cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/hr2rhjx4fz.1
title: BOILED MASH POTATOES

description: *Project Title:* Good and Bad Classification of Boiled Mash Potatoes Using OnePlus Nord CE 2 Lite 5G Mobile Camera

*Description:*

The project "Good and Bad Classification of Boiled Mash Potatoes" is focused on developing a system for automatically classifying Boiled Mash Potatoes (Solanum tuberosum L) into "good" and "bad" categories based on visual characteristics. The dataset includes over 500 images of Boiled Mash Potatoes, equally divided between good and bad samples. These images were captured using the OnePlus Nord CE 2 Lite 5G mobile camera, ensuring high-quality visual data for training a machine learning model. The images were taken against a white background in daylight conditions to ensure consistency, clarity, and minimal environmental interference.

*Dataset Composition:*

- *Good Samples (High-Quality Boiled Mash Potatoes ):* The dataset contains more than 250 images of high-quality Boiled Mash Potatoes. These samples exhibit desirable characteristics, such as consistent texture, proper fermentation, vibrant color, and no visible signs of contamination or spoilage. These images represent the positive class for the classification model, showcasing what is considered high-quality and safe Boiled Mash Potatoes.
 
- *Bad Samples (Low-Quality Boiled Mash Potatoes):* More than 250 images depict low-quality or spoiled Boiled Mash Potatoes. These samples may exhibit visual signs of spoilage, such as mold growth, discoloration, uneven texture, or over-fermentation. These images form the negative class for model training, helping to identify unwanted and unsafe products.

*Data Collection Setup:*

All images were captured using the OnePlus Nord CE 2 Lite 5G mobile camera, which provides high-resolution imaging capabilities to capture fine details. A white background was chosen to create a neutral, non-distracting environment that highlights the key features of the Boiled Mash Potatoes. Daylight conditions were used for consistent and natural lighting, allowing the camera to capture the true color, texture, and visual quality of the samples.

*Image Characteristics:*

The dataset features a wide range of Boiled Mash Potatoes samples, varying in appearance due to different stages of fermentation, levels of spoilage, and types of ingredients. This diversity ensures that the classification model can generalize across different variations in the appearance of Boiled Mash Potatoes, allowing it to effectively distinguish between good and bad samples.

*Data Annotation:*

Each image in the dataset is carefully labeled as either "good" or "bad" based on its quality. These annotations serve as the ground truth for the machine learning model, providing the necessary data for training, validating, and testing the classifier.

*Data Preprocessing:*

To ensure the model receives optimal input data, several preprocessing steps are applied:
- *Resizing:* All images are resized to a uniform resolution to maintain consistency across the dataset.
DOI: 10.17632/ymg9nsn6kn.1
title: Higher Education Students' Early Perceptions of ChatGPT: Global Survey Data

description: The introduction of ChatGPT in November 2022 marked a significant milestone in the application of artificial intelligence in higher education. Due to its advanced natural language processing capabilities, ChatGPT quickly became popular among students worldwide. However, the increasing acceptance of ChatGPT among students has attracted significant attention, sparking both excitement and scepticism globally. In order to capture early students' perceptions about ChatGPT, the most comprehensive and large-scale global survey to date was conducted between the beginning of October 2023 and the end of February 2024. The questionnaire was prepared in seven different languages: English, Italian, Spanish, Turkish, Japanese, Arabic, and Hebrew. It covered several aspects relevant to ChatGPT, including sociodemographic characteristics, usage, capabilities, regulation and ethical concerns, satisfaction and attitude, study issues and outcomes, skills development, labour market and skills mismatch, emotions, study and personal information, and general reflections. The survey targeted higher education students who are currently enrolled at any level in a higher education institution, are at least 18 years old, and have the legal capacity to provide free and voluntary consent to participate in an anonymous survey. Survey participants were recruited using a convenience sampling method, which involved promoting the survey in classrooms and through advertisements on university communication systems. The final dataset consists of 23,218 student responses from 109 different countries and territories. The data may prove useful for researchers studying students' perceptions of ChatGPT, including its implications across various aspects. Moreover, also higher education stakeholders may benefit from these data. While educators may benefit from the data in formulating curricula, including designing teaching methods and assessment tools, policymakers may consider the data when formulating strategies for higher education system development in the future.
DOI: 10.17632/ymg9nsn6kn.2
title: Higher Education Students' Early Perceptions of ChatGPT: Global Survey Data

description: The introduction of ChatGPT in November 2022 marked a significant milestone in the application of artificial intelligence in higher education. Due to its advanced natural language processing capabilities, ChatGPT quickly became popular among students worldwide. However, the increasing acceptance of ChatGPT among students has attracted significant attention, sparking both excitement and scepticism globally. In order to capture early students' perceptions about ChatGPT, the most comprehensive and large-scale global survey to date was conducted between the beginning of October 2023 and the end of February 2024. The questionnaire was prepared in seven different languages: English, Italian, Spanish, Turkish, Japanese, Arabic, and Hebrew. It covered several aspects relevant to ChatGPT, including sociodemographic characteristics, usage, capabilities, regulation and ethical concerns, satisfaction and attitude, study issues and outcomes, skills development, labour market and skills mismatch, emotions, study and personal information, and general reflections. The survey targeted higher education students who are currently enrolled at any level in a higher education institution, are at least 18 years old, and have the legal capacity to provide free and voluntary consent to participate in an anonymous survey. Survey participants were recruited using a convenience sampling method, which involved promoting the survey in classrooms and through advertisements on university communication systems. The final dataset consists of 23,218 student responses from 109 different countries and territories. The data may prove useful for researchers studying students' perceptions of ChatGPT, including its implications across various aspects. Moreover, also higher education stakeholders may benefit from these data. While educators may benefit from the data in formulating curricula, including designing teaching methods and assessment tools, policymakers may consider the data when formulating strategies for higher education system development in the future.
DOI: 10.15468/dl.6r3gbg
title: Occurrence Download

description: A dataset containing 130 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pseudobombax palmeri (S.Watson) Dugand)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 130 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016519-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p6p2d3
title: Occurrence Download

description: A dataset containing 1305 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Taxodium mucronatum Ten.",
 "Year 2020-2025"
 ]
}

 The dataset includes 1305 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016518-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tnrasm
title: Occurrence Download

description: A dataset containing 451 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Megalopta genalis Meade-Waldo, 1916",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 451 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016520-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jshr6h
title: Occurrence Download

description: A dataset containing 397 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Euodia cuspidata K.Schum., Euodia hortensis Forst., Euodia hylandii T.G.Hartley, Euodia montana T.G.Hartley, Euodia pubifolia T.G.Hartley, Euodia whitmorei T.G.Hartley)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 397 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016521-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.emrvde
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Quararibea funebris (La Llave) Vischer)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 18 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016522-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.znumtf
title: Occurrence Download

description: A dataset containing 451 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Megalopta genalis Meade-Waldo, 1916",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 451 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016523-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mne7cp
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron declivatum Ching & H.P.Yang"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016525-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d74ueq
title: Occurrence Download

description: A dataset containing 17323 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ehretia acuminata R.Br., Ehretia amoena Klotzsch, Ehretia anacua (Terán & Berl.) I.M.Johnst., Ehretia aspera Willd., Ehretia bakeri Britten, Ehretia confinis I.M.Johnst., Ehretia corylifolia C.H.Wright, Ehretia cymosa Thonn., Ehretia decaryi J.S.Mill., Ehretia densiflora F.N.Wei & H.Q.Wen, Ehretia dichotoma Blume, Ehretia dicksonii Hance, Ehretia dunniana H.Lév., Ehretia glandulosissima Verdc., Ehretia hainanensis I.M.Johnst., Ehretia javanica Blume, Ehretia keyensis Warb., Ehretia latifolia Loisel., Ehretia longiflora Champ. ex Benth., Ehretia macrophylla Wall., Ehretia meyersii J.S.Mill., Ehretia microcalyx Vaupel, Ehretia moluccana Riedl, Ehretia obtusifolia Hochst. ex A.DC., Ehretia parallela C.B.Clarke, Ehretia philippinensis A.DC., Ehretia phillipsonii J.S.Mill., Ehretia pingbianensis Y.L.Liu, Ehretia resinosa Hance, Ehretia rigida (Thunb.) Druce, Ehretia saligna R.Br., Ehretia cymosa Thonn., Ehretia seyrigii J.S.Mill., Ehretia siamensis Teijsm. & Binn., Ehretia silvana R.R.Mill, Ehretia timorensis Decne., Ehretia tinifolia L., Ehretia trachyphylla C.H.Wright, Ehretia tsangii I.M.Johnst., Ehretia wallichiana Hook.fil. & Thomson ex Gamble, Ehretia wightiana Wall. ex G.Don)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 17323 records from 296 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016524-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n422yg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Capra ibex Linnaeus, 1758"
 ]
 },
 "HasCoordinate is TRUE",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2024",
 "DecimalLatitude is greater than or equal to -13.57856525441899",
 "DecimalLatitude is less than or equal to 13.294575097139866",
 "DecimalLongitude is greater than or equal to -26.16659218575694",
 "DecimalLongitude is less than or equal to 1.228232341514007",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016527-250127130748423/datasets/export for details.
DOI: 10.15468/dl.3zxmzv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Quararibea lopezperaltae Gallardo-Hern. & Lorea-Hern.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016526-250127130748423/datasets/export for details.
DOI: 10.15468/dl.yjteqp
title: Occurrence Download

description: A dataset containing 703534 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "OccurrenceStatus is Present",
 "StateProvince is Pennsylvania",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 703534 records from 249 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016460-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0760173.10fu
title: SH0760173.10FU

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/sh0760174.10fu
title: SH0760174.10FU

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/sh0760175.10fu
title: SH0760175.10FU

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/sh0760176.10fu
title: SH0760176.10FU

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/sh0760177.10fu
title: SH0760177.10FU

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/sh0760178.10fu
title: SH0760178.10FU

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/sh0760179.10fu
title: SH0760179.10FU

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.ukd7tp
title: Occurrence Download

description: A dataset containing 562432 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((0.29548 40.61972,0.79057 40.61972,0.79057 41.17577,0.29548 41.17577,0.29548 40.61972))",
 "HasGeospatialIssue is false",
 "Year 2014-2024"
 ]
}

 The dataset includes 562432 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062865-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.