cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29306848
title: Advancing medical education in cervical cancer control with large language models for multiple-choice question generation

description: To explore the feasibility of using large language models (LLMs) to generate multiple-choice questions (MCQs) for cervical cancer control education and compare them with those created by clinicians. GPT-4o and Baichuan4 generated 40 MCQs each with iteratively refined prompts. Clinicians generated 40 MCQs for comparison. 120 MCQs were evaluated by 12 experts across five dimensions (correctness, clarity and specificity, cognitive level, clinical relevance, explainability) using a 5-point Likert scale. Difficulty and discriminatory power were tested by practitioners. Participants were asked to identify the source of each MCQ. Automated MCQs were similar to clinician-generated ones in most dimensions. However, clinician-generated MCQs had a higher cognitive level (4.00±1.08) than those from GPT-4o (3.68±1.07) and Baichuan4 (3.7±1.13). Testing with 312 practitioners revealed no significant differences in difficulty or discriminatory power among clinicians (59.51±24.50, 0.38±0.14), GPT-4o (61.89±25.36, 0.30±0.19), and Baichuan4 (59.79±26.25, 0.33±0.15). Recognition rates for LLM-generated MCQs ranged from 32% to 50%, with experts outperforming general practitioners in identifying the question setters. LLMs can generate MCQs comparable to clinician-generated ones with engineered prompts, though clinicians outperformed in cognitive level. LLM-assisted MCQ generation could enhance efficiency but requires rigorous validation to ensure educational quality.
DOI: 10.6084/m9.figshare.29306848.v1
title: Advancing medical education in cervical cancer control with large language models for multiple-choice question generation

description: To explore the feasibility of using large language models (LLMs) to generate multiple-choice questions (MCQs) for cervical cancer control education and compare them with those created by clinicians. GPT-4o and Baichuan4 generated 40 MCQs each with iteratively refined prompts. Clinicians generated 40 MCQs for comparison. 120 MCQs were evaluated by 12 experts across five dimensions (correctness, clarity and specificity, cognitive level, clinical relevance, explainability) using a 5-point Likert scale. Difficulty and discriminatory power were tested by practitioners. Participants were asked to identify the source of each MCQ. Automated MCQs were similar to clinician-generated ones in most dimensions. However, clinician-generated MCQs had a higher cognitive level (4.00±1.08) than those from GPT-4o (3.68±1.07) and Baichuan4 (3.7±1.13). Testing with 312 practitioners revealed no significant differences in difficulty or discriminatory power among clinicians (59.51±24.50, 0.38±0.14), GPT-4o (61.89±25.36, 0.30±0.19), and Baichuan4 (59.79±26.25, 0.33±0.15). Recognition rates for LLM-generated MCQs ranged from 32% to 50%, with experts outperforming general practitioners in identifying the question setters. LLMs can generate MCQs comparable to clinician-generated ones with engineered prompts, though clinicians outperformed in cognitive level. LLM-assisted MCQ generation could enhance efficiency but requires rigorous validation to ensure educational quality.
DOI: 10.6084/m9.figshare.29315710.v1
title: A multivariate analysis examining the relationship between sociodemographic differences and UK graduates’ performance on postgraduate medical exams

description: Studies examining group-level performance (differential attainment, or DA) in UK postgraduate medical examinations have, to date, focused on a limited number of exams and sociodemographic factors and used relatively simple analyses. This limits understanding of the intersectionality of different characteristics in relation to performance on these critical assessments, required for progression through training and to consultant status. This study aimed to address these gaps by identifying independent predictors of success or failure for UK medical school graduates (UKGs) across UK postgraduate medical examinations. This retrospective cohort study used multivariate logistic regression to identify independent predictors of success or failure at each examination, accounting for prior academic attainment (at point of entry to medical school). Anonymised pass/fail at the first examination attempt data were extracted from the General Medical Council (GMC) database and analysed for all UKGs examination candidates between 2014 and 2020. Between 2014–2020, 132,370 first examination attempts were made by UKGs, and 99,840 (75.4%) candidates passed at the first attempt. Multivariate analyses revealed that gender, age, ethnicity, religion, sexual orientation, disability, working less than full time and socioeconomic and educational background were all statistically significant independent predictors of success or failure in written and clinical examinations. The strongest independent predictors of failing written and/or clinical examinations were being from a minority ethnic background and having a registered disability. This large-scale study found that, even after accounting for prior academic attainment, there were significant differences in candidate examination pass rates according to key sociodemographic differences. The GMC, Medical Royal Colleges, and postgraduate training organisations now have a responsibility to use these data to guide future research and interventions that aim to reduce these attainment gaps.
DOI: 10.6084/m9.figshare.30862271
title: Herpetofauna from a Caatinga’s Brejo de altitude in Northeast Brazil

description: Inventories are essential because they can assist in decision-making about the protection of species, communities, and ecosystems. They also play a crucial role in engaging human populations, whether by involving them in citizen science initiatives or by providing a foundation for conservation actions. We provide an inventory of herpetofauna species from the Environmental Protection Area Serra de Maranguape and compare its diversity of herpetofauna with other Caatinga conservation units. The Serra de Maranguape is a pre-coastal residual massif considered a priority area for herpetofauna conservation. However, this residual massif has been subject to intense degradation and replacement of forests with crops. We found 33 species of amphibians and 52 Squamata species. The Serra de Maranguape herpetofauna consists of Caatinga’s typical species, well-distributed species, and some restricted species to the altitudinal humid forests. In addition, this herpetofauna composition is more similar to other ‘Brejos de Altitude’ rather than Caatinga. Our study’s data on richness and composition suggest that Serra de Maranguape is an area of extreme biological importance and unique because of some endemic and threatened species. This mountain range is one of the most critical areas for conserving herpetofauna in Northeastern Brazil.
DOI: 10.6084/m9.figshare.30862271.v1
title: Herpetofauna from a Caatinga’s Brejo de altitude in Northeast Brazil

description: Inventories are essential because they can assist in decision-making about the protection of species, communities, and ecosystems. They also play a crucial role in engaging human populations, whether by involving them in citizen science initiatives or by providing a foundation for conservation actions. We provide an inventory of herpetofauna species from the Environmental Protection Area Serra de Maranguape and compare its diversity of herpetofauna with other Caatinga conservation units. The Serra de Maranguape is a pre-coastal residual massif considered a priority area for herpetofauna conservation. However, this residual massif has been subject to intense degradation and replacement of forests with crops. We found 33 species of amphibians and 52 Squamata species. The Serra de Maranguape herpetofauna consists of Caatinga’s typical species, well-distributed species, and some restricted species to the altitudinal humid forests. In addition, this herpetofauna composition is more similar to other ‘Brejos de Altitude’ rather than Caatinga. Our study’s data on richness and composition suggest that Serra de Maranguape is an area of extreme biological importance and unique because of some endemic and threatened species. This mountain range is one of the most critical areas for conserving herpetofauna in Northeastern Brazil.
DOI: 10.6084/m9.figshare.29301740
title: Peer-to-peer mentorship emerges from mandatory research coursework: A social network case study

description: Research training thrives when pairing coursework with peer-to-peer mentorship. To understand how emerging collaborations promote research productivity of medical students, this study investigates the development of peer-to-peer advice-seeking behaviors and identify social mechanism that fosters collaborations. Cross-sectional surveys on advice-seeking behaviors were collected from 95 medical students awarded research presentation or publication grants from 2016 to 2023. Interrupted time series analysis (ITS) assessed the impact of research coursework, and SNA visualized the advice-seeking patterns and community structure. Path analysis and subgroup analysis identified influential factors that led to grant awarding. ITS showed an increase in grant awarding after the coursework implementation. SNA revealed a shift toward decentralized peer-to-peer advice-seeking behaviors, as group formation mediated grant awarding by 20.41%. Students preferentially seek advice from those at similar educational stages, regardless of gender and research interest. Subgroup analysis revealed advice-seeking differences across genders, educational stages, cohorts, and publication statuses. The network perspective highlights that group formation is a mediator of research productivity. Educators should consider a growing trend towards peer-to-peer mentorship and the influence of institutional policies on student behaviors. Understanding advice-seeking patterns can inform effective strategies to support and enhance undergraduate research engagement.
DOI: 10.6084/m9.figshare.29301740.v1
title: Peer-to-peer mentorship emerges from mandatory research coursework: A social network case study

description: Research training thrives when pairing coursework with peer-to-peer mentorship. To understand how emerging collaborations promote research productivity of medical students, this study investigates the development of peer-to-peer advice-seeking behaviors and identify social mechanism that fosters collaborations. Cross-sectional surveys on advice-seeking behaviors were collected from 95 medical students awarded research presentation or publication grants from 2016 to 2023. Interrupted time series analysis (ITS) assessed the impact of research coursework, and SNA visualized the advice-seeking patterns and community structure. Path analysis and subgroup analysis identified influential factors that led to grant awarding. ITS showed an increase in grant awarding after the coursework implementation. SNA revealed a shift toward decentralized peer-to-peer advice-seeking behaviors, as group formation mediated grant awarding by 20.41%. Students preferentially seek advice from those at similar educational stages, regardless of gender and research interest. Subgroup analysis revealed advice-seeking differences across genders, educational stages, cohorts, and publication statuses. The network perspective highlights that group formation is a mediator of research productivity. Educators should consider a growing trend towards peer-to-peer mentorship and the influence of institutional policies on student behaviors. Understanding advice-seeking patterns can inform effective strategies to support and enhance undergraduate research engagement.
DOI: 10.6084/m9.figshare.29315710
title: A multivariate analysis examining the relationship between sociodemographic differences and UK graduates’ performance on postgraduate medical exams

description: Studies examining group-level performance (differential attainment, or DA) in UK postgraduate medical examinations have, to date, focused on a limited number of exams and sociodemographic factors and used relatively simple analyses. This limits understanding of the intersectionality of different characteristics in relation to performance on these critical assessments, required for progression through training and to consultant status. This study aimed to address these gaps by identifying independent predictors of success or failure for UK medical school graduates (UKGs) across UK postgraduate medical examinations. This retrospective cohort study used multivariate logistic regression to identify independent predictors of success or failure at each examination, accounting for prior academic attainment (at point of entry to medical school). Anonymised pass/fail at the first examination attempt data were extracted from the General Medical Council (GMC) database and analysed for all UKGs examination candidates between 2014 and 2020. Between 2014–2020, 132,370 first examination attempts were made by UKGs, and 99,840 (75.4%) candidates passed at the first attempt. Multivariate analyses revealed that gender, age, ethnicity, religion, sexual orientation, disability, working less than full time and socioeconomic and educational background were all statistically significant independent predictors of success or failure in written and clinical examinations. The strongest independent predictors of failing written and/or clinical examinations were being from a minority ethnic background and having a registered disability. This large-scale study found that, even after accounting for prior academic attainment, there were significant differences in candidate examination pass rates according to key sociodemographic differences. The GMC, Medical Royal Colleges, and postgraduate training organisations now have a responsibility to use these data to guide future research and interventions that aim to reduce these attainment gaps.
DOI: 10.15468/dl.95h7qf
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((101.574 -0.798817, 101.50373 -0.804383, 101.43519 -0.820945, 101.370066 -0.848095, 101.309962 -0.885164, 101.256358 -0.931241, 101.210572 -0.98519, 101.173732 -1.045684, 101.146746 -1.111234, 101.130279 -1.180226, 101.124736 -1.250961, 101.130255 -1.321698, 101.146701 -1.390696, 101.17367 -1.456254, 101.210499 -1.516759, 101.256281 -1.57072, 101.309889 -1.616808, 101.370004 -1.653888, 101.435145 -1.681046, 101.503706 -1.697614, 101.574 -1.703182, 101.644294 -1.697614, 101.712855 -1.681046, 101.777996 -1.653888, 101.838111 -1.616808, 101.891719 -1.57072, 101.937501 -1.516759, 101.97433 -1.456254, 102.001299 -1.390696, 102.017745 -1.321698, 102.023264 -1.250961, 102.017721 -1.180226, 102.001254 -1.111234, 101.974268 -1.045684, 101.937428 -0.98519, 101.891642 -0.931241, 101.838038 -0.885164, 101.777934 -0.848095, 101.71281 -0.820945, 101.64427 -0.804383, 101.574 -0.798817))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047737-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uct37c
title: Occurrence Download

description: A dataset containing 7883213 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Butterfly distribution records for the UK from Butterfly Conservation, up to 2019",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2019",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 7883213 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047727-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.46jzmz
title: Occurrence Download

description: A dataset containing 2505071 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-158.75149 63.65246,-139.29775 64.99558,-134.74063 64.69837,-131.57046 64.69837,-115.71962 63.60863,-105.91191 63.31143,-94.56611 60.63283,-79.63716 58.86225,-77.38548 58.71635,-68.36203 58.13927,-13.58775 66.70773,15.0041 67.36108,15.84349 67.67586,18.36166 68.09555,21.08968 67.8857,22.24385 67.8857,91.91325 64.94783,100.93669 64.73798,144.1653 65.15768,167.58318 60.8185,179.80649 60.42458,179.96121 71.87271,179.31598 76.14686,174.53648 84.21366,106.09543 89.07519,26.77304 89.28504,2.43072 89.91458,-72.91281 88.11995,-150.96063 82.8653,-166.66386 75.77842,-178.69909 72.28713,-178.50535 64.63306,-176.87703 61.0666,-171.94562 61.27644,-161.68935 57.04226,-159.39075 59.12575,-158.75149 63.65246))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 2505071 records from 552 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047688-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eeseur
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((101.747 -0.792817, 101.67673 -0.798383, 101.60819 -0.814945, 101.543066 -0.842095, 101.482963 -0.8791639999999999, 101.429358 -0.925241, 101.383572 -0.97919, 101.346733 -1.039684, 101.319747 -1.105234, 101.30328 -1.174226, 101.297737 -1.244961, 101.303256 -1.315699, 101.319702 -1.384696, 101.346671 -1.450254, 101.3835 -1.510759, 101.429282 -1.56472, 101.48289 -1.610808, 101.543005 -1.647888, 101.608145 -1.675046, 101.676707 -1.691614, 101.747 -1.697182, 101.817293 -1.691614, 101.885855 -1.675046, 101.950995 -1.647888, 102.01111 -1.610808, 102.064718 -1.56472, 102.1105 -1.510759, 102.147329 -1.450254, 102.174298 -1.384696, 102.190744 -1.315699, 102.196263 -1.244961, 102.19072 -1.174226, 102.174253 -1.105234, 102.147267 -1.039684, 102.110428 -0.97919, 102.064642 -0.925241, 102.011037 -0.8791639999999999, 101.950934 -0.842095, 101.88581 -0.814945, 101.81727 -0.798383, 101.747 -0.792817))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047739-251120083545085/datasets/export for details.
DOI: 10.15468/dl.2kpszv
title: Occurrence Download

description: A dataset containing 412 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Salvia thymoides"
 ]
}

 The dataset includes 412 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047738-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hqvbpn
title: Occurrence Download

description: A dataset containing 1542 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ukraine",
 "Geometry POLYGON((26.60885 49.89297,26.62831 49.86725,26.64114 49.86522,26.65059 49.86117,26.63844 49.85105,26.65194 49.84362,26.65396 49.83417,26.63641 49.80515,26.62899 49.7768,26.63641 49.7687,26.64789 49.7606,26.64856 49.74845,26.64114 49.73022,26.62089 49.73697,26.61886 49.71132,26.59726 49.69512,26.58309 49.67689,26.58039 49.66812,26.58714 49.66744,26.59726 49.67284,26.61549 49.67217,26.62426 49.65934,26.63979 49.63842,26.65329 49.63369,26.64913 49.62409,26.65178 49.60978,26.6728 49.58334,26.66195 49.55486,26.68772 49.55757,26.7467 49.54063,26.82398 49.50741,26.88432 49.50673,26.90834 49.52347,27.02585 49.50361,27.05612 49.54981,27.09483 49.54981,27.13981 49.54981,27.14504 49.5676,27.18271 49.58225,27.13772 49.62305,27.14609 49.65234,27.13249 49.66699,27.13772 49.69001,27.1147 49.70884,27.10215 49.76638,27.07704 49.77266,26.98811 49.78417,27.00276 49.80823,26.98288 49.82288,27.01845 49.82392,27.02891 49.85636,26.98706 49.8574,26.98916 49.8846,27.01008 49.8867,26.96614 49.92436,26.97974 49.90762,26.93371 49.90762,26.92115 49.89297,26.91488 49.87414,26.89081 49.87937,26.82961 49.89716,26.80345 49.91494,26.77939 49.91599,26.74905 49.90553,26.71662 49.89716,26.6371 49.88356,26.60885 49.89297))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1542 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047740-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qu55dd
title: Occurrence Download

description: A dataset containing 184 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myrsine villosissima"
 ]
}

 The dataset includes 184 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047741-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5523/bris.2q31p47jp2qsa2q6yywu0kk6op
title: The challenges of eating out for young people with eating disorders: a thematic analysis of the perspectives of young people, parents and carers, and clinicians

description: This was a qualitative study exploring the challenges that people with Eating Disorders face in a café environment, specifically exploring the perspectives of People with Lived Experience of an Eating Disorder (PWLE), their parents and carers, and clinicians with experience treating them. The study was conducted in the context of the development of a virtual reality graded-exposure café intervention for people with Eating Disorders. The dataset consists of transcripts of three focus groups and 7 semi-structured interviews with People with Lived Experience of an Eating Disorder, 4 interviews with parents and carers, and 7 interviews with clinicians (all conducted using online video-conferencing software). All transcripts have been anonymised to prevent participant identification. Transcripts were analysed thematically to explore stakeholder perspectives on the challenges that people with Eating Disorders face in a café environment. This dataset also includes the participant information sheets, screening questionnaires for participation (which were completed online), consent forms, and parental assent forms used in this study, as well as the participant demographics. Focus groups and interviews were conducted between August and November 2023 by Dr Helen Bould (ORCID 0000-0001-8163-3210), Dr Laura Chapman (ORCID 0000-0002-5703-3861) and Dr Lydia Shackshaft (ORCID 0000-0002-9816-0488). Ethical approval was granted by the University of Bristol Faculty of Health Sciences Research Ethics Committee (reference: 12426).