cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/pvtys8kmv5.3
title: Data to the publication "Deciphering the sedimentary imprint of tsunamis and storms in the Lesser Antilles"

description: This is the raw data to the paper "Deciphering the sedimentary imprint of tsunami and storms in the Lesser Antilles (Saint Martin): A 3500-year record in a coastal lagoon" by Fabbri et al.
It contains:
1) XRF data to the composite Mastercores
2) Sand grain information derived from image segmentation of micro-CT data
3) Loss on ignition data to 1 core (GUI21-MC2)
4) Granulometry data to 1 core (GUI-MC2)
5) Age model based on radionuclides
6) Age model based on Bacon R package with C14 and radionuclides
7) Age-Model output in pdf format and txt
8) Uncalibrated C14-ages in csv format
DOI: 10.17632/zd2xw9tf38.1
title: Electric Vehicle Charging Stations: Model, Algorithm, Simulation, Location, and Capacity Planning

description: Data set for the positioning of electric vehicle charging stations
DOI: 10.17632/k47yjbzhcz.1
title: Lipidomics Profiling of P. falciparum pediatric malaria

description: This study investigates the lipidome profiles manifested in human serum samples originating from west African children in Burkina Faso, collected before and during P. falciparum malarial infection, with the aim of characterizing lipid metabolism perturbations associated with disease severity. Global lipidomic profiling was performed using complex lipid platform at Metabolon Inc. (Durham, NC, USA).
DOI: 10.17632/pvskpn6tbs.1
title: Dataset for Predicting the Future Price of Carbon Credits: A Systematic Review

description: This dataset forms the basis of a systematic analysis that examines different approaches and frameworks employed in forecasting future carbon credit prices.
DOI: 10.17632/pvskpn6tbs.2
title: Dataset for Predicting the Future Price of Carbon Credits: A Systematic Review

description: This dataset forms the basis of a systematic analysis that examines different approaches and frameworks employed in forecasting future carbon credit prices.
DOI: 10.17632/cwkrt43zx2.1
title: The State of IPPEs in 2023 National Survey Dataset

description: Full survey results for a study conducted in 2023 entitled, The State of Introductory Pharmacy Practice Experiences at ACPE Accredited Schools and Colleges of Pharmacy.
DOI: 10.17632/kwh7szcznk.1
title: FABP4 deficiency ameliorates alcoholic steatohepatitis

description: This is the transcriptional profiling of the liver tissue from WT and Fabp4 deficiency ASH mice. Based on the transcriptional profiling, we could get the differentially expressed genes, gene set enrichment analysis (GSEA), gene ontology (GO) terms (BP, biological process; CC, cellular component; and MF, molecular function) and kyoto encyclopedia of genes and genomes (KEGG) pathways enrichment analysis.
DOI: 10.17632/brr9x6cmyf.1
title: BoneBioreactor-updated

description: This data set contains all the CAD and STL files required to reproduce the bioreactor hardware.
DOI: 10.17632/brr9x6cmyf.2
title: BoneBioreactor-updated

description: This data set contains all the CAD and STL files required to reproduce the bioreactor hardware.
DOI: 10.17632/zjdncn6tkv.1
title: Indonesian Stand-Up Comedy Data Kompas TV

description: The "Indonesian Stand-Up Comedy Data" that was taken from Kompas TV's YouTube channel offers a thorough archive of user interactions and remarks related to stand-up comedy videos that have been broadcast on the site. Researchers and fans who want to learn more about the state of Indonesian stand-up comedy and trends in audience interaction will find this dataset to be a useful tool.
DOI: 10.17632/hkwztjwbdy.1
title: Data for Multi-Institutional FMEA and FTA on Lu-177-based RPT

description: Supplementary material for the manuscript: Enhancing Safety: Multi-Institutional FMEA and FTA on Lu-177 based RPT
DOI: 10.17632/b5zpc832mp.1
title: Genotypes of Blastocystis hominis isolated from Iraqi patients with irritable bowel syndrome

description: identify the main clinical symptoms in individuals with irritable bowel syndrome and B. hominis, as well as to identify the genotypes of B. hominis.
DOI: 10.17632/hvjzvpfgbp.1
title: Distance Data for the Different Levels of European NUTS Regions

description: The presented dataset contains the centroid distance matrix for the different levels of the European Union's (EU) Nomenclature of Territorial Units for Statistics (NUTS) regions in meters, as well as their code, name, level, and country identifier. Centroids are calculated based on the largest contiguous shape of regions. To support EU-related spatial, regional, and geographical studies, an R function is also attached that compiles the aforementioned dataset for the selected (or all) NUTS levels while complementing it with the geometrical data and centroids of regions. Optionally, this R function displays centroids on a map of Europe to ease the verification of their positions.

Please cite as:
•	(Data in Brief article)
DOI: 10.17632/hvjzvpfgbp.2
title: Distance Calculator for the Different Levels of European NUTS Regions

description: The presented R function called eudistance calculates the centroid distance matrix for the different levels of the European Union's (EU) Nomenclature of Territorial Units for Statistics (NUTS) regions in meters and complements it by their code, name, level, and country identifier, as well as their geometric data and centroids. Optionally, this R function displays centroids on a map of Europe to ease the verification of their positions.
DOI: 10.17632/hvjzvpfgbp.3
title: Distance Calculator for the Different Levels of European NUTS Regions

description: The presented R function called eudistance calculates the centroid distance matrix for the different levels of the European Union's (EU) Nomenclature of Territorial Units for Statistics (NUTS) regions in meters and complements it by their code, name, level, and country identifier, as well as their geometric data and centroids. Optionally, this R function displays centroids on a map of Europe to ease the verification of their positions.

Please cite as:
• Kurbucz, M. T., & Katona, A. I. (2022). eudistance: Distance calculator for the different levels of European NUTS regions. Software Impacts, 13, 100327.
DOI: 10.17632/hzdnxph8vg.6
title: A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms

description: The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2163 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 27, 2020. Note that this version uses 20-40-60-80-day time windows and the first test data are based on the first country reports on tests. Please cite as: • Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881. Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process. Datasets: • Country data (country_data.txt): Country data. • Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset. Raw data of figures and tables: • Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2. • Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3. • Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1. • Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2. • Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.
DOI: 10.17632/hzdnxph8vg.2
title: A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms

description: The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on May 25, 2020. Please cite as: • (Data in Brief article) Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process. Datasets: • Country data (country_data.txt): country data. • Metadata (metadata.txt): the metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): the joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): the Kendall rank correlation matrix of the joint dataset. Raw data of figures and tables: • Raw data of Fig. 2 (raw_data_fig2.txt): the raw data of Fig. 2. • Raw data of Fig. 3 (raw_data_fig3.txt): the raw data of Fig. 3. • Raw data of Table 1 (raw_data_table1.txt): the raw data of Table 1. • Raw data of Table 2 (raw_data_table2.txt): the raw data of Table 2. • Raw data of Table 3 (raw_data_table3.txt): the raw data of Table 3.
DOI: 10.17632/hzdnxph8vg.3
title: A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms

description: The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on May 25, 2020. Please cite as: • Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881. Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process. Datasets: • Country data (country_data.txt): Country data. • Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset. Raw data of figures and tables: • Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2. • Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3. • Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1. • Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2. • Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.
DOI: 10.17632/hzdnxph8vg.4
title: A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms

description: The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 7, 2020. Please cite as: • Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881. Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process. Datasets: • Country data (country_data.txt): Country data. • Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset. Raw data of figures and tables: • Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2. • Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3. • Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1. • Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2. • Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.
DOI: 10.17632/hzdnxph8vg.5
title: A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms

description: The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2163 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 13, 2020. Please cite as: • Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881. Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process. Datasets: • Country data (country_data.txt): Country data. • Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset. Raw data of figures and tables: • Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2. • Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3. • Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1. • Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2. • Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.
DOI: 10.17632/hzdnxph8vg.1
title: A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms

description: The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on May 25, 2020. Please cite as: • (Data in Brief article) Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process. Datasets: • Country data (country_data.txt): country data. • Metadata (metadata.txt): the metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): the joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): the Kendall rank correlation matrix of the joint dataset.
DOI: 10.17632/hzdnxph8vg.7
title: A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms

description: The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2163 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 27, 2020. Note that this version uses 20-40-60-80-day time windows and the first test data are based on the first country reports on tests.

Please cite as:
• Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881.
• Kurbucz, M. T., Katona, A. I., Lantos, Z., & Kosztyán, Z. T. (2021). The role of societal aspects in the formation of official COVID-19 reports: A data-driven analysis. International journal of environmental research and public health, 18(4), 1505.
• Kurbucz, M. T. (2022). Modeling the social determinants of official COVID-19 reports in the early stages of the pandemic. Journal of Applied Social Science, 16(1), 356-363.

Data generation:
• Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process.

Datasets:
• Country data (country_data.txt): Country data.
• Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators.
• Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators.
• Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset.

Raw data of figures and tables:
• Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2.
• Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3.
• Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1.
• Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2.
• Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.
DOI: 10.17632/jwkk44trj3.1
title: Panel Data Generated from Governance, Trade, and Competitiveness Indicators of World Bank Group Platforms

description: This dataset was compiled using the hdData360r R package.

More information can be found at:
•	https://github.com/mtkurbucz/hdData360r
DOI: 10.17632/vjdmnmhh4m.1
title: Acromegaly and the Risk of Cancer: A Nationwide Population-based Longitudinal Cohort Study in Korea

description: Supplement
Acromegaly and the Risk of Cancer: A Nationwide Population-based Longitudinal Cohort Study in Korea
DOI: 10.17632/kytb3472wd.1
title: Research data -processed data files-JWM

description: Reseach data for one manuscript.
DOI: 10.17632/hjr286w2km.1
title: Influence of deposition settings on sedimentary organic carbon fate in the East Siberian Arctic Shelf

description: To further understand the fate of organic carbon (OC) in the Arctic marine system, we examined the influence of deposition settings on OC degradation on the East Siberian Arctic shelf (ESAS). The sedimentary factors such as water depth, sedimentation rate (SR), and the physical chemistry of sedimentary debris and water mass that may affect OC degradation were considered in this study. This dataset includes 3 Excel spreadsheets. Sheet 1 shows the collected 210Pb-based sedimentation rates (SR) on the ESAS. Sheet 2 displays the information of 75 sediment samples investigated in this study, including the chemical environment of bottom water, and the analysis results of grain size, specific surface area (SSA), total organic carbon (TOC), carbon isotope, pyrolysis of organic matter, source apportionment, and degradation state. Sheet 3 shows the collected TOC, SSA, CPI of HMW n-alkanes, and HMW n-alkanoic acids/n-alkanes of OM in the surface sediment of the ESAS.
DOI: 10.17632/bcwm4wjzj9.1
title: Molecular Dynamics Simulations of 2D-layered Graphene Sheets with Tandem Repeat Proteins

description: Example simulation and output files of a 2D-layered composite with Graphene Oxide and Tandem Repeat Proteins. These files can be used as a template to run different combinations of 2D materials and proteins, as long as they are defined in OPLS-AA forcefield.
DOI: 10.17632/myzc6gsmh9.1
title: Effect of amubarvimab-romlusevimab for treatment of severe COVID-19 in intensive care units: a retrospective cohort study

description: Effect of amubarvimab-romlusevimab for treatment of severe COVID-19 in intensive care units: a retrospective cohort study
DOI: 10.17632/k4vf6khj9j.1
title: Persistent Somatization, Allostatic Overload, and Demoralization in Patients with Type 2 Diabetes: A Network Analysis

description: This is supplementary materials for the manuscript, including supplementary figures, tables, and scripts used to network analysis.
DOI: 10.17632/rk3y3hxnn8.1
title: The Effect of Visual Working Memory Consolidation on Long-Term Memory for Chinese Characters

description: this study aims to explore the effect of visual working memory consolidation on long-term memory for Chinese characters. Furthermore, by distinguishing the intrinsic and extrinsic contexts of Chinese characters, it examines whether visual working memory consolidation can more effectively bind contextual information to the target characters, thereby improving long-term memory performance for characters. Research on this issue will help one to understand the nature of Chinese character processing in beginner Chinese character learners and provide practical support for teaching Chinese characters to children. Our hypotheses are as follows: H1: Compared to target characters without visual working memory consolidation, characters with visual working memory consolidation will have better long-term memory performance; H2: Under visual working memory consolidation, characters will have better long-term memory performance in the context condition than in the no-context condition.
DOI: 10.17632/rnyrpzyw3h.1
title: Bias-Free Dataset of Food Delivery App Reviews with Data Poisoning Attacks

description: This dataset consists of reviews collected from restaurants on a Korean delivery app platform running a review event. A total of 128,668 reviews were collected from 136 restaurants by crawling reviews using the Selenium library in Python. The 136 chosen restaurants run review events which demand customers to write reviews with 5 stars and photos. So the annotation of data was done by considering 1) whether the review gives five-star ratings, and 2) whether the review contains photo(s).
DOI: 10.17632/rnyrpzyw3h.2
title: Bias-Free Dataset of Food Delivery App Reviews with Data Poisoning Attacks

description: This dataset consists of reviews collected from restaurants on a Korean delivery app platform running a review event. A total of 128,668 reviews were collected from 136 restaurants by crawling reviews using the Selenium library in Python. The dataset named as Korean Reviews.csv provides review data not translated to English, and the dataset named as English Reviews.csv provides review data translated to English. The 136 chosen restaurants run review events which demand customers to write reviews with 5 stars and photos. So the annotation of data was done by considering 1) whether the review gives five-star ratings, and 2) whether the review contains photo(s).
DOI: 10.17632/t99w5ksdrh.1
title: Processed Simulation Results

description: The data provided is a processed results of the dynamic simulation of near-zero rebar cutting waste level achievement, particularly for Scenarios 3.
DOI: 10.17909/6264-w578
title: Crab Nebula with JWST