cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0205048.10fu
title: SH0205048.10FU

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/sh0205049.10fu
title: SH0205049.10FU

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/sh0205050.10fu
title: SH0205050.10FU

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/sh0205051.10fu
title: SH0205051.10FU

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/sh0205052.10fu
title: SH0205052.10FU

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/sh0205053.10fu
title: SH0205053.10FU

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/sh0205054.10fu
title: SH0205054.10FU

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/sh0205055.10fu
title: SH0205055.10FU

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.8mma8u
title: Occurrence Download

description: A dataset containing 8434 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Ambystoma laterale Hallowell, 1856"
 ]
 },
 "HasCoordinate is TRUE",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2024",
 "DecimalLatitude is greater than or equal to 24.1271113347408",
 "DecimalLatitude is less than or equal to 56.572699215132005",
 "DecimalLongitude is greater than or equal to -89.31544323258166",
 "DecimalLongitude is less than or equal to -51.73636965516586",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 8434 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000899-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5f766k5r
title: MassIVE MSV000097146 - Hard Tissue paleometabolomics of early human sites yield metabolic and ecologic profiles - A: Metabolomics Data Set
title: The science of metabolic profiling exploits the chemical compound byproducts of biological functions called metabolites that, when analyzed, not only explain internal biological functions and physiological well-being, but also provide evidence of external influences specific to the habitat in which an organism lives. Here, we advance a new ultra fine scale bio molecular approach to understand the paleo environment of early human localities in eastern, central, and southern Africa using six fossil bones derived from 1.7-1.8 myr Bed I and 1.3 myr Bed II sites at Olduvai Gorge, Tanzania, one 2.4 myr sample from the Chiwondo Beds, Malawi, and one sample from the 3 myr site at Makapansgat, South Africa.
DOI: 10.6084/m9.figshare.28130015
title: Data File S1

description: Individual data points underlying all plotted figures.
DOI: 10.6084/m9.figshare.26781724
title: 2030 conservation targets for European errestrial mammals using the Favourable Conservation Status concept

description: Conservation targets for European terrestrial mammals
DOI: 10.6084/m9.figshare.28438499.v8
title: MajorType

description: MajorType allc files
DOI: 10.15468/dl.uxep5p
title: Occurrence Download

description: A dataset containing 93 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myrospermum sousanum A.Delgado & M.C.Johnst."
 ]
}

 The dataset includes 93 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000900-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57687/fk2/o7e66l
title: Automotive Industry Skills Taxonomy

description: This dataset contains skills extracted from job postings related to the automotive industry. Each skill is linked to a corresponding key term, a definition, and a classification within the KSA-O framework. The data was collected in December 2023. The dataset is organized into the following columns: *Hypernym: Key terms that group related skills under a common concept. *Hyponym: The specific skills, including different variations as they appeared in the job postings. *Definition: A description of the hypernym that explains the rationale for grouping its associated hyponyms. * Label: The classification of each hypernym within the KSA-O framework. This column contains four unique values: Knowledge Skill Ability Others
DOI: 10.15468/dl.232pc8
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sigmodon planifrons Nelson & Goldman, 1933"
 ]
}

 The dataset includes 112 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000901-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f5ug8r
title: Occurrence Download

description: A dataset containing 383 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate rounded, Geodetic datum assumed WGS84, Coordinate reprojected, Country derived from coordinates, Continent derived from coordinates)",
 "OccurrenceStatus is Present",
 "TaxonKey is Quercus germana Schltdl. & Cham."
 ]
}

 The dataset includes 383 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000902-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14878924
title: Visual Quality Assessment of Composite Images: A Compression-oriented Database and Measurement

description: A release of ciCIQADatabase, a composite compressed image database with just noticeable difference (JND) annotation and visual quality score (VQS) annotation, as proposed in "Miaohui W, et al. Visual Quality Assessment of Composite Images: Compression-oriented Database and Measurement".
DOI: 10.5281/zenodo.14878925
title: Visual Quality Assessment of Composite Images: A Compression-oriented Database and Measurement

description: A release of ciCIQADatabase, a composite compressed image database with just noticeable difference (JND) annotation and visual quality score (VQS) annotation, as proposed in "Miaohui W, et al. Visual Quality Assessment of Composite Images: Compression-oriented Database and Measurement".
DOI: 10.15468/dl.wv9fap
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sigmodon planifrons Nelson & Goldman, 1933"
 ]
}

 The dataset includes 112 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000904-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.adjwc9
title: Occurrence Download

description: A dataset containing 6929 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pan troglodytes (Blumenbach, 1775)"
 ]
}

 The dataset includes 6929 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000905-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7801/470
title: Datensatz zum Aufsatz: Carl Friedrich Lehmann-Haupt Bilder und Abbilder in historischen Zeitungen Deutschlands und Österreichs

description: Der vorliegende Datensatz sammelt nun die zeitgenössischen Meldungen in Österreich und Deutschland, die sowohl über Lehmann-Haupt berichten als auch solche, die aus seiner eigenen Feder stammen. Die Suche wurde mittels optischer Zeichenerkennung (optical character recognition = OCR) und der fortschreitenden Digitalisierung der historischen Zeitungen ermöglicht. Für die österreichischen Zeitungen wurde ANNO (https://anno.onb.ac.at/) und für die deutschen Zeitungen das Deutsche Zeitungsportal (https://www.deutsche-digitale-bibliothek.de/newspaper) nach Lehmann-Haupt durchsucht. Diese Datensammlung dient als Grundlage für deren Auswertung im Aufsatz: Angelika Kellner, Carl Friedrich Lehmann-Haupt. Bilder und Abbilder in zeitgenössischen Zeitungen in: Kai Ruffing – Brigitte Truschnegg – Andreas Rudigier – Julian Degen – Sebastian Fink – Kordula Schnegg (Hrsg.), Navigating the Worlds of History. Studies in Honor of Robert Rollinger on the Occasion of his 60th Birthday (Wiesbaden 2024) 1369–1388.
DOI: 10.15468/dl.ry5wtc
title: Occurrence Download

description: A dataset containing 4035 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Lophius Linnaeus, 1758"
 ]
}

 The dataset includes 4035 records from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000907-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14889966
title: Replication Timing Data
DOI: 10.5281/zenodo.14889967
title: Replication Timing Data
DOI: 10.15468/dl.s9qyu5
title: Occurrence Download

description: A dataset containing 140 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mus musculus Linnaeus, 1758",
 "Year 2010-2025"
 ]
}

 The dataset includes 140 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000908-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14228303
title: Artificial Intelligence Identifies Individuals with Prediabetes from Single-Lead Electrocardiograms

description: Contents



codes.zip



For ECG feature extraction (this will need original ECG signal data)



ecg_feature_extraction.sh

ecg_feature_extraction.py

feature_extractor.py



For training with hyperparameter optimization



train.sh

train.py



For prediction of prediabetes/diabetes from ECG feature



test.sh

test.py





raw_ecg_data: 16,766 ECG records used in our analyses. Each record is a 5,000 x 12 matrix in a CSV file.

participant_characteristics.csv: health check records of 16,766 participants where the information below are stored



participant_id: IDs for participant. Some IDs are duplicated because the dataset contains multiple records from some of the participants.

ecg_id: IDs for ECG records, all of which are unique

age: The age of each participant at the time of the health checkup

male_sex: If the participant is male, "True" is recorded

smoking: if the participant smokes, "True" is recorded

drinking: 1 for "rarely", 2 for "occasionally" and 3 for "regularly" is recorded according to the frequency of drinking

height: participant's height in centimeters (cm)

weight: participant's body weight in kilograms (kg)

BMI: body mass index, calculated using the formula: weight (kg) / [height (m)]^2

pulse_rate:  pulse rate in pulse per minute (/min)  

sBP: systolic blood pressure in mmHg

dBP: diastolic blood pressure in mmHg

FPG: fasting plasma glucose levels measured in milligrams per deciliter (mg/dL)

HbA1c: hemoglobin A1c levels in %

dm_under_treatment: if the participant was undergoing treatment for known diabetes, "True" is recorded

prediabetes_diabetes: classification label which is "True" if a participant meet either of the following criteria



FPG ≥ 110 mg/dL

HbA1c ≥ 6.0%

Undergoing treatment for diabetes



development_data: "True" in records used as development data in our study



ecg_feature_data.zip: extracted ECG features (unprocessed), for 12-lead and 1-lead ECG



ecg_features_1-lead.csv    [Single-lead (lead I) ECG]

ecg_features_12-lead.csv  [12-lead ECG]



feature_list.zip: List of ECG features used (to be used for ECG extraction for original data)



feature_list_269_12-lead.csv   [269 features for 12-lead ECG analysis]

feature_list_28_1-lead.csv     [28 features for single-lead (lead I) analysis]



model_12-lead, model_1-lead: model trained with our 12-lead or single-lead (lead I) ECG data, and the classification thresholds, used for test



model_fold_1.pkl - model_fold_10.pkl : model for each of 10-fold cross validation

average_threshold.pkl : classification threshold, which is the average of 10-fold




Codes and data for test are also available in (https://github.com/dkoga4116/diabetes_detector)
DOI: 10.5281/zenodo.14235273
title: Artificial Intelligence Identifies Individuals with Prediabetes from Single-Lead Electrocardiograms

description: Contents



codes.zip



For ECG feature extraction (this will need original ECG signal data)



ecg_feature_extraction.sh

ecg_feature_extraction.py

feature_extractor.py



For training with hyperparameter optimization



train.sh

train.py



For prediction of prediabetes/diabetes from ECG feature



test.sh

test.py





raw_ecg_data: 16,766 ECG records used in our analyses. Each record is a 5,000 x 12 matrix in a CSV file.

participant_characteristics.csv: health check records of 16,766 participants where the information below are stored



participant_id: IDs for participant. Some IDs are duplicated because the dataset contains multiple records from some of the participants.

ecg_id: IDs for ECG records, all of which are unique

age: The age of each participant at the time of the health checkup

male_sex: If the participant is male, "True" is recorded

smoking: if the participant smokes, "True" is recorded

drinking: 1 for "rarely", 2 for "occasionally" and 3 for "regularly" is recorded according to the frequency of drinking

height: participant's height in centimeters (cm)

weight: participant's body weight in kilograms (kg)

BMI: body mass index, calculated using the formula: weight (kg) / [height (m)]^2

pulse_rate:  pulse rate in pulse per minute (/min)  

sBP: systolic blood pressure in mmHg

dBP: diastolic blood pressure in mmHg

FPG: fasting plasma glucose levels measured in milligrams per deciliter (mg/dL)

HbA1c: hemoglobin A1c levels in %

dm_under_treatment: if the participant was undergoing treatment for known diabetes, "True" is recorded

prediabetes_diabetes: classification label which is "True" if a participant meet either of the following criteria



FPG ≥ 110 mg/dL

HbA1c ≥ 6.0%

Undergoing treatment for diabetes



development_data: "True" in records used as development data in our study



ecg_feature_data.zip: extracted ECG features (unprocessed), for 12-lead and 1-lead ECG



ecg_features_1-lead.csv    [Single-lead (lead I) ECG]

ecg_features_12-lead.csv  [12-lead ECG]



feature_list.zip: List of ECG features used (to be used for ECG extraction for original data)



feature_list_269_12-lead.csv   [269 features for 12-lead ECG analysis]

feature_list_28_1-lead.csv     [28 features for single-lead (lead I) analysis]



model_12-lead, model_1-lead: model trained with our 12-lead or single-lead (lead I) ECG data, and the classification thresholds, used for test



model_fold_1.pkl - model_fold_10.pkl : model for each of 10-fold cross validation

average_threshold.pkl : classification threshold, which is the average of 10-fold




Codes and data for demo are also available in (https://github.com/dkoga4116/diabetes_detector)
DOI: 10.5281/zenodo.14227987
title: Artificial Intelligence Identifies Individuals with Prediabetes from Single-Lead Electrocardiograms