cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/2zpg636krc
title: ReligiHate: A Balanced Corpus for Religious Hate Comment Detection in Bangla (Bangladesh and India)

description: The religious dataset consisting of Hindu and Muslim hate comments from Bangladesh and India in the Bangla language is a collection of online comments that contain religious hate speech targeting either the Hindu or Muslim communities. These comments were gathered from various sources such as newspapers, social media platforms, and online forums. The purpose of collecting this data is to analyze the prevalence of religious intolerance, identify patterns in hate speech, and contribute to the development of tools for automatically detecting and mitigating such content.

Key Features of the Dataset:
Source and Collection:

Comments were sourced from both Bangladesh and India, reflecting religious sentiments in these neighboring countries where tensions between religious groups have often been a social issue.
Sources include Bangla-language social media, news articles, opinion pieces, and comments sections on websites.

Content:
The dataset contains a mix of both Hindu-targeted hate speech and Muslim-targeted hate speech, with derogatory, offensive, and inflammatory remarks based on religion.
Hate comments include stereotypical statements, incitement to violence, communal hatred, and discriminatory language directed at members of the opposing community.

Purpose and Use Cases:
Hate Speech Detection: This dataset is useful for developing machine learning models that can automatically identify and flag harmful content on social media platforms.
Social Science Research: Researchers can study the psychological and sociopolitical factors that drive such hate speech.
Policy and Moderation Tools: Governments, social media platforms, and civil society organizations can use insights from this dataset to design anti-hate speech policies and create moderation systems that reduce online hate.

Challenges:
Contextual Nuances: Understanding the cultural and religious context of Bangla comments is crucial for accurately identifying hate speech. A comment that might seem neutral in one context could be deeply offensive in another.
Code-Switching: Some comments might mix Bangla with English or regional languages, complicating the classification and sentiment analysis process.
Bias in Data: The dataset might reflect a certain level of social bias depending on the region from which it was collected, which needs to be addressed when training AI models.

Conclusion:
This dataset offers valuable insights into the dynamics of religious hate speech in Bangladesh and India, two countries with diverse religious populations and a history of interfaith tension. It can help in the development of tools for mitigating online hate speech, while also fostering better understanding and tolerance across religious communities.
DOI: 10.34847/nkl.64ce633l
title: Marseilleveyre_2012_Delphine_Isoardi

description: La base repose sur les prospections de S. Bouffier et D. Isoardi. S'agissant de données de prospections, tous les sites sont à requêter en "mobilier/céramique".
DOI: 10.15468/dl.876mwr
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grevillea robusta A.Cunn. ex R.Br.",
 "Country is Tonga",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081607-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2zpg636krc.3
title: ReligiHate: A Balanced Corpus for Religious Hate Comment Detection in Bangla (Bangladesh and India)

description: The religious dataset consisting of Hindu and Muslim hate comments from Bangladesh and India in the Bangla language is a collection of online comments that contain religious hate speech targeting either the Hindu or Muslim communities. These comments were gathered from various sources such as newspapers, social media platforms, and online forums. The purpose of collecting this data is to analyze the prevalence of religious intolerance, identify patterns in hate speech, and contribute to the development of tools for automatically detecting and mitigating such content.

Key Features of the Dataset:
Source and Collection:

Comments were sourced from both Bangladesh and India, reflecting religious sentiments in these neighboring countries where tensions between religious groups have often been a social issue.
Sources include Bangla-language social media, news articles, opinion pieces, and comments sections on websites.

Content:
The dataset contains a mix of both Hindu-targeted hate speech and Muslim-targeted hate speech, with derogatory, offensive, and inflammatory remarks based on religion.
Hate comments include stereotypical statements, incitement to violence, communal hatred, and discriminatory language directed at members of the opposing community.

Purpose and Use Cases:
Hate Speech Detection: This dataset is useful for developing machine learning models that can automatically identify and flag harmful content on social media platforms.
Social Science Research: Researchers can study the psychological and sociopolitical factors that drive such hate speech.
Policy and Moderation Tools: Governments, social media platforms, and civil society organizations can use insights from this dataset to design anti-hate speech policies and create moderation systems that reduce online hate.

Challenges:
Contextual Nuances: Understanding the cultural and religious context of Bangla comments is crucial for accurately identifying hate speech. A comment that might seem neutral in one context could be deeply offensive in another.
Code-Switching: Some comments might mix Bangla with English or regional languages, complicating the classification and sentiment analysis process.
Bias in Data: The dataset might reflect a certain level of social bias depending on the region from which it was collected, which needs to be addressed when training AI models.

Conclusion:
This dataset offers valuable insights into the dynamics of religious hate speech in Bangladesh and India, two countries with diverse religious populations and a history of interfaith tension. It can help in the development of tools for mitigating online hate speech, while also fostering better understanding and tolerance across religious communities.
DOI: 10.15468/dl.aw2a7h
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grevillea robusta A.Cunn. ex R.Br.",
 "Country is Türkiye",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081608-250525065834625/datasets/export for details.
DOI: 10.15468/dl.stnhj4
title: Occurrence Download

description: A dataset containing 1323 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grevillea robusta A.Cunn. ex R.Br.",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1323 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081609-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pbnjsgdd5b
title: Peel-and-Swiss Roll Biopsy: A Practical Technique for Tissue Sampling in Fragile Blistering Skin Conditions

description: 1. Video demonstration of the peel-and-Swiss roll biopsy technique in a representative patient with pemphigus vulgaris, illustrating the atraumatic retrieval and preparation of a detached epidermal sheet for histopathological examination
2. Supplementary Figure 1: (a) Representative tissue section from a patient with Staphylococcal scalded skin syndrome (SSS), which shows a rolled-up, detached horny layer (100x); (b) High-power view shows mounds of parakeratosis, with focal cluster of acantholytic cells (400x). 
3. Supplementary Figure 2: (a) Representative tissue sections from a patient with pemphigus vulgaris, showing “Swiss-roll” configuration at low-power magnification (100x); (b) A close-up view showing the roof of intraepidermal with distinct acantholysis (400x).
DOI: 10.17632/pbnjsgdd5b.2
title: Peel-and-Swiss Roll Biopsy: A Practical Technique for Tissue Sampling in Fragile Blistering Skin Conditions

description: 1. Video demonstration of the peel-and-Swiss roll biopsy technique in a representative patient with pemphigus vulgaris, illustrating the atraumatic retrieval and preparation of a detached epidermal sheet for histopathological examination
2. Supplementary Figure 1: (a) Representative tissue section from a patient with Staphylococcal scalded skin syndrome (SSS), which shows a rolled-up, detached horny layer (100x); (b) High-power view shows mounds of parakeratosis, with focal cluster of acantholytic cells (400x). 
3. Supplementary Figure 2: (a) Representative tissue sections from a patient with pemphigus vulgaris, showing “Swiss-roll” configuration at low-power magnification (100x); (b) A close-up view showing the roof of intraepidermal with distinct acantholysis (400x).
DOI: 10.15468/dl.3ctbbe
title: Occurrence Download

description: A dataset containing 832 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Melanocorypha calandra (Linnaeus, 1766)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 832 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081610-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/sjybczmfkh
title: Dataset for Esports Study (Excluding ID9, 16, 17)

description: This dataset contains behavioral and physiological data collected during the Phase 2.0 experiment of an esports study investigating the effects of GABA intake on performance and EEG-based flow states.

It includes anonymized records from participants, excluding IDs 9, 16, and 17 due to incomplete or invalid responses.

Demographic variables and measured outcomes include MPG performance indicators and EEG-derived attention metrics (e.g., sensorimotor rhythm [SMR] at Fp1).

This dataset is intended to support reproducibility and secondary analyses related to nutritional interventions in competitive gaming.
DOI: 10.17632/sjybczmfkh.1
title: Dataset for Esports Study (Excluding ID9, 16, 17)

description: This dataset contains behavioral and physiological data collected during the Phase 2.0 experiment of an esports study investigating the effects of GABA intake on performance and EEG-based flow states.

It includes anonymized records from participants, excluding IDs 9, 16, and 17 due to incomplete or invalid responses.

Demographic variables and measured outcomes include MPG performance indicators and EEG-derived attention metrics (e.g., sensorimotor rhythm [SMR] at Fp1).

This dataset is intended to support reproducibility and secondary analyses related to nutritional interventions in competitive gaming.
DOI: 10.15468/dl.767ak9
title: Occurrence Download

description: A dataset containing 115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Guatemala",
 "OccurrenceStatus is Present",
 "TaxonKey is Elasmobranchii"
 ]
}

 The dataset includes 115 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081612-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.euwtcp
title: Occurrence Download

description: A dataset containing 284 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "Geometry POLYGON((-96.64723 -8.75579,-60.35277 -8.75579,-60.35277 5.75579,-96.64723 5.75579,-96.64723 -8.75579))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (90fd6680-349f-11d8-aa2d-b8a03c50a862, ae447c50-b8a8-11d8-92a4-b8a03c50a862, 8e1a97a0-3ca8-11d9-8439-b8a03c50a862, 7b8aff00-a9f8-11d8-944b-b8a03c50a862, 33d232b5-5cc2-4521-8bd6-121c5bb41c32, 396d5f30-dea9-11db-8ab4-b8a03c50a862, 061b4f20-f241-11da-a328-b8a03c50a862, 66522820-055c-11d8-b84e-b8a03c50a862, d0e01e0c-7e8c-49aa-aeed-15fab7dfee46)",
 "TaxonKey is Ceratostema alatum (Hoerold) Sleumer"
 ]
}

 The dataset includes 284 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081611-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dwz6vc
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grevillea robusta A.Cunn. ex R.Br.",
 "Country is Zimbabwe",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081613-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/y74tv2jbz3
title: Survey Dataset on Sleep Patterns, Health Effects, and Lifestyle Factors in Bangladesh

description: Description:

Proper sleep is essential for maintaining physical and mental well-being. Daily activities, stress, and lifestyle choices can significantly impact sleep quality. Inadequate or irregular sleep can lead to fatigue, reduced productivity, and long-term health issues. In countries like Bangladesh, where work demands, social habits, and environmental factors often disrupt sleep routines, understanding sleep behavior is crucial.

This dataset was collected through a structured questionnaire distributed via Google Forms and in-person interviews across various regions of Bangladesh. It captures a wide range of sleep-related behaviors and health indicators from individuals of different ages, occupations, and living environments.

Key Features of the Dataset:

The dataset includes a mix of categorical, numerical, and multi-select textual data. Key variables include:

Demographics: Age range, gender, occupation, weight, height

Sleep schedule: Bedtime and wake-up times on weekdays and weekends

Sleep quality: Average sleep duration, time taken to fall asleep

Sleep disturbances: Breathing difficulties, restlessness, medical conditions

Behavioral factors: Reasons for staying up late (e.g., social media, stress)

Health impacts: Side effects of poor sleep (e.g., fatigue, lack of focus)

Coping strategies: Methods used to manage sleep deprivation (e.g., caffeine, exercise)

Environment: Self-rated comfort of the sleeping environment (scale of 1–5)

Usage:

This dataset offers valuable insights for researchers, data scientists, and public health professionals. Potential applications include:

Calculating BMI from height and weight to explore correlations with sleep quality

Building predictive models using machine learning algorithms such as:

Sleep Quality Classification (Logistic Regression, Random Forest, XGBoost)

Sleep Duration Prediction (Linear Regression, Random Forest)

Sleep Behavior Clustering (K-Means, DBSCAN)

Coping Strategy Recommendation (ML-kNN, Content-Based Filtering)

Fatigue and Focus Drop Prediction (XGBoost, SVM)

These models can help identify at-risk individuals, inform public health interventions, and support personalized wellness recommendations.

Data Sources:

Data was collected from 2,610 individuals across Bangladesh, including university students, professionals, and residents of both urban and rural areas. Responses were gathered through online forms and face-to-face interviews, with all entries standardized via Google Forms.

Dataset Size:

Total entries: 2,610

Number of features: 20

File format: CSV

File size: ~750 KB
DOI: 10.17632/y74tv2jbz3.1
title: Survey Dataset on Sleep Patterns, Health Effects, and Lifestyle Factors in Bangladesh

description: Description:

Proper sleep is essential for maintaining physical and mental well-being. Daily activities, stress, and lifestyle choices can significantly impact sleep quality. Inadequate or irregular sleep can lead to fatigue, reduced productivity, and long-term health issues. In countries like Bangladesh, where work demands, social habits, and environmental factors often disrupt sleep routines, understanding sleep behavior is crucial.

This dataset was collected through a structured questionnaire distributed via Google Forms and in-person interviews across various regions of Bangladesh. It captures a wide range of sleep-related behaviors and health indicators from individuals of different ages, occupations, and living environments.

Key Features of the Dataset:

The dataset includes a mix of categorical, numerical, and multi-select textual data. Key variables include:

Demographics: Age range, gender, occupation, weight, height

Sleep schedule: Bedtime and wake-up times on weekdays and weekends

Sleep quality: Average sleep duration, time taken to fall asleep

Sleep disturbances: Breathing difficulties, restlessness, medical conditions

Behavioral factors: Reasons for staying up late (e.g., social media, stress)

Health impacts: Side effects of poor sleep (e.g., fatigue, lack of focus)

Coping strategies: Methods used to manage sleep deprivation (e.g., caffeine, exercise)

Environment: Self-rated comfort of the sleeping environment (scale of 1–5)

Usage:

This dataset offers valuable insights for researchers, data scientists, and public health professionals. Potential applications include:

Calculating BMI from height and weight to explore correlations with sleep quality

Building predictive models using machine learning algorithms such as:

Sleep Quality Classification (Logistic Regression, Random Forest, XGBoost)

Sleep Duration Prediction (Linear Regression, Random Forest)

Sleep Behavior Clustering (K-Means, DBSCAN)

Coping Strategy Recommendation (ML-kNN, Content-Based Filtering)

Fatigue and Focus Drop Prediction (XGBoost, SVM)

These models can help identify at-risk individuals, inform public health interventions, and support personalized wellness recommendations.

Data Sources:

Data was collected from 2,610 individuals across Bangladesh, including university students, professionals, and residents of both urban and rural areas. Responses were gathered through online forms and face-to-face interviews, with all entries standardized via Google Forms.

Dataset Size:

Total entries: 2,610

Number of features: 20

File format: CSV

File size: ~750 KB
DOI: 10.15468/dl.7qafhv
title: Occurrence Download

description: A dataset containing 340 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Melichneutes robustus (Bates, 1909)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 340 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081614-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/dy9w96z3hw
title: LINF_210016200

description: Cyclase-associated protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/dy9w96z3hw.1
title: LINF_210016200

description: Cyclase-associated protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/h5xkbrhkgf
title: LINF_210017200

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/h5xkbrhkgf.1
title: LINF_210017200

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/xw6t9tf3tp
title: LINF_210017500

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/xw6t9tf3tp.1
title: LINF_210017500

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/8x578xt4nt
title: LINF_210017700

description: epsilon tubulin; Leishmania infantum (strain JPCM5)
DOI: 10.17632/8x578xt4nt.1
title: LINF_210017700

description: epsilon tubulin; Leishmania infantum (strain JPCM5)
DOI: 10.17632/xbv29jrwtv
title: LINF_210017900

description: CSPP1-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/xbv29jrwtv.1
title: LINF_210017900

description: CSPP1-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/w673t69c66
title: LINF_210019300

description: Golgin-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/w673t69c66.1
title: LINF_210019300

description: Golgin-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.5517/ccdc.csd.cc2m4mm1
title: CCDC 2417523: Experimental Crystal Structure Determination

description: Related Article: Hui-Peng Lv, Sheng-Qian Hu, Yong-Ju Bai, Jun-Si Zhou, Luan-Ying Ji, Zhong-Xia Wang, Yong Ai, Yan Qin, Xiao-Gang Chen|2025|Chemical Science|16|9109|doi:10.1039/D5SC00620A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/rsj7w86m8b
title: LINF_210019400

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/rsj7w86m8b.1
title: LINF_210019400

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.15468/dl.nxchgf
title: Occurrence Download

description: A dataset containing 143 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Anolis cuvieri Merrem, 1820"
 ]
}

 The dataset includes 143 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081615-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/vtzxp39png
title: LINF_210019600

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/vtzxp39png.1
title: LINF_210019600

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)