cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3929/ethz-b-000602648
title: Data collection for CS-D Experiment in Mont Terri - geochemical

description: Here, we present geochemical data from an in-situ injection experiment, where CO2-enriched fluid was continuously injected in a faulted caprock analogue. The joint analysis of noble gases, ion concentrations and carbon isotopes allows to quantify mixing between injected CO2-enriched fluid and resident formation water and to describe the temporal evolution of water-rock interaction processes. The data presented here are a crucial complement to the geophysical monitoring data at the fracture scale, highlighting a unique migration of CO2 in fault zones. The following files contain the time evolution of various parameters measured from April 2019 to August 2020. The files contain respectively: 1. Circulation: Electrical Conductivity “EC” in [microS/m], and pH 2. fluid_analysis_ions: Concentration of Na, K, Mg, Ca, Sr, Cl, Sulfate in [ppm] measured in the injection water “Inj” and in the six intervals of the monitoring borehole “Int1, Int2, Int3, Int4, Int5, Int6” from bottom to top of the borehole respectively. 3. fluid_analysisDIC&Oxigen: Stables Isotopes concentrations of δ13C and δ18O expressed in [ ] and measured in the injection water “Inj” and in the six intervals of the monitoring borehole “Int1, Int2, Int3, Int4, Int5, Int6” from bottom to top of the borehole respectively. 4. gas_partialpressures: partial pressure expressed in [hPa] of the following gases dissolved in water: He,CO2 ,N2, Kr ,Ar, O2 5. NG_concentrations Concentration of the noble gases Ar and He in [cm3/g], and corresponding Temperature [K], and Pressure [atm] conditions. 6. Pressure injection pressure [MPa] and pressure [MPa] at monitoring interval M1 7. Pump Injection flow rate [ml/min]
DOI: 10.5281/zenodo.18183883
title: Tuning re-entrant phase behavior of silica nanoparticles in polymer suspension via interplay of interactions

description: Sushma, S. Kumar, J. Kohlbrecher and V. K. Aswal, Dataset of “Tuning re-entrant phase Behavior of silica nanoparticles in polymer suspension via interplay of interactions,” [Data set], https://doi.org/10.1103/xvgy-d27r
DOI: 10.15468/dl.pj3b6v
title: Occurrence Download

description: A dataset listing the 5 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Continent is Asia",
 "Country is one of (Chinese Taipei, China)",
 "OccurrenceStatus is Present",
 "TaxonKey is Tetrasporales"
 ]
}

 The dataset's 5 records were derived from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003386-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e241544
title: A pplication of Machine Learning Approaches to D evelop P redictive M odels for Diabetes and Hypertension among Bangladesh Adults

description: <br>AbstractIntroduction:With rapidurbanization, lifestyle changes, and an aging population, non-communicablediseases (NCDs),includinghypertension and diabetes,pose significant public health challenges inBangladesh andmanyotherlow-and middle-incomecountries. This studyusedmachine learning (ML)approachesto develop predictive models forhypertension and diabetesamong adultsin this country.Methods:BangladeshDemographic andHealthSurvey2022datawere analyzed. This isa nationallyrepresentative cross-sectional survey.Participantre classified as hypertensivewhen theirsystolic bloodpressurewas≥140mmHg, diastolicblood pressure was≥90mmHg, orif they usedantihypertensivemedication.They were classified asdiabetic if theirfasting plasma glucosewas≥7.0mmol/L orthey usedglucose-lowering drugs. Potential predictors included age, gender, education, wealth quintile,overweight/obesity,rural-urbanresidence, and divisionof residence.Descriptiveanalysis was conducted,andsix ML modelswere applied: artificialneuralnetwork (ANN),randomforest,adaptive boosting(AdaBoost),gradientboosting, XGBoost, andsupportvectormachine (SVM). Models’performancewasevaluated via accuracy,area under the curve (AUC), sensitivity, specificity, and F1-score. Featureimportance was assessed to rankrisk factors.Results:The study included13,847 adults, 55% of whom were females.Sensitivity was high across models(up to 0.96 for diabetes and 0.90 for hypertension). However, the overall specificity was low, particularlyfor diabetes (as low as 0.13 in XGBoost).Diabetes and hypertension had prevalence of 16.3% and 20.5%,respectively.The prevalence of both conditionsincreasedwith age, and the highest prevalencewas24.4%for diabetes and 43.3% for hypertensionamongindividuals aged 65 and older. Wealthier and urban residentsexperienced higher rates (diabetes: 24.9% among the richest compared to 9.9% among the poorest;hypertension: 23.3% in urban versus 19.2% in rural areas). Additionally, overweight/obesity was a strongpredictor for both conditions.For diabetes, AdaBoosthadthe highest AUC (0.699) and SVMhadthehighest accuracy (0.836); for hypertension, AdaBoosthad the greatestAUC (0.775) and accuracy (0.799).Hypertension topped diabetes predictors, while overweight/obesitywas the top predictorfor hypertension,followed by age and diabetes. Wealth and gender were moderately influential, with education andgeographic factors less so. Low specificity across models indicated challenges in identifying non-cases.Conclusion:This ML-driven analysisidentifiedthe bidirectional relationship ofhypertensionanddiabetesalong with several other predictors, includingoverweight/obesity,older age, and richer household wealthquintiles.Ourfindings underscore the need for integrated screening and lifestyle interventions targetinghigh-risk groups to mitigatefutureNCD burden.
DOI: 10.3886/e241544v2
title: A pplication of Machine Learning Approaches to D evelop P redictive M odels for Diabetes and Hypertension among Bangladesh Adults

description: <br>AbstractIntroduction:With rapidurbanization, lifestyle changes, and an aging population, non-communicablediseases (NCDs),includinghypertension and diabetes,pose significant public health challenges inBangladesh andmanyotherlow-and middle-incomecountries. This studyusedmachine learning (ML)approachesto develop predictive models forhypertension and diabetesamong adultsin this country.Methods:BangladeshDemographic andHealthSurvey2022datawere analyzed. This isa nationallyrepresentative cross-sectional survey.Participantre classified as hypertensivewhen theirsystolic bloodpressurewas≥140mmHg, diastolicblood pressure was≥90mmHg, orif they usedantihypertensivemedication.They were classified asdiabetic if theirfasting plasma glucosewas≥7.0mmol/L orthey usedglucose-lowering drugs. Potential predictors included age, gender, education, wealth quintile,overweight/obesity,rural-urbanresidence, and divisionof residence.Descriptiveanalysis was conducted,andsix ML modelswere applied: artificialneuralnetwork (ANN),randomforest,adaptive boosting(AdaBoost),gradientboosting, XGBoost, andsupportvectormachine (SVM). Models’performancewasevaluated via accuracy,area under the curve (AUC), sensitivity, specificity, and F1-score. Featureimportance was assessed to rankrisk factors.Results:The study included13,847 adults, 55% of whom were females.Sensitivity was high across models(up to 0.96 for diabetes and 0.90 for hypertension). However, the overall specificity was low, particularlyfor diabetes (as low as 0.13 in XGBoost).Diabetes and hypertension had prevalence of 16.3% and 20.5%,respectively.The prevalence of both conditionsincreasedwith age, and the highest prevalencewas24.4%for diabetes and 43.3% for hypertensionamongindividuals aged 65 and older. Wealthier and urban residentsexperienced higher rates (diabetes: 24.9% among the richest compared to 9.9% among the poorest;hypertension: 23.3% in urban versus 19.2% in rural areas). Additionally, overweight/obesity was a strongpredictor for both conditions.For diabetes, AdaBoosthadthe highest AUC (0.699) and SVMhadthehighest accuracy (0.836); for hypertension, AdaBoosthad the greatestAUC (0.775) and accuracy (0.799).Hypertension topped diabetes predictors, while overweight/obesitywas the top predictorfor hypertension,followed by age and diabetes. Wealth and gender were moderately influential, with education andgeographic factors less so. Low specificity across models indicated challenges in identifying non-cases.Conclusion:This ML-driven analysisidentifiedthe bidirectional relationship ofhypertensionanddiabetesalong with several other predictors, includingoverweight/obesity,older age, and richer household wealthquintiles.Ourfindings underscore the need for integrated screening and lifestyle interventions targetinghigh-risk groups to mitigatefutureNCD burden.
DOI: 10.25442/hku.30944585
title: Supporting data for "VISIBLE LIGHT-ACTIVATED COVALENT LABELING PROBES ENABLE SPATIOTEMPORAL MAPPING OF LOCALIZED PROTEIN ENVIRONMENTS"

description: All the data below these files are about the mitochondrial proteomic analysis by using our probes, particularly for HeLa cells. The data include the procedures of the mitochondrial proteomics and the RAW data presented by Excel. The data was proceeded in Excel. We developed the first red-light-induced covalent crosslinking strategy for endogenous protein labeling in living cells that bypasses additional photocatalysts or photosensitizers. This files present the first MIM-localized small-molecule probes for mitochondrial proteomic analysis, particularly by using RAW cells under LPS stimulation. The data was proceeded in Excel, GOCC, and metascape. By using RAW cells as models, we proved that our approach circumvents genetic manipulation, exhibits minimal cytotoxicity, and offers a versatile tool for studying mitochondrial proteomics in live cells, advancing our understanding of mitochondrial adaptations in health and disease.Chapter 2: we developed two red-shifted coumarin-scaffold fluorescent probes that bypass photocatalysts, directly generating reactive carbene intermediates under green or red LED light irradiation for covalent protein labeling. Both probes exhibited efficient labeling of bovine serum albumin (BSA) without amino acid preference, including for leucine residues.Chapter 3: Probes derived from a carbonic anhydrase (CA) inhibitor and red light-activated pyridinium-diazo-coumarin successfully labeled CA-II in vitro upon red LED light irradiation. However, detection of endogenous CAs using our probes was unsuccessful. Similarly, probes derived from bromodomain-containing protein (BRD) inhibitors and pyridinium-diazo-coumarin failed to detect endogenous BRDs, likely due to insufficient ligand affinity or poor intracellular accessibility.Chapter 4: Notably, through hydrophobicity optimization, our red light-activated pyridinium-diazo-coumarin probes enabled selective, high-efficiency labeling of the mitochondrial inner membrane (MIM) across diverse cell lines. Applications in HeLa cells, hypoxia-stimulated HEK293T cells, and LPS-activated RAW264.7 macrophages further confirmed their utility in capturing mitochondrial proteomics, particularly MIM proteomic dynamics under physiological conditions.<br>
DOI: 10.25442/hku.30944585.v1
title: Supporting data for "VISIBLE LIGHT-ACTIVATED COVALENT LABELING PROBES ENABLE SPATIOTEMPORAL MAPPING OF LOCALIZED PROTEIN ENVIRONMENTS"

description: All the data below these files are about the mitochondrial proteomic analysis by using our probes, particularly for HeLa cells. The data include the procedures of the mitochondrial proteomics and the RAW data presented by Excel. The data was proceeded in Excel. We developed the first red-light-induced covalent crosslinking strategy for endogenous protein labeling in living cells that bypasses additional photocatalysts or photosensitizers. This files present the first MIM-localized small-molecule probes for mitochondrial proteomic analysis, particularly by using RAW cells under LPS stimulation. The data was proceeded in Excel, GOCC, and metascape. By using RAW cells as models, we proved that our approach circumvents genetic manipulation, exhibits minimal cytotoxicity, and offers a versatile tool for studying mitochondrial proteomics in live cells, advancing our understanding of mitochondrial adaptations in health and disease.Chapter 2: we developed two red-shifted coumarin-scaffold fluorescent probes that bypass photocatalysts, directly generating reactive carbene intermediates under green or red LED light irradiation for covalent protein labeling. Both probes exhibited efficient labeling of bovine serum albumin (BSA) without amino acid preference, including for leucine residues.Chapter 3: Probes derived from a carbonic anhydrase (CA) inhibitor and red light-activated pyridinium-diazo-coumarin successfully labeled CA-II in vitro upon red LED light irradiation. However, detection of endogenous CAs using our probes was unsuccessful. Similarly, probes derived from bromodomain-containing protein (BRD) inhibitors and pyridinium-diazo-coumarin failed to detect endogenous BRDs, likely due to insufficient ligand affinity or poor intracellular accessibility.Chapter 4: Notably, through hydrophobicity optimization, our red light-activated pyridinium-diazo-coumarin probes enabled selective, high-efficiency labeling of the mitochondrial inner membrane (MIM) across diverse cell lines. Applications in HeLa cells, hypoxia-stimulated HEK293T cells, and LPS-activated RAW264.7 macrophages further confirmed their utility in capturing mitochondrial proteomics, particularly MIM proteomic dynamics under physiological conditions.<br>
DOI: 10.34740/kaggle/dsv/14471043
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.18218395
title: Ipseity Daily Data

description: At what rate do individuals say they are X?

Begining on 2025-07-08, I surveyed a small random sample of American adults on a daily cadence.  They responded to many items of the form: Does <signifier> describe you today?

The file ipseity-daily-aggregated.csv in this repository contains the aggregated results through 2025-07-28.  The columns are as follows:



signifier - a word, phrase or emoji

yes_count - the number of respondents who answered yes (aggregated all-time).

no_count - the number of respondents who answered no (aggregated all-time).

prevalence_per_10k - an estimate of the prevalence per 10,000.  If we were to survey 10,000 American adults, how many would we expect to respond yes to this signifier?  The number is calculated as yes_count / (yes_count + no_count) * 10,000.

earliest_obs_date - this date represents the first time this particular signifier was ever displayed to a respondent.  Signifiers are chosen with stochasticity from a long list; new signifiers are sometimes added, old signifiers are sometimes deleted, and a signifier may be moved higher or lower in priority for sampling.

latest_obs_date - the date closest to the present date on which signifier was most recently presented to a respondent.


Version created on 2026-01-12 — cumulative data.
DOI: 10.34740/kaggle/dsv/14471099
title: Contrastive Language Image Pretraining by openai
DOI: 10.15468/dl.mrjgey
title: Occurrence Download

description: A dataset listing the 2 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Continent is Asia",
 "Country is one of (China, Chinese Taipei)",
 "OccurrenceStatus is Present",
 "TaxonKey is Chlamydomonadales"
 ]
}

 The dataset's 2 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003388-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25442/hku.30918353
title: Supporting data for “Constructing Influencer Personae of Chinese Intellectuals on Social Media”

description: This dataset comprises the complete set of qualitative interview data collected for the PhD research project titled “Constructing Influencer Personae of Chinese Intellectuals on Social Media,” conducted at The University of Hong Kong.The dataset is structured into two comparative cohorts for analysis:Baseline Cohort: 14 non-academic social media influencers (SMIs) from diverse content verticals, including lifestyle, beauty, technology, and knowledge-sharing.Focal Cohort: 6 academic-turned social media influencers (Aca-influencers), including both established and early-career scholars. All participants were purposively sampled based on their established online presence (minimum ~50,000 followers) on major Chinese platforms such as Douyin, RedNote/Xiaohongshu, Bilibili, and Weibo.Date Content:This archive contains the anonymized verbatim transcripts of all 20 semi-structured interviews conducted between May 2023 and May 2025. The interviews looked at why people wanted to become influencers, focusing on both "push" and "pull" factors. They further addressed content-creation strategies and personal-branding practices, as well as participants’ perceptions of platform algorithms and governance structures. Additionally, participants shared their experiences related to monetization and collaborations with Multi-Channel Networks (MCNs).Note on Audio: The original audio recordings are not included in this public dataset due to confidentiality concerns, as the voices are direct personal identifiers. All analysis should be based on the provided transcripts.<br>
DOI: 10.25442/hku.30918353.v1
title: Supporting data for “Constructing Influencer Personae of Chinese Intellectuals on Social Media”

description: This dataset comprises the complete set of qualitative interview data collected for the PhD research project titled “Constructing Influencer Personae of Chinese Intellectuals on Social Media,” conducted at The University of Hong Kong.The dataset is structured into two comparative cohorts for analysis:Baseline Cohort: 14 non-academic social media influencers (SMIs) from diverse content verticals, including lifestyle, beauty, technology, and knowledge-sharing.Focal Cohort: 6 academic-turned social media influencers (Aca-influencers), including both established and early-career scholars. All participants were purposively sampled based on their established online presence (minimum ~50,000 followers) on major Chinese platforms such as Douyin, RedNote/Xiaohongshu, Bilibili, and Weibo.Date Content:This archive contains the anonymized verbatim transcripts of all 20 semi-structured interviews conducted between May 2023 and May 2025. The interviews looked at why people wanted to become influencers, focusing on both "push" and "pull" factors. They further addressed content-creation strategies and personal-branding practices, as well as participants’ perceptions of platform algorithms and governance structures. Additionally, participants shared their experiences related to monetization and collaborations with Multi-Channel Networks (MCNs).Note on Audio: The original audio recordings are not included in this public dataset due to confidentiality concerns, as the voices are direct personal identifiers. All analysis should be based on the provided transcripts.<br>
DOI: 10.5281/zenodo.18218431
title: COVID-19-Todesfälle in Deutschland

description: Im Datensatz "COVID-19-Todesfälle in Deutschland" des Robert Koch-Instituts werden täglich aktualisierte Informationen zu COVID-19-bedingten Todesfällen in Deutschland bereitgestellt. Die Datenerhebung erfolgt im Rahmen des Infektionsschutzgesetzes (IfSG). Der Datensatz umfasst aggregierte Todesfallzahlen nach Berichtsdatum, Altersgruppen und Bundesländern und wird ergänzt durch den Fall-Verstorbenen-Anteil. Der Datensatz ermöglicht fundierte Analysen zur Bewertung der Krankheitsschwere und Verbreitung von COVID-19 in Deutschland.
DOI: 10.5281/zenodo.18218432
title: 7-Tage-Inzidenz der COVID-19-Fälle in Deutschland

description: Im Datensatz "7-Tage-Inzidenz der COVID-19-Fälle in Deutschland" des Robert Koch-Instituts werden die täglich aktualisierten COVID-19-Inzidenzen auf Bundes-, Landes- und Landkreisebene basierend auf den Meldungen nach dem Infektionsschutzgesetz (IfSG) bereitgestellt. Die 7-Tage-Inzidenz wird definiert als Anzahl gemeldeter Fälle der letzten sieben Tage pro 100.000 Einwohner:innen und differenziert nach Altersgruppen und Regionen. Die Datenverarbeitung erfolgt vollständig elektronisch über das Deutsche Elektronische Melde- und Informationssystem (DEMIS).
DOI: 10.34740/kaggle/dsv/14471163
title: TLDR pages translation pairs dataset
DOI: 10.25442/hku.30919613
title: Supporting data for “Effects of Angong Niuhuang Pill on ischemic stroke via the regulation of gut microbiota targeting TMA/TMAO metabolic pathway”

description: BackgroundIschemic stroke is a cerebrovascular event with underlying pathology in which gut microbiota and their metabolites participate via the gut-brain axis. The traditional Chinese medicine Angong Niuhuang Pill (AGNHP) is widely used in treatment of stroke, but its mechanisms via gut microbiota modulation remain obscure. ResultsIn this study, we established a middle cerebral artery occlusion (MCAO) mouse model and administered AGNHP at different timepoints after stroke. Multi-omics approaches including 16S rRNA sequencing and LC-MS/MS-based targeted metabolomics were conducted to analyze gut microbiota composition and TMA/TMAO metabolic pathways dynamically. The results showed that AGNHP treatment significantly reduced infarct volume, improved neurological function, relieve inflammatory conditions and restored gut and blood-brain barrier (BBB) integrity. Mechanistically, AGNHP altered gut microbiota composition. Through integrating abundance profiling, Lefse analysis, and correlation analysis related to metabolites, further study revealed AGNHP exclusively enriched the probiotic Akkermansia muciniphila, which suppressed TMAproducing-related genes (CntA/B, CutC/D, YeaW/X), thereby reducing systemic and cerebral TMA/TMAO accumulation. Complementary in vitro studies demonstrated that TMA may directly disrupted tight junction proteins in BBB and intestinal barrier. ConclusionsThis study elucidates the neuroprotective mechanism of AGNHP on ischemic stroke through modulation of the A. muciniphila-TMA/TMAO. By establishing a novel “Complex Drugs-Core Strains-Key Metabolites” paradigm, our study elucidates how AGNHP protects against ischemic stroke through time-dependent regulation of the gut microbiota-TMA/TMAO axis, with A. muciniphila playing a pivotal role. These findings not only advance our understanding of stroke pathophysiology, but also provide a methodological framework for investigating complex traditional medicines in modern biomedical research.