cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57783/8qeg-qa23
title: Marketscan Health Risk Assessment Database Set B

description: For technical support with Columbia Data Platform please contact: dataplatform@columbia.edu. 

For User Group and Access questions please contact: rit@cumc.columbia.edu. 

For specific technical questions of the Marketscan data, please contact marketscan.support@merative.com, or the Merative Support Community Portal at https://merative.my.site.com/mysupport.
description: The MarketScan® Research Databases aggregate claims & enrollment data from commercial, federal, state and public health plans, linking paid claims to real-world data for healthcare research, economics and treatment outcomes for ~300m patients.
description: The Merative™ MarketScan® Health Risk Assessment (HRA) Database is an integrated database that contains self-reported biometric and health-related behavioral data. The data come from surveys of employees of large U.S. corporations and health plans. This information is linkable to the medical, pharmacy, and enrollment data in the Merative MarketScan Research Databases for these employees, making the HRA Database a unique and valuable resource for examining the relationship between health risks and medical expenditures. For some employees, data also can be linked to the MarketScan Health and Productivity Management Database, which contains information on work absence, disability, and worker’s compensation cases.
DOI: 10.57783/tz3t-k127
title: Marketscan Health Risk Assessment Database Set A

description: For technical support with Columbia Data Platform please contact: dataplatform@columbia.edu. 

For User Group and Access questions please contact: rit@cumc.columbia.edu. 

For specific technical questions of the Marketscan data, please contact marketscan.support@merative.com, or the Merative Support Community Portal at https://merative.my.site.com/mysupport.
description: The MarketScan® Research Databases aggregate claims & enrollment data from commercial, federal, state and public health plans, linking paid claims to real-world data for healthcare research, economics and treatment outcomes for ~300m patients.
description: The Merative™ MarketScan® Health Risk Assessment (HRA) Database is an integrated database that contains self-reported biometric and health-related behavioral data. The data come from surveys of employees of large U.S. corporations and health plans. This information is linkable to the medical, pharmacy, and enrollment data in the Merative MarketScan Research Databases for these employees, making the HRA Database a unique and valuable resource for examining the relationship between health risks and medical expenditures. For some employees, data also can be linked to the MarketScan Health and Productivity Management Database, which contains information on work absence, disability, and worker’s compensation cases.
DOI: 10.5281/zenodo.18775630
title: THE IMPORTANCE OF TOYS IN CHILD UPBRINGING

description: This article analyzes the role of toys in the upbringing of preschool children, their classifications, and functional tasks from a scientific and pedagogical perspective. It outlines the pedagogical, hygienic, and artistic requirements for toys and highlights the significance of various toy categories in a child’s intellectual, physical, aesthetic, and social development. Furthermore, the article substantiates the importance of toy systems in folk pedagogy for the advancement of sensory abilities.
DOI: 10.57783/bnwh-y611
title: Marketscan Medicaid

description: For technical support with Columbia Data Platform please contact: dataplatform@columbia.edu. 

For User Group and Access questions please contact: rit@cumc.columbia.edu. 

For specific technical questions of the Marketscan data, please contact marketscan.support@merative.com, or the Merative Support Community Portal at https://merative.my.site.com/mysupport.
description: The MarketScan® Research Databases aggregate claims & enrollment data from commercial, federal, state and public health plans, linking paid claims to real-world data for healthcare research, economics and treatment outcomes for ~300m patients.
DOI: 10.57783/as00-g015
title: Marketscan Medicare Set A Mortality Edition

description: For technical support with Columbia Data Platform please contact: dataplatform@columbia.edu. 

For User Group and Access questions please contact: rit@cumc.columbia.edu. 

For specific technical questions of the Marketscan data, please contact marketscan.support@merative.com, or the Merative Support Community Portal at https://merative.my.site.com/mysupport.
description: The MarketScan® Research Databases aggregate claims & enrollment data from commercial, federal, state and public health plans, linking paid claims to real-world data for healthcare research, economics and treatment outcomes for ~300m patients.
description: The Merative™ MarketScan® Research Databases capture person-specific clinical utilization, expenditures, and enrollment across inpatient, outpatient, prescription drug, and carve-out services. The data come from a selection of large employers, health plans, and government and public organizations. The MarketScan Research Databases link paid claims and encounter data to detailed patient information across sites and types of providers and over time. The annual medical databases include private-sector health data from approximately 350 payers. Historically, more than 20 billion service records are available in the MarketScan databases. These data represent the medical experience of insured employees and their dependents for active employees, early retirees, Consolidated Omnibus Budget Reconciliation Act (COBRA) continuees, and Medicare-eligible retirees with employer-provided Medicare Supplemental and Medicare Advantage plans.
DOI: 10.57783/r4dq-7d24
title: Marketscan Medicare Set B Mortality Edition

description: For technical support with Columbia Data Platform please contact: dataplatform@columbia.edu. 

For User Group and Access questions please contact: rit@cumc.columbia.edu. 

For specific technical questions of the Marketscan data, please contact marketscan.support@merative.com, or the Merative Support Community Portal at https://merative.my.site.com/mysupport.
description: The MarketScan® Research Databases aggregate claims & enrollment data from commercial, federal, state and public health plans, linking paid claims to real-world data for healthcare research, economics and treatment outcomes for ~300m patients.
description: The Merative™ MarketScan® Research Databases capture person-specific clinical utilization, expenditures, and enrollment across inpatient, outpatient, prescription drug, and carve-out services. The data come from a selection of large employers, health plans, and government and public organizations. The MarketScan Research Databases link paid claims and encounter data to detailed patient information across sites and types of providers and over time. The annual medical databases include private-sector health data from approximately 350 payers. Historically, more than 20 billion service records are available in the MarketScan databases. These data represent the medical experience of insured employees and their dependents for active employees, early retirees, Consolidated Omnibus Budget Reconciliation Act (COBRA) continuees, and Medicare-eligible retirees with employer-provided Medicare Supplemental and Medicare Advantage plans.
DOI: 10.5281/zenodo.18775629
title: THE IMPORTANCE OF TOYS IN CHILD UPBRINGING

description: This article analyzes the role of toys in the upbringing of preschool children, their classifications, and functional tasks from a scientific and pedagogical perspective. It outlines the pedagogical, hygienic, and artistic requirements for toys and highlights the significance of various toy categories in a child’s intellectual, physical, aesthetic, and social development. Furthermore, the article substantiates the importance of toy systems in folk pedagogy for the advancement of sensory abilities.
DOI: 10.57783/d336-2n27
title: Marketscan Medicare Set B

description: For technical support with Columbia Data Platform please contact: dataplatform@columbia.edu. 

For User Group and Access questions please contact: rit@cumc.columbia.edu. 

For specific technical questions of the Marketscan data, please contact marketscan.support@merative.com, or the Merative Support Community Portal at https://merative.my.site.com/mysupport.
description: The MarketScan® Research Databases aggregate claims & enrollment data from commercial, federal, state and public health plans, linking paid claims to real-world data for healthcare research, economics and treatment outcomes for ~300m patients.
description: The Merative™ MarketScan® Research Databases capture person-specific clinical utilization, expenditures, and enrollment across inpatient, outpatient, prescription drug, and carve-out services. The data come from a selection of large employers, health plans, and government and public organizations. The MarketScan Research Databases link paid claims and encounter data to detailed patient information across sites and types of providers and over time. The annual medical databases include private-sector health data from approximately 350 payers. Historically, more than 20 billion service records are available in the MarketScan databases. These data represent the medical experience of insured employees and their dependents for active employees, early retirees, Consolidated Omnibus Budget Reconciliation Act (COBRA) continuees, and Medicare-eligible retirees with employer-provided Medicare Supplemental and Medicare Advantage plans.
DOI: 10.57783/t7tn-mt12
title: Marketscan Commercial National Weights

description: For technical support with Columbia Data Platform please contact: dataplatform@columbia.edu. 

For User Group and Access questions please contact: rit@cumc.columbia.edu. 

For specific technical questions of the Marketscan data, please contact marketscan.support@merative.com, or the Merative Support Community Portal at https://merative.my.site.com/mysupport.
description: The MarketScan® Research Databases aggregate claims & enrollment data from commercial, federal, state and public health plans, linking paid claims to real-world data for healthcare research, economics and treatment outcomes for ~300m patients.
description: The Merative™ MarketScan® Commercial (CCAE) Database and Medicare (MDCR) Database contain data from individuals who have employer-sponsored insurance (ESI) as either primary coverage or Medicare supplemental coverage. To project this population to the national population of individuals with ESI, the MarketScan Commercial Insurance Weights were constructed using the Public Use Microdata Sample (PUMS) of the American Community Survey (ACS) conducted by the U.S. Census Bureau.1 2 These estimates are designed to weight individuals in the MarketScan databases to reflect the national ESI population by demographic stratum; health care use varies appreciably across demographic strata.



The MarketScan Commercial Insurance Weights currently project to the total U.S. ESI population. This group represents a large market share of health care services, drugs, and medical devices.
DOI: 10.15483/3020451
title: 220EV Box Truck Telemetry Dataset

description: This dataset contains telemetry data from the 220EV box truck as provided by the manufacturer, Dana. Data are recorded in a time series interval of 1 second. Recorded data contain odometer reading (kilometers), axle speed, state of charge of the battery, and cabin heater setting.
DOI: 10.15483/3020452
title: ChargePoint Level 2 Charging Data

description: This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
DOI: 10.15483/3020453
title: SWS Building Electric Demand Profile

description: This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
DOI: 10.15468/dl.rd5kqn
title: Occurrence Download

description: A dataset containing 26180410 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Costa Rica",
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations, Museum of Comparative Zoology, Harvard University)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 26180410 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000455-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18775657
title: TEACHINGMEDICALENGLISHVOCABULARYTHROUGHTASK-BASEDLEARNING:ACASESTUDYOFMEDICALSTUDENTS

description: ThegrowingdominanceofEnglishasthelanguageofglobalmedicinehasincreasedthedemandforeffectiveMedicalEnglishinstructioninnon-English-speakingcountries.Medicalstudentsareexpectednotonlytounderstandspecializedterminologybutalsotoapplyitaccuratelyinprofessionalcommunication.However,traditionalvocabularyteachingmethodsoftenfailtoprovidesufficientcontextualizationandcommunicativepractice.ThisstudyinvestigatestheeffectivenessofTask-BasedLearning(TBL)inteachingMedicalEnglishvocabularytouniversitymedicalstudentsinanEnglishasaForeignLanguage(EFL)context.Usingamixed-methodsresearchdesign,datawerecollectedthroughpre-andpost-tests,task-basedclassroominterventions,questionnaires,andclassroomobservations.Theresultsdemonstrateasignificantimprovementinstudents’medicalvocabularyknowledgeandtheirabilitytouseterminologyinrealisticcommunicativesituations.Furthermore,studentsexpressedpositiveattitudestowardtask-basedinstruction,highlightingincreasedmotivationandconfidence.ThefindingssuggestthatTBLisapedagogicallyeffectiveapproachforMedicalEnglishvocabularyinstructionandshouldbeintegratedmorewidelyintoEnglishforSpecificPurposes(ESP)curricula
DOI: 10.5281/zenodo.18775656
title: TEACHINGMEDICALENGLISHVOCABULARYTHROUGHTASK-BASEDLEARNING:ACASESTUDYOFMEDICALSTUDENTS

description: ThegrowingdominanceofEnglishasthelanguageofglobalmedicinehasincreasedthedemandforeffectiveMedicalEnglishinstructioninnon-English-speakingcountries.Medicalstudentsareexpectednotonlytounderstandspecializedterminologybutalsotoapplyitaccuratelyinprofessionalcommunication.However,traditionalvocabularyteachingmethodsoftenfailtoprovidesufficientcontextualizationandcommunicativepractice.ThisstudyinvestigatestheeffectivenessofTask-BasedLearning(TBL)inteachingMedicalEnglishvocabularytouniversitymedicalstudentsinanEnglishasaForeignLanguage(EFL)context.Usingamixed-methodsresearchdesign,datawerecollectedthroughpre-andpost-tests,task-basedclassroominterventions,questionnaires,andclassroomobservations.Theresultsdemonstrateasignificantimprovementinstudents’medicalvocabularyknowledgeandtheirabilitytouseterminologyinrealisticcommunicativesituations.Furthermore,studentsexpressedpositiveattitudestowardtask-basedinstruction,highlightingincreasedmotivationandconfidence.ThefindingssuggestthatTBLisapedagogicallyeffectiveapproachforMedicalEnglishvocabularyinstructionandshouldbeintegratedmorewidelyintoEnglishforSpecificPurposes(ESP)curricula
DOI: 10.5281/zenodo.18021612
title: Struct2Query: Curated SiteHopper Database and Benchmark Datasets for Structure-Based Composite Query Generation

description: Dataset accompanying the Struct2Query method for structure-based composite query generation in virtual screening.


Overview


Struct2Query bridges structure-based and ligand-based virtual screening by leveraging protein binding site similarity to construct composite-molecule ROCS queries compatabile with rapid ligand-based screening. Given a target protein structure, it identifies proteins with similar binding pockets, retrieves their co-crystallized ligands, and assembles multi-molecule queries that capture binding modes across structurally related sites.


This dataset provides the associated curated databases and benchmark data.


Contents


1. SiteHopper Database (`sitehopper_databases/Struct2Query.shdb`)



Curated database of 78,806 protein-ligand complexes

Formatted for OpenEye SiteHopper pocket similarity searches


2. DEKOIS 2.0 Benchmark (`dekois/`)



81 protein targets with prepared structures

Each target includes: protein structure (PDB), bound ligand (MOL2), active compounds (SDF), and decoys (SDF)


3. DUDE-Z Benchmark (`dudez/`)



43 protein targets

Each target includes: active ligands (SMILES) and property-matched decoys (SMILES)


Usage


See the Struct2Query GitHub repository for installation instructions and usage examples.
DOI: 10.5281/zenodo.18021610
title: Struct2Query: Curated SiteHopper Database and Benchmark Datasets for Structure-Based Composite Query Generation

description: Dataset accompanying the Struct2Query method for structure-based composite query generation in virtual screening.


Overview


Struct2Query bridges structure-based and ligand-based virtual screening by leveraging protein binding site similarity to construct composite-molecule ROCS queries compatabile with rapid ligand-based screening. Given a target protein structure, it identifies proteins with similar binding pockets, retrieves their co-crystallized ligands, and assembles multi-molecule queries that capture binding modes across structurally related sites.


This dataset provides the associated curated databases and benchmark data.


Contents


1. SiteHopper Database (`sitehopper_databases/Struct2Query.shdb`)



Curated database of 78,806 protein-ligand complexes

Formatted for OpenEye SiteHopper pocket similarity searches


2. DEKOIS 2.0 Benchmark (`dekois/`)



81 protein targets with prepared structures

Each target includes: protein structure (PDB), bound ligand (MOL2), active compounds (SDF), and decoys (SDF)


3. DUDE-Z Benchmark (`dudez/`)



43 protein targets

Each target includes: active ligands (SMILES) and property-matched decoys (SMILES)


Usage


See the Struct2Query GitHub repository for installation instructions and usage examples.
DOI: 10.25430/researchdata.cab.unipd.it.00001767
title: ASIACO: Asiago Spectroscopy and Imaging Atlas of COmets. Ten years of cometary science at the Asiago Astrophysical Observatory

description: This collection contains the complete set of reduced one-dimensional optical spectra of 47 comets observed at the Asiago Astrophysical Observatory (University of Padova) with the 1.22 m Galileo Telescope over the period 2012–2026. The dataset includes all spectra presented in Farina et al. (2026) and constitutes the ASIACO: Asiago Spectroscopy and Imaging Atlas of COmets. The spectra are distributed as TXT files, each consisting of two columns (wavelength and calibrated flux) for individual comet observations.
DOI: 10.15468/dl.wp8mfv
title: Occurrence Download

description: A dataset containing 2998 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ambystoma velasci"
 ]
}

 The dataset includes 2998 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000521-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18509155
title: Data for "Challenges and Opportunities in Single-Sample Network Modeling"

description: This repository stores the data that were used as the basis for the analyses in "Challenges and Opportunities for Single Sample Network Modeling".

Corresponding code can be found at https://github.com/kimberlyglass/single-sample-networks.

To easily run the github code, the three files in this repository should be added into the "Data" directory and "GTExData.zip" should be un-zipped.

 

The files included in this repository include:

(1) InSilicoData.mat -- a "mat" file containing generated data for 6 genes and 600 samples based on three known network structures (250 in sample set 1, 250 in sample set 2, and 100 in sample set 3). Used for the "toy data" analysis and created using MakeInSilicoData.m. This file stores the following variables:

-- 'ExpData': a 6 by 600 matrix containing simulated gene expression values.

-- 'SSNets': a 6 element cell array. Each element contains a 15 by 600 matrix with the inferred edge weights for each sample based on applying six different single-sample network modeling approaches: LIONESS::PCC, SSN, SWEET, BONOBO, LIONESS::MI, CSN.

-- 'numsampOrg': the number of samples in sample set 1 (one clique) and in sample set 2 (two cliques); value is 250.

-- 'numsampRnd': the number of "noisy" samples in sample set 3; value is 100.

-- 'NodeIdx': a 6-choose-2 = 15 by 2 matrix with the edge information for the inferred single-sample networks.

 

(2) GTEx_MixData3.mat -- a "mat" file containing gene expression data for 250 samples in each of 15 different tissues. The 13281 genes in these data are protein-coding genes with a median expression of at least 2 in all 15 tissues. Used for the GTEx / human data analysis and created using MakeGTEx_MixData3.m. This file stores the following variables:

-- 'ExpData': a 13281 by 3750 matrix containg gene expression values from GTEx.

-- 'GeneSymbols': the names of genes in the rows of 'ExpData'

-- 'Tissue': the tissues for the samples in the columns of 'ExpData'

 

(3) GTExData.zip -- a zip of the "GTExData" subdirectory, which contains GTEx data files that were parsed to create the GTEx_MaxData3.mat file. These files include:

-- GTEx_NormExpressionData.txt: a file containing gene expression data from GTEx, processed as described in PMID#29069589.

-- GTEx_AnnInfo_AllGenes.txt: a file containing information about the genes in "GTEx_NormExpressionData.txt"

-- GTEx_SampleInfo.txt: a file containing information about the samples in "GTEx_NormExpressionData.txt"