cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.qqtd2n
title: Occurrence Download

description: A dataset containing 236390 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 61c539d8-47e0-4b82-875f-b95aad66dcd4",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 236390 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010733-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yeb8hf
title: Occurrence Download

description: A dataset containing 583125 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is d6bd7c74-ff29-48f8-b5f5-e9dc1afff3db",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 583125 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010734-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.drbvxt
title: Occurrence Download

description: A dataset containing 408230 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Panopeidae, Rhodomelaceae, Acanthuridae, Gobiidae, Pilumnidae, Alpheidae, Vesiculariidae, Balanidae, Esperiopsidae, Sertulariidae, Actiniidae, Nolellidae, Anomiidae, Ceramiaceae, Corophiidae, Electridae, Mytilidae, Ascidiidae, Chordariaceae, Asteriidae, Austrobalanidae, Elminiidae, Capitellidae, Barentsiidae, Membraniporidae, Spionidae, Styelidae, Bugulidae, Buskiidae, Carcinidae, Caulerpaceae, Hormathiidae, Callyspongiidae, Cancridae, Caprellidae, Limulidae, Portunidae, Celleporidae, Chaetodontidae, Cladophoraceae, Champiaceae, Chironemidae, Scytosiphonaceae, Chondropsidae, Chthamalidae, Sphaeromatidae, Cionidae, Cirolanidae, Cirrholoveniidae, Cladonematidae, Microcionidae, Clavelinidae, Campanulariidae, Codiaceae, Cordylophoridae, Corellidae, Corynidae, Candidae, Calloporidae, Cryptosulidae, Cutleriaceae, Cyclicoporidae, Ostreidae, Dendyidae, Diadematidae, Diadumenidae, Dictyotaceae, Didemnidae, Holozoidae, Tubulariidae, Maeridae, Elphidiidae, Endeidae, Engraulidae, Limapontiidae, Ischyroceridae, Varunidae, Cypraeidae, Sabellidae, Cidaridae, Eudendriidae, Polycitoridae, Serpulidae, Lafoeidae, Galatheidae, Heteropiidae, Halymeniaceae, Wrangeliaceae, Paguridae, Haleciidae, Halisarcidae, Pyuridae, Hippopodinidae, Bitectiporidae, Holothuriidae, Nephropidae, Caryophylliidae, Halichondriidae, Gryphaeidae, Janolidae, Lepadidae, Grantiidae, Ligiidae, Limidae, Limnoriidae, Loxoconchidae, Eunicidae, Lysmatidae, Melitidae, Sinoflustridae, Penaeidae, Grapsidae, Pectinidae, Molgulidae, Crambeidae, Mycalidae, Menippidae, Holocentridae, Sesarmidae, Trapezidae, Nereididae, Diaperoeciidae, Tetraclitidae, Goniodorididae, Blenniidae, Salmonidae, Ophiactidae, Ophiodermatidae, Ophiocomidae, Ophionereididae, Squillidae, Polynoidae, Smittinidae, Microdesmidae, Pennariidae, Amphinomidae, Phoronidae, Mastigiidae, Sargassaceae, Margaritidae, Plicatulidae, Plumulariidae, Polyceridae, Corbulidae, Inachoididae, Bangiaceae, Raspailiidae, Clupeidae, Savignyellidae, Majidae, Schizoporellidae, Schizymeniaceae, Scophthalmidae, Siphogenerinoididae, Poaceae, Stenopodidae, Stenothoidae, Archaeobalanidae, Hesionidae, Symplectoscyphidae, Teredinidae, Tethyidae, Thalamoporellidae, Semelidae, Nassariidae, Ulvaceae, Alariaceae, Virgulinellidae, Watersiporidae, Scombridae, Serranidae)",
 "Geometry POLYGON ((-181.131592 -37.12775, -187.78141 -33.078885, -189.068184 -33.987495, -186.411552 -37.925243, -192.774353 -44.251839, -195.387383 -46.345032, -192.379532 -48.31083, -188.062592 -45.989329, -183.377609 -41.69727, -180.416107 -38.542795, -181.131592 -37.12775))"
 ]
}

 The dataset includes 408230 records from 288 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010725-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cysq67
title: Occurrence Download

description: A dataset containing 683711 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 683711 records from 460 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010720-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31322272
title: NMPA-MedDevice: A Multimodal Regulatory Dataset of 43,000 Chinese Medical Devices for Risk Classification Research

description: NMPA-MedDevice is a large-scale regulatory dataset derived from China's National Medical Products Administration (NMPA) Unique Device Identification (UDI) registry (full release: July 2024). It comprises two complementary components:1. **Labeled multimodal subset** -- 1,005 medical devices with product descriptions, regulatory metadata, and manufacturer-assigned risk classification labels (Class I / II / III).2. **Unlabeled text-only corpus** -- Approximately 42,000 unique device records (after deduplication and quality filtering from the full 66,472-record registry) with complete regulatory text fields.Risk class labels are programmatically derived from the ninth character of the NMPA registration number, ensuring objective and reproducible annotation:- `1` = Class I (low risk)- `2` = Class II (moderate risk)- `3` = Class III (high risk)<br>### Image ModalityFor privacy and copyright reasons, raw product images are **not** distributed with this dataset. Instead, we provide **pre-extracted feature vectors** using standard deep learning backbones, following established practice in the scientific community (cf. UNI2-h features [Chen et al., Nature Medicine 2024], LAION-5B embeddings [Schuhmann et al., NeurIPS 2022]):Text features: BERT-base-Chinese [CLS] token embeddings (768 dimensions per sample)Image features:EfficientNet-B5 average-pool embeddings (2,048 dimensions per sample)These pre-computed embeddings enable full reproducibility of all downstream classification experiments reported in the companion ESWA paper. Users requiring original images may use the provided `scripts/image_retrieval.py` to download them directly from manufacturer websites.<br>
DOI: 10.6084/m9.figshare.31322272.v1
title: NMPA-MedDevice: A Multimodal Regulatory Dataset of 43,000 Chinese Medical Devices for Risk Classification Research

description: NMPA-MedDevice is a large-scale regulatory dataset derived from China's National Medical Products Administration (NMPA) Unique Device Identification (UDI) registry (full release: July 2024). It comprises two complementary components:1. **Labeled multimodal subset** -- 1,005 medical devices with product descriptions, regulatory metadata, and manufacturer-assigned risk classification labels (Class I / II / III).2. **Unlabeled text-only corpus** -- Approximately 42,000 unique device records (after deduplication and quality filtering from the full 66,472-record registry) with complete regulatory text fields.Risk class labels are programmatically derived from the ninth character of the NMPA registration number, ensuring objective and reproducible annotation:- `1` = Class I (low risk)- `2` = Class II (moderate risk)- `3` = Class III (high risk)<br>### Image ModalityFor privacy and copyright reasons, raw product images are **not** distributed with this dataset. Instead, we provide **pre-extracted feature vectors** using standard deep learning backbones, following established practice in the scientific community (cf. UNI2-h features [Chen et al., Nature Medicine 2024], LAION-5B embeddings [Schuhmann et al., NeurIPS 2022]):Text features: BERT-base-Chinese [CLS] token embeddings (768 dimensions per sample)Image features:EfficientNet-B5 average-pool embeddings (2,048 dimensions per sample)These pre-computed embeddings enable full reproducibility of all downstream classification experiments reported in the companion ESWA paper. Users requiring original images may use the provided `scripts/image_retrieval.py` to download them directly from manufacturer websites.<br>
DOI: 10.15468/dl.v5tqyd
title: Occurrence Download

description: A dataset containing 338 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stryphnodendron guianense",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 338 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010744-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p4fmxt
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tecophilaea violiflora",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 12 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010745-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8me436
title: Occurrence Download

description: A dataset containing 207 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Year is greater than or equal to 1900",
 "TaxonKey is Cymbopogon martini"
 ]
}

 The dataset includes 207 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010746-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/9nq3-bg08
title: "CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING"

description: "Credit card fraud is an escalating problem in the digital age, where transactions are increasingly conducted online. The convenience of credit cards comes with the inherent risk of fraud, leading to significant financial losses for consumers, businesses, and financial institutions. Traditional methods of fraud detection, primarily rule-based systems, have proven to be insufficient in effectively identifying fraudulent activities due to their static nature and inability to adapt to evolving fraud patterns. This necessitates the development and implementation of more sophisticated and adaptive techniques. Machine learning, with its ability to analyze large volumes of data and detect complex patterns, presents a promising solution to this problem.Machine learning (ML) algorithms have the potential to revolutionize credit card fraud detection by offering a dynamic, data-driven approach to identifying fraudulent transactions. Unlike traditional rule-based systems, machine learning models can learn from historical data, adapt to new fraud patterns, and improve their performance over time. This adaptability is crucial given the constantly changing tactics of fraudsters. Various machine learning techniques, including supervised and unsupervised learning, are employed to detect anomalies and predict fraudulent behavior. Supervised learning models, such as logistic regression, decision trees, random forests, and support vector machines, are trained on labeled datasets where transactions are marked as either fraudulent or legitimate. These models learn the characteristics of fraudulent transactions and can predict the likelihood of new transactions being fraudulent based on the learned patterns. On the other hand, unsupervised learning models, such as clustering and anomaly detection algorithms, do not require labeled data. They identify outliers in the data that deviate from the norm, which may indicate potential fraud."
DOI: 10.15468/dl.uxw95c
title: Occurrence Download

description: A dataset containing 935 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Occurrence records of mammal species in Tana River Basin, Kenya"
 ]
}

 The dataset includes 935 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010747-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18616928
title: Functionalized sepiolite incorporated chitosan/poly(ethylene glycol) hydrogels for wound dressing and sustained silver sulfadiazine release

description: Silver sulfadiazine (AgSD) belongs to the class of sulfonamides, a broad spectrum antibiotic. However, topically AgSD has limited bioavailability, less solubility and poor penetration. Consequently, we have prepared functionalized sepiolite (F-SEP) incorporating chitosan (CS) and poly(ethylene glycol) (PEG) hydrogels for improved AgSD delivery and wound dressing applications. In 30 hours, 81.27% of the drug was released which is superior from numerous studies and depicted best fit for Korsmyer-Peppas model (R2-0.978). The biodegradable and pH responsive CS/PEG/F-SEP hydrogel platform could be a valuable choice for sustained delivery of AgSD and therapeutic payloads for development of efficient dressings for healing wounds.
description: Firstly, sepilite clay was functionalized using   3-aminopropyl(triethoxy)silane (APTS)  cross-linker. 

Subsequently, chitosan, PEG, functionalized sepilite (F-SEP) were used to prepare hydrogels using solution casting technique. 

The data was collected by the synthesis, characterization and analysis of hydrogels.

 
description: Five hydrogels were prepared coded below:

SAR(cont) = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (0.0 mg)

SAR-5 = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (5.0 mg)

SAR-10 = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (10.0 mg)

SAR-15 = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (15.0 mg)

SAR-20 = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (20.0 mg)

DSAR-15 = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (15.0 mg) + AgSD (100.0 mg)
description: Further, the data was acquired from IR, TGA, swelling analysis (in distilled water, buffer/non-buffer and ionic solutions), cell viability assay,shore A hardness, encapsulation efficacy, AgSD release and release kinetics.
DOI: 10.5281/zenodo.18617328
title: FOUNDATIONS OF CORRECTIVE PEDAGOGY AND INCLUSIVE EDUCATION.
DOI: 10.5281/zenodo.18617329
title: FOUNDATIONS OF CORRECTIVE PEDAGOGY AND INCLUSIVE EDUCATION.
DOI: 10.5281/zenodo.18599207
title: Functionalized sepiolite incorporated chitosan/poly(ethylene glycol) hydrogels for wound dressing and sustained silver sulfadiazine release

description: Silver sulfadiazine (AgSD) belongs to the class of sulfonamides, a broad spectrum antibiotic. However, topically AgSD has limited bioavailability, less solubility and poor penetration. Consequently, we have prepared functionalized sepiolite (F-SEP) incorporating chitosan (CS) and poly(ethylene glycol) (PEG) hydrogels for improved AgSD delivery and wound dressing applications. In 30 hours, 81.27% of the drug was released which is superior from numerous studies and depicted best fit for Korsmyer-Peppas model (R2-0.978). The biodegradable and pH responsive CS/PEG/F-SEP hydrogel platform could be a valuable choice for sustained delivery of AgSD and therapeutic payloads for development of efficient dressings for healing wounds.
description: Firstly, sepilite clay was functionalized using   3-aminopropyl(triethoxy)silane (APTS)  cross-linker. 

Subsequently, chitosan, PEG, functionalized sepilite (F-SEP) were used to prepare hydrogels using solution casting technique. 

The data was collected by the synthesis, characterization and analysis of hydrogels.

 
description: Five hydrogels were prepared coded below:

SAR(cont) = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (0.0 mg)

SAR-5 = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (5.0 mg)

SAR-10 = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (10.0 mg)

SAR-15 = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (15.0 mg)

SAR-20 = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (20.0 mg)

DSAR-15 = Chitosan (0.5g)+ PEG (0.5g) + F-SEP (15.0 mg) + AgSD (100.0 mg)
description: Further, the data was acquired from IR, TGA, swelling analysis (in distilled water, buffer/non-buffer and ionic solutions), cell viability assay,shore A hardness, encapsulation efficacy, AgSD release and release kinetics.
DOI: 10.15468/dl.ncy6kh
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Robrichia schomburgkii",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 108 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010748-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18617330
title: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING.

description: Credit card fraud is an escalating problem in the digital age, where transactions are increasingly

conducted online. The convenience of credit cards comes with the inherent risk of fraud,

leading to significant financial losses for consumers, businesses, and financial institutions.

Traditional methods of fraud detection, primarily rule-based systems, have proven to be

insufficient in effectively identifying fraudulent activities due to their static nature and inability

to adapt to evolving fraud patterns. This necessitates the development and implementation of

more sophisticated and adaptive techniques. Machine learning, with its ability to analyze large

volumes of data and detect complex patterns, presents a promising solution to this problem.

Machine learning (ML) algorithms have the potential to revolutionize credit card fraud

detection by offering a dynamic, data-driven approach to identifying fraudulent transactions.

Unlike traditional rule-based systems, machine learning models can learn from historical data,

adapt to new fraud patterns, and improve their performance over time. This adaptability is

crucial given the constantly changing tactics of fraudsters. Various machine learning

techniques, including supervised and unsupervised learning, are employed to detect anomalies

and predict fraudulent behavior. Supervised learning models, such as logistic regression,

decision trees, random forests, and support vector machines, are trained on labeled datasets

where transactions are marked as either fraudulent or legitimate. These models learn the

characteristics of fraudulent transactions and can predict the likelihood of new transactions

being fraudulent based on the learned patterns. On the other hand, unsupervised learning

models, such as clustering and anomaly detection algorithms, do not require labeled data. They

identify outliers in the data that deviate from the norm, which may indicate potential fraud.
DOI: 10.5281/zenodo.18617331
title: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING.

description: Credit card fraud is an escalating problem in the digital age, where transactions are increasingly

conducted online. The convenience of credit cards comes with the inherent risk of fraud,

leading to significant financial losses for consumers, businesses, and financial institutions.

Traditional methods of fraud detection, primarily rule-based systems, have proven to be

insufficient in effectively identifying fraudulent activities due to their static nature and inability

to adapt to evolving fraud patterns. This necessitates the development and implementation of

more sophisticated and adaptive techniques. Machine learning, with its ability to analyze large

volumes of data and detect complex patterns, presents a promising solution to this problem.

Machine learning (ML) algorithms have the potential to revolutionize credit card fraud

detection by offering a dynamic, data-driven approach to identifying fraudulent transactions.

Unlike traditional rule-based systems, machine learning models can learn from historical data,

adapt to new fraud patterns, and improve their performance over time. This adaptability is

crucial given the constantly changing tactics of fraudsters. Various machine learning

techniques, including supervised and unsupervised learning, are employed to detect anomalies

and predict fraudulent behavior. Supervised learning models, such as logistic regression,

decision trees, random forests, and support vector machines, are trained on labeled datasets

where transactions are marked as either fraudulent or legitimate. These models learn the

characteristics of fraudulent transactions and can predict the likelihood of new transactions

being fraudulent based on the learned patterns. On the other hand, unsupervised learning

models, such as clustering and anomaly detection algorithms, do not require labeled data. They

identify outliers in the data that deviate from the norm, which may indicate potential fraud.
DOI: 10.15468/dl.6f8qxg
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xeronema callistemon",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 43 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010749-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kzwea5
title: Occurrence Download

description: A dataset containing 2825094 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Specimen)",
 "Geometry POLYGON((-129.94 45.36,-111.58 45.36,-111.58 49.39,-129.94 49.39,-129.94 45.36))",
 "License is one of (CC-BY-NC 4.0, CC-BY 4.0, CC0 1.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 2825094 records from 467 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010617-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mrwatn
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001151-160526112335914/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vxj7my
title: Occurrence Download

description: A dataset containing 139445087 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Geometry POLYGON((98 -90,180 -90,180 19,98 19,98 -90))",
 "Geometry POLYGON((-98.22409 19.53736,-98.14444 19.53277,-98.0736 19.62205,-98.19614 19.64618,-98.22409 19.53736))"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 139445087 records from 5240 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013444-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.