cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ewg4qg
title: Occurrence Download

description: A dataset containing 142548 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-12-27",
 "EventDate is less than or equal to 2026-01-26",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 },
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 142548 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003264-260120142942310/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.28623506
title: MEG group-level characteristics of finger movement

description: The group-level data files to generate the figures in the article "Multidimensional Dynamic Characterization and Decoding of Finger Movements Using Magnetoencephalography". The analysis results include SQUID-MEG data from 16 participants performing single extension movements of the right thumb, index, middle, and little fingers. The data analysis pipeline and results visualization code are available at https://github.com/Yzheng9725/MEG-study-fingermovement.
DOI: 10.6084/m9.figshare.28623506.v2
title: MEG group-level characteristics of finger movement

description: The group-level data files to generate the figures in the article "Multidimensional Dynamic Characterization and Decoding of Finger Movements Using Magnetoencephalography". The analysis results include SQUID-MEG data from 16 participants performing single extension movements of the right thumb, index, middle, and little fingers. The data analysis pipeline and results visualization code are available at https://github.com/Yzheng9725/MEG-study-fingermovement.
DOI: 10.6084/m9.figshare.28623506.v1
title: MEG group-level characteristics of finger movement

description: The group-level data files to generate the figures in the article "Multidimensional Dynamic Characterization and Decoding of Finger Movements Using Magnetoencephalography". The analysis results include SQUID-MEG data from 16 participants performing single extension movements of the right thumb, index, middle, and little fingers. The data analysis pipeline and results visualization code are available at https://github.com/Yzheng9725/MEG-study-fingermovement. For access to the raw MEG data, please contact Prof. Zhuo (email: yzhuo@ibp.ac.cn), and we will provide it upon reasonable request.
DOI: 10.15468/dl.4jrsyj
title: Occurrence Download

description: A dataset containing 200 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "TaxonKey is Selenicereus spinulosus"
 ]
}

 The dataset includes 200 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003274-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57935/agr.25602312
title: Life Cycle Assessment using GWP and GWP-star

description: This dataset is provided as supplementary material for the paper (entitled: Effects of management and technology scenarios on the carbon footprint of milk from New Zealand pasture-based dairy farm systems) to be published in Journal of Dairy Science.The file describes calculations for the carbon footprint of milk production in New Zealand, exploring two different approaches for the use of the new GWP* metric.
DOI: 10.57935/agr.25602312.v1
title: Life Cycle Assessment using GWP and GWP-star

description: This dataset is provided as supplementary material for the paper (entitled: Effects of management and technology scenarios on the carbon footprint of milk from New Zealand pasture-based dairy farm systems) to be published in Journal of Dairy Science.The file describes calculations for the carbon footprint of milk production in New Zealand, exploring two different approaches for the use of the new GWP* metric.
DOI: 10.15468/dl.bqfh28
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((100.166 2.306179, 100.095681 2.30061, 100.027094 2.284042, 99.96193100000001 2.256882, 99.901797 2.219799, 99.84817200000001 2.173709, 99.802378 2.119745, 99.765542 2.059238, 99.738569 1.993678, 99.72212399999999 1.92468, 99.71660900000001 1.853943, 99.722159 1.783208, 99.738636 1.714218, 99.76563400000001 1.64867, 99.802486 1.588179, 99.848286 1.534233, 99.901905 1.488159, 99.962023 1.451093, 100.027161 1.423945, 100.095716 1.407385, 100.166 1.401819, 100.236284 1.407385, 100.304839 1.423945, 100.369977 1.451093, 100.430095 1.488159, 100.483714 1.534233, 100.529514 1.588179, 100.566366 1.64867, 100.593364 1.714218, 100.609841 1.783208, 100.615391 1.853943, 100.609876 1.92468, 100.593431 1.993678, 100.566458 2.059238, 100.529622 2.119745, 100.483828 2.173709, 100.430203 2.219799, 100.370069 2.256882, 100.304906 2.284042, 100.236319 2.30061, 100.166 2.306179))"},{"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/0003275-260120142942310/datasets/export for details.
DOI: 10.15468/dl.p2z4rg
title: Occurrence Download

description: A dataset containing 1755 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Brazil",
 "TaxonKey is Brachyura"
 ]
}

 The dataset includes 1755 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003276-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.njq56a
title: Occurrence Download

description: A dataset containing 109 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-73.456 8.156098, -73.52697499999999 8.150525999999999, -73.596197 8.133948, -73.661956 8.106773, -73.722629 8.069672000000001, -73.77672 8.023561000000001, -73.822895 7.969579, -73.86001899999999 7.909057, -73.887181 7.843489, -73.903716 7.77449, -73.909221 7.70376, -73.903567 7.633042, -73.886899 7.564077, -73.85963099999999 7.49856, -73.82243800000001 7.438104, -73.776239 7.384194, -73.722172 7.338156, -73.66156700000001 7.301121, -73.59591500000001 7.273998, -73.526827 7.257453, -73.456 7.251892, -73.38517299999999 7.257453, -73.316085 7.273998, -73.250433 7.301121, -73.18982800000001 7.338156, -73.135761 7.384194, -73.089562 7.438104, -73.052369 7.49856, -73.02510100000001 7.564077, -73.008433 7.633042, -73.002779 7.70376, -73.008284 7.77449, -73.02481899999999 7.843489, -73.051981 7.909057, -73.089105 7.969579, -73.13527999999999 8.023561000000001, -73.18937099999999 8.069672000000001, -73.250044 8.106773, -73.315803 8.133948, -73.385025 8.150525999999999, -73.456 8.156098))"},{"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 109 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003277-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.njftvj
title: Occurrence Download

description: A dataset containing 85 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Continent is North America",
 "Country is Mexico",
 "OccurrenceStatus is Present",
 "TaxonKey is Leptofreya ambigua"
 ]
}

 The dataset includes 85 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003279-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w2efng
title: Occurrence Download

description: A dataset containing 527 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Computarización de la Colección de mamíferos del Centro de Investigaciones Biológicas del Noroeste SC",
 "TaxonKey is Thomomys"
 ]
}

 The dataset includes 527 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003280-260120142942310/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.30920051
title: Learning and identity development during interprofessional hospital placements: A qualitative exploration using rich pictures

description: Increasing numbers of healthcare students are trained within interprofessional hospital placements, where they learn to be part of the landscape of healthcare practice. Explicitly facilitating (inter)professional identity development has been recommended as a goal of these placements. We aimed to explore students’ experiences during their placement, and its relation to their learning and identity development. In this multicenter qualitative study, nine medical students, six midwifery students, and six nursing students drew rich pictures of one satisfying and one challenging experience during their interprofessional placement, capturing complex, nonverbal elements of these experiences. We used semi-structured interviews to deepen understanding of students’ experiences and their developing identities, adopting an inductive constructivist thematic analysis. During their interprofessional encounters, a range of emotions supported or challenged students’ learning and identity development. These emotions played a pivotal role throughout the three themes we identified: (1) Understanding and appreciating differences; (2) Navigating identity tensions; and (3) Gaining confidence in patient-centered learning and collaboration. During interprofessional placements, most students engage in learning about each other’s responsibilities and values, enhancing knowledgeability. Tutors should be aware of students’ emotions during interprofessional encounters, and stimulate reflection on them, as emotions can foster or hamper students’ knowledgeability and identity development.
DOI: 10.6084/m9.figshare.30920051.v1
title: Learning and identity development during interprofessional hospital placements: A qualitative exploration using rich pictures

description: Increasing numbers of healthcare students are trained within interprofessional hospital placements, where they learn to be part of the landscape of healthcare practice. Explicitly facilitating (inter)professional identity development has been recommended as a goal of these placements. We aimed to explore students’ experiences during their placement, and its relation to their learning and identity development. In this multicenter qualitative study, nine medical students, six midwifery students, and six nursing students drew rich pictures of one satisfying and one challenging experience during their interprofessional placement, capturing complex, nonverbal elements of these experiences. We used semi-structured interviews to deepen understanding of students’ experiences and their developing identities, adopting an inductive constructivist thematic analysis. During their interprofessional encounters, a range of emotions supported or challenged students’ learning and identity development. These emotions played a pivotal role throughout the three themes we identified: (1) Understanding and appreciating differences; (2) Navigating identity tensions; and (3) Gaining confidence in patient-centered learning and collaboration. During interprofessional placements, most students engage in learning about each other’s responsibilities and values, enhancing knowledgeability. Tutors should be aware of students’ emotions during interprofessional encounters, and stimulate reflection on them, as emotions can foster or hamper students’ knowledgeability and identity development.
DOI: 10.6084/m9.figshare.31118734
title: <b>Python workflow (Google Colab) for visualisation generation and the associated datasets (NVivo exports) for: Indigenous knowledge techniques associated with the treatment of venereal diseases in Okitipupa Local Government Area, Nigeria</b>

description: This repository contains the the Python source code and associated raw data matrices + ehnobotanical index computation worksheets for an ethnobotanical study on venereal diseases treatment in Okitipupa LGA. Specifically, the dataset includes NVivo-exported matrix coding queries used to analyse relationships between traditional medicine practitioners (TMPs), plant species, and therapeutic rationales. It also provides the worksheets for quantitative ethnobotanical indices including Use Value (UV), Fidelity Level (FL), and Factor of Informant Consensus (Fic). <b>Content:</b><b>Biomed_Ethnobotanical_Visualisations.ipynb: </b>A Python Jupyter Notebook (Google Colab) containing the complete computational pipeline. This includes scripts for importing the .xlsx matrices, calculating ethnobotanical indices, and generating the various charts used in the manuscript.<b>Biomed_PlantCitationAcrossTMPs_Evaluation.xlsx:</b> A cross-tabulation matrix of TMPs versus plant species. This file captures the citation frequency of each plant by individual practitioners and serves as the primary dataset for citation dispersion analysis.​Biomed_PotencyDeterminationAcrossTMPs_MatrixCodingQuery.xlsx: An NVivo-exported matrix coding output that identifies the qualitative rationales (e.g., consulter feedback, personal experience, gift appreciation) provided by TMPs to justify the potency of their venereal disease remedies.<b>Biomed_PrepMethod&AdminModeRelationship_MatrixCodingQuery.xlsx:</b> A matrix coding query file illustrating the relationship between preparation methods (e.g., decoction, infusion, maceration) and their corresponding modes of administration (oral vs. topical).<b>Biomed_UseValueEvaluation.xlsx: </b>A computation worksheet containing the values for Number of Uses (Nur) and Number of Informants (N) used to calculate the Use Value (UV) for each plant species across venereal disease categories.<b>Biomed_FidelityLevelEvaluation.xlsx:</b> A quantitative worksheet used for calculating the Fidelity Level (FL) percentages, representing the preference of practitioners for specific plants in treating certain STI categories.<b>Biomed_UseReport(PartsUsed)ofPlantSpecies_MatrixCodingQuery.xlsx: </b>A detailed matrix of plant species versus the specific morphological parts used (e.g., roots, leaves, seeds) in remedy preparations, based on the accumulated use-reports.<b>Biomed_FicEvaluation.xlsx: </b>An Informant Consensus Factor (Fic/ICF) support sheet containing the categorised venereal diseases and the respective counts of use-reports and taxa, used to evaluate the consistency of Indigenous knowledge.
DOI: 10.6084/m9.figshare.31118734.v2
title: <b>Python workflow (Google Colab) for visualisation generation and the associated datasets (NVivo exports) for: Indigenous knowledge techniques associated with the treatment of venereal diseases in Okitipupa Local Government Area, Nigeria</b>

description: This repository contains the the Python source code and associated raw data matrices + ehnobotanical index computation worksheets for an ethnobotanical study on venereal diseases treatment in Okitipupa LGA. Specifically, the dataset includes NVivo-exported matrix coding queries used to analyse relationships between traditional medicine practitioners (TMPs), plant species, and therapeutic rationales. It also provides the worksheets for quantitative ethnobotanical indices including Use Value (UV), Fidelity Level (FL), and Factor of Informant Consensus (Fic). <b>Content:</b><b>Biomed_Ethnobotanical_Visualisations.ipynb: </b>A Python Jupyter Notebook (Google Colab) containing the complete computational pipeline. This includes scripts for importing the .xlsx matrices, calculating ethnobotanical indices, and generating the various charts used in the manuscript.<b>Biomed_PlantCitationAcrossTMPs_Evaluation.xlsx:</b> A cross-tabulation matrix of TMPs versus plant species. This file captures the citation frequency of each plant by individual practitioners and serves as the primary dataset for citation dispersion analysis.​Biomed_PotencyDeterminationAcrossTMPs_MatrixCodingQuery.xlsx: An NVivo-exported matrix coding output that identifies the qualitative rationales (e.g., consulter feedback, personal experience, gift appreciation) provided by TMPs to justify the potency of their venereal disease remedies.<b>Biomed_PrepMethod&AdminModeRelationship_MatrixCodingQuery.xlsx:</b> A matrix coding query file illustrating the relationship between preparation methods (e.g., decoction, infusion, maceration) and their corresponding modes of administration (oral vs. topical).<b>Biomed_UseValueEvaluation.xlsx: </b>A computation worksheet containing the values for Number of Uses (Nur) and Number of Informants (N) used to calculate the Use Value (UV) for each plant species across venereal disease categories.<b>Biomed_FidelityLevelEvaluation.xlsx:</b> A quantitative worksheet used for calculating the Fidelity Level (FL) percentages, representing the preference of practitioners for specific plants in treating certain STI categories.<b>Biomed_UseReport(PartsUsed)ofPlantSpecies_MatrixCodingQuery.xlsx: </b>A detailed matrix of plant species versus the specific morphological parts used (e.g., roots, leaves, seeds) in remedy preparations, based on the accumulated use-reports.<b>Biomed_FicEvaluation.xlsx: </b>An Informant Consensus Factor (Fic/ICF) support sheet containing the categorised venereal diseases and the respective counts of use-reports and taxa, used to evaluate the consistency of Indigenous knowledge.
DOI: 10.6084/m9.figshare.31118734.v1
title: <b>Python workflow (Google Colab) and associated datasets (NVivo exports) for: Indigenous knowledge techniques associated with the treatment of venereal diseases in Okitipupa Local Government Area, Nigeria</b>

description: This repository contains the the Python source code and associated raw data matrices + ehnobotanical index computation worksheets for an ethnobotanical study on venereal diseases treatment in Okitipupa LGA. Specifically, the dataset includes NVivo-exported matrix coding queries used to analyse relationships between traditional medicine practitioners (TMPs), plant species, and therapeutic rationales. It also provides the worksheets for quantitative ethnobotanical indices including Use Value (UV), Fidelity Level (FL), and Factor of Informant Consensus (Fic). <b>Content:</b><b>Biomed_Ethnobotanical_Visualisations.ipynb: </b>A Python Jupyter Notebook (Google Colab) containing the complete computational pipeline. This includes scripts for importing the .xlsx matrices, calculating ethnobotanical indices, and generating the various charts used in the manuscript.<b>Biomed_PlantCitationAcrossTMPs_Evaluation.xlsx:</b> A cross-tabulation matrix of TMPs versus plant species. This file captures the citation frequency of each plant by individual practitioners and serves as the primary dataset for citation dispersion analysis.​Biomed_PotencyDeterminationAcrossTMPs_MatrixCodingQuery.xlsx: An NVivo-exported matrix coding output that identifies the qualitative rationales (e.g., consulter feedback, personal experience, gift appreciation) provided by TMPs to justify the potency of their venereal disease remedies.<b>Biomed_PrepMethod&AdminModeRelationship_MatrixCodingQuery.xlsx:</b> A matrix coding query file illustrating the relationship between preparation methods (e.g., decoction, infusion, maceration) and their corresponding modes of administration (oral vs. topical).<b>Biomed_UseValueEvaluation.xlsx: </b>A computation worksheet containing the values for Number of Uses (Nur) and Number of Informants (N) used to calculate the Use Value (UV) for each plant species across venereal disease categories.<b>Biomed_FidelityLevelEvaluation.xlsx:</b> A quantitative worksheet used for calculating the Fidelity Level (FL) percentages, representing the preference of practitioners for specific plants in treating certain STI categories.<b>Biomed_UseReport(PartsUsed)ofPlantSpecies_MatrixCodingQuery.xlsx: </b>A detailed matrix of plant species versus the specific morphological parts used (e.g., roots, leaves, seeds) in remedy preparations, based on the accumulated use-reports.<b>Biomed_FicEvaluation.xlsx: </b>An Informant Consensus Factor (Fic/ICF) support sheet containing the categorised venereal diseases and the respective counts of use-reports and taxa, used to evaluate the consistency of Indigenous knowledge.
DOI: 10.15468/dl.v58cg7
title: Occurrence Download

description: A dataset containing 733 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Platyrrhinus umbratus, Platyrrhinus umbratus aquilius, Platyrrhinus umbratus umbratus, Platyrrhinus umbratus aquilus, Platyrrhinus umbratus oratus)"
 ]
}

 The dataset includes 733 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003281-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.