cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31395288.v1
title: Optimizing Social Media Campaigns Through Engagement Topology and Behavioral Clustering

description: Social media engagement drives both individual behavior and content dissemination, yet traditional analytics often reduce interactions to simple counts, obscuring the complex structures underlying user activity. In the highly competitive digital ecosystem, understanding how users engage with content is critical for businesses seeking to optimize social media campaigns and maximize ROI. Traditional engagement metrics, such as likes and shares, fail to capture the underlying structure and dynamics of user behavior. This study investigates the latent patterns of engagement by combining topological data analysis (TDA) with behavioral clustering across 100,000 posts on multiple platforms. Using persistent homology and k-nearest neighbor graphs, we reveal a primary bifurcation between Active (validation-focused) and Passive (consumption/propagation) users, nested four-strain substructures, and over 650 significant $H_1$ loops indicating recurring feedback cycles. Active users exhibit strong cluster cohesion and high engagement rates, while Passive users contribute broadly to content diffusion with slightly higher loop counts, highlighting distinct functional roles in social media dynamics. These findings provide a principled framework for targeting content, reinforcing feedback loops, and leveraging hub posts to amplify engagement. By linking topological structure to behavioral patterns, this work advances both the theoretical understanding of digital interaction and the practical design of more effective social media campaigns.
DOI: 10.15468/dl.devm4h
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "TaxonKey is Hyporthodus mystacinus"
 ]
}

 The dataset includes 45 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004401-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nnbya7
title: Occurrence Download

description: A dataset containing 12167 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Delphinapterus leucas"
 ]
}

 The dataset includes 12167 records from 128 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004400-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/p8w694yjkv
title: Raw data of Synchronized scalp acupuncture for stroke: A systematic review and meta-analysis

description: This dataset contains the raw data and analytical files supporting the systematic review and meta-analysis titled "Synchronizing Scalp Acupuncture with Rehabilitation Training for Post-Stroke Recovery: A Systematic Review and Meta-Analysis".
The dataset includes:
1.Outcome Measures (Numerical Data): Raw numerical data for all primary and secondary outcomes, including sample sizes (n), means, and standard deviations (SD) for continuous variables.
2.Subgroup Analysis Category: Detailed categorization used for subgroup analyses to explore potential sources of heterogeneity.
3.Meta-regression Data: Comprehensive datasets used for meta-regression analyses.
DOI: 10.17632/p8w694yjkv.1
title: Raw data of Synchronized scalp acupuncture for stroke: A systematic review and meta-analysis

description: This dataset contains the raw data and analytical files supporting the systematic review and meta-analysis titled "Synchronizing Scalp Acupuncture with Rehabilitation Training for Post-Stroke Recovery: A Systematic Review and Meta-Analysis".
The dataset includes:
1.Outcome Measures (Numerical Data): Raw numerical data for all primary and secondary outcomes, including sample sizes (n), means, and standard deviations (SD) for continuous variables.
2.Subgroup Analysis Category: Detailed categorization used for subgroup analyses to explore potential sources of heterogeneity.
3.Meta-regression Data: Comprehensive datasets used for meta-regression analyses.
DOI: 10.17632/fyzy2z8nzx
title: A Large-Scale Dataset for Arrogance Detection in the Bengali Language

description: This dataset contains 46,128 labeled Bengali comments curated for the task of arrogance detection. While existing datasets focus heavily on hate speech or cyberbullying, this dataset addresses the subtle linguistic nuances of "arrogance", characterized by overbearing pride, lack of empathy, and social superiority, which is often expressed without overt toxicity.
The data was compiled to support research in Bengali NLP. It serves as the primary resource for training the high-performing BanglaBERT model (96% accuracy) described in the accompanying research paper.
Dataset Structure
The dataset is provided in a single .csv file with the following columns:
comment: The raw Bengali text.
source: The origin of the comment (online or AI).
weak_label: Initial label assigned by heuristic functions.
snorkel_label: Refined label produced by the Snorkel framework.
final_label: The target label for classification.
1: Arrogant
0:Non-arrogant
DOI: 10.17632/fyzy2z8nzx.2
title: A Large-Scale Dataset for Arrogance Detection in the Bengali Language

description: This dataset contains 46,128 labeled Bengali comments curated for the task of arrogance detection. While existing datasets focus heavily on hate speech or cyberbullying, this dataset addresses the subtle linguistic nuances of "arrogance", characterized by overbearing pride, lack of empathy, and social superiority, which is often expressed without overt toxicity.
The data was compiled to support research in Bengali NLP. It serves as the primary resource for training the high-performing BanglaBERT model (96% accuracy) described in the accompanying research paper.
Dataset Structure
The dataset is provided in a single .csv file with the following columns:
comment: The raw Bengali text.
source: The origin of the comment (online or AI).
weak_label: Initial label assigned by heuristic functions.
snorkel_label: Refined label produced by the Snorkel framework.
final_label: The target label for classification.
1: Arrogant
0:Non-arrogant
DOI: 10.17632/ctt5kwppwt
title: A High Fidelity Synthetic IoT Dataset for Smart Waste Management (10 Million Records)

description: This dataset provides 10 million simulated records of smart waste bin levels across major neighborhoods in Dhaka, Bangladesh (Gulshan, Motijheel, Dhanmondi, Mirpur, and Old Dhaka). Unlike generic datasets, this simulation incorporates regional socio-cultural factors, such as the "Friday Mosque Effect" (spikes in waste generation during prayer times), weekend patterns (Friday to Saturday), and extreme seasonal events like Eid-ul-Adha. The data also includes real-world IoT challenges, such as sensor noise, battery depletion patterns influenced by weather conditions, and missing data points resulting from connectivity failures. It is designed for researchers working on time-series forecasting, anomaly detection, and smart city logistics. 

Key Features:
1. 10 Million Records: High-volume time-series data.
2. Spatial Integrity: 5,000 unique bin IDs locked to specific Dhaka coordinates.
3. Socio-Cultural Seasonality: Includes the "Friday Mosque Effect" and "Eid-ul-Adha" seasonal spikes.
4. IoT Realism: Simulated sensor noise, battery drain, and connectivity failures. 
5. Target Variables: Dual targets for ML Classification (needs_collection) and Regression (overflow_kg).
DOI: 10.17632/ctt5kwppwt.2
title: A High Fidelity Synthetic IoT Dataset for Smart Waste Management (10 Million Records)

description: This dataset provides 10 million simulated records of smart waste bin levels across major neighborhoods in Dhaka, Bangladesh (Gulshan, Motijheel, Dhanmondi, Mirpur, and Old Dhaka). Unlike generic datasets, this simulation incorporates regional socio-cultural factors, such as the "Friday Mosque Effect" (spikes in waste generation during prayer times), weekend patterns (Friday to Saturday), and extreme seasonal events like Eid-ul-Adha. The data also includes real-world IoT challenges, such as sensor noise, battery depletion patterns influenced by weather conditions, and missing data points resulting from connectivity failures. It is designed for researchers working on time-series forecasting, anomaly detection, and smart city logistics. 

Key Features:
1. 10 Million Records: High-volume time-series data.
2. Spatial Integrity: 5,000 unique bin IDs locked to specific Dhaka coordinates.
3. Socio-Cultural Seasonality: Includes the "Friday Mosque Effect" and "Eid-ul-Adha" seasonal spikes.
4. IoT Realism: Simulated sensor noise, battery drain, and connectivity failures. 
5. Target Variables: Dual targets for ML Classification (needs_collection) and Regression (overflow_kg).
DOI: 10.17632/vk2s2yyby5
title: Pertanyaan dan Hasil USE Questionnaire

description: 30 pertanyaan USE Questionnaire yang dibagi menjadi 4 komponen indikator, yaitu usefulness (tingkat kegunaan), ease of use (kemudahan dalam penggunaan), ease of learning (kemudahan untuk dipelajari), dan satisfaction (tingkat kepuasan).
DOI: 10.17632/vk2s2yyby5.1
title: Pertanyaan dan Hasil USE Questionnaire

description: 30 pertanyaan USE Questionnaire yang dibagi menjadi 4 komponen indikator, yaitu usefulness (tingkat kegunaan), ease of use (kemudahan dalam penggunaan), ease of learning (kemudahan untuk dipelajari), dan satisfaction (tingkat kepuasan).
DOI: 10.17632/6pr47pty85
title: Enterococcus durans B21

description: Genome-scale metabolic model-guided assessment of safety and probiotic potential of Enterococcus durans B21
DOI: 10.17632/6pr47pty85.2
title: Enterococcus durans B21

description: Genome-scale metabolic model-guided assessment of safety and probiotic potential of Enterococcus durans B21
DOI: 10.15468/dl.kk28xj
title: Occurrence Download

description: A dataset containing 535 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anoura geoffroyi",
 "Country is Colombia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 535 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004402-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/86gv9c3fp4
title: Supplementary data of CFTR

description: Supplementary table and uncropped Gels and Blots images
DOI: 10.17632/86gv9c3fp4.1
title: Supplementary data of CFTR

description: Supplementary table and uncropped Gels and Blots images
DOI: 10.17632/ryhfyy87wj
title: OsHPPD structural data

description: This database contains the crystal structure of OsHPPD and the predicted structures of relevant mutants simulated using AlphaFold.
DOI: 10.17632/ryhfyy87wj.2
title: OsHPPD structural data

description: This database contains the crystal structure of OsHPPD and the predicted structures of relevant mutants simulated using AlphaFold.
DOI: 10.17632/w7cbjb5fmt
title: Exploring Gen Z’s Environmental Knowledge and Consumer Hope in Driving Sustainable Consumption Behavior: Evidence from Hokky Supermarket Surabaya

description: Penelitian ini bertujuan untuk mengkaji mekanisme psikologis yang mendorong perilaku konsumsi berkelanjutan pada Generasi Z dengan menelaah peran green self-identity, perceived natural content, dan perceived greenwashing melalui consumer hope, serta mempertimbangkan environmental knowledge sebagai variabel moderasi. Penelitian ini menggunakan pendekatan kuantitatif dengan metode survei terhadap 270 konsumen Generasi Z yang pernah berbelanja di Hokky Supermarket Surabaya. Data dianalisis menggunakan Partial Least Squares–Structural Equation Modeling (PLS-SEM). Hasil penelitian menunjukkan bahwa green self-identity dan perceived natural content berpengaruh positif dan signifikan terhadap consumer hope, sedangkan perceived greenwashing berpengaruh negatif dan signifikan. Selanjutnya, consumer hope terbukti berpengaruh positif terhadap perilaku konsumsi berkelanjutan. Namun, environmental knowledge tidak terbukti memoderasi maupun memediasi hubungan antara persepsi konsumen dan consumer hope. Meskipun penelitian sebelumnya menekankan peran faktor kognitif dalam konsumsi berkelanjutan, studi ini memperluas literatur dengan menegaskan peran emosi prospektif, khususnya consumer hope, sebagai mekanisme kunci yang menghubungkan persepsi konsumen dengan perilaku aktual. Temuan ini memberikan implikasi praktis bagi peritel dan pemasar dalam merancang strategi keberlanjutan yang autentik untuk mendorong konsumsi berkelanjutan di kalangan Generasi Z.
DOI: 10.17632/w7cbjb5fmt.1
title: Exploring Gen Z’s Environmental Knowledge and Consumer Hope in Driving Sustainable Consumption Behavior: Evidence from Hokky Supermarket Surabaya

description: Penelitian ini bertujuan untuk mengkaji mekanisme psikologis yang mendorong perilaku konsumsi berkelanjutan pada Generasi Z dengan menelaah peran green self-identity, perceived natural content, dan perceived greenwashing melalui consumer hope, serta mempertimbangkan environmental knowledge sebagai variabel moderasi. Penelitian ini menggunakan pendekatan kuantitatif dengan metode survei terhadap 270 konsumen Generasi Z yang pernah berbelanja di Hokky Supermarket Surabaya. Data dianalisis menggunakan Partial Least Squares–Structural Equation Modeling (PLS-SEM). Hasil penelitian menunjukkan bahwa green self-identity dan perceived natural content berpengaruh positif dan signifikan terhadap consumer hope, sedangkan perceived greenwashing berpengaruh negatif dan signifikan. Selanjutnya, consumer hope terbukti berpengaruh positif terhadap perilaku konsumsi berkelanjutan. Namun, environmental knowledge tidak terbukti memoderasi maupun memediasi hubungan antara persepsi konsumen dan consumer hope. Meskipun penelitian sebelumnya menekankan peran faktor kognitif dalam konsumsi berkelanjutan, studi ini memperluas literatur dengan menegaskan peran emosi prospektif, khususnya consumer hope, sebagai mekanisme kunci yang menghubungkan persepsi konsumen dengan perilaku aktual. Temuan ini memberikan implikasi praktis bagi peritel dan pemasar dalam merancang strategi keberlanjutan yang autentik untuk mendorong konsumsi berkelanjutan di kalangan Generasi Z.
DOI: 10.17632/b9tryr388p
title: A dataset of Early fault diagnosis of electric vehicle batteries based on physics-informed DeepSeekV3 architecture

description: Accurate early warning of battery failure is essential to ensure the safety of electric vehicles (EVs). However, the scarcity of early fault samples, the difficulty in capturing weak features, and the fact that pure data - driven models are prone to violate physical laws still pose challenges for early fault diagnosis. To address these issues, an unsupervised joint diagnosis framework combining generative pre-trained transformer and weighted one-class support vector machine (GPT-WOCSVM) is proposed for the first time in this paper. The charge data GPT (Charge-dataGPT) is constructed based on the physics-informed DeepSeekV3 architecture. It can use the charging data of the first 10 minutes to generate the charging sequence for the next 20 minutes in accordance with the electrochemical law through autoregression; WOCSVM is trained only with normal data, and early fault recognition is realized by judging whether the generated sequence deviates from the normal mode. 
This dataset contains the implementation code and model of GPT-WOCSVM.
DOI: 10.17632/b9tryr388p.2
title: A dataset of Early fault diagnosis of electric vehicle batteries based on physics-informed DeepSeekV3 architecture

description: Accurate early warning of battery failure is essential to ensure the safety of electric vehicles (EVs). However, the scarcity of early fault samples, the difficulty in capturing weak features, and the fact that pure data - driven models are prone to violate physical laws still pose challenges for early fault diagnosis. To address these issues, an unsupervised joint diagnosis framework combining generative pre-trained transformer and weighted one-class support vector machine (GPT-WOCSVM) is proposed for the first time in this paper. The charge data GPT (Charge-dataGPT) is constructed based on the physics-informed DeepSeekV3 architecture. It can use the charging data of the first 10 minutes to generate the charging sequence for the next 20 minutes in accordance with the electrochemical law through autoregression; WOCSVM is trained only with normal data, and early fault recognition is realized by judging whether the generated sequence deviates from the normal mode. 
This dataset contains the implementation code and model of GPT-WOCSVM.
DOI: 10.17632/vjhrgy6pf2
title: Mercury intrusion porosimetry and X-ray Diffraction data of carbonaceous shale under oxalic acid-induced chemical etching

description: This dataset contains mercury intrusion porosimetry (MIP) and X-ray Diffraction (XRD) data of Permian carbonaceous shale from Southwest China, collected under controlled oxalic acid-induced chemical etching conditions. It aims to support mechanistic studies on the coupled chemical-mechanical degradation of carbonaceous shale weak interlayers—critical geohazard drivers in the region—by linking mineral dissolution and microstructural evolution.
The data were obtained from shale samples exposed to oxalic acid solutions at concentrations of 0.005, 0.011, and 0.268 mol/L (plus a distilled water control) for 7, 14, and 21 days, capturing changes in mineral composition (e.g., calcite, illite) and pore-structure parameters (e.g., pore size distribution). This dataset enables validation of multi-scale kinetic models and informs sustainable geohazard mitigation strategies by quantifying the link between chemical etching and shear strength degradation.
DOI: 10.17632/vjhrgy6pf2.1
title: Mercury intrusion porosimetry and X-ray Diffraction data of carbonaceous shale under oxalic acid-induced chemical etching

description: This dataset contains mercury intrusion porosimetry (MIP) and X-ray Diffraction (XRD) data of Permian carbonaceous shale from Southwest China, collected under controlled oxalic acid-induced chemical etching conditions. It aims to support mechanistic studies on the coupled chemical-mechanical degradation of carbonaceous shale weak interlayers—critical geohazard drivers in the region—by linking mineral dissolution and microstructural evolution.
The data were obtained from shale samples exposed to oxalic acid solutions at concentrations of 0.005, 0.011, and 0.268 mol/L (plus a distilled water control) for 7, 14, and 21 days, capturing changes in mineral composition (e.g., calcite, illite) and pore-structure parameters (e.g., pore size distribution). This dataset enables validation of multi-scale kinetic models and informs sustainable geohazard mitigation strategies by quantifying the link between chemical etching and shear strength degradation.
DOI: 10.17632/htkpynj4vb
title: UzSentiNER v1: A 30,000-Sentence Uzbek Corpus for Joint Sentiment Analysis and NER (6 Entity Types) with Emoji Signals

description: UzSentiNER v1 is a 30,000-sentence Uzbek corpus designed for joint sentiment analysis (Positive/Negative/Neutral) and named entity recognition with six entity types: PERSON, POSITION, ORGANIZATION, LOCATION, DATE, PRODUCT.
The dataset combines 24,000 synthetic sentences (generated and calibrated with two rounds of manual quality control) and 6,000 linguist-curated sentences, merged into a single unified schema. Emoji usage is included to simulate real user writing patterns; emojis are placed naturally within sentences (not appended at the end).
The package provides multiple formats for reproducible research: XLSX, CSV, JSONL, plus token-level BIO tags and CoNLL (train/val/test) exports for NER training. Character-level entity spans are also included.
A manual audit on a random subset of 1,000 sentences reports 95.1% acceptable samples; remaining issues relate only to sentiment calibration, with 0% NER-type errors observed.
This dataset is intended for benchmarking and developing Uzbek NLP models for sentiment classification, NER, and multi-task learning. See README and documentation files for schema, annotation guidelines, validation report, and reproducibility instructions.
DOI: 10.17632/htkpynj4vb.1
title: UzSentiNER v1: A 30,000-Sentence Uzbek Corpus for Joint Sentiment Analysis and NER (6 Entity Types) with Emoji Signals

description: UzSentiNER v1 is a 30,000-sentence Uzbek corpus designed for joint sentiment analysis (Positive/Negative/Neutral) and named entity recognition with six entity types: PERSON, POSITION, ORGANIZATION, LOCATION, DATE, PRODUCT.
The dataset combines 24,000 synthetic sentences (generated and calibrated with two rounds of manual quality control) and 6,000 linguist-curated sentences, merged into a single unified schema. Emoji usage is included to simulate real user writing patterns; emojis are placed naturally within sentences (not appended at the end).
The package provides multiple formats for reproducible research: XLSX, CSV, JSONL, plus token-level BIO tags and CoNLL (train/val/test) exports for NER training. Character-level entity spans are also included.
A manual audit on a random subset of 1,000 sentences reports 95.1% acceptable samples; remaining issues relate only to sentiment calibration, with 0% NER-type errors observed.
This dataset is intended for benchmarking and developing Uzbek NLP models for sentiment classification, NER, and multi-task learning. See README and documentation files for schema, annotation guidelines, validation report, and reproducibility instructions.
DOI: 10.17632/bk4rnwxrmp
title: Data

description: Data for my research paper
DOI: 10.17632/bk4rnwxrmp.1
title: Data

description: Data for my research paper
DOI: 10.17632/8fwwjpm79y
title: ShinyFMBN, a Shiny app to access FoodMicrobionet

description: This data set contains the ShinyFMBN app and the FoodMicrobionet database v5.1.2
The ShinyFMBN app allows you to access FoodMicrobionet 5.1.2, a repository of data on food microbiome studies including data for both bacteria and fungi which can be used to retrieve and analyze data on the fungal and bacterial microbiota of a large variety of foods. To run the app you need to install R and R Studio. Data are available in R (.rds) format. The FoodMicrobionet database and related scripts are available on GitHub (https://github.com/ep142/FoodMicrobionet)

This compressed folder contains folder shiny_FMBN_4_light: this includes the app folder, the runShinyFMBN_4.R script (a R script to install all needed packages and run the app) and the app manual in .html and .md format
Note that an online version of the app is also available at https://ep142.shinyapps.io/ShinyFMBN_4_light/

This version includes a few fixes: an inconsistency with study 308 was fixed and the text in the about and welcome panes was made more self explainatory. A .md version of the manual was added.