cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/fdxf3p5kt4
title: Low-grade blueschists in the central Yarlung Zangbo Suture Zone, southern Tibet: Petrogenesis and tectonic implications for the Neo-Tethyan Ocean evolution

description: The Yarlung Zangbo Suture Zone (YZSZ) in southern Tibet preserves critical geological records of the Neo-Tethyan Ocean, from oceanic crust formation to final subduction and continental collision. This study presents new petrological, mineral chemical, geochronological, and thermodynamic modelling data from two isolated occurrences of blueschists in the Sangsang and Kadui areas of the central YZSZ, with the aim of constraining their pressure–temperature–time (P–T–t) evolution, protolith nature, and tectonic significance. The Sangsang blueschists record a peak mineral assemblage of magnesioriebeckite, aegirine-augite, phengite, epidote, chlorite, albite, and sphene, whereas the Kadui blueschists lack phengite and contain a peak assemblage of riebeckite, aegirine-augite, epidote, chlorite, albite, and sphene. Phase equilibrium modelling constrains peak metamorphic conditions to 5.9–7.5 kbar at 314–368 °C for Sangsang and 5.5–7.1 kbar at 325–380 °C for Kadui, indicating high geothermal gradients and shallow subduction. Zircon U–Pb dating yields protolith ages of 150–148 Ma for Sangsang and 192–188 Ma for Kadui, the latter further supported by apatite U–Pb results. Published 40Ar/39Ar ages of sodium amphiboles from both localities indicate blueschist-facies metamorphism during the Paleocene (65–59 Ma), synchronous with the onset of the India–Asia collision. Field relationships show that the blueschists occur as layered or lenticular bodies intercalated with metachert and marble within sedimentary-matrix mélanges situated south of the YZSZ ophiolite belt. These blueschists exhibit distinctly higher thermal gradients and lower peak pressures compared to the high-P/low-T metamorphic rocks in the Indus Suture Zone and the Nagaland Ophiolite Complex. These characteristics suggest that the Sangsang and Kadui blueschists represent upper oceanic crustal fragments, likely derived from seamounts, that were offscraped and metamorphosed within the basal part of an accretionary wedge rather than descending into the subduction channel. By integrating new metamorphic, geochemical, and geochronological constraints with existing regional data, this study reconstructs a refined tectonic evolution model of the Neo-Tethyan Ocean. It outlines key transitions from oceanic crust generation to shallow subduction, accretion, and eventual continental collision along the southern margin of the Lhasa Block, providing new insights into the geodynamic evolution of the Tibetan Plateau.
DOI: 10.17632/fdxf3p5kt4.1
title: Low-grade blueschists in the central Yarlung Zangbo Suture Zone, southern Tibet: Petrogenesis and tectonic implications for the Neo-Tethyan Ocean evolution

description: The Yarlung Zangbo Suture Zone (YZSZ) in southern Tibet preserves critical geological records of the Neo-Tethyan Ocean, from oceanic crust formation to final subduction and continental collision. This study presents new petrological, mineral chemical, geochronological, and thermodynamic modelling data from two isolated occurrences of blueschists in the Sangsang and Kadui areas of the central YZSZ, with the aim of constraining their pressure–temperature–time (P–T–t) evolution, protolith nature, and tectonic significance. The Sangsang blueschists record a peak mineral assemblage of magnesioriebeckite, aegirine-augite, phengite, epidote, chlorite, albite, and sphene, whereas the Kadui blueschists lack phengite and contain a peak assemblage of riebeckite, aegirine-augite, epidote, chlorite, albite, and sphene. Phase equilibrium modelling constrains peak metamorphic conditions to 5.9–7.5 kbar at 314–368 °C for Sangsang and 5.5–7.1 kbar at 325–380 °C for Kadui, indicating high geothermal gradients and shallow subduction. Zircon U–Pb dating yields protolith ages of 150–148 Ma for Sangsang and 192–188 Ma for Kadui, the latter further supported by apatite U–Pb results. Published 40Ar/39Ar ages of sodium amphiboles from both localities indicate blueschist-facies metamorphism during the Paleocene (65–59 Ma), synchronous with the onset of the India–Asia collision. Field relationships show that the blueschists occur as layered or lenticular bodies intercalated with metachert and marble within sedimentary-matrix mélanges situated south of the YZSZ ophiolite belt. These blueschists exhibit distinctly higher thermal gradients and lower peak pressures compared to the high-P/low-T metamorphic rocks in the Indus Suture Zone and the Nagaland Ophiolite Complex. These characteristics suggest that the Sangsang and Kadui blueschists represent upper oceanic crustal fragments, likely derived from seamounts, that were offscraped and metamorphosed within the basal part of an accretionary wedge rather than descending into the subduction channel. By integrating new metamorphic, geochemical, and geochronological constraints with existing regional data, this study reconstructs a refined tectonic evolution model of the Neo-Tethyan Ocean. It outlines key transitions from oceanic crust generation to shallow subduction, accretion, and eventual continental collision along the southern margin of the Lhasa Block, providing new insights into the geodynamic evolution of the Tibetan Plateau.
DOI: 10.15468/6hrs5u
title: Herbarium UGDA - Vascular Plants Collection

description: The collection of vascular plants of the Herbarium Universitatis Gedanensis (UGDA). Current GBIF distributed data include c. 130 000 specimens at the collections. The collection represent specimens from Poland, especially from Pomerania, including the area of ​​Słowiński and Woliński National Parks.
DOI: 10.5281/zenodo.15726135
title: Design of Borole and Silylene-Based Frustrated Lewis Pairs for Dinitrogen Activation Promoted by Aromaticity: A Combined DFT and Machine Learning Study
DOI: 10.5281/zenodo.15729072
title: Design of Borole and Silylene-Based Frustrated Lewis Pairs for Dinitrogen Activation Promoted by Aromaticity: A Combined DFT and Machine Learning Study
DOI: 10.17632/czzk5zgr69
title: OSA Three-level metaanalysis

description: Filter data from original literatures and R code for effect size analysis
DOI: 10.17632/czzk5zgr69.1
title: OSA Three-level metaanalysis

description: Filter data from original literatures and R code for effect size analysis
DOI: 10.5281/zenodo.15727778
title: Raw data for the project Prevention of unintended pregnancies by an integral healthy school approach

description: Desciption of quantitative data collected during the ZonMw project Prevention of unintended pregnancies by an integral healthy school approach in the region of Hollands Midden in the Netherlands
DOI: 10.5281/zenodo.15727779
title: Raw data for the project Prevention of unintended pregnancies by an integral healthy school approach

description: Desciption of quantitative data collected during the ZonMw project Prevention of unintended pregnancies by an integral healthy school approach in the region of Hollands Midden in the Netherlands
DOI: 10.5281/zenodo.15728884
title: Supplementary Materials: AI Diagnostic Models for Sickle Cell Severity Stratification - Datasets, Code, Checklists

description: This repository supports the manuscript:  

"Diagnostic Accuracy of Artificial Intelligence Models for Stratifying Disease Severity in Sickle Cell Disorders..."  

 

**Contents**:  

1. PRISMA-DTA/PROBAST-AI/CLAIM Checklists  

2. Complete database search strategies (MEDLINE, Embase, etc.)  

3. R scripts for meta-analysis (metafor, meta4diag)  

4. Python code for SHAP analysis and LightGBM deployment  

5. Ethnicity-specific threshold calibration tables  

6. FHIR API templates for EHR integration  

 

**Validation**:  

- Reproduces all figures/tables in the manuscript  

- CPU-optimized models for low-resource settings (Raspberry Pi compatible)
DOI: 10.5281/zenodo.15728885
title: Supplementary Materials: AI Diagnostic Models for Sickle Cell Severity Stratification - Datasets, Code, Checklists

description: This repository supports the manuscript:  

"Diagnostic Accuracy of Artificial Intelligence Models for Stratifying Disease Severity in Sickle Cell Disorders..."  

 

**Contents**:  

1. PRISMA-DTA/PROBAST-AI/CLAIM Checklists  

2. Complete database search strategies (MEDLINE, Embase, etc.)  

3. R scripts for meta-analysis (metafor, meta4diag)  

4. Python code for SHAP analysis and LightGBM deployment  

5. Ethnicity-specific threshold calibration tables  

6. FHIR API templates for EHR integration  

 

**Validation**:  

- Reproduces all figures/tables in the manuscript  

- CPU-optimized models for low-resource settings (Raspberry Pi compatible)
DOI: 10.17632/27j2gp4w34
title: Incidencia del Neuromarketing en el Comportamiento del Consumidor en el Sector Retail de Manta.

description: La presente investigación permitió examinar la incidencia del neuromarketing en el comportamiento del consumidor en el sector retail de Manta, destacando estrategias visuales, auditivas y cinestésicas. Los resultados 
evidenciaron la incidencia del neuromarketing en la motivación emocional en el comportamiento del consumidor. 
El análisis permitió conocer la interacción entre el cerebro humano y las funciones del neocórtex, cerebro límbico 
y primitivo. Se desarrolló un estudio de tipo cuantitativo y cualitativo. Los análisis estadísticos, con una alta 
confiabilidad (α = 0,922) y correlaciones significativas (τb de Kendall = 0,818, p &lt; 0,001; ρ de Spearman = 0,889, 
p &lt; 0,001), muestran una fuerte asociación entre neuromarketing y comportamiento del consumidor. Los resultados 
indican que las técnicas de neuromarketing impactan significativamente en la motivación emocional y las 
decisiones de compra, resaltando la importancia del entorno de la tienda y factores ambientales en la percepción 
del consumidor.
Palabras Claves: Neuromarketing, Comportamiento, Consumidor, Retail, Cerebro.
DOI: 10.21947/2570094
title: Site G - Distributed Temperature System / Reviewed Data

description: This dataset contains fiber optic distributed temperature system (DTS) data deployed on tethered balloon system at AWAKEN site G. The DTS data include time stamp, altitude, and temperature measurements.
DOI: 10.17632/27j2gp4w34.2
title: Incidencia del Neuromarketing en el Comportamiento del Consumidor en el Sector Retail de Manta.

description: La presente investigación permitió examinar la incidencia del neuromarketing en el comportamiento del consumidor en el sector retail de Manta, destacando estrategias visuales, auditivas y cinestésicas. Los resultados 
evidenciaron la incidencia del neuromarketing en la motivación emocional en el comportamiento del consumidor. 
El análisis permitió conocer la interacción entre el cerebro humano y las funciones del neocórtex, cerebro límbico 
y primitivo. Se desarrolló un estudio de tipo cuantitativo y cualitativo. Los análisis estadísticos, con una alta 
confiabilidad (α = 0,922) y correlaciones significativas (τb de Kendall = 0,818, p &lt; 0,001; ρ de Spearman = 0,889, 
p &lt; 0,001), muestran una fuerte asociación entre neuromarketing y comportamiento del consumidor. Los resultados 
indican que las técnicas de neuromarketing impactan significativamente en la motivación emocional y las 
decisiones de compra, resaltando la importancia del entorno de la tienda y factores ambientales en la percepción 
del consumidor.
Palabras Claves: Neuromarketing, Comportamiento, Consumidor, Retail, Cerebro.
DOI: 10.17632/k59mh7vkyn
title: Neuromarketing, Big Data e IA: La ciencia detrás del deseo de compra.

description: This report investigates the phenomenon of purchase desire through the lens of neuromarketing, a discipline that merges neuroscience and psychology to uncover how consumers make decisions. Neuromarketing is defined as the analysis of brain responses to marketing stimuli, highlighting its ability to shape consumer behavior and preferences. The report examines the brain processes involved in decision-making, identifying key areas such as the prefrontal cortex, responsible for planning and reasoning; the limbic system, which regulates emotions; and the reptilian brain, which controls basic instincts. These findings demonstrate that purchasing decisions are predominantly emotional rather than rational, challenging the traditional notion that consumers act logically. The report also explores the intersection of neuromarketing and emerging technologies such as Big Data and Artificial Intelligence (AI). It discusses how these tools enable companies to personalize their marketing strategies by analyzing behavior patterns and individual preferences, thereby intensifying the desire to purchase by emotionally resonating with consumers. Furthermore, the ethical implications of neuromarketing are addressed, emphasizing the need for responsible practices that respect consumer privacy. In an environment where emotional manipulation can be effective, it is crucial to establish a balance between persuasion and ethics.
DOI: 10.17632/k59mh7vkyn.1
title: Neuromarketing, Big Data e IA: La ciencia detrás del deseo de compra.

description: This report investigates the phenomenon of purchase desire through the lens of neuromarketing, a discipline that merges neuroscience and psychology to uncover how consumers make decisions. Neuromarketing is defined as the analysis of brain responses to marketing stimuli, highlighting its ability to shape consumer behavior and preferences. The report examines the brain processes involved in decision-making, identifying key areas such as the prefrontal cortex, responsible for planning and reasoning; the limbic system, which regulates emotions; and the reptilian brain, which controls basic instincts. These findings demonstrate that purchasing decisions are predominantly emotional rather than rational, challenging the traditional notion that consumers act logically. The report also explores the intersection of neuromarketing and emerging technologies such as Big Data and Artificial Intelligence (AI). It discusses how these tools enable companies to personalize their marketing strategies by analyzing behavior patterns and individual preferences, thereby intensifying the desire to purchase by emotionally resonating with consumers. Furthermore, the ethical implications of neuromarketing are addressed, emphasizing the need for responsible practices that respect consumer privacy. In an environment where emotional manipulation can be effective, it is crucial to establish a balance between persuasion and ethics.
DOI: 10.17632/mvcbj5hvbz.2
title: supporting figures

description: The supporting figures related to the manuscript on microplastic distribution and characteristics in India's different coastal and estuarine ecosystems. For the conciseness of the manuscript, supporting figures are added here instead of the main manuscript. Anyone can access the figures for detailed knowledge about data distribution.
DOI: 10.17632/prn6p3fds8
title: Ecological Footprint of Microplastics in Coastal and Estuarine Environments of India: Sediment-Water Interface Analysis

description: Microplastic distribution was investigated at different coastal and estuarine regions of India. The chemical nature of microplastic and macroplastic debris was determined using Raman spectroscopy. SEM-EDX was done to analyze the surface morphology of microplastic. The heavy metal concentrations on plastic debris were measured using ICPOES.
DOI: 10.15468/dl.eqm3bz
title: Occurrence Download

description: A dataset containing 1671 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantago lanceolata L.",
 "Year 2000-2025"
 ]
}

 The dataset includes 1671 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067103-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/prn6p3fds8.2
title: Ecological Footprint of Microplastics in Coastal and Estuarine Environments of India: Sediment-Water Interface Analysis

description: Microplastic distribution was investigated at different coastal and estuarine regions of India. The chemical nature of microplastic and macroplastic debris was determined using Raman spectroscopy. SEM-EDX was done to analyze the surface morphology of microplastic. The heavy metal concentrations on plastic debris were measured using ICPOES.
DOI: 10.17632/6jz6hfcp5m
title: Controls of F content and pH on the formation of greisen quantified by reactive transport modeling

description: Code and raw data for all figures in the manuscript by Qiao et al., 2024 to be published by Geochimica et Cosmochimica Acta as ' Controls of F content and pH on the formation of greisen quantified by reactive transport modeling'.
DOI: 10.17632/6jz6hfcp5m.1
title: Controls of F content and pH on the formation of greisen quantified by reactive transport modeling

description: Code and raw data for all figures in the manuscript by Qiao et al., 2024 to be published by Geochimica et Cosmochimica Acta as ' Controls of F content and pH on the formation of greisen quantified by reactive transport modeling'.
DOI: 10.17632/k2f6ff56fh
title: Carbonate adsorption to goethite and proton charging

description: Carbonate adsorption data and CD-MUSIC model simulations on goethite of 76 m2/g under closed and open conditions in NaCl and NaNO3.
Proton charging data and CD-MUSIC model simulations on goethite of 76 m2/g in NaCl and NaNO3.
DOI: 10.17632/k2f6ff56fh.1
title: Carbonate adsorption to goethite and proton charging

description: Carbonate adsorption data and CD-MUSIC model simulations on goethite of 76 m2/g under closed and open conditions in NaCl and NaNO3.
Proton charging data and CD-MUSIC model simulations on goethite of 76 m2/g in NaCl and NaNO3.
DOI: 10.17632/w6nk65bgwp
title: Systematic Literature Review For Stock Price Prediction with Machine Learning

description: This systematic literature review assesses stock price prediction methodologies, concentrating on research employing machine learning, time series analysis, and statistical models. Following the implementation of rigorous inclusion and exclusion criteria, we examine research according to their aims, techniques, datasets, and outcomes. The comprehensive literature assessment of these 10 studies uncovers significant insights and consequences for the three study issues. The paper highlights the efficacy of AI models, especially in stock market prediction, utilizing Long Short-Term Memory (LSTM) networks due to their ability to process sequential data and recognize long-term relationships. Sentiment analysis derived from intermarket data, news, and social media boosts predicted accuracy, particularly when integrated with conventional technical analysis. Market variables, including historical returns, momentum, implied volatility (VIX), and cash flow financial data, enhance predictive accuracy by offering further context. Standard assessment indicators, such as accuracy, return on investment (ROI), profitability, and Mean Absolute Error (MAE), indicate that AI models surpass conventional techniques. Nonetheless, obstacles persist, including dependence on erratic sentiment data, dangers of overfitting, and possible biases arising from unstructured sentiment data and the limited volume of sentiment data. Future research may increase sentiment analysis by including additional sentiment data and implementing quality control, integrating diverse AI methodologies, and encompassing a wider range of financial and economic data to strengthen model robustness. This article seeks to emphasize the growing capability of deep learning to enhance stock market prediction methodologies.
DOI: 10.17632/w6nk65bgwp.1
title: Systematic Literature Review For Stock Price Prediction with Machine Learning

description: This systematic literature review assesses stock price prediction methodologies, concentrating on research employing machine learning, time series analysis, and statistical models. Following the implementation of rigorous inclusion and exclusion criteria, we examine research according to their aims, techniques, datasets, and outcomes. The comprehensive literature assessment of these 10 studies uncovers significant insights and consequences for the three study issues. The paper highlights the efficacy of AI models, especially in stock market prediction, utilizing Long Short-Term Memory (LSTM) networks due to their ability to process sequential data and recognize long-term relationships. Sentiment analysis derived from intermarket data, news, and social media boosts predicted accuracy, particularly when integrated with conventional technical analysis. Market variables, including historical returns, momentum, implied volatility (VIX), and cash flow financial data, enhance predictive accuracy by offering further context. Standard assessment indicators, such as accuracy, return on investment (ROI), profitability, and Mean Absolute Error (MAE), indicate that AI models surpass conventional techniques. Nonetheless, obstacles persist, including dependence on erratic sentiment data, dangers of overfitting, and possible biases arising from unstructured sentiment data and the limited volume of sentiment data. Future research may increase sentiment analysis by including additional sentiment data and implementing quality control, integrating diverse AI methodologies, and encompassing a wider range of financial and economic data to strengthen model robustness. This article seeks to emphasize the growing capability of deep learning to enhance stock market prediction methodologies.
DOI: 10.17632/nvhjgjtg88
title: Creditor Protection Shocks and Corporate Cash Holdings: Insights from Online Judicial Auctions

description: This replication folder contains the code and data used to obtain the results presented in the paper “Creditor Protection Shocks and Corporate Cash Holdings: Insights from Online Judicial Auctions” by Weilun Tian, Junru Guo, Dongyan Li, and Jiarui Wang.
DOI: 10.17632/nvhjgjtg88.1
title: Creditor Protection Shocks and Corporate Cash Holdings: Insights from Online Judicial Auctions

description: This replication folder contains the code and data used to obtain the results presented in the paper “Creditor Protection Shocks and Corporate Cash Holdings: Insights from Online Judicial Auctions” by Weilun Tian, Junru Guo, Dongyan Li, and Jiarui Wang.
DOI: 10.17632/3sykgw27jy
title: Ephemeral communication dataset
DOI: 10.17632/3sykgw27jy.1
title: Ephemeral communication dataset
DOI: 10.17632/kxmthnkx4c
title: Global dust emission dataset

description: Global dust emission simulations and relative datasets.
DOI: 10.17632/kxmthnkx4c.1
title: Global dust emission dataset

description: Global dust emission simulations and relative datasets.
DOI: 10.17632/rkp539khzd
title: TechnologyInMathTeacherNoticingReview

description: The dataset includes all the reviewed articles in the article.
DOI: 10.17632/rkp539khzd.1
title: TechnologyInMathTeacherNoticingReview

description: The dataset includes all the reviewed articles in the article.