cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/7y66xb3gmc
title: Student Smoking, Alcohol, and Psychological Wellness Data

description: This dataset consists of 1163 responses and 14 columns (features), focusing on the relationship between smoking, alcohol consumption, and psychological wellness among university students. It includes self-reported data on smoking and drinking habits, coping mechanisms, mental health, and help-seeking behaviors. The survey was designed to capture insights into how these behaviors impact students' overall well-being.

Data Collection: The data was collected through an online survey administered via Google Forms in [month/year]. Respondents provided information about their lifestyle habits, reasons for engaging in these behaviors, and their psychological wellness.

Key Features:
1. Demographics: Age, year of study, and field of study.
2. Smoking Habits: Frequency of smoking, age of starting, and reasons for smoking.
3. Alcohol Consumption: Frequency of alcohol use, typical weekly intake, and reasons for drinking.
4. Stress Management: Methods for coping with stress, including smoking and alcohol.
5. Mental Health: Self-reported psychological wellness and willingness to reduce or quit smoking and drinking.
6. Help-Seeking Behavior: Responses regarding help-seeking for mental health concerns.

This dataset can be used for:
1. Machine learning analysis to model and predict psychological wellness based on lifestyle behaviors.
2. Statistical studies on the interplay between smoking, alcohol, and mental health.
3. Development of intervention strategies to improve student well-being and reduce harmful habits.

Format: The dataset contains 14 columns with categorical, ordinal, and numerical data. It is formatted for ease of analysis and ready for use in statistical and machine learning applications.
DOI: 10.15468/dl.8w52rn
title: Occurrence Download

description: A dataset containing 167 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hemionitis subcordata"
 ]
}

 The dataset includes 167 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004768-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7y66xb3gmc.3
title: Student Smoking, Alcohol, and Psychological Wellness Data

description: This dataset consists of 1163 responses and 14 columns (features), focusing on the relationship between smoking, alcohol consumption, and psychological wellness among university students. It includes self-reported data on smoking and drinking habits, coping mechanisms, mental health, and help-seeking behaviors. The survey was designed to capture insights into how these behaviors impact students' overall well-being.

Data Collection: The data was collected through an online survey administered via Google Forms in [month/year]. Respondents provided information about their lifestyle habits, reasons for engaging in these behaviors, and their psychological wellness.

Key Features:
1. Demographics: Age, year of study, and field of study.
2. Smoking Habits: Frequency of smoking, age of starting, and reasons for smoking.
3. Alcohol Consumption: Frequency of alcohol use, typical weekly intake, and reasons for drinking.
4. Stress Management: Methods for coping with stress, including smoking and alcohol.
5. Mental Health: Self-reported psychological wellness and willingness to reduce or quit smoking and drinking.
6. Help-Seeking Behavior: Responses regarding help-seeking for mental health concerns.

This dataset can be used for:
1. Machine learning analysis to model and predict psychological wellness based on lifestyle behaviors.
2. Statistical studies on the interplay between smoking, alcohol, and mental health.
3. Development of intervention strategies to improve student well-being and reduce harmful habits.

Format: The dataset contains 14 columns with categorical, ordinal, and numerical data. It is formatted for ease of analysis and ready for use in statistical and machine learning applications.
DOI: 10.17632/x2f23gh2pk
title: Replication Package: Argentina: The Honor Student—By Merit and By Mistake. A Natural Experiment on "Information Effects"

description: This replication package contains all Python Jupyter notebooks required to reproduce every table and figure in the paper Argentina: The Honor Student—By Merit and By Mistake. A Natural Experiment on “Information Effects”, including the Online Appendix. Each result is generated by its own notebook. A README accompanies the project as a whole, providing instructions and additional details.
DOI: 10.17632/x2f23gh2pk.1
title: Replication Package: Argentina: The Honor Student—By Merit and By Mistake. A Natural Experiment on "Information Effects"

description: This replication package contains all Python Jupyter notebooks required to reproduce every table and figure in the paper Argentina: The Honor Student—By Merit and By Mistake. A Natural Experiment on “Information Effects”, including the Online Appendix. Each result is generated by its own notebook. A README accompanies the project as a whole, providing instructions and additional details.
DOI: 10.17632/7kvwbrz43c
title: Supplementary data of Metabolic syndrome is associated with excessive scarring: A prospective cohort study including 466625 participants

description: This is the supplementary dataset for Article：Metabolic syndrome is associated with excessive scarring: A prospective cohort study including 466625 participants
DOI: 10.17632/7kvwbrz43c.1
title: Supplementary data of Metabolic syndrome is associated with excessive scarring: A prospective cohort study including 466625 participants

description: This is the supplementary dataset for Article：Metabolic syndrome is associated with excessive scarring: A prospective cohort study including 466625 participants
DOI: 10.17632/f8r7cpgyjn
title: Medicine-Food Homologous Herbs

description: This dataset consists of near-infrared (NIR) spectral data of 48 kinds of medicinal and edible homologous herbs.
DOI: 10.17632/f8r7cpgyjn.1
title: Medicine-Food Homologous Herbs

description: This dataset consists of near-infrared (NIR) spectral data of 48 kinds of medicinal and edible homologous herbs.
DOI: 10.17632/jy53gzrzf2
title: Consumer Perceptions and Loyalty Survey for Luxury Products Dataset

description: This dataset captures consumer perceptions of luxury products, including shopping frequency, satisfaction levels, service quality, and loyalty behaviors, offering valuable insights into purchasing motivations and brand loyalty in the luxury sector.

Dataset Overview
• Total responses: 272
• Number of features: 11
• Focus: Customer satisfaction and loyalty in luxury products
• Captures: Shopping frequency, product/service satisfaction, issues, and loyalty intentions
• Goal: Understand consumer behavior, satisfaction drivers, and loyalty patterns in the luxury market

Data Collection
• Method: Online survey
• Format: 11 columns, mix of categorical and ordinal data
• Prepared for: Statistical analysis and machine learning applications

Key Features
• Age group
• Gender
• Frequency of luxury shopping
• Main reason for choosing luxury products
• Satisfaction with product quality
• Shopping experience rating
• Delivery service rating
• Satisfaction with customer service support
• Issues faced with luxury shop services
• Likelihood of recommending luxury products to others
• Plans to shop again at a luxury shop

Potential Applications
• Identify drivers of customer loyalty
• Segment customers based on satisfaction and demographics
• Analyze shopping frequency and consumer behavior
• Study motivations behind luxury purchases
• Predict likelihood of repeat purchases
• Predict recommendation behavior
• Detect service pain points
• Design targeted loyalty and retention programs
DOI: 10.17632/jy53gzrzf2.1
title: Consumer Perceptions and Loyalty Survey for Luxury Products Dataset

description: This dataset captures consumer perceptions of luxury products, including shopping frequency, satisfaction levels, service quality, and loyalty behaviors, offering valuable insights into purchasing motivations and brand loyalty in the luxury sector.

Dataset Overview
• Total responses: 272
• Number of features: 11
• Focus: Customer satisfaction and loyalty in luxury products
• Captures: Shopping frequency, product/service satisfaction, issues, and loyalty intentions
• Goal: Understand consumer behavior, satisfaction drivers, and loyalty patterns in the luxury market

Data Collection
• Method: Online survey
• Format: 11 columns, mix of categorical and ordinal data
• Prepared for: Statistical analysis and machine learning applications

Key Features
• Age group
• Gender
• Frequency of luxury shopping
• Main reason for choosing luxury products
• Satisfaction with product quality
• Shopping experience rating
• Delivery service rating
• Satisfaction with customer service support
• Issues faced with luxury shop services
• Likelihood of recommending luxury products to others
• Plans to shop again at a luxury shop

Potential Applications
• Identify drivers of customer loyalty
• Segment customers based on satisfaction and demographics
• Analyze shopping frequency and consumer behavior
• Study motivations behind luxury purchases
• Predict likelihood of repeat purchases
• Predict recommendation behavior
• Detect service pain points
• Design targeted loyalty and retention programs
DOI: 10.17632/t5tty6vbh2
title: Consumer Insights for Marketing Strategy

description: This dataset contains 333 responses with 13 features, focusing on consumer insights for marketing strategy in the fashion and apparel industry.

It captures roles within the industry, product interests, supplier preferences, and factors influencing purchasing decisions. The dataset also explores perceptions of product quality, pricing, promotions, and communication channels, making it valuable for strategic marketing analysis.

Data Collection
• Method: Online survey
• Format: 13 columns, with categorical, ordinal, and textual responses
• Prepared for: Market research, statistical analysis, and business strategy development

Key Features
• Role in the apparel industry
• Geographic location
• Product interests in fashion shops
• Key values when choosing garment suppliers
• Rating of product quality
• Perception of pricing
• Willingness to pay more (e.g., for faster shipping, sustainability)
• First awareness of the fashion shop
• Preferred platform for product catalogs
• Promotions influencing buying decisions
• Likelihood of recommending the shop
• Preferred type of content from the shop

Potential Applications
• Analyze consumer roles and geographic distribution in the apparel industry
• Identify most valued supplier attributes (price, delivery, quality, sustainability)
• Understand product interests and market demand trends
• Evaluate perceptions of pricing and product quality
• Model recommendation likelihood based on satisfaction and values
• Assess the effectiveness of promotional strategies
• Identify preferred communication and catalog platforms
• Inform targeted marketing and content strategy for fashion brands
DOI: 10.17632/t5tty6vbh2.1
title: Consumer Insights for Marketing Strategy

description: This dataset contains 333 responses with 13 features, focusing on consumer insights for marketing strategy in the fashion and apparel industry.

It captures roles within the industry, product interests, supplier preferences, and factors influencing purchasing decisions. The dataset also explores perceptions of product quality, pricing, promotions, and communication channels, making it valuable for strategic marketing analysis.

Data Collection
• Method: Online survey
• Format: 13 columns, with categorical, ordinal, and textual responses
• Prepared for: Market research, statistical analysis, and business strategy development

Key Features
• Role in the apparel industry
• Geographic location
• Product interests in fashion shops
• Key values when choosing garment suppliers
• Rating of product quality
• Perception of pricing
• Willingness to pay more (e.g., for faster shipping, sustainability)
• First awareness of the fashion shop
• Preferred platform for product catalogs
• Promotions influencing buying decisions
• Likelihood of recommending the shop
• Preferred type of content from the shop

Potential Applications
• Analyze consumer roles and geographic distribution in the apparel industry
• Identify most valued supplier attributes (price, delivery, quality, sustainability)
• Understand product interests and market demand trends
• Evaluate perceptions of pricing and product quality
• Model recommendation likelihood based on satisfaction and values
• Assess the effectiveness of promotional strategies
• Identify preferred communication and catalog platforms
• Inform targeted marketing and content strategy for fashion brands
DOI: 10.17632/bd2dr7k84j
title: Non-linear dynamics of inflation aversion in the Euro Area: Evidence from a Panel Smooth Transition model

description: The R code and the dataset for reproducing all the results (tables and figures) in the paper "Non-linear dynamics of inflation aversion in the Euro Area: Evidence from a Panel Smooth Transition model"
DOI: 10.17632/bd2dr7k84j.2
title: Non-linear dynamics of inflation aversion in the Euro Area: Evidence from a Panel Smooth Transition model

description: The R code and the dataset for reproducing all the results (tables and figures) in the paper "Non-linear dynamics of inflation aversion in the Euro Area: Evidence from a Panel Smooth Transition model"
DOI: 10.17632/dgvf9wp8vz
title: Part-of-Speech Tagged Corpus for the Pahari Language

description: This dataset contains a manually annotated Part-of-Speech (POS) tagged corpus for the Pahari language, a low-resource Indo-Aryan language spoken in parts of Pakistan and India. The dataset consists of 28,900 samples, structured in CSV format with two columns:

Words – the Pahari tokens

Tags – the corresponding POS tags

A total of 23 POS tags are used in the annotation. The basic POS categories were obtained from Karnai (2007), “Pahari aur Urdu: Ik Taqabali Jaiza”, and the tagging process was guided by previously designed Urdu POS tagsets to ensure consistency and linguistic validity.

This resource provides a valuable contribution toward the computational processing of the Pahari language, supporting research in Natural Language Processing (NLP), low-resource language modeling, machine learning applications, and linguistic analysis.
DOI: 10.17632/dgvf9wp8vz.1
title: Part-of-Speech Tagged Corpus for the Pahari Language

description: This dataset contains a manually annotated Part-of-Speech (POS) tagged corpus for the Pahari language, a low-resource Indo-Aryan language spoken in parts of Pakistan and India. The dataset consists of 28,900 samples, structured in CSV format with two columns:

Words – the Pahari tokens

Tags – the corresponding POS tags

A total of 23 POS tags are used in the annotation. The basic POS categories were obtained from Karnai (2007), “Pahari aur Urdu: Ik Taqabali Jaiza”, and the tagging process was guided by previously designed Urdu POS tagsets to ensure consistency and linguistic validity.

This resource provides a valuable contribution toward the computational processing of the Pahari language, supporting research in Natural Language Processing (NLP), low-resource language modeling, machine learning applications, and linguistic analysis.
DOI: 10.17632/mz5pkd397s
title: Data: Identifying uncertainty factors affecting the dynamics of U.S. electricity retail sales

description: Data used for empirical analysis of this study.
DOI: 10.17632/mz5pkd397s.1
title: Data: Identifying uncertainty factors affecting the dynamics of U.S. electricity retail sales

description: Data used for empirical analysis of this study.
DOI: 10.17632/jf62zyt7nk
title: Primary and secondary steroid acids in crude oils

description: The chromatograms and mass spectra used in the manuscript figures were provided in numerical format in this dataset.
DOI: 10.17632/jf62zyt7nk.1
title: Primary and secondary steroid acids in crude oils

description: The chromatograms and mass spectra used in the manuscript figures were provided in numerical format in this dataset.
DOI: 10.17632/d3thrf7n58
title: ECMODE-D-24-01599R1

description: Research Data for ECMODE-D-24-01599R1: How Bank Digitalization Benefits Firm Borrowing: Evidence from China
DOI: 10.17632/d3thrf7n58.1
title: ECMODE-D-24-01599R1

description: Research Data for ECMODE-D-24-01599R1: How Bank Digitalization Benefits Firm Borrowing: Evidence from China
DOI: 10.5517/ccdc.csd.cc2l1qcr
title: CCDC 2384934: Experimental Crystal Structure Determination

description: Related Article: Russell M. Main, Romy Ettlinger, Tia K. Tajnšek, Deborah A. Brako-Amoafo, Maximillian G. Stanzione, Morven J. Duncan, Philip Ettlinger, Gaynor B. Lawrence, Mark R. Warren, Christopher J. Heard, Russell E. Morris|2025|J.Am.Chem.Soc.|147|31260|doi:10.1021/jacs.5c10395
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: 10.17632/mhvhrhmvk2
title: Holocene Sediment and Hydrological Records of Dongting Lake

description: Holocene Sediment and Hydrological Records of Dongting Lake
DOI: 10.5517/ccdc.csd.cc2l1qds
title: CCDC 2384935: Experimental Crystal Structure Determination

description: Related Article: Russell M. Main, Romy Ettlinger, Tia K. Tajnšek, Deborah A. Brako-Amoafo, Maximillian G. Stanzione, Morven J. Duncan, Philip Ettlinger, Gaynor B. Lawrence, Mark R. Warren, Christopher J. Heard, Russell E. Morris|2025|J.Am.Chem.Soc.|147|31260|doi:10.1021/jacs.5c10395
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: 10.13012/b2idb-0816875_v1
title: Data from Sugar Production from Bioenergy Sorghum by Using Pilot-Scale Continuous Hydrothermal Pretreatment Combined with Disk Refining

description: Chemical-free pretreatments are attracting increased interest because they generate less inhibitor in hydrolysates. In this study, pilot-scaled continuous hydrothermal (PCH) pretreatment followed by disk refining was evaluated and compared to laboratory-scale batch hot water (LHW) pretreatment. Bioenergy sorghum bagasse (BSB) was pretreated at 160-190 °C for 10 min with and without subsequent disk milling. Hydrothermal pretreatment and disk milling synergistically improved glucose and xylose release by 10-20% compared to hydrothermal pretreatment alone. Maximum yields of glucose and xylose of 82.55% and 70.78%, respectively were achieved, when BSB was pretreated at 190 °C and 180 °C followed by disk milling. LHW pretreated BSB had 5-15% higher sugar yields compared to PCH for all pretreatment conditions. The surface area improvement was also performed. PCH pretreatment combined with disk milling increased BSB surface area by 31.80-106.93%, which was greater than observed using LHW pretreatment.
DOI: 10.17632/mhvhrhmvk2.1
title: Holocene Sediment and Hydrological Records of Dongting Lake

description: Holocene Sediment and Hydrological Records of Dongting Lake
DOI: 10.5517/ccdc.csd.cc2l1qft
title: CCDC 2384936: Experimental Crystal Structure Determination

description: Related Article: Russell M. Main, Romy Ettlinger, Tia K. Tajnšek, Deborah A. Brako-Amoafo, Maximillian G. Stanzione, Morven J. Duncan, Philip Ettlinger, Gaynor B. Lawrence, Mark R. Warren, Christopher J. Heard, Russell E. Morris|2025|J.Am.Chem.Soc.|147|31260|doi:10.1021/jacs.5c10395
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: 10.5517/ccdc.csd.cc2l1qhw
title: CCDC 2384938: Experimental Crystal Structure Determination

description: Related Article: Russell M. Main, Romy Ettlinger, Tia K. Tajnšek, Deborah A. Brako-Amoafo, Maximillian G. Stanzione, Morven J. Duncan, Philip Ettlinger, Gaynor B. Lawrence, Mark R. Warren, Christopher J. Heard, Russell E. Morris|2025|J.Am.Chem.Soc.|147|31260|doi:10.1021/jacs.5c10395
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: 10.5517/ccdc.csd.cc2l1qjx
title: CCDC 2384939: Experimental Crystal Structure Determination

description: Related Article: Russell M. Main, Romy Ettlinger, Tia K. Tajnšek, Deborah A. Brako-Amoafo, Maximillian G. Stanzione, Morven J. Duncan, Philip Ettlinger, Gaynor B. Lawrence, Mark R. Warren, Christopher J. Heard, Russell E. Morris|2025|J.Am.Chem.Soc.|147|31260|doi:10.1021/jacs.5c10395
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: 10.5517/ccdc.csd.cc2l1qky
title: CCDC 2384940: Experimental Crystal Structure Determination

description: Related Article: Russell M. Main, Romy Ettlinger, Tia K. Tajnšek, Deborah A. Brako-Amoafo, Maximillian G. Stanzione, Morven J. Duncan, Philip Ettlinger, Gaynor B. Lawrence, Mark R. Warren, Christopher J. Heard, Russell E. Morris|2025|J.Am.Chem.Soc.|147|31260|doi:10.1021/jacs.5c10395
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: 10.5517/ccdc.csd.cc2l1qlz
title: CCDC 2384941: Experimental Crystal Structure Determination

description: Related Article: Russell M. Main, Romy Ettlinger, Tia K. Tajnšek, Deborah A. Brako-Amoafo, Maximillian G. Stanzione, Morven J. Duncan, Philip Ettlinger, Gaynor B. Lawrence, Mark R. Warren, Christopher J. Heard, Russell E. Morris|2025|J.Am.Chem.Soc.|147|31260|doi:10.1021/jacs.5c10395
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: 10.15468/dl.9s9k9c
title: Occurrence Download

description: A dataset containing 748 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Records of amphibians (Amphibia) and reptiles (Reptilia) in Ukraine according to the results of monitoring of Facebook public groups in 2019-2022"
 ]
}

 The dataset includes 748 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004769-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.