cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18229193
title: Anonymized Dataset and Scripts for LLM Intervention in Chinese EFL: Reducing FLA and Enhancing Proficiency

description: This dataset supports the study titled "The Role of Large Language Models in Reducing Foreign Language Anxiety and Enhancing English Proficiency for Chinese EFL Learners." The research employed a sequential explanatory mixed-methods design to evaluate an 8-week asynchronous speaking intervention using large language models (LLMs such as DeepSeek and Kimi Chat) delivered via WeChat assignments to 52 intermediate-level Chinese EFL students at Chengdu University of Technology.The intervention aimed to provide low-stakes, non-judgmental speaking practice to address foreign language anxiety (FLA), increase speaking self-efficacy, and improve oral proficiency in a high-anxiety sociocultural context characterized by Confucian heritage values (e.g., fear of losing face) and exam-oriented education.Quantitative findings showed significant reductions in FLA (FLCAS: t(44) = -3.964, p < .001, d = 0.59), increases in self-efficacy (adapted ESSES: Wilcoxon signed-rank test, p < .001, r = 0.63), and gains in proficiency (VRT rubric: t(11) = 4.011, p = .002, d = 1.16), with engagement (OSE) positively correlated with proficiency gains (r = .682, p < .05). Qualitative thematic analysis of 470 interview turns from 12 participants identified key mechanisms, including non-judgmental AI feedback, cultural relevance of prompts, and risks of over-reliance.Contents of the dataset

Quantitative_Data: Anonymized pre- and post-intervention scores for FLCAS, ESSES, OSE, and VRT; processed CSV files; master merged dataset.Qualitative_Data: Anonymized interview transcripts (full and individual speaker files using C-XX codes); initial coding and final refined themes (CSV).Code_and_Scripts: Python scripts for data cleaning, processing, statistical analysis (paired t-tests, Wilcoxon, correlations), thematic coding support, and VRT scoring.Documentation: Intervention summary PDF, anonymized consent form template, README with variable descriptions and replication guide, ethics and anonymization notes.

How to replicateAll participant data is fully anonymized: no names, IDs, locations, or identifiable information remain. Transcripts and qualitative files use speaker codes (e.g., C-01 to C-30). Quantitative files use pseudonymized participant codes where applicable.To replicate quantitative results: Load Master_Quantitative_Data_Anonymized.csv and run quant_analysis_final.py or vrt_real_analysis.py for descriptive statistics, paired tests, effect sizes, and correlations.To replicate qualitative analysis: Use Final_Refined_Themes.csv and Initial_Human_Coding_Anonymized.csv together with read_transcripts.py or initial_coding.py for theme verification and LDA support.See README.txt for detailed variable descriptions, folder structure, and step-by-step instructions.This dataset enables full reproduction of the reported statistical results and thematic findings while strictly protecting participant privacy in accordance with informed consent and ethical guidelines.
DOI: 10.5281/zenodo.18229194
title: Anonymized Dataset and Scripts for LLM Intervention in Chinese EFL: Reducing FLA and Enhancing Proficiency

description: This dataset supports the study titled "The Role of Large Language Models in Reducing Foreign Language Anxiety and Enhancing English Proficiency for Chinese EFL Learners." The research employed a sequential explanatory mixed-methods design to evaluate an 8-week asynchronous speaking intervention using large language models (LLMs such as DeepSeek and Kimi Chat) delivered via WeChat assignments to 52 intermediate-level Chinese EFL students at Chengdu University of Technology.The intervention aimed to provide low-stakes, non-judgmental speaking practice to address foreign language anxiety (FLA), increase speaking self-efficacy, and improve oral proficiency in a high-anxiety sociocultural context characterized by Confucian heritage values (e.g., fear of losing face) and exam-oriented education.Quantitative findings showed significant reductions in FLA (FLCAS: t(44) = -3.964, p < .001, d = 0.59), increases in self-efficacy (adapted ESSES: Wilcoxon signed-rank test, p < .001, r = 0.63), and gains in proficiency (VRT rubric: t(11) = 4.011, p = .002, d = 1.16), with engagement (OSE) positively correlated with proficiency gains (r = .682, p < .05). Qualitative thematic analysis of 470 interview turns from 12 participants identified key mechanisms, including non-judgmental AI feedback, cultural relevance of prompts, and risks of over-reliance.Contents of the dataset

Quantitative_Data: Anonymized pre- and post-intervention scores for FLCAS, ESSES, OSE, and VRT; processed CSV files; master merged dataset.Qualitative_Data: Anonymized interview transcripts (full and individual speaker files using C-XX codes); initial coding and final refined themes (CSV).Code_and_Scripts: Python scripts for data cleaning, processing, statistical analysis (paired t-tests, Wilcoxon, correlations), thematic coding support, and VRT scoring.Documentation: Intervention summary PDF, anonymized consent form template, README with variable descriptions and replication guide, ethics and anonymization notes.

How to replicateAll participant data is fully anonymized: no names, IDs, locations, or identifiable information remain. Transcripts and qualitative files use speaker codes (e.g., C-01 to C-30). Quantitative files use pseudonymized participant codes where applicable.To replicate quantitative results: Load Master_Quantitative_Data_Anonymized.csv and run quant_analysis_final.py or vrt_real_analysis.py for descriptive statistics, paired tests, effect sizes, and correlations.To replicate qualitative analysis: Use Final_Refined_Themes.csv and Initial_Human_Coding_Anonymized.csv together with read_transcripts.py or initial_coding.py for theme verification and LDA support.See README.txt for detailed variable descriptions, folder structure, and step-by-step instructions.This dataset enables full reproduction of the reported statistical results and thematic findings while strictly protecting participant privacy in accordance with informed consent and ethical guidelines.
DOI: 10.15156/bio/sh1375893.10fu
title: SH1375893.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1375894.10fu
title: SH1375894.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1375896.10fu
title: SH1375896.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1375897.10fu
title: SH1375897.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1375898.10fu
title: SH1375898.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.fqzzmx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"BRA.12.91_2","matchCase":false},{"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/0005653-260108223611665/datasets/export for details.
DOI: 10.15156/bio/sh1375901.10fu
title: SH1375901.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.w5j5wj
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Greece",
 "Geometry POLYGON((18.02201 34.4479,30.75619 34.4479,30.75619 42.72512,18.02201 42.72512,18.02201 34.4479))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Geum coccineum Sibth. & Sm."
 ]
}

 The dataset includes 23 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0271054-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4z7y2z
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lefebvrea tenuis (C.C.Towns.) P.J.D.Winter"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005554-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f24que
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Oxypetalum gracile (Malme)"
 ]
}

 The dataset includes 29 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067294-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dgf574
title: Occurrence Download

description: A dataset containing 106 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lachenalia mediana Jacq."
 ]
}

 The dataset includes 106 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002476-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w3csb2
title: Occurrence Download

description: A dataset containing 445 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhinoceros unicornis Linnaeus, 1758"
 ]
}

 The dataset includes 445 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0271057-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mjbnzt
title: Occurrence Download

description: A dataset containing 163 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Legenere valdiviana (Phil.) E.Wimm."
 ]
}

 The dataset includes 163 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005556-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.megk3a
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Colletotrichum fructicola Prihast., L.Cai & K.D.Hyde"
 ]
}

 The dataset includes 44 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0271060-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4r67k9
title: Occurrence Download

description: A dataset containing 257 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lycium ruthenicum Murray"
 ]
}

 The dataset includes 257 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005558-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7f6uf6
title: Occurrence Download

description: A dataset containing 1295 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Native Bee Inventory of Missisquoi National Wildlife Refuge"
 ]
}

 The dataset includes 1295 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0274192-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z8z949
title: Occurrence Download

description: A dataset containing 89646 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Lecythidaceae"
 ]
}

 The dataset includes 89646 records from 416 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0274222-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6hd47z
title: Occurrence Download

description: A dataset containing 13450 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Israeli Butterfly Monitoring Scheme (BMS-IL)"
 ]
}

 The dataset includes 13450 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0271061-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.awfzb6
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Legousia julianii (Batt.) Briq."
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005562-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wqhxjp
title: Occurrence Download

description: A dataset containing 7841 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Macroglossum stellatarum Linnaeus, 1758",
 "Year is greater than 2013",
 "Geometry MULTIPOLYGON (((19.51 47.46,19.51 36.21,6.41 36.21,6.41 47.46,19.51 47.46)))"
 ]
}

 The dataset includes 7841 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036561-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g8vhx2
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((60.32214 10.23654,141.67786 10.23654,141.67786 42.76346,60.32214 42.76346,60.32214 10.23654))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Colletotrichum aenigma B.S.Weir & P.R.Johnst."
 ]
}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0271069-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j2gcds
title: Occurrence Download

description: A dataset containing 65 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lefebvrea angustisecta Engl."
 ]
}

 The dataset includes 65 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005537-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wrjhdp
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ocotea hirtostyla van der Werff"
 ]
}

 The dataset includes 30 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067295-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8upbsk
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lachenalia minima W.F.Barker"
 ]
}

 The dataset includes 13 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002477-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vj3jp6
title: Occurrence Download

description: A dataset containing 304 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Colletotrichum siamense Prihast., L.Cai & K.D.Hyde"
 ]
}

 The dataset includes 304 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0271064-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.