cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28628162.v1
title: <b>Using a cash transfer plus SMS nudge package to improve the wellbeing among caregivers of adolescents living with HIV during the COVID-19 epidemic in South Africa: A pilot randomised controlled trial</b>

description: <b>Background: </b>The mental and financial strain linked to unpaid caregiving has been amplified during the COVID-19 pandemic. In sub-Saharan Africa, carers of adolescents living with HIV (ALHIV) are critical for maintenance of optimum HIV treatment outcomes. However, the ability of caregivers to provide quality care to ALHIV is undermined by their ability to maintain their own wellbeing due to lack of finances and poor mental health. Nudges are a behavioural economics strategy used to influence healthcare decision-making by targeting behavioural barriers. Nudges such as cash incentives and SMS reminders are linked to improved health and wellbeing. There is a lack of evidence on whether these nudges could promote caregiver wellbeing during COVID-19.<br><b>Objectives:</b> To compare the feasibility, acceptability and preliminary effectiveness of an economic incentive nudge package (cash + motivational SMS) for improving caregiver wellbeing.<br><b>Methods: </b>We conducted a pilot individual-randomised controlled trial (November 2021-March 2022), with N=100 caregivers of ALHIV (10–19-year-olds), sampled from HIV clinics in Durban, KwaZulu-Natal. Participants were randomly assigned to the intervention or control arm. The intervention arm (n=50) received an unconditional cash incentive (~ $23 USD) via mobile banking services and a positive wellbeing SMS per month, over a three-month period. The control arm (n=50) received one standard SMS encouraging linkage to care. The intervention was co-designed with our caregiver advisory board. The nudge targeted three behavioural economic principles (aspiration framing, altruism, loss aversion), drawing on facilitators of wellbeing in this setting. Participants were interviewed at baseline and end-line (12 weeks) to collect socio-demographic, health, and wellbeing data. The primary outcome was change in wellbeing measured using the Mental Health Continuum Short-Form. Caregivers (n=8) in each arm underwent in-depth interviews to understand lived experiences and perceptions of the intervention. An intention-to-treat analysis was conducted. Qualitative data were analysed using the framework method.
DOI: 10.34740/kaggle/dsv/11102697
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.15468/dl.xsd8dv
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Australia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Paraporpidia Rambold & Pietschmann"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018409-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uzpndh
title: Occurrence Download

description: A dataset containing 1632 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Insektmobilen - National citizen science and DNA metabarcoding survey of flying insects in June 2018 and 2019",
 "TaxonKey is Culicoides Latreille, 1809"
 ]
}

 The dataset includes 1632 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018411-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xbj397
title: Occurrence Download

description: A dataset containing 1339 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Liriomyza sativae Blanchard, 1938"
 ]
}

 The dataset includes 1339 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018410-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rj4raj
title: Occurrence Download

description: A dataset containing 298 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Giant Devil Rays in the North-Western Mediterranean Sea"
 ]
}

 The dataset includes 298 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018412-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8p6ese
title: Occurrence Download

description: A dataset containing 4031 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation)",
 "Continent is Europe",
 "Country is Netherlands",
 "OccurrenceStatus is Present",
 "TaxonKey is Castor fiber Linnaeus, 1758",
 "Year 2005-2010"
 ]
}

 The dataset includes 4031 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018414-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uyab9g
title: Occurrence Download

description: A dataset containing 9836 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Estudio Diagnóstico Levantamiento Biodiversidad Región Arica y Parinacota 2013"
 ]
}

 The dataset includes 9836 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018413-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qrwve2
title: Occurrence Download

description: A dataset containing 504 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Homotoma ficus (Linnaeus, 1758)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 504 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018415-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fyb7ab
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Australia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lecidea Ach., 1803"
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018416-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.29vcyy
title: Occurrence Download

description: A dataset containing 99 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kenya",
 "TaxonKey is Panthera pardus (Linnaeus, 1758)",
 "Year 1000-2000"
 ]
}

 The dataset includes 99 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018418-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15056544
title: 호지 추측의 공리적 증명

description: https://x.com/MathLutin/status/1902679243609485363?t=Iys-XwAdiQoGqHh6iBVZqA&s=19
DOI: 10.5281/zenodo.15056545
title: 호지 추측의 공리적 증명

description: https://x.com/MathLutin/status/1902679243609485363?t=Iys-XwAdiQoGqHh6iBVZqA&s=19
DOI: 10.5281/zenodo.10777441
title: EyeOnWater training dataset for assessing the inclusion of water images

description: Training dataset

The EyeOnWater app is designed to assess the ocean's water quality using images captured by regular citizens. In order to have an extra helping hand in determining whether an image meets the criteria for inclusion in the app, the YOLOv8 model for image classification is employed. With the help of this model all uploaded pictures are assessed. If the model deems a water image unsuitable, it is excluded from the app's online database. In order to train this model a training dataset containing a large pool of different images is required. The training dataset includes 12,357 'good' and 10,019 'bad' water quality images that were submitted to the EyeOnWater app. 

Technical details

Data preprocessing

In order to create a larger training dataset the set of original images (containing a total of 1700 images) are augmented, by rotating, displacing and resizing them. Using the following settings:



Maximum rotation of 45 degrees in both directions

Maximum displacement of 20% times the width or height

Horizontal and vertical flip

Maximum shear range of 20% times the width

Pixel range of 10 units


Data splitting

The training dataset is 80% used for training, 10% for validation and 10% for prediction. 

Classes, labels and annotations

The training dataset contains 2 classes with 2 labels 'good' and 'bad'. The 'good' images contain water images that are suited to determine the water quality using the Forel-Ule scale. The 'bad' images can include for example too much water reflection, a visible bottom surface, objects or not even include water at all.

Parameters

From the images the water quality can be obtained by comparing the water color to the 21 colors in the Forel-Ule scale.

Parameter: http://vocab.nerc.ac.uk/collection/P01/current/CLFORULE/  

Data sources

The images are taken by citizen scientists, often with a smartphone.

Data quality

As the images are taken by smartphones, the image quality can be low. Next to this, the images are taken outside, in a non-confined space, meaning that there can be bad lightning, reflections and other problems occurring. Therefore, the images need first to be checked before they can be included in the app.

Image resolution

Larger images are resized to 256px by 256px, smaller images are excluded from the training dataset.

Spatial coverage

Images are taken on a global scale.

Contact information

For more information on the training dataset and/or the app, you can contact tjerk@maris.nl.
DOI: 10.5281/zenodo.15056570
title: Wheat Plant Diseases Dataset

description: Description:

The Wheat Plant Diseases Dataset is a comprehensive collection of high-resolution images designed to assist researchers, agronomists, and developers in the development of advanced machine learning models for the classification and diagnosis of various wheat plant diseases. This dataset aims to contribute to the sustainable management of wheat crops by enabling the early detection and treatment of diseases, ultimately safeguarding food security.

Download Dataset

Dataset Content





Total Number of Images: 14,155




Image Quality: High-resolution images capturing real-world disease conditions, devoid of any artificial augmentations to preserve the authenticity and natural variability of the dataset.




Disease Classes: The dataset covers a wide range of wheat plant diseases, categorized into the following classes:




Pest-related Diseases:




Aphid: A common pest known to cause yellowing and stunted growth in wheat plants.




Mite: Tiny arachnids that feed on the plant sap, leading to discoloration and leaf curling.




Stem Fly: Insects that lay eggs in the stems of wheat plants, causing structural damage and reduced yield.




Fungal Diseases:




Rusts: A group of fungal diseases, each causing different symptoms but all leading to significant crop loss.




Black Rust / Stem Rust: Causes dark, elongated pustules on stems and leaves.




Brown Rust / Leaf Rust: Results in orange-brown pustules primarily on the leaves.




Yellow Rust / Stripe Rust: Characterized by yellow stripes running along the length of the leaves.



Benefits of the Wheat Plant Diseases Dataset





Extensive Coverage: With over 14,000 images, the dataset provides a robust foundation for developing machine learning models capable of identifying a wide range of wheat diseases.




Authenticity: The dataset contains real-world images, free from artificial augmentation, ensuring that the trained models are more likely to perform well in practical scenarios.




Educational Value: The inclusion of disease causes and visual monitoring guides makes this dataset not only a tool for machine learning but also an educational resource for understanding wheat plant health.




Enhanced Agricultural Practices: By utilizing this dataset, stakeholders in agriculture can adopt more proactive and informed approaches to disease management, leading to healthier crops and higher yields.



Conclusion

The Wheat Plant Diseases Dataset is an indispensable resource for anyone involved in agricultural research, disease diagnosis, and crop management. Its extensive and varied image collection, coupled with detailed disease information, makes it a powerful tool for advancing wheat disease detection through Al and machine learning.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15056571
title: Wheat Plant Diseases Dataset

description: Description:

The Wheat Plant Diseases Dataset is a comprehensive collection of high-resolution images designed to assist researchers, agronomists, and developers in the development of advanced machine learning models for the classification and diagnosis of various wheat plant diseases. This dataset aims to contribute to the sustainable management of wheat crops by enabling the early detection and treatment of diseases, ultimately safeguarding food security.

Download Dataset

Dataset Content





Total Number of Images: 14,155




Image Quality: High-resolution images capturing real-world disease conditions, devoid of any artificial augmentations to preserve the authenticity and natural variability of the dataset.




Disease Classes: The dataset covers a wide range of wheat plant diseases, categorized into the following classes:




Pest-related Diseases:




Aphid: A common pest known to cause yellowing and stunted growth in wheat plants.




Mite: Tiny arachnids that feed on the plant sap, leading to discoloration and leaf curling.




Stem Fly: Insects that lay eggs in the stems of wheat plants, causing structural damage and reduced yield.




Fungal Diseases:




Rusts: A group of fungal diseases, each causing different symptoms but all leading to significant crop loss.




Black Rust / Stem Rust: Causes dark, elongated pustules on stems and leaves.




Brown Rust / Leaf Rust: Results in orange-brown pustules primarily on the leaves.




Yellow Rust / Stripe Rust: Characterized by yellow stripes running along the length of the leaves.



Benefits of the Wheat Plant Diseases Dataset





Extensive Coverage: With over 14,000 images, the dataset provides a robust foundation for developing machine learning models capable of identifying a wide range of wheat diseases.




Authenticity: The dataset contains real-world images, free from artificial augmentation, ensuring that the trained models are more likely to perform well in practical scenarios.




Educational Value: The inclusion of disease causes and visual monitoring guides makes this dataset not only a tool for machine learning but also an educational resource for understanding wheat plant health.




Enhanced Agricultural Practices: By utilizing this dataset, stakeholders in agriculture can adopt more proactive and informed approaches to disease management, leading to healthier crops and higher yields.



Conclusion

The Wheat Plant Diseases Dataset is an indispensable resource for anyone involved in agricultural research, disease diagnosis, and crop management. Its extensive and varied image collection, coupled with detailed disease information, makes it a powerful tool for advancing wheat disease detection through Al and machine learning.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.11102127
title: ECS_Estaun2024_Figures_DataSet
DOI: 10.5281/zenodo.11102128
title: ECS_Estaun2024_Figures_DataSet
DOI: 10.5281/zenodo.11104038
title: ECS_Estaun2024_Figures_DataSet
DOI: 10.5281/zenodo.14057229
title: ECS_Estaun2024_Figures_DataSet
DOI: 10.5281/zenodo.15056592
title: ECS_Estaun2024_Figures_DataSet
DOI: 10.15468/dl.rbvp2h
title: Occurrence Download

description: A dataset containing 7216 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Korea, Republic of",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Symplocos coreana (H.Lév.) Ohwi, Symplocos sawafutagi Nagam., Symplocos prunifolia Siebold & Zucc., Symplocos tanakana Nakai)"
 ]
}

 The dataset includes 7216 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018419-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w9nyka
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Australia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Heterodea Nyl."
 ]
}

 The dataset includes 34 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018420-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7utwxt
title: Occurrence Download

description: A dataset containing 78005 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Netherlands",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lissotriton vulgaris (Linnaeus, 1758)",
 "Year 1964-2024"
 ]
}

 The dataset includes 78005 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018407-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qktcca
title: Occurrence Download

description: A dataset containing 10096 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation)",
 "Continent is Europe",
 "Country is Netherlands",
 "OccurrenceStatus is Present",
 "TaxonKey is Castor fiber Linnaeus, 1758",
 "Year 2010-2015"
 ]
}

 The dataset includes 10096 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018422-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.59rrv5
title: Occurrence Download

description: A dataset containing 697 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aleochara tristis Gravenhorst, 1806",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 697 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018423-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7488/60450947-a1af-4f25-9003-491291daf44e
title: Decentralised Digital Economy (DECaDE) - ORAgen Interview Study

description: ## Access ##
This dataset is held in the Edinburgh DataVault, directly accessible only to authorised University of Edinburgh staff. External users may request access to a copy of the data by contacting the Principal Investigator, Contact Person or Data Manager named on this page. Requests for access will not necessarily be granted. University of Edinburgh users who wish to have direct access should consult the information about retrieving data from the DataVault at: https://www.ed.ac.uk/is/research-support/datavault.
description: DECaDE ORAgen interview research study. Data can be used by DECaDE research staff at the University of Edinburgh and University of Surrey in developing academic reports, journal articles and presentations only. Data will be stored on secure University of Edinburgh data storage indefinitely (subject to review every three years
DOI: 10.15468/dl.cj5m8h
title: Occurrence Download

description: A dataset listing the 255 species recorded in GBIF matching the query:
{
 "and" : [
 "CollectionKey is 65f33b76-9146-4058-b769-75b7d9ffe2b4",
 "RecordedBy is one of (langlois, annabelle; turgon, jean-pierre, langlois, annabelle; mcneil, raymond; turgon, jean-pierre, archambault, raymond; langlois, annabelle; turgon, jean-pierre, langlois, annabelle; lebeuf, renee; turgon, jean-pierre, langlois, annabelle; mcneil, raymond, archambault, raymond; langlois, annabelle; mcneil, raymond; nuzzolese, joseph; turgon, jean-pierre, langlois, annabelle; lebeuf, renee; paul, andre; turgon, jean-pierre, archambault, raymond; dahger, dimitri; langlois, annabelle; mcneil, raymond, archambault, raymond; langlois, annabelle; marchand, charlotte, langlois, annabelle; mcneil, raymond; turgon, jean-pierre; archambault, raymond)"
 ]
}

 The dataset's 255 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018417-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15048151
title: Dataset for publication "Magneto-Raman effect in van der Waals layered two-dimensional CrSBr antiferromagnet"
DOI: 10.5281/zenodo.15048152
title: Dataset for publication "Magneto-Raman effect in van der Waals layered two-dimensional CrSBr antiferromagnet"
DOI: 10.15468/dl.uj5knv
title: Occurrence Download

description: A dataset containing 21032 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Microtus agrestis (Linnaeus, 1761)"
 ]
}

 The dataset includes 21032 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018424-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hq8r9r
title: Occurrence Download

description: A dataset containing 17445 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation)",
 "Country is Netherlands",
 "OccurrenceStatus is Present",
 "TaxonKey is Castor fiber Linnaeus, 1758",
 "Year 2015-2020"
 ]
}

 The dataset includes 17445 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018426-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u7srkm
title: Occurrence Download

description: A dataset containing 927 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "OccurrenceStatus is Present",
 "TaxonKey is Procambarus clarkii (Girard, 1852)"
 ]
}

 The dataset includes 927 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018428-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.