cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5803233
title: Frailty and delirium data from UHA

description: Data collected from sept. 2017 up to sept. 2018 at the Hospital Universitario de Alava with a follow up of two years to assess mortality. 

The content of this repository is as follows:



The actual excel file with the data collected (full_dataset.xls)

The details of the recruitment process and study design (study-design-details.pdf)

The demographics and descriptive statistics of the dataset (demographics_and_descriptive_statistics.pdf)

The explanation of the meaning of variables in the dataset (explanation of variables.docx)

A graphical description of the recruitment process (recruitment.jpg)


 

acknowledgements: project funded by the spanish MICIN with code PID2020-116346GB-I00/ MICIN/AEI/10.13039/501100011033
DOI: 10.5281/zenodo.18702700
title: GFED5NRT: Global Fire Emissions Database Near-Real-Time Extension

description: GFED5NRT: Global Fire Emissions Database Near-Real-Time Extension

[Version] 5.1

[Authors]Yang Chen, Guido R. van der Werf, Mingquan Mu, Dave van Wees, Joanne V. Hall, Louis Giglio, Roland Vernooij, Douglas C. Morton, Li Xu, Tianjia Liu, Rebecca C. Scholten, Shane R. Coffield, Melanie B. Follette-Cook, Lesley Ott, James T. Randerson

[Overview]This repository contains the 2023–2024 dataset for the Global Fire Emissions Database Near-Real-Time extension (GFED5NRT). GFED5NRT provides daily global estimates of burned area and emissions for multiple trace gases and aerosols, using active fire observations from the Visible Infrared Imaging Radiometer Suite (VIIRS).

This dataset is designed to provide continuity with the long-term GFED5 record (1997–2022) by using biome- and region-specific lookup tables of effective fire area and fuel consumption to bridge the transition from MODIS to VIIRS sensors.

[Data Description and Source Sensors]The data are categorized based on the VIIRS sensor(s) used for the derivation:    CMB (Combined): Data derived using both Suomi NPP (SNPP) and NOAA-20 (JPSS-1) VIIRS active fire observations.    VNP: Data derived using Suomi NPP (SNPP) VIIRS observations only.    VJ1: Data derived using NOAA-20 (JPSS-1) VIIRS observations only.

[Directory Structure]The data are organized by temporal resolution (Daily/Monthly), data type (Species/Ecosystem), and sensor source (CMB/VNP/VJ1). Each sensor subfolder contains separate directories for the years 2023 and 2024.

Root/├── Daily/│   ├── Species/          (Trace gas and aerosol emissions)│   │   ├── CMB/ [2023, 2024]│   │   ├── VNP/ [2023, 2024]│   │   └── VJ1/ [2023, 2024]│   └── Ecosystem/        (Ecosystem-specific burned area and emissions)│       ├── CMB/ [2023, 2024]│       ├── VNP/ [2023, 2024]│       └── VJ1/ [2023, 2024]└── Monthly/    ├── Species/    │   ├── CMB/ [2023, 2024]    │   ├── VNP/ [2023, 2024]    │   └── VJ1/ [2023, 2024]    └── Ecosystem/        ├── CMB/ [2023, 2024]        ├── VNP/ [2023, 2024]        └── VJ1/ [2023, 2024]

[Data Content]    Species: Includes daily and monthly gridded emissions for carbon (C) and various trace gases and aerosols (e.g., CO, CH4, NOx, PM2.5, BC, OC).    Ecosystem: Includes burned area and emissions partitioned by fire type (e.g., boreal forest fires, temperate forest fires, tropical deforestation and degradation, peat fires, agricultural waste burning, and savanna/grassland/shrubland fires).

[Contact]Yang Chen (yang.chen@uci.edu)
DOI: 10.5281/zenodo.18702699
title: GFED5NRT: Global Fire Emissions Database Near-Real-Time Extension

description: GFED5NRT: Global Fire Emissions Database Near-Real-Time Extension

[Version] 5.1

[Authors]Yang Chen, Guido R. van der Werf, Mingquan Mu, Dave van Wees, Joanne V. Hall, Louis Giglio, Roland Vernooij, Douglas C. Morton, Li Xu, Tianjia Liu, Rebecca C. Scholten, Shane R. Coffield, Melanie B. Follette-Cook, Lesley Ott, James T. Randerson

[Overview]This repository contains the 2023–2024 dataset for the Global Fire Emissions Database Near-Real-Time extension (GFED5NRT). GFED5NRT provides daily global estimates of burned area and emissions for multiple trace gases and aerosols, using active fire observations from the Visible Infrared Imaging Radiometer Suite (VIIRS).

This dataset is designed to provide continuity with the long-term GFED5 record (1997–2022) by using biome- and region-specific lookup tables of effective fire area and fuel consumption to bridge the transition from MODIS to VIIRS sensors.

[Data Description and Source Sensors]The data are categorized based on the VIIRS sensor(s) used for the derivation:    CMB (Combined): Data derived using both Suomi NPP (SNPP) and NOAA-20 (JPSS-1) VIIRS active fire observations.    VNP: Data derived using Suomi NPP (SNPP) VIIRS observations only.    VJ1: Data derived using NOAA-20 (JPSS-1) VIIRS observations only.

[Directory Structure]The data are organized by temporal resolution (Daily/Monthly), data type (Species/Ecosystem), and sensor source (CMB/VNP/VJ1). Each sensor subfolder contains separate directories for the years 2023 and 2024.

Root/├── Daily/│   ├── Species/          (Trace gas and aerosol emissions)│   │   ├── CMB/ [2023, 2024]│   │   ├── VNP/ [2023, 2024]│   │   └── VJ1/ [2023, 2024]│   └── Ecosystem/        (Ecosystem-specific burned area and emissions)│       ├── CMB/ [2023, 2024]│       ├── VNP/ [2023, 2024]│       └── VJ1/ [2023, 2024]└── Monthly/    ├── Species/    │   ├── CMB/ [2023, 2024]    │   ├── VNP/ [2023, 2024]    │   └── VJ1/ [2023, 2024]    └── Ecosystem/        ├── CMB/ [2023, 2024]        ├── VNP/ [2023, 2024]        └── VJ1/ [2023, 2024]

[Data Content]    Species: Includes daily and monthly gridded emissions for carbon (C) and various trace gases and aerosols (e.g., CO, CH4, NOx, PM2.5, BC, OC).    Ecosystem: Includes burned area and emissions partitioned by fire type (e.g., boreal forest fires, temperate forest fires, tropical deforestation and degradation, peat fires, agricultural waste burning, and savanna/grassland/shrubland fires).

[Contact]Yang Chen (yang.chen@uci.edu)
DOI: 10.15468/dl.edfc7k
title: Occurrence Download

description: A dataset containing 427 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CAN.2_1",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Hyoscyamus niger, Egeria densa, Peganum harmala, Genista monspessulana, Cabomba caroliniana, Alhagi maurorum, Crupina vulgaris, Phragmites australis, Spartina densiflora, Spartina patens, Spartina alterniflora, Spartina anglica, Isatis tinctoria, Euphorbia oblongata, Brachypodium sylvaticum, Butomus umbellatus, Alopecurus myosuroides, Salvinia molesta, Galega officinalis, Halogeton glomeratus, Hieracium pilosella, Eichhornia crassipes, Hydrilla verticillata, Sorghum halepense, Aegilops cylindrica, Centaurea virgata, Taeniatherum caput-medusae, Pueraria montana, Solanum elaeagnifolium, Cyperus rotundus, Echium plantagineum, Lepidium latifolium, Odontites verna serotina, Arundo donax, Salvia aethiopis, Milium vernale, Thymelaea passerina, Centaurea iberica, Centaurea calcitrapa, Centaurea solstitialis, Vincetoxicum nigrum, Vincetoxicum hirundinaria, Zygophyllum fabago, Helianthus ciliaris, Carduus pycnocephalus, Carduus tenuiflorus, Pistia stratiotes, Stratiotes aloides, Nymphoides peltata)"
 ]
}

 The dataset includes 427 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000511-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tqgryq
title: Occurrence Download

description: A dataset containing 0 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)"
 ]
 }
 },
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "Geometry POLYGON ((105.610544 14.999999, 179.999961 14.999999, 179.999961 65.08290100000001, 105.610544 65.08290100000001, 105.610544 14.999999))",
 "TaxonKey is Nannothrissa stewarti"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000515-260225131425191/datasets/export for details.
DOI: 10.15468/dl.dxqdaz
title: Occurrence Download

description: A dataset containing 65159 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aedes aegypti"
 ]
}

 The dataset includes 65159 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000450-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18775571
title: THE IMPORTANCE OF IMPROVING THE EFFECTIVE APPLICATION OF PEDAGOGICAL MASTERY IN PRACTICE

description: This article examines the importance of improving the effective application of pedagogical mastery in modern educational practice within the context of globalization and rapid technological advancement. It emphasizes that the quality of education largely depends on teachers’ professional competence, methodological preparedness, psychological awareness, and ability to implement innovative approaches. The study highlights the multifaceted nature of pedagogical mastery, including professional knowledge, methodological skills, pedagogical technique, digital competence, leadership abilities, inclusive education practices, assessment culture, creativity, and professional ethics.

Special attention is given to the practical implementation of pedagogical skills through lesson planning, instructional strategies, reflection, formative assessment, and research-based approaches. The article also addresses contemporary challenges such as digital transformation, inclusive education, global competencies, collaboration, and prevention of professional burnout. It concludes that pedagogical mastery is a dynamic and continuous process of professional growth that directly influences educational quality, student development, and societal progress.
DOI: 10.5281/zenodo.18775570
title: THE IMPORTANCE OF IMPROVING THE EFFECTIVE APPLICATION OF PEDAGOGICAL MASTERY IN PRACTICE

description: This article examines the importance of improving the effective application of pedagogical mastery in modern educational practice within the context of globalization and rapid technological advancement. It emphasizes that the quality of education largely depends on teachers’ professional competence, methodological preparedness, psychological awareness, and ability to implement innovative approaches. The study highlights the multifaceted nature of pedagogical mastery, including professional knowledge, methodological skills, pedagogical technique, digital competence, leadership abilities, inclusive education practices, assessment culture, creativity, and professional ethics.

Special attention is given to the practical implementation of pedagogical skills through lesson planning, instructional strategies, reflection, formative assessment, and research-based approaches. The article also addresses contemporary challenges such as digital transformation, inclusive education, global competencies, collaboration, and prevention of professional burnout. It concludes that pedagogical mastery is a dynamic and continuous process of professional growth that directly influences educational quality, student development, and societal progress.
DOI: 10.25824/redu/wmqpjg
title: Effect of gelatinization degree on the properties of sweet potato (Ipomoea batatas L.) starch aerogels

description: This dataset contains experimental structural and functional data in which the rheological properties of sweet potato starch hydrogels were correlated with the structural and functional properties of the resulting aerogels, prepared at different starch concentrations and gelatinization times.
DOI: 10.5281/zenodo.18743666
title: Processed dataset for the deliverable D1.2 – Report summarising farmer needs
DOI: 10.5281/zenodo.18743667
title: Processed dataset for the deliverable D1.2 – Report summarising farmer needs
DOI: 10.15468/dl.vjxtxq
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Historic Records of Boreal Chorus Frog in Vermont, USA"
 ]
}

 The dataset includes 38 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000517-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r939rx
title: Occurrence Download

description: A dataset containing 978 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vochysia ferruginea",
 "Country is Colombia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 978 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000516-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18772107
title: BPLD: Bangladeshi Plant Leave Dataset

description: Overview

The Bangladeshi  Plant Leave Dataset is a self-collected hierarchical plant leaf image dataset designed to support research in hierarchical image classification, plant species recognition, and deep learning-based agricultural applications. The dataset contains 7,200 leaf images organized into a two-level hierarchical taxonomy consisting of three primary classes Flower, Fruit, and Medicinal plants and eighteen fine-grained subclasses.

All images were captured in real-world outdoor environments using a personal mobile phone camera, ensuring natural variations in illumination, orientation, background complexity, and scale. The dataset is balanced at both hierarchical levels, making it suitable for multi-stage classification frameworks and comparative deep learning studies.

Dataset Structure

The dataset follows a two-stage hierarchical structure:

Level 1 – Main Classes (3 classes) :



Flower

Fruit

Medicinal


Level 2 – Subclasses (6 per main class):



Flower: Hibiscus, Lotus, Marigold, Rose, Rose Periwinkle, Togor.

Fruit: Guava, Jackfruit, Lemon, Mango, Papaya, Wood Apple.

Medicine: Bitter Vine, Neem, Pathorkuci, Thankuni, Tulshi, Vasaka.


Dataset Statistics



Total Images: 7,200

Total Main Classes: 3

Total Subclasses: 18

Images per Subclass: 400

Images per Main Class: 2,400

Class Distribution: Fully balanced


Image Acquisition Protocol

All images were captured by the dataset creator using a personal mobile device under natural environmental conditions. The acquisition process intentionally preserved real-world variability, including differences in lighting, background clutter, leaf pose, and scale. No synthetic image generation was used. This real-world collection strategy enhances the dataset’s applicability for robust deep learning model development.

Intended Use

The dataset is intended for:



 Hierarchical image classification research

 Multi-stage deep learning frameworks

 Transfer learning evaluation 

 Fine-grained plant leaf recognition

 Agricultural and medicinal plant identification systems
DOI: 10.5281/zenodo.18772106
title: BPLD: Bangladeshi Plant Leave Dataset

description: Overview

The Bangladeshi  Plant Leave Dataset is a self-collected hierarchical plant leaf image dataset designed to support research in hierarchical image classification, plant species recognition, and deep learning-based agricultural applications. The dataset contains 7,200 leaf images organized into a two-level hierarchical taxonomy consisting of three primary classes Flower, Fruit, and Medicinal plants and eighteen fine-grained subclasses.

All images were captured in real-world outdoor environments using a personal mobile phone camera, ensuring natural variations in illumination, orientation, background complexity, and scale. The dataset is balanced at both hierarchical levels, making it suitable for multi-stage classification frameworks and comparative deep learning studies.

Dataset Structure

The dataset follows a two-stage hierarchical structure:

Level 1 – Main Classes (3 classes) :



Flower

Fruit

Medicinal


Level 2 – Subclasses (6 per main class):



Flower: Hibiscus, Lotus, Marigold, Rose, Rose Periwinkle, Togor.

Fruit: Guava, Jackfruit, Lemon, Mango, Papaya, Wood Apple.

Medicine: Bitter Vine, Neem, Pathorkuci, Thankuni, Tulshi, Vasaka.


Dataset Statistics



Total Images: 7,200

Total Main Classes: 3

Total Subclasses: 18

Images per Subclass: 400

Images per Main Class: 2,400

Class Distribution: Fully balanced


Image Acquisition Protocol

All images were captured by the dataset creator using a personal mobile device under natural environmental conditions. The acquisition process intentionally preserved real-world variability, including differences in lighting, background clutter, leaf pose, and scale. No synthetic image generation was used. This real-world collection strategy enhances the dataset’s applicability for robust deep learning model development.

Intended Use

The dataset is intended for:



 Hierarchical image classification research

 Multi-stage deep learning frameworks

 Transfer learning evaluation 

 Fine-grained plant leaf recognition

 Agricultural and medicinal plant identification systems
DOI: 10.15468/dl.mxcum2
title: Occurrence Download

description: A dataset containing 1045716 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lepus europaeus"
 ]
}

 The dataset includes 1045716 records from 3119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000406-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fvs33k
title: Occurrence Download

description: A dataset listing the 94 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Indonesia",
 "DatasetKey is one of (iNaturalist Research-grade Observations, Observation.org, Nature data from around the World, Pl@ntNet observations)",
 "GadmGid is one of (IDN.9.24_1, IDN.9.24.26_1, IDN.9.5.28_1, IDN.9.24.8_1, IDN.9.5.27_1, IDN.9.4_1, IDN.9.4.2_1)",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (28eb1a3f-1c15-4a95-931a-4af90ecb574d, c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f, da86174a-a605-43a4-a5e8-53d484152cd3)",
 "TaxonKey is Aves",
 "Year is 2025"
 ]
}

 The dataset's 94 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0085887-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.phfvzr
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ipomoea thunbergioides Welw."
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033882-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dhxjaj
title: Occurrence Download

description: A dataset containing 32 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 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001189-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.neozea
title: Occurrence Download

description: A dataset containing 32 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 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003228-180730143533302/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hyvj56
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Justicia vagabunda Benoist"
 ]
}

 The dataset includes 32 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033885-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fotd7w
title: Occurrence Download

description: A dataset containing 32 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 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003248-180730143533302/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gxseax
title: Occurrence Download

description: A dataset containing 382196 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2024-01-29",
 "EventDate is less than or equal to 2024-02-28",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 382196 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033691-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rf0ohc
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/0036145-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3e6w7h
title: Occurrence Download

description: A dataset containing 32 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 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003282-180730143533302/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.