cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/rc5htd5dfr
title: University Student Stress Dataset

description: This dataset contains responses from 3000 undergraduate students studying at public, private, and national universities in Bangladesh. The primary goal of the dataset is to support machine learning research on classifying student stress levels using academic, lifestyle, social, and psychological factors. Data were collected through a structured Google Form survey, which was distributed via email to 3710 students, and a total of 3000 complete and anonymous responses were used in the final dataset.

The dataset includes 18 attributes, selected based on their relevance to academic stress in higher education. Demographic features include Age (19–24) and Gender, while University_Type captures differences between institutional environments. Academic indicators—Study_Hours, Class_Attendance, Exam_Frequency, Assignment_Load, and Tuition (Yes/No)—reflect students’ academic workload and overall study pressure.

Lifestyle-related features include Sleep_Hours, Social_Media_Use, Screen_Time, and Physical_Exercise, as daily habits often influence stress and time management. Socio-economic and psychological attributes—Family_Income_Level, Peer_Pressure, Family_Support, and Anxiety_Level—provide additional context on student wellbeing and external influences.

A derived variable, Stress_Score, was computed by combining workload indicators, psychological ratings, and lifestyle habits. Based on this score, each student was assigned a target label, Stress_Level, categorized as Low, Medium, or High. This label enables supervised learning tasks and stress prediction modeling.

The dataset is clean, structured, and suitable for multi-class classification, correlation analysis, and educational data science research. It offers a valuable resource for understanding how academic demands and personal habits interact to influence stress among university students in Bangladesh.
DOI: 10.17632/rc5htd5dfr.1
title: University Student Stress Dataset

description: This dataset contains responses from 3000 undergraduate students studying at public, private, and national universities in Bangladesh. The primary goal of the dataset is to support machine learning research on classifying student stress levels using academic, lifestyle, social, and psychological factors. Data were collected through a structured Google Form survey, which was distributed via email to 3710 students, and a total of 3000 complete and anonymous responses were used in the final dataset.

The dataset includes 18 attributes, selected based on their relevance to academic stress in higher education. Demographic features include Age (19–24) and Gender, while University_Type captures differences between institutional environments. Academic indicators—Study_Hours, Class_Attendance, Exam_Frequency, Assignment_Load, and Tuition (Yes/No)—reflect students’ academic workload and overall study pressure.

Lifestyle-related features include Sleep_Hours, Social_Media_Use, Screen_Time, and Physical_Exercise, as daily habits often influence stress and time management. Socio-economic and psychological attributes—Family_Income_Level, Peer_Pressure, Family_Support, and Anxiety_Level—provide additional context on student wellbeing and external influences.

A derived variable, Stress_Score, was computed by combining workload indicators, psychological ratings, and lifestyle habits. Based on this score, each student was assigned a target label, Stress_Level, categorized as Low, Medium, or High. This label enables supervised learning tasks and stress prediction modeling.

The dataset is clean, structured, and suitable for multi-class classification, correlation analysis, and educational data science research. It offers a valuable resource for understanding how academic demands and personal habits interact to influence stress among university students in Bangladesh.
DOI: 10.17632/8vvwhgf42f
title: Blautia-derived L-arginine promotes osteoblast differentiation and alleviates osteoporosis via lactylation modification

description: Osteoporosis is a systemic metabolic disorder in which the gut microbiota plays a pivotal regulatory role. However, the specific microbial taxa and metabolic pathways involved in bone homeostasis remain poorly understood. This study aimed to explore the contribution of intestinal microbiota, particularly Blautia, and its metabolites in osteoporosis. Fecal samples were collected from 71 osteoporosis patients and 28 healthy controls. Through 16S rRNA and metagenomic shotgun sequencing, a notable decrease in Blautia and disruption of arginine metabolic pathways within this genus were identified in osteoporosis patients. Fecal microbiota transplantation experiments demonstrated that gut microbial composition significantly influences ovariectomy (OVX)-induced bone loss. Colonization with the Blautia model strain, a representative species of the genus, improved bone mineral density and mitigated osteoporosis in OVX mice. Fecal metabolomic analysis revealed systemic alterations in arginine metabolism among patients. Supplementation with L-arginine in OVX mice effectively alleviated bone loss. In vitro studies showed that L-arginine promotes osteoblast differentiation by enhancing lactylation modifications. These findings establish Blautia and its metabolite L-arginine as critical regulators of bone homeostasis, highlighting the potential for targeting Blautia abundance or modulating the arginine-lactylation pathway as therapeutic strategies for osteoporosis.
DOI: 10.17632/8vvwhgf42f.1
title: Blautia-derived L-arginine promotes osteoblast differentiation and alleviates osteoporosis via lactylation modification

description: Osteoporosis is a systemic metabolic disorder in which the gut microbiota plays a pivotal regulatory role. However, the specific microbial taxa and metabolic pathways involved in bone homeostasis remain poorly understood. This study aimed to explore the contribution of intestinal microbiota, particularly Blautia, and its metabolites in osteoporosis. Fecal samples were collected from 71 osteoporosis patients and 28 healthy controls. Through 16S rRNA and metagenomic shotgun sequencing, a notable decrease in Blautia and disruption of arginine metabolic pathways within this genus were identified in osteoporosis patients. Fecal microbiota transplantation experiments demonstrated that gut microbial composition significantly influences ovariectomy (OVX)-induced bone loss. Colonization with the Blautia model strain, a representative species of the genus, improved bone mineral density and mitigated osteoporosis in OVX mice. Fecal metabolomic analysis revealed systemic alterations in arginine metabolism among patients. Supplementation with L-arginine in OVX mice effectively alleviated bone loss. In vitro studies showed that L-arginine promotes osteoblast differentiation by enhancing lactylation modifications. These findings establish Blautia and its metabolite L-arginine as critical regulators of bone homeostasis, highlighting the potential for targeting Blautia abundance or modulating the arginine-lactylation pathway as therapeutic strategies for osteoporosis.
DOI: 10.17632/gpp2szh4b4
title: Research Data for JRESS-D-25-01971: A Two-Stage Resilient Restoration Framework for Large-Scale Urban Electric Power Infrastructure Systems under Extreme Hurricane Events Considering Social Vulnerability and Economic Factors

description: This repository contains the research data accompanying the study “A Two-Stage Resilient Restoration Framework for Large-Scale Urban Electric Power Infrastructure Systems under Extreme Hurricane Events Considering Social Vulnerability and Economic Factors.” The dataset is organized into two main parts: hurricane hazard data and synthetic power distribution network data for large-scale urban electric power infrastructure systems.

The hurricane data are sourced from the International Best Track Archive for Climate Stewardship (IBTrACS) and can be accessed at the National Centers for Environmental Information (NCEI): https://www.ncei.noaa.gov/products/international-best-track-archive
. For details on IBTrACS, please refer to:
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone best track data. Bulletin of the American Meteorological Society, 91, 363–376. doi:10.1175/2009BAMS2755.1.

The power grid data are synthetically generated following the methodology proposed in our previous work on large-scale synthetic distribution networks under historical hurricane hazards:
S. Li, S. Wang, Q. Zhao, D. Liu and C. K. Tse, “A Risk Assessment Framework for Large-Scale Synthetic Power Distribution Networks Considering Historical Hurricane Disasters,” IEEE Transactions on Power Systems, vol. 40, no. 5, pp. 3846–3858, Sept. 2025, doi:10.1109/TPWRS.2025.3530401.

Together, these data enable quantitative analysis of grid resilience, restoration strategies, and risk assessment under extreme hurricane events, while explicitly incorporating social vulnerability and economic factors to support more equitable and cost-effective decision-making in urban power system planning and operation.
DOI: 10.17632/gpp2szh4b4.2
title: Research Data for JRESS-D-25-01971: A Two-Stage Resilient Restoration Framework for Large-Scale Urban Electric Power Infrastructure Systems under Extreme Hurricane Events Considering Social Vulnerability and Economic Factors

description: This repository contains the research data accompanying the study “A Two-Stage Resilient Restoration Framework for Large-Scale Urban Electric Power Infrastructure Systems under Extreme Hurricane Events Considering Social Vulnerability and Economic Factors.” The dataset is organized into two main parts: hurricane hazard data and synthetic power distribution network data for large-scale urban electric power infrastructure systems.

The hurricane data are sourced from the International Best Track Archive for Climate Stewardship (IBTrACS) and can be accessed at the National Centers for Environmental Information (NCEI): https://www.ncei.noaa.gov/products/international-best-track-archive
. For details on IBTrACS, please refer to:
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone best track data. Bulletin of the American Meteorological Society, 91, 363–376. doi:10.1175/2009BAMS2755.1.

The power grid data are synthetically generated following the methodology proposed in our previous work on large-scale synthetic distribution networks under historical hurricane hazards:
S. Li, S. Wang, Q. Zhao, D. Liu and C. K. Tse, “A Risk Assessment Framework for Large-Scale Synthetic Power Distribution Networks Considering Historical Hurricane Disasters,” IEEE Transactions on Power Systems, vol. 40, no. 5, pp. 3846–3858, Sept. 2025, doi:10.1109/TPWRS.2025.3530401.

Together, these data enable quantitative analysis of grid resilience, restoration strategies, and risk assessment under extreme hurricane events, while explicitly incorporating social vulnerability and economic factors to support more equitable and cost-effective decision-making in urban power system planning and operation.
DOI: 10.17632/bwk59k85xz
title: Survey of Exploring Multi-Club Ownership (MCO) in Cultivating Relationships between Supporters of City Football Group (CFG)’s Chinese Local Team and Global Football Brands

description: This questionnaire was used to collect survey data for the study “Exploring Multi-Club Ownership (MCO) in Cultivating Relationships between Supporters of City Football Group (CFG)’s Chinese Local Team and Global Football Brands.”
DOI: 10.17632/bwk59k85xz.1
title: Survey of Exploring Multi-Club Ownership (MCO) in Cultivating Relationships between Supporters of City Football Group (CFG)’s Chinese Local Team and Global Football Brands

description: This questionnaire was used to collect survey data for the study “Exploring Multi-Club Ownership (MCO) in Cultivating Relationships between Supporters of City Football Group (CFG)’s Chinese Local Team and Global Football Brands.”
DOI: 10.17632/9jhm839ck5
title: Automated scoring of tumour infiltrating lymphocytes informs risk of death from thin melanoma: a nested case-case study [Supplementary Tables]

description: Supplementary de-identified data regarding automated analysis of tumour-infiltrating lymphocytes in a nested case-case cohort of patients with thin melanoma from Queensland, Australia
DOI: 10.17632/9jhm839ck5.2
title: Automated scoring of tumour infiltrating lymphocytes informs risk of death from thin melanoma: a nested case-case study [Supplementary Tables]

description: Supplementary de-identified data regarding automated analysis of tumour-infiltrating lymphocytes in a nested case-case cohort of patients with thin melanoma from Queensland, Australia
DOI: 10.17632/zgsg4kyvtd
title: Dataset for: A huge mantle reservoir for subducted carbon beneath Central-East Asia

description: This dataset originates from the article “A huge mantle reservoir for subducted carbon beneath Central‑East Asia.” Mg‑Zn‑Sr‑Nd‑Hf‑Pb isotopic and major‑trace element data from Russian Cenozoic basalts reveal mantle metasomatism by slab‑derived melts/fluids, providing key evidence for deep carbon recycling and slab–mantle/crust–mantle interactions in Central‑East Asia. Detailed geochemical analyses were conducted on well‑correlated late Cenozoic volcanic systems in southeastern Siberia—Oka, Vitim, Udokan, and Tok. The samples exhibit SiO₂ (41.2–52.0 wt.%), total alkali (Na₂O+K₂O, 3.8–7.9 wt.%), Al₂O₃ (11.2–16.0 wt.%), MgO (6.6–12.0 wt.%), and TiO₂ (1.6–3.2 wt.%) contents similar to low‑silica basaltic melts in East Asia. They are enriched in light rare earth and large‑ion lithophile elements, depleted in heavy rare earths, with (La/Yb)ₙ = 8.78–31.0, resembling ocean‑island basalts. Radiogenic isotopes vary as (⁸⁷Sr/⁸⁶Sr)i = 0.703577–0.705135 and εNd(t) = +4.93 to –2.24, indicating the mantle source with multiple components. Magnesium isotopes show δ²⁶Mg values of –0.25±0.03‰ to –0.43±0.03‰, lower than peridotitic mantle and mid‑ocean ridge basalts (MORBs). Zinc isotopes show δ⁶⁶Zn values of +0.30±0.010‰ to +0.44±0.02‰, higher than normal mantle and MORBs. This coupled light‑Mg–heavy‑Zn signature points to the presence of carbonate in the melts (carbonate minerals can have δ²⁶Mg as low as –5.57‰ and δ⁶⁶Zn up to +1.67‰). Subducted slabs can deliver substantial carbon carriers (marine sediments, altered oceanic crust) into the deep mantle; melting of these slabs at depth generates low‑silica, carbonate‑bearing melts, offering a novel perspective on the origin of Russian Cenozoic basalts.
DOI: 10.17632/zgsg4kyvtd.1
title: Dataset for: A huge mantle reservoir for subducted carbon beneath Central-East Asia

description: This dataset originates from the article “A huge mantle reservoir for subducted carbon beneath Central‑East Asia.” Mg‑Zn‑Sr‑Nd‑Hf‑Pb isotopic and major‑trace element data from Russian Cenozoic basalts reveal mantle metasomatism by slab‑derived melts/fluids, providing key evidence for deep carbon recycling and slab–mantle/crust–mantle interactions in Central‑East Asia. Detailed geochemical analyses were conducted on well‑correlated late Cenozoic volcanic systems in southeastern Siberia—Oka, Vitim, Udokan, and Tok. The samples exhibit SiO₂ (41.2–52.0 wt.%), total alkali (Na₂O+K₂O, 3.8–7.9 wt.%), Al₂O₃ (11.2–16.0 wt.%), MgO (6.6–12.0 wt.%), and TiO₂ (1.6–3.2 wt.%) contents similar to low‑silica basaltic melts in East Asia. They are enriched in light rare earth and large‑ion lithophile elements, depleted in heavy rare earths, with (La/Yb)ₙ = 8.78–31.0, resembling ocean‑island basalts. Radiogenic isotopes vary as (⁸⁷Sr/⁸⁶Sr)i = 0.703577–0.705135 and εNd(t) = +4.93 to –2.24, indicating the mantle source with multiple components. Magnesium isotopes show δ²⁶Mg values of –0.25±0.03‰ to –0.43±0.03‰, lower than peridotitic mantle and mid‑ocean ridge basalts (MORBs). Zinc isotopes show δ⁶⁶Zn values of +0.30±0.010‰ to +0.44±0.02‰, higher than normal mantle and MORBs. This coupled light‑Mg–heavy‑Zn signature points to the presence of carbonate in the melts (carbonate minerals can have δ²⁶Mg as low as –5.57‰ and δ⁶⁶Zn up to +1.67‰). Subducted slabs can deliver substantial carbon carriers (marine sediments, altered oceanic crust) into the deep mantle; melting of these slabs at depth generates low‑silica, carbonate‑bearing melts, offering a novel perspective on the origin of Russian Cenozoic basalts.
DOI: 10.17632/p8dbpcgbtb
title: Noncanonical open reading frames from lncRNAs encode functional micropeptides that help shape agronomic traits in rice

description: Original raw image files for the study "Noncanonical open reading frames from lncRNAs encode functional micropeptides that help shape agronomic traits in rice" by Cheng et al.
DOI: 10.17632/p8dbpcgbtb.1
title: Noncanonical open reading frames from lncRNAs encode functional micropeptides that help shape agronomic traits in rice

description: Original raw image files for the study "Noncanonical open reading frames from lncRNAs encode functional micropeptides that help shape agronomic traits in rice" by Cheng et al.
DOI: 10.17632/6zszgtcdd4
title: Dual-Tank Mixed Cloud Chamber

description: Temperature, humidity, and pressure measurements in a dual-chamber cloud chamber, and temperature, humidity, pressure, particle size spectrometer, droplet size spectrometer, and water vapor data in quantitative catalytic experiments using hygroscopic catalysts and AgI.
DOI: 10.17632/6zszgtcdd4.1
title: Dual-Tank Mixed Cloud Chamber

description: Temperature, humidity, and pressure measurements in a dual-chamber cloud chamber, and temperature, humidity, pressure, particle size spectrometer, droplet size spectrometer, and water vapor data in quantitative catalytic experiments using hygroscopic catalysts and AgI.
DOI: 10.17632/xc99xrn3ys
title: 1901-1945 Corpus FLATIF (Fashion Languages and Terminologies across Italian and French) - 1
DOI: 10.17632/xc99xrn3ys.1
title: 1901-1945 Corpus FLATIF (Fashion Languages and Terminologies across Italian and French) - 1
DOI: 10.17632/69k294mhkh
title: Dataset for surrogate-assisted multi-objective optimization of fin geometries in hydrate-based thermochemical energy storage

description: This dataset contains 320 simulation cases generated using COMSOL Multiphysics for training and validation of a machine-learning surrogate model. Each case includes geometric parameters of branched-fin structures and the corresponding thermal performance metrics, including total heat storage capacity and thermal power. The dataset is intended to support transparency, reproducibility, and comparative studies of surrogate-assisted optimization in hydrate-based thermochemical energy storage systems.
DOI: 10.17632/69k294mhkh.1
title: Dataset for surrogate-assisted multi-objective optimization of fin geometries in hydrate-based thermochemical energy storage

description: This dataset contains 320 simulation cases generated using COMSOL Multiphysics for training and validation of a machine-learning surrogate model. Each case includes geometric parameters of branched-fin structures and the corresponding thermal performance metrics, including total heat storage capacity and thermal power. The dataset is intended to support transparency, reproducibility, and comparative studies of surrogate-assisted optimization in hydrate-based thermochemical energy storage systems.
DOI: 10.17632/nyd9dc9wp3
title: Dataset of Who Is Most at Risk? Academic Contexts and Demographic Patterns of Cybersecurity Vulnerability in Ghanaian Higher Education

description: This dataset contains anonymised survey data collected from undergraduate students at Kwame Nkrumah University of Science and Technology (KNUST), Ghana, examining demographic and academic differences in cybersecurity vulnerability. Cybersecurity vulnerability is operationalised as routine risky online behaviours such as password reuse, rapid clicking on shared links, and unsafe account management.
Data were collected through a cross-sectional online survey between April and August 2025, with 373 undergraduate students included after data cleaning. The dataset comprises demographic variables (gender, age group, academic level, and college) and responses to the Risky Cybersecurity Behaviours Scale (RScB) (Hadlington, 2017), a validated behavioural measure focusing on everyday online practices rather than awareness.
The dataset supports analysis of how cybersecurity vulnerability varies across academic levels and disciplinary contexts in Ghanaian higher education. It is suitable for secondary analysis, comparative studies, and research on behavioural cybersecurity risk in university settings, particularly in Global South contexts.
DOI: 10.17632/nyd9dc9wp3.1
title: Dataset of Who Is Most at Risk? Academic Contexts and Demographic Patterns of Cybersecurity Vulnerability in Ghanaian Higher Education

description: This dataset contains anonymised survey data collected from undergraduate students at Kwame Nkrumah University of Science and Technology (KNUST), Ghana, examining demographic and academic differences in cybersecurity vulnerability. Cybersecurity vulnerability is operationalised as routine risky online behaviours such as password reuse, rapid clicking on shared links, and unsafe account management.
Data were collected through a cross-sectional online survey between April and August 2025, with 373 undergraduate students included after data cleaning. The dataset comprises demographic variables (gender, age group, academic level, and college) and responses to the Risky Cybersecurity Behaviours Scale (RScB) (Hadlington, 2017), a validated behavioural measure focusing on everyday online practices rather than awareness.
The dataset supports analysis of how cybersecurity vulnerability varies across academic levels and disciplinary contexts in Ghanaian higher education. It is suitable for secondary analysis, comparative studies, and research on behavioural cybersecurity risk in university settings, particularly in Global South contexts.
DOI: 10.17632/4c34pxksb5
title: Accelerated Screening of Hydrogen Evolution Reaction Catalysts in Carbon Nanotube Materials Based on DFT Calculations and Machine Learning

description: We hypothesized that single-atom transition metal catalysts embedded in carbon nanotubes (CNT@TM) could achieve hydrogen evolution reaction (HER) performance comparable to platinum (Pt). The "(n, n) CNT@TM" folder contains the optimized structural models calculated using VASP, while the "HER" folder contains models for H atom adsorption calculations.
DOI: 10.17632/4c34pxksb5.2
title: Accelerated Screening of Hydrogen Evolution Reaction Catalysts in Carbon Nanotube Materials Based on DFT Calculations and Machine Learning

description: We hypothesized that single-atom transition metal catalysts embedded in carbon nanotubes (CNT@TM) could achieve hydrogen evolution reaction (HER) performance comparable to platinum (Pt). The "(n, n) CNT@TM" folder contains the optimized structural models calculated using VASP, while the "HER" folder contains models for H atom adsorption calculations.
DOI: 10.15468/n3grze
title: New Material Of Alagomyidae (Mammalia, Glires) From The Late Paleocene Subeng Locality, Inner Mongolia

description: This dataset contains the digitized treatments in Plazi based on the original journal article MENG, JIN, NI, XIJUN, LI, CHUANKUI, BEARD, K. CHRISTOPHER, GEBO, DANIEL L., WANG, YUANQING, WANG, HONGJIANG (2007): New Material Of Alagomyidae (Mammalia, Glires) From The Late Paleocene Subeng Locality, Inner Mongolia. American Museum Novitates 3597 (1): 1-32, DOI: 10.1206/0003-0082(2007)3597[1:NMOAMG]2.0.CO;2, URL: http://www.bioone.org/perlserv/?request=get-abstract&doi=10.1206%2F0003-0082(2007)3597%5B1%3ANMOAMG%5D2.0.CO%3B2