cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e130621
title: Parents' online school reviews reflect several racial and socioeconomic disparities in K-12 education

description: The files included on this page contain a description of the datasets that were used for this project, along with information about how access can be requested. They also contain the code repositories used to collect, prepare, and analyze the data.<br><br>The abstract for the paper is below:<br><br>Parents often select schools by relying on subjective assessments of quality made by other parents, which are increasingly becoming available through written reviews on school ratings websites. To identify relationships between review content and school quality, we apply recent advances in natural language processing to nearly half a million parent reviews posted for over 50,000 publicly-funded US K-12 schools on a popular ratings website. We find: i) schools in urban areas and those serving affluent families are more likely to receive reviews; ii) review language correlates with standardized test scores—which generally track race and family income—but not school effectiveness, measured by how much students improve in their test scores over time; and iii) the linguistics of reviews reveal several racial and income-based disparities in K-12 education. These findings suggest that parents who reference school reviews may be accessing, and making decisions based on, biased perspectives that reinforce achievement gaps.<br>
description: other; web scraping;
description: All reviews posted by those self-identified as parents on GreatSchools.org until approximately December 2019 / February 2020 (due to iterative data collection)Schools and corresponding characteristics / outcomes measures available in the Stanford Educational Data ArchiveAll US-based census tracts with available demographics and other relevant characteristics, based on the 2010 census and 2015 American Community Survey (prepared by Opportunity Insights).
DOI: 10.3886/e130621v1-176703
title: Parents' online school reviews reflect several racial and socioeconomic disparities in K-12 education

description: The files included on this page contain a description of the datasets that were used for this project, along with information about how access can be requested. They also contain the code repositories used to collect, prepare, and analyze the data.<br><br>The abstract for the paper is below:<br><br>Parents often select schools by relying on subjective assessments of quality made by other parents, which are increasingly becoming available through written reviews on school ratings websites. To identify relationships between review content and school quality, we apply recent advances in natural language processing to nearly half a million parent reviews posted for over 50,000 publicly-funded US K-12 schools on a popular ratings website. We find: i) schools in urban areas and those serving affluent families are more likely to receive reviews; ii) review language correlates with standardized test scores—which generally track race and family income—but not school effectiveness, measured by how much students improve in their test scores over time; and iii) the linguistics of reviews reveal several racial and income-based disparities in K-12 education. These findings suggest that parents who reference school reviews may be accessing, and making decisions based on, biased perspectives that reinforce achievement gaps.<br>
description: All reviews posted by those self-identified as parents on GreatSchools.org until approximately December 2019 / February 2020 (due to iterative data collection)Schools and corresponding characteristics / outcomes measures available in the Stanford Educational Data ArchiveAll US-based census tracts with available demographics and other relevant characteristics, based on the 2010 census and 2015 American Community Survey (prepared by Opportunity Insights).
description: other; web scraping;
DOI: 10.15468/dl.qhv36w
title: Occurrence Download

description: A dataset containing 2098 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Periplaneta americana",
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "Country is one of (Albania, Austria, Bosnia and Herzegovina, Belgium, Bulgaria, Belarus, Switzerland, Cyprus, Czechia, Germany, Denmark, Estonia, Spain, Finland, France, United Kingdom of Great Britain and Northern Ireland, Greece, Croatia, Hungary, Ireland, Iceland, Italy, Lithuania, Luxembourg, Latvia, Moldova, Republic of, Montenegro, North Macedonia, Malta, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Sweden, Slovenia, Slovakia, Ukraine)",
 "HasCoordinate is true",
 "MediaType is Image"
 ]
}

 The dataset includes 2098 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002482-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g9jyhk
title: Occurrence Download

description: A dataset containing 1059 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)"
 ]
 }
 },
 "TaxonKey is Aristida benthamii"
 ]
}

 The dataset includes 1059 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002483-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29208914
title: <b>Emotion Regulation and Visual Attention in Body Dysmorphic Disorder: A Multimodal Study Using EEG, GSR, and Eye-Tracking</b>

description: <b>Background</b> Body Dysmorphic Disorder (BDD) is a psychiatric condition characterized by obsessive preoccupation with perceived physical flaws, often accompanied by emotional dysregulation and attentional biases. Despite increasing clinical recognition, the neurophysiological and attentional mechanisms underlying BDD remain poorly understood.<b>Methods</b> This study employed a multimodal experimental design to compare individuals with BDD (n = 27) and healthy controls (n = 27). Participants completed standardized psychometric assessments and were exposed to emotionally valenced facial and body-related images while undergoing electroencephalography (EEG), galvanic skin response (GSR), and eye-tracking. Group-by-valence interactions were analyzed using mixed-model ANOVAs.<b>Results</b> The BDD group showed significantly higher depression, anxiety, body image disturbance, and suppression scores, alongside reduced cognitive reappraisal and self-efficacy. EEG analyses revealed increased N170 amplitudes, attenuated P300 amplitudes, and greater right-sided frontal alpha asymmetry, suggesting early hypervigilance, reduced evaluative processing, and affective withdrawal. GSR data indicated heightened sympathetic arousal and delayed physiological recovery. Eye-tracking data revealed gender-specific attentional biases in BDD: females fixated more on facial and lower-body regions, while males focused on muscular upper-body features. These patterns were absent in the control group.<b>Conclusion</b> BDD is associated with dysregulated multisystem responses to appearance-related stimuli, characterized by neural hyperreactivity, impaired cognitive-emotional regulation, and gender-specific visual attention biases. These findings support the utility of integrating psychophysiological and attentional markers into individualized assessment and intervention strategies for BDD.
DOI: 10.6084/m9.figshare.29208914.v1
title: <b>Emotion Regulation and Visual Attention in Body Dysmorphic Disorder: A Multimodal Study Using EEG, GSR, and Eye-Tracking</b>

description: <b>Background</b> Body Dysmorphic Disorder (BDD) is a psychiatric condition characterized by obsessive preoccupation with perceived physical flaws, often accompanied by emotional dysregulation and attentional biases. Despite increasing clinical recognition, the neurophysiological and attentional mechanisms underlying BDD remain poorly understood.<b>Methods</b> This study employed a multimodal experimental design to compare individuals with BDD (n = 27) and healthy controls (n = 27). Participants completed standardized psychometric assessments and were exposed to emotionally valenced facial and body-related images while undergoing electroencephalography (EEG), galvanic skin response (GSR), and eye-tracking. Group-by-valence interactions were analyzed using mixed-model ANOVAs.<b>Results</b> The BDD group showed significantly higher depression, anxiety, body image disturbance, and suppression scores, alongside reduced cognitive reappraisal and self-efficacy. EEG analyses revealed increased N170 amplitudes, attenuated P300 amplitudes, and greater right-sided frontal alpha asymmetry, suggesting early hypervigilance, reduced evaluative processing, and affective withdrawal. GSR data indicated heightened sympathetic arousal and delayed physiological recovery. Eye-tracking data revealed gender-specific attentional biases in BDD: females fixated more on facial and lower-body regions, while males focused on muscular upper-body features. These patterns were absent in the control group.<b>Conclusion</b> BDD is associated with dysregulated multisystem responses to appearance-related stimuli, characterized by neural hyperreactivity, impaired cognitive-emotional regulation, and gender-specific visual attention biases. These findings support the utility of integrating psychophysiological and attentional markers into individualized assessment and intervention strategies for BDD.
DOI: 10.6084/m9.figshare.29208914.v2
title: <b>Emotion Regulation and Visual Attention in Body Dysmorphic Disorder: A Multimodal Study Using EEG, GSR, and Eye-Tracking</b>

description: <b>Background</b> Body Dysmorphic Disorder (BDD) is a psychiatric condition characterized by obsessive preoccupation with perceived physical flaws, often accompanied by emotional dysregulation and attentional biases. Despite increasing clinical recognition, the neurophysiological and attentional mechanisms underlying BDD remain poorly understood.<b>Methods</b> This study employed a multimodal experimental design to compare individuals with BDD (n = 27) and healthy controls (n = 27). Participants completed standardized psychometric assessments and were exposed to emotionally valenced facial and body-related images while undergoing electroencephalography (EEG), galvanic skin response (GSR), and eye-tracking. Group-by-valence interactions were analyzed using mixed-model ANOVAs.<b>Results</b> The BDD group showed significantly higher depression, anxiety, body image disturbance, and suppression scores, alongside reduced cognitive reappraisal and self-efficacy. EEG analyses revealed increased N170 amplitudes, attenuated P300 amplitudes, and greater right-sided frontal alpha asymmetry, suggesting early hypervigilance, reduced evaluative processing, and affective withdrawal. GSR data indicated heightened sympathetic arousal and delayed physiological recovery. Eye-tracking data revealed gender-specific attentional biases in BDD: females fixated more on facial and lower-body regions, while males focused on muscular upper-body features. These patterns were absent in the control group.<b>Conclusion</b> BDD is associated with dysregulated multisystem responses to appearance-related stimuli, characterized by neural hyperreactivity, impaired cognitive-emotional regulation, and gender-specific visual attention biases. These findings support the utility of integrating psychophysiological and attentional markers into individualized assessment and intervention strategies for BDD.
DOI: 10.5281/zenodo.18544868
title: INNOVATIVE PEDAGOGICAL APPROACHES TO SHAPING SOCIAL ACTIVITY IN THE CONTEXT OF INCLUSION

description: This scientific thesis highlights the role and significance of innovative pedagogical approaches in shaping students' social activity within inclusive education settings. In addition, pedagogical technologies, methods, and modern approaches that ensure social integration are analyzed.
DOI: 10.5281/zenodo.18544869
title: INNOVATIVE PEDAGOGICAL APPROACHES TO SHAPING SOCIAL ACTIVITY IN THE CONTEXT OF INCLUSION

description: This scientific thesis highlights the role and significance of innovative pedagogical approaches in shaping students' social activity within inclusive education settings. In addition, pedagogical technologies, methods, and modern approaches that ensure social integration are analyzed.
DOI: 10.5281/zenodo.18543089
title: Bifurcation-PCI Outcomes Dataset

description: Accurate prognostication after percutaneous coronary intervention (PCI) of coronary bifurcation lesions requires standardized, well-documented datasets with clear usage guidance.
DOI: 10.5281/zenodo.18444866
title: Bifurcation-PCI Outcomes Dataset

description: Accurate prognostication after percutaneous coronary intervention (PCI) of coronary bifurcation lesions requires standardized, well-documented datasets with clear usage guidance.
DOI: 10.15468/dl.femsb5
title: Occurrence Download

description: A dataset containing 731 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "GadmGid is CHN.29_1",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 731 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002485-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xsjt95
title: Occurrence Download

description: A dataset containing 281 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)"
 ]
 }
 },
 "TaxonKey is Aristida biglandulosa"
 ]
}

 The dataset includes 281 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002486-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.afsbay
title: Occurrence Download

description: A dataset containing 1934 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gorilla gorilla"
 ]
}

 The dataset includes 1934 records from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002487-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31292419
title: Mapping Multiclass-Targeted Hate Speech in Online Discourse: An Open Dataset

description: The Multiclass English Hate Speech Dataset is a resource for digital humanities research, enabling scholars to study how online hate speech reflects and shapes identity, power, and social boundaries in online communication. The data is an extended and fine-grained version of the original binary-labelled hate-speech dataset released as part of the TweetEval [1] benchmark (Hate sub-task). While the original dataset contained English posts annotated only as Hate or Non-Hate, this work substantially enhances it by applying a detailed manual re-annotation process to create multiple specific hate-speech categories. This provides greater granularity, enables more accurate modelling of real-world online hate, and enables sociolinguistic analysis.All posts were manually reviewed and reclassified by trained annotators following a structured annotation guideline. The dataset introduces a comprehensive multiclass taxonomy capturing different forms of explicit and implicit hate, including:Gender-Based Hate Speech (Misogyny)Gender-Based Hate Speech (Misandry)Racial and Ethnic Hate Speech (Anti-Black Hate Speech)Racial and Ethnic Hate Speech (Anti-Hispanic Hate Speech)Racial and Ethnic Hate Speech (Anti-Asian Hate Speech)Racial and Ethnic Hate Speech (Anti-Semitic Hate Speech)Immigration & Xenophobic Hate Speech (Anti-Immigrant)Immigration & Xenophobic Hate Speech (Anti-Refugee)Immigration & Xenophobic Hate Speech (Xenophobia)Religious Hate Speech (Islamophobia)Religious Hate Speech (Anti-Christian Hate Speech)Profanity and General AbuseThreats and ViolenceHate speech toward CountriesThrough this re-annotation effort, the dataset transforms a simple binary classification problem into a 14-class fine-grained hate-speech categorization task. This enables robust research on model sensitivity, bias analysis, explainability, and safety evaluation.The dataset is suitable for a range of applications, including:Content moderation research and safety evaluationSociolinguistic and discourse analysis of targeted abuseDigital humanities studies on identity, power, and social boundariesAll user identifiers and personally identifiable information (PII) have been removed or masked to ensure privacy and ethical compliance. Each record includes the anonymized text and the newly assigned multiclass label. The samples presented in the data are hate records extracted from TweetEval.This resource aims to support researchers, practitioners, and policymakers in building safer and more responsible AI systems capable of detecting nuanced forms of online hate and targeted harassment.[1] https://github.com/cardiffnlp/tweeteval/tree/main/datasets/hate
DOI: 10.6084/m9.figshare.31292419.v2
title: Mapping Multiclass-Targeted Hate Speech in Online Discourse: An Open Dataset

description: The Multiclass English Hate Speech Dataset is a resource for digital humanities research, enabling scholars to study how online hate speech reflects and shapes identity, power, and social boundaries in online communication. The data is an extended and fine-grained version of the original binary-labelled hate-speech dataset released as part of the TweetEval [1] benchmark (Hate sub-task). While the original dataset contained English posts annotated only as Hate or Non-Hate, this work substantially enhances it by applying a detailed manual re-annotation process to create multiple specific hate-speech categories. This provides greater granularity, enables more accurate modelling of real-world online hate, and enables sociolinguistic analysis.All posts were manually reviewed and reclassified by trained annotators following a structured annotation guideline. The dataset introduces a comprehensive multiclass taxonomy capturing different forms of explicit and implicit hate, including:Gender-Based Hate Speech (Misogyny)Gender-Based Hate Speech (Misandry)Racial and Ethnic Hate Speech (Anti-Black Hate Speech)Racial and Ethnic Hate Speech (Anti-Hispanic Hate Speech)Racial and Ethnic Hate Speech (Anti-Asian Hate Speech)Racial and Ethnic Hate Speech (Anti-Semitic Hate Speech)Immigration & Xenophobic Hate Speech (Anti-Immigrant)Immigration & Xenophobic Hate Speech (Anti-Refugee)Immigration & Xenophobic Hate Speech (Xenophobia)Religious Hate Speech (Islamophobia)Religious Hate Speech (Anti-Christian Hate Speech)Profanity and General AbuseThreats and ViolenceHate speech toward CountriesThrough this re-annotation effort, the dataset transforms a simple binary classification problem into a 14-class fine-grained hate-speech categorization task. This enables robust research on model sensitivity, bias analysis, explainability, and safety evaluation.The dataset is suitable for a range of applications, including:Content moderation research and safety evaluationSociolinguistic and discourse analysis of targeted abuseDigital humanities studies on identity, power, and social boundariesAll user identifiers and personally identifiable information (PII) have been removed or masked to ensure privacy and ethical compliance. Each record includes the anonymized text and the newly assigned multiclass label. The samples presented in the data are hate records extracted from TweetEval.This resource aims to support researchers, practitioners, and policymakers in building safer and more responsible AI systems capable of detecting nuanced forms of online hate and targeted harassment.[1] https://github.com/cardiffnlp/tweeteval/tree/main/datasets/hate
DOI: 10.6084/m9.figshare.31292419.v1
title: Mapping Multiclasss-Targeted Hate Speech in Online Discourse: An Open Dataset

description: The Multiclass English Hate Speech Dataset is a resource for digital humanities research, enabling scholars to study how online hate speech reflects and shapes identity, power, and social boundaries in online communication. The data is an extended and fine-grained version of the original binary-labelled hate-speech dataset released as part of the TweetEval [1] benchmark (Hate sub-task). While the original dataset contained English posts annotated only as Hate or Non-Hate, this work substantially enhances it by applying a detailed manual re-annotation process to create multiple specific hate-speech categories. This provides greater granularity, enables more accurate modelling of real-world online hate, and enables sociolinguistic analysis.All posts were manually reviewed and reclassified by trained annotators following a structured annotation guideline. The dataset introduces a comprehensive multiclass taxonomy capturing different forms of explicit and implicit hate, including:Gender-Based Hate Speech (Misogyny)Gender-Based Hate Speech (Misandry)Racial and Ethnic Hate Speech (Anti-Black Hate Speech)Racial and Ethnic Hate Speech (Anti-Hispanic Hate Speech)Racial and Ethnic Hate Speech (Anti-Asian Hate Speech)Racial and Ethnic Hate Speech (Anti-Semitic Hate Speech)Immigration & Xenophobic Hate Speech (Anti-Immigrant)Immigration & Xenophobic Hate Speech (Anti-Refugee)Immigration & Xenophobic Hate Speech (Xenophobia)Religious Hate Speech (Islamophobia)Religious Hate Speech (Anti-Christian Hate Speech)Profanity and General AbuseThreats and ViolenceHate speech toward CountriesThrough this re-annotation effort, the dataset transforms a simple binary classification problem into a 14-class fine-grained hate-speech categorization task. This enables robust research on model sensitivity, bias analysis, explainability, and safety evaluation.The dataset is suitable for a range of applications, including:Content moderation research and safety evaluationSociolinguistic and discourse analysis of targeted abuseDigital humanities studies on identity, power, and social boundariesAll user identifiers and personally identifiable information (PII) have been removed or masked to ensure privacy and ethical compliance. Each record includes the anonymized text and the newly assigned multiclass label. The samples presented in the data are hate records extracted from TweetEval.This resource aims to support researchers, practitioners, and policymakers in building safer and more responsible AI systems capable of detecting nuanced forms of online hate and targeted harassment.[1] https://github.com/cardiffnlp/tweeteval/tree/main/datasets/hate