cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18265458
title: M4Health: A Multi-Modal, Multi-Domain, Multi-Platform, and Multi-Task Benchmark for Video-Driven Health Communication on Social Media

description: Short video platforms have transformed how the public consumes health information on web and social media where uncurated content poses significant risks to vulnerable populations. While prior work has primarily focused on text-only or single-platform analyses, comprehensive benchmarks for multi-modal health communication in short videos remain limited. In this paper, we introduce M4Health, a multi-modal, multi-domain, multi-platform, and multi-task benchmark for health communication in short videos. M4Health comprises over 436,398 videos from TikTok, YouTube Shorts, and Reddit, spanning diverse health domains such as nutrition, fitness, mental health, and wellness. We provide expert annotations for 3,000 videos across three interrelated tasks, including credibility assessment, AI-generation detection, and theme classification. Extensive benchmarking experiments show that current state-of-the-art models, including task-specific approaches and large vision-language models (LVLMs), achieve suboptimal performance. We share the M4Health dataset with research communities to foster collaborative research toward supporting informed health decision-making on web and social media.
DOI: 10.5281/zenodo.18272989
title: Diaphoranthema Beer. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18272988
title: Diaphoranthema Beer. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18272990
title: Diaphoranthema capillaris (Ruiz & Pav.) Beer. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18272991
title: Diaphoranthema capillaris (Ruiz & Pav.) Beer. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.15442256
title: Vector-QM24 (VQM24) dataset

description: Quantum chemistry dataset of ~836 thousand small organic and inorganic molecules.

Density Functional Theory (DFT) properties for all 784,875 conformers in local minima; 258,242 constitutional isomers (most stable conformer) and 51,072 saddle point structures are available in the DFT_all.npz, DFT_uniques.npz and DFT_saddles.npz files respectively.Diffusion quantum Monte Carlo (DMC) data for 10,793 constitutional isomers is available in the DMC.npz file.

All molecules are ordered in the same way across every array.

Keys for accessing each property are tabulated in the paper.

Usage example :

import numpy as np

data = np.load('DFT_all.npz', allow_pickle=True)
print(data.files) #see a list of all properties

key = 'freqs'

property = data[key] #DFT vibrational frequencies of all moleculesprint(property[42]) #Frequencies of molecule number 42 in the array (HSCl, Thiohypochlorous acid)

 

Input file samples, tools and kernel ridge regression, graph neural network models : https://github.com/dkhan42/VQM24Atomic energies (in Hartree) used to calculate the atomization energies :



#atomic energies wB97X-D3/cc-pVDZ (PSI4 v1.7)

eatomic = {'Hydrogen' : -0.5012728848846926,

'Carbon' : -37.83859584856468,

'Nitrogen' : -54.5760607136932450,

'Oxygen' : -75.0474818911551438,

'Fluorine' : -99.7031524437270917,

'Bromine' : -2574.01253635198464,

'Chlorine' : -460.13960793480203,

'Phosphorous' : -341.2510291850040858,

'Sulfur' : -398.1021030909759020,

'Silicon' : -289.3578409507016431}

 

 

Wavefunctions of all 836 thousand molecules from the dataset are available as .molden files in wavefunctions.tar.gz.molden file for a specific molecule from the dataset can be found using the 'compounds' array in 'DFT_all.npz' file.For instance : the 0-th entry in the 'compounds' array of DFT_all.npz corresponds to 'SH2_0/conformer_1'Wavefunction file for this molecule will be found at 'wavefunctions/SH2_0/conformer_1.molden' after untarring wavefunctions.tar.gzMultiwfn (http://sobereva.com/multiwfn/) can be used to read the .molden wavefunction files

 



Dataset is described in the paper : https://www.nature.com/articles/s41597-025-05428-4
DOI: 10.5281/zenodo.15442257
title: Vector-QM24 (VQM24) dataset

description: Quantum chemistry dataset of ~836 thousand small organic and inorganic molecules.

Density Functional Theory (DFT) properties for all 784,875 conformers in local minima; 258,242 constitutional isomers (most stable conformer) and 51,072 saddle point structures are available in the DFT_all.npz, DFT_uniques.npz and DFT_saddles.npz files respectively.Diffusion quantum Monte Carlo (DMC) data for 10,793 constitutional isomers is available in the DMC.npz file.

All molecules are ordered in the same way across every array.

Keys for accessing each property are tabulated in the paper.

Usage example :

import numpy as np

data = np.load('DFT_all.npz', allow_pickle=True)
print(data.files) #see a list of all properties

key = 'freqs'

property = data[key] #DFT vibrational frequencies of all moleculesprint(property[42]) #Frequencies of molecule number 42 in the array (HSCl, Thiohypochlorous acid)

 

Input file samples, tools and kernel ridge regression, graph neural network models : https://github.com/dkhan42/VQM24Atomic energies (in Hartree) used to calculate the atomization energies :



#atomic energies wB97X-D3/cc-pVDZ (PSI4 v1.7)

eatomic = {'Hydrogen' : -0.5012728848846926,

'Carbon' : -37.83859584856468,

'Nitrogen' : -54.5760607136932450,

'Oxygen' : -75.0474818911551438,

'Fluorine' : -99.7031524437270917,

'Bromine' : -2574.01253635198464,

'Chlorine' : -460.13960793480203,

'Phosphorous' : -341.2510291850040858,

'Sulfur' : -398.1021030909759020,

'Silicon' : -289.3578409507016431}

 

 

Wavefunctions of all 836 thousand molecules from the dataset are available as .molden files in wavefunctions.tar.gz.molden file for a specific molecule from the dataset can be found using the 'compounds' array in 'DFT_all.npz' file.For instance : the 0-th entry in the 'compounds' array of DFT_all.npz corresponds to 'SH2_0/conformer_1'Wavefunction file for this molecule will be found at 'wavefunctions/SH2_0/conformer_1.molden' after untarring wavefunctions.tar.gzMultiwfn (http://sobereva.com/multiwfn/) can be used to read the .molden wavefunction files

 



Dataset is described in the paper : https://www.nature.com/articles/s41597-025-05428-4
DOI: 10.57761/m1av-t757
title: partner-mpib-de-processed
DOI: 10.57761/yyff-et91
title: partner-mpib-de-processed
DOI: 10.5281/zenodo.18273031
title: Diaphoranthema recurvata (L.) Beer. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273030
title: Diaphoranthema recurvata (L.) Beer. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273039
title: Diaphoranthema virescens (Ruiz & Pav.) Beer. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273038
title: Diaphoranthema virescens (Ruiz & Pav.) Beer. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273048
title: Dichanthelium (Hitchc. & Chase) Gould. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273049
title: Dichanthelium (Hitchc. & Chase) Gould. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273053
title: Dichanthelium sabulorum (Lam.) Gould & C.A.Clark. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273052
title: Dichanthelium sabulorum (Lam.) Gould & C.A.Clark. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.15468/dl.p5x3uf
title: Occurrence Download

description: A dataset containing 4138 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pacifastacus leniusculus",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4138 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015602-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18273034
title: ВЛИЯНИЕ СОЦИАЛЬНЫХ СЕТЕЙ НА ВЫБОР ПРОДУКТОВ ПИТАНИЯ ПОДРОСТКАМИ: СИСТЕМАТИЧЕСКИЙ ОБЗОР С ИСПОЛЬЗОВАНИЕМ СМЕШАННЫХ МЕТОДОВ

description: Подростки представляют собой одну из наиболее уязвимых групп населения, подверженных влиянию цифрового маркетинга и контента социальных сетей. Этот систематический обзор с использованием смешанных методов анализирует, в какой степени воздействие социальных сетей влияет на выбор продуктов питания, пищевое поведение и психологические модели питания подростков. Отбор исследований, опубликованных в период с 2018 по 2024 год, проводился на основе  в базах данных Скопус. В общей сложности 42 исследования соответствовали критериям включения. Количественные результаты показывают сильную связь между воздействием высококалорийных продуктов, рекламируемых влиятельными лицами, и повышенной вероятностью их потребления (ОР = 1,78; 95% ДИ: 1,52–2,01). Качественные исследования выявляют эмоциональные триггеры (стресс, скука, сравнение с другими), которые усиливают нездоровые пищевые привычки. Напротив, контент, ориентированный на здоровье, показал умеренное положительное влияние, но имел значительно меньший охват. Результаты подчеркивают необходимость программ медиаграмотности, политических ограничений на маркетинг нездоровой пищи и школьных цифровых программ по вопросам питания.
DOI: 10.5281/zenodo.18273035
title: ВЛИЯНИЕ СОЦИАЛЬНЫХ СЕТЕЙ НА ВЫБОР ПРОДУКТОВ ПИТАНИЯ ПОДРОСТКАМИ: СИСТЕМАТИЧЕСКИЙ ОБЗОР С ИСПОЛЬЗОВАНИЕМ СМЕШАННЫХ МЕТОДОВ

description: Подростки представляют собой одну из наиболее уязвимых групп населения, подверженных влиянию цифрового маркетинга и контента социальных сетей. Этот систематический обзор с использованием смешанных методов анализирует, в какой степени воздействие социальных сетей влияет на выбор продуктов питания, пищевое поведение и психологические модели питания подростков. Отбор исследований, опубликованных в период с 2018 по 2024 год, проводился на основе  в базах данных Скопус. В общей сложности 42 исследования соответствовали критериям включения. Количественные результаты показывают сильную связь между воздействием высококалорийных продуктов, рекламируемых влиятельными лицами, и повышенной вероятностью их потребления (ОР = 1,78; 95% ДИ: 1,52–2,01). Качественные исследования выявляют эмоциональные триггеры (стресс, скука, сравнение с другими), которые усиливают нездоровые пищевые привычки. Напротив, контент, ориентированный на здоровье, показал умеренное положительное влияние, но имел значительно меньший охват. Результаты подчеркивают необходимость программ медиаграмотности, политических ограничений на маркетинг нездоровой пищи и школьных цифровых программ по вопросам питания.
DOI: 10.5281/zenodo.18273060
title: Dichanthelium sabulorum var. polycladum (Ekman) Zuloaga. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273061
title: Dichanthelium sabulorum var. polycladum (Ekman) Zuloaga. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18272183
title: SuperDARN data in netCDF format (1995-Sep)

description: 1995-Sep SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.18273069
title: EFFECTIVENESS OF AN 8-WEEK STRENGTH-ORIENTED TRAINING PROGRAM ON THE STRENGTH QUALITIES OF 14–16-YEAR-OLD RUGBY PLAYERS

description: The purpose of this study was to evaluate the effectiveness of strength-oriented training programs aimed at improving strength qualities in 14–16-year-old rugby players. The research was conducted at an Olympic and Paralympic Sports Training Center and involved 20 healthy male rugby players. A pre-test–post-test experimental design was applied over an 8-week training intervention. Training sessions were held twice daily from Monday to Friday, each lasting 90 minutes, while one 120-minute session was conducted on Saturdays. The strength-oriented exercises were primarily integrated into the morning training sessions, focusing on both general and rugby-specific strength development. Strength indicators were assessed before and after the intervention using standardized physical fitness tests. The results demonstrated statistically significant improvements in key strength parameters following the training program, confirming the effectiveness of the proposed methodology. The findings suggest that a systematically structured strength training program can significantly enhance strength qualities in young rugby players and may be effectively implemented in specialized sports training centers.
DOI: 10.5281/zenodo.6811412
title: SuperDARN data in netCDF format (1995-Sep)

description: 1995-Sep SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.18273070
title: EFFECTIVENESS OF AN 8-WEEK STRENGTH-ORIENTED TRAINING PROGRAM ON THE STRENGTH QUALITIES OF 14–16-YEAR-OLD RUGBY PLAYERS

description: The purpose of this study was to evaluate the effectiveness of strength-oriented training programs aimed at improving strength qualities in 14–16-year-old rugby players. The research was conducted at an Olympic and Paralympic Sports Training Center and involved 20 healthy male rugby players. A pre-test–post-test experimental design was applied over an 8-week training intervention. Training sessions were held twice daily from Monday to Friday, each lasting 90 minutes, while one 120-minute session was conducted on Saturdays. The strength-oriented exercises were primarily integrated into the morning training sessions, focusing on both general and rugby-specific strength development. Strength indicators were assessed before and after the intervention using standardized physical fitness tests. The results demonstrated statistically significant improvements in key strength parameters following the training program, confirming the effectiveness of the proposed methodology. The findings suggest that a systematically structured strength training program can significantly enhance strength qualities in young rugby players and may be effectively implemented in specialized sports training centers.
DOI: 10.5281/zenodo.18272215
title: Trajectories obtained for different N (2D and 3D)

description: The filenames are self-explanatory. The file format is .xyz, for visualization using Ovito. Timestep, $\Delta t$ for each simulation is $10^{-4}$ and dimensionless $f=1$. The simulations show only the steady state trajectories.
DOI: 10.5281/zenodo.18273080
title: Dichanthelium sabulorum var. sabulorum . In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273081
title: Dichanthelium sabulorum var. sabulorum . In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18272214
title: Trajectories obtained for different N (2D and 3D)

description: The filenames are self-explanatory. The file format is .xyz, for visualization using Ovito. Timestep, $\Delta t$ for each simulation is $10^{-4}$ and dimensionless $f=1$. The simulations show only the steady state trajectories.
DOI: 10.5281/zenodo.18273085
title: Dichondra J.R.Forst. & G.Forst. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273084
title: Dichondra J.R.Forst. & G.Forst. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273094
title: Dichondra argentea Willd. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.5281/zenodo.18273093
title: Dichondra argentea Willd. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:45
DOI: 10.35092/yhjc31080034
title: BUAN Bundle Profiles Data

description: The example data used in this tutorial consist of the left arcuate fasciculus (AF) from a single subject randomly selected from the PPMI dataset. Specifically, the dataset includes:<code>AF_L_recognized_orig</code>: the subject’s left arcuate fasciculus bundle in native diffusion space,<code>AF_L_recognized</code>: the same subject’s left arcuate fasciculus bundle transformed into MNI space,<code>AF_L_model</code>: a model (template) left arcuate fasciculus bundle in MNI space, and<code>fa.nii</code>: the subject’s fractional anisotropy (FA) map in native space.Together, these files provide all components needed to demonstrate BUAN bundle profiles approach: a subject-specific bundle, its aligned representation in a common space, a model bundle used to define the centroid and weighting, and an along-tract scalar metric. This setup mirrors a typical tractometry workflow and allows users to compute weighted mean along-tract FA profiles for the arcuate fasciculus in a fully reproducible manner.
DOI: 10.35092/yhjc31080034.v1
title: BUAN Bundle Profiles Data

description: The example data used in this tutorial consist of the left arcuate fasciculus (AF) from a single subject randomly selected from the PPMI dataset. Specifically, the dataset includes:<code>AF_L_recognized_orig</code>: the subject’s left arcuate fasciculus bundle in native diffusion space,<code>AF_L_recognized</code>: the same subject’s left arcuate fasciculus bundle transformed into MNI space,<code>AF_L_model</code>: a model (template) left arcuate fasciculus bundle in MNI space, and<code>fa.nii</code>: the subject’s fractional anisotropy (FA) map in native space.Together, these files provide all components needed to demonstrate BUAN bundle profiles approach: a subject-specific bundle, its aligned representation in a common space, a model bundle used to define the centroid and weighting, and an along-tract scalar metric. This setup mirrors a typical tractometry workflow and allows users to compute weighted mean along-tract FA profiles for the arcuate fasciculus in a fully reproducible manner.
DOI: 10.5281/zenodo.15101777
title: Framework for Measuring Cancer Treatment Cost and Quality

description: The Framework contains clinical evidence scores and anticipated treatment costs of all recommended medical treatment options for all major cancer types. The sets of recommended treatments and clinical evidence scores are based on recommendations from the National Comprehensive Cancer Network (NCCN). Treatment costs use Medicare reimbursement rates. Data are updated every 6 months (Jan 1 and July 1) of each year, to reflect changes in NCCN recommendations, clinical evidence, and drug prices. Each treatment has a unique identifier specific to the drug combination and cancer indication, to allow individual treatments to be identified and tracked over time. Data currently extend from January 2016 through January 2023; additional more recent time points will be added in future uploads.