cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7284/167100
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR91a using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/167040
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR91b using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5281/zenodo.17931576
title: A Design-Oriented Framework for Mediating Generative AI in Programming Learning Technologies: Moderators, Causal Pathways, and Operational Guidelines

description: Generative Artificial Intelligence (GenAI) systems are increasingly integrated into programming education, yet empirical evidence reports highly heterogeneous learning outcomes, ranging from productivity gains to erosion of fundamental competencies. These inconsistencies suggest that learning effectiveness is not an intrinsic property of GenAI, but rather an emergent result of how learning technologies are designed, orchestrated, and embedded within instructional contexts. This study aims to derive design oriented knowledge to inform the development and evaluation of GenAI based learning technologies for programming education. We conducted a systematic mapping of 101 peer-reviewed studies published between 2020 and 2025 across seven digital libraries, following PRISMA 2020 guidelines. The synthesis integrates thematic analysis and comparative evidence to identify pedagogical mediation strategies, moderators of effectiveness, and process level transformations in programming learning supported by GenAI. The results reveal three interdependent design dimensions that shape learning outcomes in GenAI powered learning technologies: (i) temporal design, emphasizing phased introduction of AI following the consolidation of programming fundamentals; (ii) structural design, operationalized through calibrated scaffolding, adaptive assistance, and fade-out mechanisms; and (iii) instructional design, focusing on metacognitive prompting, reflective code evaluation, and process-oriented tasks. Across the corpus, three critical moderator interactions explain the prevalence of mixed outcomes: knowledge level × timing of AI introduction, meta-cognition × type of GenAI system, and mediation design × usage patterns. These interactions expose systematic risks such as dependency and illusion of competence, particularly when generic large language models are deployed without pedagogical control. Based on these findings, we propose a design oriented integrative framework that articulates inputs, mediation mechanisms, moderator interactions, and multidimensional learning outcomes through three empirically grounded causal pathways. The framework is accompanied by testable propositions and operational guidelines that translate empirical evidence into actionable design principles for GenAI based programming learning technologies. The study contributes actionable insights for researchers and practitioners seeking to design, implement, and evaluate learning technologies that leverage generative AI while preserving deep conceptual understanding and learner
DOI: 10.5281/zenodo.17931577
title: A Design-Oriented Framework for Mediating Generative AI in Programming Learning Technologies: Moderators, Causal Pathways, and Operational Guidelines

description: Generative Artificial Intelligence (GenAI) systems are increasingly integrated into programming education, yet empirical evidence reports highly heterogeneous learning outcomes, ranging from productivity gains to erosion of fundamental competencies. These inconsistencies suggest that learning effectiveness is not an intrinsic property of GenAI, but rather an emergent result of how learning technologies are designed, orchestrated, and embedded within instructional contexts. This study aims to derive design oriented knowledge to inform the development and evaluation of GenAI based learning technologies for programming education. We conducted a systematic mapping of 101 peer-reviewed studies published between 2020 and 2025 across seven digital libraries, following PRISMA 2020 guidelines. The synthesis integrates thematic analysis and comparative evidence to identify pedagogical mediation strategies, moderators of effectiveness, and process level transformations in programming learning supported by GenAI. The results reveal three interdependent design dimensions that shape learning outcomes in GenAI powered learning technologies: (i) temporal design, emphasizing phased introduction of AI following the consolidation of programming fundamentals; (ii) structural design, operationalized through calibrated scaffolding, adaptive assistance, and fade-out mechanisms; and (iii) instructional design, focusing on metacognitive prompting, reflective code evaluation, and process-oriented tasks. Across the corpus, three critical moderator interactions explain the prevalence of mixed outcomes: knowledge level × timing of AI introduction, meta-cognition × type of GenAI system, and mediation design × usage patterns. These interactions expose systematic risks such as dependency and illusion of competence, particularly when generic large language models are deployed without pedagogical control. Based on these findings, we propose a design oriented integrative framework that articulates inputs, mediation mechanisms, moderator interactions, and multidimensional learning outcomes through three empirically grounded causal pathways. The framework is accompanied by testable propositions and operational guidelines that translate empirical evidence into actionable design principles for GenAI based programming learning technologies. The study contributes actionable insights for researchers and practitioners seeking to design, implement, and evaluate learning technologies that leverage generative AI while preserving deep conceptual understanding and learner
DOI: 10.15468/dl.qa8tmt
title: Occurrence Download

description: A dataset containing 1729 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Occurrence evidence)",
 "Continent is South America",
 "OccurrenceStatus is Present",
 "TaxonKey is Scinax fuscovarius",
 "Year 2000-2020"
 ]
}

 The dataset includes 1729 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007428-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kky758
title: Occurrence Download

description: A dataset containing 12334 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mnemiopsis leidyi",
 "HasCoordinate is true",
 "Year is greater than or equal to 2008",
 "Year is less than or equal to 2026"
 ]
}

 The dataset includes 12334 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007427-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/8s947y
title: Replication Code for "Assessment Regressivity and the Homestead Exemption"

description: Replication code for Assessment Regressivity and the Homestead Exemption
DOI: 10.15468/dl.d8xw9h
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sarcocaulon marlothii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007429-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a7ssby
title: Occurrence Download

description: A dataset containing 16194 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Aposeris foetida, Aposeris foetida)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 16194 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007430-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q3sdzc
title: Occurrence Download

description: A dataset containing 502 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alzatea verticillata",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 502 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007433-260129131611470/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.30209830
title: Bidirectional Longitudinal Correlations Between Alcohol Use Frequency and Quantity, and Anxiety in Adults

description: Alcohol use and anxiety both contribute significantly to the global burden of disease, and understanding their longitudinal relationships may clarify whether they exacerbate each another. However, previous research on the topic has yielded mixed results, potentially due to a lack of differentiation between alcohol use patterns. This study aimed to explore the bidirectional longitudinal correlations between alcohol use frequency and quantity, and anxiety. A longitudinal random-intercept cross-lagged panel model was constructed using data from 18+ year-old individuals (<i>N</i> = 14,665), collected in the 2017 and 2019 waves of the nationally representative Household, Income, and Labor Dynamics in Australia (HILDA) survey. Anxiety was measured with the Kessler Psychological Distress Scale anxiety subscale and participants’ reported frequency and quantity of alcohol use. For individuals, anxiety was positively associated with subsequent alcohol use quantity, and alcohol use frequency and quantity were negatively and positively associated with subsequent anxiety, respectively. Cross-lagged effects were small in magnitude suggesting that alcohol use patterns and anxiety accounted for a small amount of change in each other. Results strengthen support for a reciprocal relationship between heavier alcohol use and anxiety and provide novel insight into a relationship between frequent, low quantity alcohol use, and subsequent lower anxiety. These results underscore the importance of differentiating between alcohol use patterns when evaluating mental health outcomes and emphasize that heavier drinking patterns are particularly detrimental to psychological well-being.
DOI: 10.6084/m9.figshare.30209830.v1
title: Bidirectional Longitudinal Correlations Between Alcohol Use Frequency and Quantity, and Anxiety in Adults

description: Alcohol use and anxiety both contribute significantly to the global burden of disease, and understanding their longitudinal relationships may clarify whether they exacerbate each another. However, previous research on the topic has yielded mixed results, potentially due to a lack of differentiation between alcohol use patterns. This study aimed to explore the bidirectional longitudinal correlations between alcohol use frequency and quantity, and anxiety. A longitudinal random-intercept cross-lagged panel model was constructed using data from 18+ year-old individuals (<i>N</i> = 14,665), collected in the 2017 and 2019 waves of the nationally representative Household, Income, and Labor Dynamics in Australia (HILDA) survey. Anxiety was measured with the Kessler Psychological Distress Scale anxiety subscale and participants’ reported frequency and quantity of alcohol use. For individuals, anxiety was positively associated with subsequent alcohol use quantity, and alcohol use frequency and quantity were negatively and positively associated with subsequent anxiety, respectively. Cross-lagged effects were small in magnitude suggesting that alcohol use patterns and anxiety accounted for a small amount of change in each other. Results strengthen support for a reciprocal relationship between heavier alcohol use and anxiety and provide novel insight into a relationship between frequent, low quantity alcohol use, and subsequent lower anxiety. These results underscore the importance of differentiating between alcohol use patterns when evaluating mental health outcomes and emphasize that heavier drinking patterns are particularly detrimental to psychological well-being.
DOI: 10.15468/dl.b82h9h
title: Occurrence Download

description: A dataset containing 21163 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Aquilegia atrata, Aquilegia vulgaris, Aquilegia vulgaris, Aquilegia vulgaris, Aquilegia vulgaris, Aquilegia atroviolacea, Aquilegia atroviolacea)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 21163 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007434-260129131611470/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.30330158
title: The Role of Sleep Impairment in Associations Between Pain and Nicotine/Tobacco Dependence in Wave 6 of the Population Assessment of Tobacco and Health (PATH) Study

description: Tobacco use is a significant national health concern that frequently co-occurs with chronic pain. Both pain and use of nicotine/tobacco have been linked to greater sleep impairments (i.e., trouble initiating and maintaining sleep), and there is reason to believe that more severe pain may indirectly contribute to greater nicotine dependence <i>via</i> sleep impairments. Therefore, the goal of this study was to examine indirect associations between pain severity and nicotine dependence <i>via</i> sleep impairment severity among adults who use cigarettes and electronic nicotine delivery systems (ENDS). Data were drawn from Wave 6 (March – November 2021) of the Population Assessment of Tobacco and Health Study (PATH), limited to <i>N</i> = 9,682 (54% male; 22% ages 25–24; 77% White) participants who reported regular cigarette smoking and/or ENDS use. Pain severity and sleep impairment were assessed using single items, and nicotine/tobacco dependence was assessed using the Wisconsin Inventory of Smoking Dependence Motives (WISDM). Results indicated positive indirect associations between pain severity and both tobacco and ENDS dependence <i>via</i> sleep impairment severity. Sleep impairment may play an important role in associations between pain severity and nicotine dependence among adults who use cigarettes or ENDS. These findings and future work may warrant the development of interventions that address sleep dysfunction to support nicotine and tobacco cessation, particularly among adults who experience pain.
DOI: 10.6084/m9.figshare.30330158.v1
title: The Role of Sleep Impairment in Associations Between Pain and Nicotine/Tobacco Dependence in Wave 6 of the Population Assessment of Tobacco and Health (PATH) Study

description: Tobacco use is a significant national health concern that frequently co-occurs with chronic pain. Both pain and use of nicotine/tobacco have been linked to greater sleep impairments (i.e., trouble initiating and maintaining sleep), and there is reason to believe that more severe pain may indirectly contribute to greater nicotine dependence <i>via</i> sleep impairments. Therefore, the goal of this study was to examine indirect associations between pain severity and nicotine dependence <i>via</i> sleep impairment severity among adults who use cigarettes and electronic nicotine delivery systems (ENDS). Data were drawn from Wave 6 (March – November 2021) of the Population Assessment of Tobacco and Health Study (PATH), limited to <i>N</i> = 9,682 (54% male; 22% ages 25–24; 77% White) participants who reported regular cigarette smoking and/or ENDS use. Pain severity and sleep impairment were assessed using single items, and nicotine/tobacco dependence was assessed using the Wisconsin Inventory of Smoking Dependence Motives (WISDM). Results indicated positive indirect associations between pain severity and both tobacco and ENDS dependence <i>via</i> sleep impairment severity. Sleep impairment may play an important role in associations between pain severity and nicotine dependence among adults who use cigarettes or ENDS. These findings and future work may warrant the development of interventions that address sleep dysfunction to support nicotine and tobacco cessation, particularly among adults who experience pain.
DOI: 10.6084/m9.figshare.30402575
title: Supervised Manifold Learning for Functional Data

description: Classification is a core topic in functional data analysis. A large number of functional classifiers have been proposed in the literature, most of which are based on functional principal component analysis or functional regression. In contrast, we investigate this topic from the perspective of manifold learning. It is assumed that functional data lie on an unknown low-dimensional manifold, and we expect that superior classifiers can be developed based on the manifold structure. To this end, we propose a novel proximity measure that takes the label information into account to learn the low-dimensional representations, also known as the supervised manifold learning outcomes. When the outcomes are coupled with multivariate classifiers, the procedure induces a new family of functional classifiers. In theory, we prove that our functional classifier induced by the k-NN classifier is asymptotically optimal. In practice, we show that our method, coupled with several classical multivariate classifiers, achieves highly competitive classification performance compared to existing functional classifiers across both synthetic and real data examples. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.30402575.v1
title: Supervised Manifold Learning for Functional Data

description: Classification is a core topic in functional data analysis. A large number of functional classifiers have been proposed in the literature, most of which are based on functional principal component analysis or functional regression. In contrast, we investigate this topic from the perspective of manifold learning. It is assumed that functional data lie on an unknown low-dimensional manifold, and we expect that superior classifiers can be developed based on the manifold structure. To this end, we propose a novel proximity measure that takes the label information into account to learn the low-dimensional representations, also known as the supervised manifold learning outcomes. When the outcomes are coupled with multivariate classifiers, the procedure induces a new family of functional classifiers. In theory, we prove that our functional classifier induced by the k-NN classifier is asymptotically optimal. In practice, we show that our method, coupled with several classical multivariate classifiers, achieves highly competitive classification performance compared to existing functional classifiers across both synthetic and real data examples. Supplementary materials are available online.
DOI: 10.5281/zenodo.18460384
title: PSYCHOLOGICAL ADAPTATION AND MOTIVATIONAL FACTORS IN TEACHERS' USE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES

description: This article analyzes the psychological adaptation and motivational factors that emerge when teachers in general education and higher educational institutions use artificial intelligence technologies. Specifically, it examines the interrelationships between attitudes towards technology, technological anxiety, intrinsic and extrinsic motivation, the desire for professional self-development, and innovative competencies. The study employed a mixed-method approach, combining questionnaires and semi-structured interviews; data collected from 180 teachers were processed using descriptive statistics and correlation analysis. Results indicate that perceived usefulness and ease of use of the technology, technological self-efficacy, and intrinsic motivation are strong predictors of teachers' positive attitudes and intention to use [1,3,6,17]. While technological anxiety remains at a moderate level, it is found that professional development programs focused on self-improvement can reduce this anxiety and significantly enhance innovative competencies [4,9,13,19]. The conclusions drawn provide practical recommendations for educational policy, pedagogical psychology, and the design of teacher training programs.
DOI: 10.5281/zenodo.18460385
title: PSYCHOLOGICAL ADAPTATION AND MOTIVATIONAL FACTORS IN TEACHERS' USE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES

description: This article analyzes the psychological adaptation and motivational factors that emerge when teachers in general education and higher educational institutions use artificial intelligence technologies. Specifically, it examines the interrelationships between attitudes towards technology, technological anxiety, intrinsic and extrinsic motivation, the desire for professional self-development, and innovative competencies. The study employed a mixed-method approach, combining questionnaires and semi-structured interviews; data collected from 180 teachers were processed using descriptive statistics and correlation analysis. Results indicate that perceived usefulness and ease of use of the technology, technological self-efficacy, and intrinsic motivation are strong predictors of teachers' positive attitudes and intention to use [1,3,6,17]. While technological anxiety remains at a moderate level, it is found that professional development programs focused on self-improvement can reduce this anxiety and significantly enhance innovative competencies [4,9,13,19]. The conclusions drawn provide practical recommendations for educational policy, pedagogical psychology, and the design of teacher training programs.
DOI: 10.15468/dl.2remnm
title: Occurrence Download

description: A dataset containing 45437 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "TaxonKey is Rudbeckia hirta",
 "Year 1945-2026"
 ]
}

 The dataset includes 45437 records from 247 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007431-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2ehbhx
title: Occurrence Download

description: A dataset containing 56162 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Anemone nemorosa, Anemone nemorosa)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 56162 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007394-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.