cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14859098
title: Data from Phenocam (PHE) measurements at Heraklion – FORTH (HEFORT) from 2024-01-01 to 2024-12-31 [RAW]

description: Original phenocam images separated into near-infrared (NIR) and visible (VIS).
description: TECHNICAL INFO

Files were submitted and recorded as part of the global Phenocam Network with the site name "forthgr". Access to the "forthgr" phenocam on the global phenocam network through: https://phenocam.nau.edu/webcam/sites/forthgr/

Files are organised in sub-directories per year and month of the year.

LICENSE

Creative Commons Attribution 4.0 International. 

Please read the Phenocam Fair Use Policy.

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005).

Data were managed and attributed by the PhenoCam Network, which has been supported by the National Science Foundation, the Long-Term Agroecosystem Research (LTAR) network which is supported by the United States Department of Agriculture (USDA), the U.S. Department of Energy, the U.S. Geological Survey, the Northeastern States Research Cooperative, and the USA National Phenology Network. 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project

Global Phenocam Network
DOI: 10.5281/zenodo.13293733
title: Cichlidae: Cichlidae - life span

description: body mass, life span

[
![][id]
[id]: http://media.eol.org/content/2009/07/24/11/83028_580_360.jpg "Cichlids"
][eolpath]
[eolpath]: http://eol.org/pages/5344/overview
DOI: 10.5281/zenodo.13293734
title: Cichlidae: Cichlidae - life span

description: body mass, life span

[
![][id]
[id]: http://media.eol.org/content/2009/07/24/11/83028_580_360.jpg "Cichlids"
][eolpath]
[eolpath]: http://eol.org/pages/5344/overview
DOI: 10.15468/dl.m6p5b8
title: Occurrence Download

description: A dataset containing 20641 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Capsicum annuum L.",
 "HasCoordinate is true"
 ]
}

 The dataset includes 20641 records from 422 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001491-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.djafpc
title: Occurrence Download

description: A dataset listing the 22 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "GadmGid is IND.24_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Hesperiidae"
 ]
}

 The dataset's 22 records were derived from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001479-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.58145/awf/s3s2ms
title: What you don't know can hurt others. A systematic review on calibration of stimulus intensity in pain research

description: Data for a systematic review on calibration of stimulus intensity in pain research
DOI: 10.6084/m9.figshare.28407497
title: Dataset for Generative artificial intelligence models in clinical infectiousdisease consultations: a cross-sectional analysis among specialists andresident trainees

description: In this cross-sectional analysis, researchers evaluated the performance and safety of four generative artificial intelligence (GenAI) chatbots in the context of clinical infectious disease consultations. The study involved GPT-4.0, a custom GPT-4.0 chatbot (cGPT-4) optimized via retrieval-augmented generation, Gemini Pro, and Claude 2. Forty unique clinical scenarios were created from real patient consultations and systematically anonymized and categorized into relevant sections. Six clinical experts, including specialists and resident trainees, independently evaluated 160 AI-generated responses using a 5-point Likert scale across four domains: factual consistency, comprehensiveness, coherence, and potential medical harmfulness.Results demonstrated that GPT-4.0-based models achieved significantly higher composite scores compared to Gemini Pro and Claude 2, particularly in factual accuracy and comprehensiveness. However, across all models, less than two-fifths of responses were deemed “Harmless,” raising concerns about the clinical safety of deploying these systems without supervision. Specialists consistently rated the responses more favorably than resident trainees, highlighting a discrepancy in clinical judgment. Cost analysis revealed decreasing operating expenses over time, but model performance was not directly correlated with cost. The study emphasizes that despite promising advancements, current GenAI models require further refinement and human oversight before being integrated into direct clinical care. Collectively, these findings inform future clinical application.
DOI: 10.6084/m9.figshare.28407497.v1
title: Dataset for Generative artificial intelligence models in clinical infectiousdisease consultations: a cross-sectional analysis among specialists andresident trainees

description: In this cross-sectional analysis, researchers evaluated the performance and safety of four generative artificial intelligence (GenAI) chatbots in the context of clinical infectious disease consultations. The study involved GPT-4.0, a custom GPT-4.0 chatbot (cGPT-4) optimized via retrieval-augmented generation, Gemini Pro, and Claude 2. Forty unique clinical scenarios were created from real patient consultations and systematically anonymized and categorized into relevant sections. Six clinical experts, including specialists and resident trainees, independently evaluated 160 AI-generated responses using a 5-point Likert scale across four domains: factual consistency, comprehensiveness, coherence, and potential medical harmfulness.Results demonstrated that GPT-4.0-based models achieved significantly higher composite scores compared to Gemini Pro and Claude 2, particularly in factual accuracy and comprehensiveness. However, across all models, less than two-fifths of responses were deemed “Harmless,” raising concerns about the clinical safety of deploying these systems without supervision. Specialists consistently rated the responses more favorably than resident trainees, highlighting a discrepancy in clinical judgment. Cost analysis revealed decreasing operating expenses over time, but model performance was not directly correlated with cost. The study emphasizes that despite promising advancements, current GenAI models require further refinement and human oversight before being integrated into direct clinical care. Collectively, these findings inform future clinical application.
DOI: 10.5281/zenodo.14863339
title: 4DHydro_Results_Tetis_Tugela_15sec_daily_exp31_20070101_20221231

description: Modeling results with TETIS software, for Tugela River with a spatial resolution of 15 seconds, and temporal resolution daily. Experiment 31. Forcing "mergedera5gpmsm2a". Calibrating methodology MOSCEM, calibrated variable SM

Includes results for the following variables:



Total evapotranspiration flux in kg m-2 s-1

Discharge flux in m-3 s-1

Volumetric soil moisture content in percentage

Total water storage content in kg m-2
DOI: 10.5281/zenodo.14863340
title: 4DHydro_Results_Tetis_Tugela_15sec_daily_exp31_20070101_20221231

description: Modeling results with TETIS software, for Tugela River with a spatial resolution of 15 seconds, and temporal resolution daily. Experiment 31. Forcing "mergedera5gpmsm2a". Calibrating methodology MOSCEM, calibrated variable SM

Includes results for the following variables:



Total evapotranspiration flux in kg m-2 s-1

Discharge flux in m-3 s-1

Volumetric soil moisture content in percentage

Total water storage content in kg m-2
DOI: 10.15468/dl.g3bhm5
title: Occurrence Download

description: A dataset containing 271 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Siganus argenteus (Quoy & Gaimard, 1825)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 271 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001493-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e277dy
title: Occurrence Download

description: A dataset listing the 30 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is IND.24_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Papilionidae"
 ]
}

 The dataset's 30 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001487-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4csg69
title: Occurrence Download

description: A dataset containing 564 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hexagrammos otakii Jordan & Starks, 1895",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 564 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001495-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14861091
title: FaciaVox a Multimodal Biometric Dataset

description: The FaciaVox dataset is an extensive multimodal biometric resource designed to enable in-depth exploration of face-image and voice recording research areas in both masked and unmasked scenarios.

Features of the Dataset:

1. Multimodal Data: A total of 1,800 face images (JPG) and 6,000 audio recordings (WAV) were collected, enabling cross-domain analysis of visual and auditory biometrics.

2. Participants were categorized into four age groups for structured labeling:Label 1: Under 16 yearsLabel 2: 16 to less than 31 yearsLabel 3: 31 to less than 46 yearsLabel 4: 46 years and above

3. Sibling Data: Some participants are siblings, adding a challenging layer for speaker identification and facial recognition tasks due to genetic similarities in vocal and facial features. Sibling relationships are documented in the accompanying "FaciaVox List" data file.

4. Standardized Filenames: The dataset uses a consistent, intuitive naming convention for both facial images and voice recordings. Each filename includes:Type (F: Face Image, V: Voice Recording)Participant ID (e.g., sub001)Mask Type (e.g., a: unmasked, b: disposable mask, etc.)Zoom Level or Sentence ID (e.g., 1x, 3x, 5x for images or specific sentence identifier {01, 02, 03, ..., 10} for recordings)

5. Diverse Demographics: 19 different countries.

6. A challenging face recognition problem involving reflective mask shields and severe lighting conditions.

7. Each participant uttered 7 English statements and 3 Arabic statements, regardless of their native language. This adds a challenge for speaker identification. 

Research Applications

FaciaVox is a versatile dataset supporting a wide range of research domains, including but not limited to:•    Speaker Identification (SI) and Face Recognition (FR): Evaluating biometric systems under varying conditions.•    Impact of Masks on Biometrics: Investigating how different facial coverings affect recognition performance.•    Language Impact on SI: Exploring the effects of native and non-native speech on speaker identification.•    Age and Gender Estimation: Inferring demographic information from voice and facial features.•    Race and Ethnicity Matching: Studying biometrics across diverse populations.•    Synthetic Voice and Deepfake Detection: Detecting cloned or generated speech.•    Cross-Domain Biometric Fusion: Combining facial and vocal data for robust authentication.•    Speech Intelligibility: Assessing how masks influence speech clarity.•    Image Inpainting: Reconstructing occluded facial regions for improved recognition.

Researchers can use the facial images and voice recordings independently or in combination to explore multimodal biometric systems. The standardized filenames and accompanying metadata make it easy to align visual and auditory data for cross-domain analyses. Sibling relationships and demographic labels add depth for tasks such as familial voice recognition, demographic profiling, and model bias evaluation.
DOI: 10.15468/dl.jm3db9
title: Occurrence Download

description: A dataset listing the 42 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is IND.24_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Lycaenidae"
 ]
}

 The dataset's 42 records were derived from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001492-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14024335
title: Raw AFM curves for displayed curves
DOI: 10.5281/zenodo.14024336
title: Raw AFM curves for displayed curves
DOI: 10.15468/dl.q53heq
title: Occurrence Download

description: A dataset containing 4065 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chelonia mydas (Linnaeus, 1758)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4065 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001497-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ndz5mb
title: Occurrence Download

description: A dataset listing the 18 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is IND.24_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Pieridae"
 ]
}

 The dataset's 18 records were derived from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001494-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.em2xjm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Testudo mydas Linnaeus, 1758",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001499-250212154643175/datasets/export for details.
DOI: 10.15468/dl.w443pw
title: Occurrence Download

description: A dataset containing 36883165 species occurrences available in GBIF matching the query:
{
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
}

 The dataset includes 36883165 records from 4619 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001481-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0196091.10fu
title: SH0196091.10FU

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/sh0196092.10fu
title: SH0196092.10FU

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/sh0196093.10fu
title: SH0196093.10FU

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/sh0196094.10fu
title: SH0196094.10FU

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/sh0196095.10fu
title: SH0196095.10FU

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/sh0196096.10fu
title: SH0196096.10FU

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/sh0196097.10fu
title: SH0196097.10FU

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.