for these prescription cannabinoids in the USA is unknown. This study examined Medicaid claims for two FDA-approved prescription cannabinoids, dronabinol and cannabidiol, approved in 1985 and 2018, respectively, from 2016–2020 to better understand the pharmacoepidemiologic trends and distribution of these drugs in US Medicaid amidst the increasing use of non-pharmaceutical formulations of cannabis. <b><i>Methods:</i></b> The longitudinal study analyzed Medicaid prescription claims that were calculated by extracting the prescriptions on a state level from 2016 to 2020 for two cannabinoids, dronabinol and cannabidiol, where outcomes over each year were calculated. Outcomes were (1) the number of prescriptions for each state corrected for the number of Medicaid enrollees and (2) dronabinol and cannabidiol spending. Spending refers to the amount reimbursed by the state Medicaid program. <b><i>Results:</i></b> Dronabinol prescriptions per state decreased by 25.3% from 2016 to 2020, while cannabidiol prescriptions increased by 16,272.99% from 2018 to 2020. The spending on these drugs parallels that of their prescription trend with a 66.3% decrease in reimbursement for dronabinol ($5.7 million in 2020), whereas cannabidiol increased by +26,582.0% ($233.3 million in 2020). Dronabinol prescriptions, when corrected for the number of enrollees, in Connecticut were 136.4 times larger than in New Mexico, and seventeen states had zero prescriptions. Idaho’s prescriptions of cannabidiol (27.8/10,000 enrollees) were significantly elevated relative to the national average and were 15.4-fold higher than Washington, DC (1.8/10K enrollees). <b><i>Conclusions:</i></b> The prescriptions of pharmaceutical-grade tetrahydrocannabinol decreased while those of cannabidiol increased. This study also identified pronounced state-level variation in cannabinoid prescribing to Medicaid patients. State formularies and prescription drug list variation may contribute to the drug reimbursements in Medicaid, though further research is needed to identify the health policy or pharmacoeconomic origins of these disparities.
DOI: 10.18712/nsd-nsd3030-v1
title: The State Employee Survey 2021

description: The State Employee Survey 2021 has been carried out as a collaboration between Rambøll Management Consulting, DFØ and Statistics Norway. It's a representative survey which is carried out among government employees (NOT the armed forces, health corporations, etc.). A main purpose of the survey is mapping what it's like to work in the state, which provides important knowledge for further developing personnel policy and management. In 2021, topics such as commitment, ability to implement, competence, autonomy, management, trust, digitalisation, innovation, etc.. were dealt with.
DOI: 10.34740/kaggle/ds/3096725
title: Bitcoin & Fear and Greed
DOI: 10.3886/e111666v1-139561
title: Data + code supplement for "Health Care Demand Under Simple Prices"

description: This paper shows that consumers respond to prices for complex health care when they can easily assess out-of-pocket prices. Health care cost containment efforts increasingly incentivize price-shopping, despite a dearth of evidence that this steers consumers toward lower-priced care for major medical services. I show that consumers shift toward lower-priced hospitals in the highly simplified price information environment of insurance plans with tiered hospital networks. Consumers observe a single predictable, well-defined price that applies to a broad range of services within each of at most three hospital tiers. Within three years, expected partial-equilibrium savings reach 8–17 percent of baseline spending.
description: Hospitals and private health insurance in Massachusetts.
DOI: 10.5517/ccdc.csd.cc2cxwmt
title: CCDC 2202507: Experimental Crystal Structure Determination

description: Related Article: Tamrat Yimenu Zeleke, Bipul Sarma|2022|Cryst.Growth Des.|22|7405|doi:10.1021/acs.cgd.2c00996
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.19276061
title: Raw data for body weight

description: Body weight raw dataFlow cytometry raw data<br>
DOI: 10.6084/m9.figshare.19276061.v1
title: Raw data for body weight and flow cytometry.pdf

description: Body weight raw dataFlow cytometry raw data<br>
DOI: 10.5517/ccdc.csd.cc2846k6
title: CCDC 2089417: Experimental Crystal Structure Determination

description: Related Article: Huiwen Dong, Yunhu Zhang, Tse Wai Ming, Shuhui Wang, Jiawei Li, Shaobing Fu, Qingying Zhang, Kewu Zeng, Pengfei Tu, Hong Liang|2022|Phytochemistry|197|113129|doi:10.1016/j.phytochem.2022.113129
DOI: 10.5517/ccdc.csd.cc2846l7
title: CCDC 2089418: Experimental Crystal Structure Determination

description: Related Article: Huiwen Dong, Yunhu Zhang, Tse Wai Ming, Shuhui Wang, Jiawei Li, Shaobing Fu, Qingying Zhang, Kewu Zeng, Pengfei Tu, Hong Liang|2022|Phytochemistry|197|113129|doi:10.1016/j.phytochem.2022.113129
DOI: 10.5517/ccdc.csd.cc2846n9
title: CCDC 2089420: Experimental Crystal Structure Determination

description: Related Article: Huiwen Dong, Yunhu Zhang, Tse Wai Ming, Shuhui Wang, Jiawei Li, Shaobing Fu, Qingying Zhang, Kewu Zeng, Pengfei Tu, Hong Liang|2022|Phytochemistry|197|113129|doi:10.1016/j.phytochem.2022.113129
DOI: 10.5517/ccdc.csd.cc2893d3
title: CCDC 2094124: Experimental Crystal Structure Determination

description: Related Article: Marta S. Krawczyk, Irena Majerz|2022|Cryst.Growth Des.|22|1554|doi:10.1021/acs.cgd.1c00768
DOI: 10.5517/ccdc.csd.cc2893f4
title: CCDC 2094125: Experimental Crystal Structure Determination

description: Related Article: Marta S. Krawczyk, Irena Majerz|2022|Cryst.Growth Des.|22|1554|doi:10.1021/acs.cgd.1c00768
DOI: 10.5517/ccdc.csd.cc2893g5
title: CCDC 2094126: Experimental Crystal Structure Determination

description: Related Article: Marta S. Krawczyk, Irena Majerz|2022|Cryst.Growth Des.|22|1554|doi:10.1021/acs.cgd.1c00768
DOI: 10.5517/ccdc.csd.cc2893h6
title: CCDC 2094127: Experimental Crystal Structure Determination

description: Related Article: Marta S. Krawczyk, Irena Majerz|2022|Cryst.Growth Des.|22|1554|doi:10.1021/acs.cgd.1c00768
DOI: 10.5517/ccdc.csd.cc2893j7
title: CCDC 2094128: Experimental Crystal Structure Determination

description: Related Article: Marta S. Krawczyk, Irena Majerz|2022|Cryst.Growth Des.|22|1554|doi:10.1021/acs.cgd.1c00768
DOI: 10.5517/ccdc.csd.cc2893k8
title: CCDC 2094129: Experimental Crystal Structure Determination

description: Related Article: Marta S. Krawczyk, Irena Majerz|2022|Cryst.Growth Des.|22|1554|doi:10.1021/acs.cgd.1c00768
DOI: 10.5517/ccdc.csd.cc2893l9
title: CCDC 2094130: Experimental Crystal Structure Determination

description: Related Article: Marta S. Krawczyk, Irena Majerz|2022|Cryst.Growth Des.|22|1554|doi:10.1021/acs.cgd.1c00768
DOI: 10.5517/ccdc.csd.cc2893mb
title: CCDC 2094131: Experimental Crystal Structure Determination

description: Related Article: Marta S. Krawczyk, Irena Majerz|2022|Cryst.Growth Des.|22|1554|doi:10.1021/acs.cgd.1c00768
DOI: 10.5517/ccdc.csd.cc289k7d
title: CCDC 2094553: Experimental Crystal Structure Determination

description: Related Article: Jinshan Wang, Yuguang Yang, Cuifeng Jiang, Meng He, Chuang Yao, Jianfeng Zhang|2022|J.Mater.Chem.C|10|3163|doi:10.1039/D1TC05497J
DOI: 10.5517/ccdc.csd.cc28j566
title: CCDC 2100907: Experimental Crystal Structure Determination

description: Related Article: Aaron Gerwien, Benjamin Jehle, Marvin Irmler, Peter Mayer, Henry Dube|2022|J.Am.Chem.Soc.|144|3029|doi:10.1021/jacs.1c11183
DOI: 10.5517/ccdc.csd.cc28j577
title: CCDC 2100908: Experimental Crystal Structure Determination

description: Related Article: Aaron Gerwien, Benjamin Jehle, Marvin Irmler, Peter Mayer, Henry Dube|2022|J.Am.Chem.Soc.|144|3029|doi:10.1021/jacs.1c11183
DOI: 10.5517/ccdc.csd.cc28j588
title: CCDC 2100909: Experimental Crystal Structure Determination

description: Related Article: Aaron Gerwien, Benjamin Jehle, Marvin Irmler, Peter Mayer, Henry Dube|2022|J.Am.Chem.Soc.|144|3029|doi:10.1021/jacs.1c11183
DOI: 10.5517/ccdc.csd.cc28j599
title: CCDC 2100910: Experimental Crystal Structure Determination

description: Related Article: Aaron Gerwien, Benjamin Jehle, Marvin Irmler, Peter Mayer, Henry Dube|2022|J.Am.Chem.Soc.|144|3029|doi:10.1021/jacs.1c11183
DOI: 10.5517/ccdc.csd.cc28j5bb
title: CCDC 2100911: Experimental Crystal Structure Determination

description: Related Article: Aaron Gerwien, Benjamin Jehle, Marvin Irmler, Peter Mayer, Henry Dube|2022|J.Am.Chem.Soc.|144|3029|doi:10.1021/jacs.1c11183
DOI: 10.5517/ccdc.csd.cc28nbx6
title: CCDC 2104928: Experimental Crystal Structure Determination

description: Related Article: Yanyu Chen, Zhaobo Wang, Wutong Zhao, Shitao Sun, Lu Yang, Junpeng Zhang, Di Zhang, Maosheng Cheng, Bin Lin, Yongxiang Liu|2022|Chem.Commun.|58|3051|doi:10.1039/D1CC07298F
DOI: 10.5281/zenodo.6323964
title: Dataset: Sentiment Analysis annotation of News headlines covering the Olympic legacy of Rio 2016 and London 2012 published by the Brazilian and British online media

description: Dataset of 464 news headlines with sentiment manually annotated by a domain expert using the labels positive, negative and neutral. Data contains URLs for news articles published between 2004-2020 by the British and Brazilian media in English and Brazilian Portuguese covering the Olympic legacies of London 2012 and Rio 2016. Articles were collected from the news outlets’ websites using Google search engine. News outlets: The Guardian Daily Mail Globo Estadao
DOI: 10.5281/zenodo.6323963
title: Dataset: Sentiment Analysis annotation of News headlines covering the Olympic legacy of Rio 2016 and London 2012 published by the Brazilian and British online media

description: Dataset of 464 news headlines with sentiment manually annotated by a domain expert using the labels positive, negative and neutral. Data contains URLs for news articles published between 2004-2020 by the British and Brazilian media in English and Brazilian Portuguese covering the Olympic legacies of London 2012 and Rio 2016. Articles were collected from the news outlets’ websites using Google search engine. News outlets: The Guardian Daily Mail Globo Estadao
DOI: 10.25346/s6/ignjal/t4t2tq
title: DVSE SS 01 understand users and their needs.txt

description: A document used in DevOps - a set of practices that combines software development (Dev) and IT operations (Ops). It aims to shorten the business intelligence development cycle.
DOI: 10.5281/zenodo.6324218
title: First-Principles Core Spectroscopy of LiCoO2 and CoO2

description: Input and results data for the paper "First-Principles Core Spectroscopy of LiCoO2 and CoO2"
DOI: 10.5281/zenodo.6324217
title: First-Principles Core Spectroscopy of LiCoO2 and CoO2

description: Input and results data for the paper "First-Principles Core Spectroscopy of LiCoO2 and CoO2"
DOI: 10.25810/ns7n-vh24.25
title: African Newspaper Coverage of Climate Change or Global Warming, 2004-2022 - January 2022
DOI: 10.18118/g6v95b
title: Site Visit and Drone Based Reconnaissance Study of the Severely Affected Infrastructure by February 7th, 2021, Chamoli Rock-Ice Avalanche Disaster Uttarakhand India
DOI: 10.6069/j8w9-pr66
title: Pre-Vaccine Era COVID-19 Infection Fatality Ratio Estimates by Age, Time, and Location 2020-2021

description: This dataset contains estimates of the COVID-19 infection-fatality ratio (IFR) for global populations during the period April 15, 2020 to January 1, 2021. IFR is defined as the probability of an individual dying from COVID-19 once infected with the SARS-CoV-2 virus. In these files, IFR is expressed as a percent: deaths divided by infections multiplied by 100. Location-specific estimates include 190 countries and territories, as well as subnational locations in 11 countries and territories. Specific time points for each location include April 15, 2020; July 15, 2020; October 15, 2020; and January 1, 2021. The age-specific IFR estimates are time-invariant and pool data from all locations with age-stratified data; only data from prior to vaccine introduction in each location was used for the age-specific estimates. The analytic process that produced these estimates accounted for several known biases, such as under-reporting of COVID-19 deaths and the waning sensitivity of SARS-CoV-2 antibody tests.
DOI: 10.6069/xd80-8h39
title: COVID-19 Pandemic Preparedness - Infection Rate and Infection Fatality Ratio Estimates 2020-2021

description: This dataset contains national-level estimates of unadjusted and adjusted COVID-19 infections per capita and infection fatality ratio (IFR) between January 1, 2020 and September 30, 2021 for 177 countries. Cumulative COVID-19 infections per 1,000 population include both unadjusted estimates and estimates standardized for environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. IFR per 1,000 infections include both unadjusted estimated and estimates standardized for the age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardized prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita.
DOI: 10.5281/zenodo.6323447
title: Supplementary Data for: Human pathogenic RNA viruses establish non-competing lineages by occupying independent niches

description: <strong>Supplementary Data</strong> <strong>Tables</strong> <strong>Table S1:</strong> Virus characteristics including abbreviation, family, order, tax id, sequence length threshold (for nearly complete genomes), vaccination status, mode of transmission, circulation (human or human/zoonotic), available treatment, and disease progression (acute or chronic). <strong>Table S2:</strong> Sequence IDs of outgroup constituents. <strong>Table S3:</strong> GISAID acknowledgements for the SARS-CoV-2 sequences used in this study. <strong>Table S4:</strong> Summary of manual sequence curation indicating lab- or vaccine-related keywords used to prune sequences. <strong>Table S5:</strong> Sequence IDs for EVA and H3N2 e10, e100, d10, and d100 subtrees. <strong>Table S6:</strong> Sequence IDs for ML and GL lineages. <strong>Table S7:</strong> Sequence IDs for the H3N2 “new” and “old” subtrees. <strong>Table S8:</strong> Mutation rates (in units of nucleotide substitutions per site per year) and estimated dates for the last common ancestor of all tree/subtrees. Negative dates represent years prior to 0 CE. <strong>Table S9:</strong> Mean dN/dS values. <strong>Table S10:</strong> Yearly viral cases and generation time estimations (with references). <strong>Table S11:</strong> Metadata including the sequence ID, host, date of isolation, location of isolation, and subtype. <strong>Table S12:</strong> Overview of key parameters including number of GLs N, Ne, and MRCA for each virus species as well as the mean over all species. <strong>Directories</strong> <strong>Alignments</strong> ORFeome alignments (excluding stop codons) with the exception of SARS-CoV-2. <strong>Correlated Subtrees</strong> Subtrees representing all correlated-clades (genealogical lineages; GL) in Newick format. <strong>Diversity TMRCA and Skyline Plots</strong> .png files. <strong>e/d/n/o Subtrees</strong> EVA and H3N2 subtrees, evenly and diversely sampled as well as “early” and “late” subtrees for H3N2. <strong>Genealogical Trees</strong> Including global topologies (.main), subtrees, and grafted trees (.grafted). <strong>Maximally Diverse Subtrees:</strong> Genealogical trees sampled to contain the same number of leaves as each respective tree in the Simulated Trees directory. <strong>ORF References</strong> The first and last nucleotide of each ORF in the reference sequence. <strong>Redundancy Tables</strong> List of all redundant isolates and the corresponding representative in the ORFeome-unique alignment. <strong>Reference Sequences</strong> Genbank page for all reference sequences. <strong>Rooted Trees</strong> Global topologies for each virus. <strong>Simulated Trees:</strong> Neutral models, based on Yule-Harding, for each virus.<br> Ultrametric Trees For each global topology. <strong>Ultrametric Trees </strong>For each global topology.
DOI: 10.5281/zenodo.5711959
title: Supplementary Data for: Human pathogenic RNA viruses establish non-competing lineages by occupying independent niches

description: <strong>Supplementary Data</strong> <strong>Tables</strong> <strong>Table S1:</strong> Virus characteristics including abbreviation, family, order, tax id, sequence length threshold (for nearly complete genomes), vaccination status, mode of transmission, circulation (human or human/zoonotic), available treatment, and disease progression (acute or chronic). <strong>Table S2:</strong> Sequence IDs of outgroup constituents. <strong>Table S3:</strong> GISAID acknowledgements for the SARS-CoV-2 sequences used in this study. <strong>Table S4:</strong> Summary of manual sequence curation indicating lab- or vaccine-related keywords used to prune sequences. <strong>Table S5:</strong> Sequence IDs for EVA and H3N2 e10, e100, d10, and d100 subtrees. <strong>Table S6:</strong> Sequence IDs for ML and GL lineages. <strong>Table S7:</strong> Sequence IDs for the H3N2 “new” and “old” subtrees. <strong>Table S8:</strong> Mutation rates (in units of nucleotide substitutions per site per year) and estimated dates for the last common ancestor of all tree/subtrees. Negative dates represent years prior to 0 CE. <strong>Table S9:</strong> Mean dN/dS values. <strong>Table S10:</strong> Yearly viral cases and generation time estimations (with references). <strong>Table S11:</strong> Metadata including the sequence ID, host, date of isolation, location of isolation, and subtype. <strong>Table S12:</strong> Overview of key parameters including number of GLs N, Ne, and MRCA for each virus species as well as the mean over all species. <strong>Directories</strong> <strong>Alignments</strong> ORFeome alignments (excluding stop codons) with the exception of SARS-CoV-2. <strong>Correlated Subtrees</strong> Subtrees representing all correlated-clades (genealogical lineages; GL) in Newick format. <strong>Diversity TMRCA and Skyline Plots</strong> .png files. <strong>e/d/n/o Subtrees</strong> EVA and H3N2 subtrees, evenly and diversely sampled as well as “early” and “late” subtrees for H3N2. <strong>Genealogical Trees</strong> Including global topologies (.main), subtrees, and grafted trees (.grafted). <strong>Maximally Diverse Subtrees:</strong> Genealogical trees sampled to contain the same number of leaves as each respective tree in the Simulated Trees directory. <strong>ORF References</strong> The first and last nucleotide of each ORF in the reference sequence. <strong>Redundancy Tables</strong> List of all redundant isolates and the corresponding representative in the ORFeome-unique alignment. <strong>Reference Sequences</strong> Genbank page for all reference sequences. <strong>Rooted Trees</strong> Global topologies for each virus. <strong>Simulated Trees:</strong> Neutral models, based on Yule-Harding, for each virus.<br> Ultrametric Trees For each global topology. <strong>Ultrametric Trees </strong>For each global topology.
DOI: 10.5281/zenodo.6324227
title: Canadian Timber Ghost Acres Data

description: Data collected for a paper under review with the Journal of Historical Research.
DOI: 10.5281/zenodo.6324226
title: Canadian Timber Ghost Acres Data

description: Data collected for a paper under review with the Journal of Historical Research.
DOI: 10.25810/es52-xr23.46
title: Canadian Newspaper Coverage of Climate Change or Global Warming, 2000-2022 - January 2022

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 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: 10.25810/8pez-h060.25
title: Asian Newspaper Coverage of Climate Change or Global Warming, 2004-2022 - January 2022

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 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: 10.18434/mds2-2434
title: The NIST Scan Framework for ARTIQ