cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/v46jkbbzv3.1
title: A Benchmark Dataset for Detecting Disease in Plant Leaves: An Essential Resource for Deep Learning Models.

description: This dataset has been developed for research on plant leaf disease detection, specifically targeting Gourd, Zucchini, Bitter melon, Bean, Aubergine and Yardlong bean leaves. It contains high-resolution images capturing various disease symptoms to support the development and evaluation of deep learning models in agriculture. The dataset is ideal for use in image classification, segmentation, and disease diagnosis applications.

Dataset Overview:
Contains images of plant leaves affected by various diseases (Bacterial, YMV, Fungal, Viral infections) commonly found in agricultural crops.
Captured under controlled conditions, ensuring realistic and diverse real-world scenarios.
Includes images from multiple plant species and varying disease stages.

Types of Plant Leafs:
1. Gourd (537)
2. Zucchini (186)
3. Bitter melon (300)
4. Bean (517)
5. Aubergine (242)
6. Yardlong bean (500)
 
Key Features:
Number of Images: 2820
Number of Augmented Images: 2561(to increase dataset variability and model robustness)
File Formats: JPEG

Disease Types: Bacterial, YMV, Fungal, and Viral infections.

Applications:
Deep Learning: This dataset is specifically designed for the development and evaluation of deep learning models for plant disease detection. It can be used to train convolutional neural networks (CNNs) and other advanced deep learning architectures for tasks such as image classification, object detection, and segmentation, providing insights into plant health status and disease severity.

Machine Learning: In addition to deep learning, this dataset can also be utilised in traditional machine learning methods for disease recognition, but its primary value lies in enabling the use of cutting-edge neural networks for automated, scalable disease detection.

Agricultural Technology: The dataset supports the development of mobile applications and automated systems for real-time plant health monitoring. Deep learning models trained on this data can be integrated into mobile platforms or drone-based systems to provide instant, accurate disease diagnosis in agricultural settings, aiding farmers in timely interventions.

Agricultural Research: Researchers can use this dataset to better understand the impact of diseases on various plant species and their visual symptoms. The rich diversity of images will also help in studying disease progression, improving the design of predictive models, and contributing to better crop protection practices.

Dataset Collection:
1. Compiled from publicly available plant images and field-controlled environments.
2. Includes varying lighting conditions, leaf positions, and disease stages for robust model training.

By providing detailed, high-quality images of diseased plant leaves from multiple species, this dataset plays a critical role in advancing deep learning applications in agriculture. It enables more accurate disease detection, which can lead to faster responses and more sustainable farming practices.
DOI: 10.17632/v46jkbbzv3.2
title: A Benchmark Dataset for Detecting Disease in Plant Leaves: An Essential Resource for Deep Learning Models.

description: This dataset has been developed for research on plant leaf disease detection, specifically targeting Gourd, Zucchini, Bitter melon, Bean, Aubergine and Yardlong bean leaves. It contains high-resolution images capturing various disease symptoms to support the development and evaluation of deep learning models in agriculture. The dataset is ideal for use in image classification, segmentation, and disease diagnosis applications.

Dataset Overview:
Contains images of plant leaves affected by various diseases (Bacterial, YMV, Fungal, Viral infections) commonly found in agricultural crops.
Captured under controlled conditions, ensuring realistic and diverse real-world scenarios.
Includes images from multiple plant species and varying disease stages.

Types of Plant Leafs:
1. Gourd (537)
2. Zucchini (186)
3. Bitter melon (300)
4. Bean (517)
5. Aubergine (242)
6. Yardlong bean (500)
 
Key Features:
Number of Images: 2820
Number of Augmented Images: 2561(to increase dataset variability and model robustness)
File Formats: JPEG

Disease Types: Bacterial, YMV, Fungal, and Viral infections.

Applications:
Deep Learning: This dataset is specifically designed for the development and evaluation of deep learning models for plant disease detection. It can be used to train convolutional neural networks (CNNs) and other advanced deep learning architectures for tasks such as image classification, object detection, and segmentation, providing insights into plant health status and disease severity.

Machine Learning: In addition to deep learning, this dataset can also be utilised in traditional machine learning methods for disease recognition, but its primary value lies in enabling the use of cutting-edge neural networks for automated, scalable disease detection.

Agricultural Technology: The dataset supports the development of mobile applications and automated systems for real-time plant health monitoring. Deep learning models trained on this data can be integrated into mobile platforms or drone-based systems to provide instant, accurate disease diagnosis in agricultural settings, aiding farmers in timely interventions.

Agricultural Research: Researchers can use this dataset to better understand the impact of diseases on various plant species and their visual symptoms. The rich diversity of images will also help in studying disease progression, improving the design of predictive models, and contributing to better crop protection practices.

Dataset Collection:
1. Compiled from publicly available plant images and field-controlled environments.
2. Includes varying lighting conditions, leaf positions, and disease stages for robust model training.

By providing detailed, high-quality images of diseased plant leaves from multiple species, this dataset plays a critical role in advancing deep learning applications in agriculture. It enables more accurate disease detection, which can lead to faster responses and more sustainable farming practices.
DOI: 10.18739/a2v210
title: Micropulse lidar (MPL) measurements taken at Summit Station, Greenland, 2015

description: This data set contains measurements from a MicroPulse LIDAR (MPL) that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project.
DOI: 10.17632/tnf7fg3m4g.1
title: Averaging positions, velocities, forces, etc., for atoms in crystallographic layers

description: A self-explanatory Perl script with sample data files to work with. Useful for data analysis of lammps' dump files. Has been used in:
Zbigniew Kozioł, Stretched-exponential stress dynamics in chain of springs and masses model of crystals: analytical results and MD simulations, (2025)
DOI: 10.18739/a2s200
title: Micropulse lidar (MPL) measurements taken at Summit Station, Greenland, 2016

description: This data set contains measurements from a MicroPulse LIDAR (MPL) that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project.
DOI: 10.18739/a2649t
title: Cloud and Aerosol Polarization and Backscatter LiDAR measurements taken at Summit Station, Greenland, 2010

description: This data set contains measurements from a Cloud and Aerosol Polarization and Backscatter LiDAR (CAPABL) that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This LiDAR points near-vertically and measures backscatter in three polarization states. Parameters include: backscatter in the parallel channel, the depolarization ratio, and the diattenuation. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The LiDAR is owned by the National Oceanic and Atmospheric Administration's Earth System Research Laboratory. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.18739/a27z1c
title: Cloud and Aerosol Polarization and Backscatter LiDAR measurements taken at Summit Station, Greenland, 2011

description: This data set contains measurements from a Cloud and Aerosol Polarization and Backscatter LiDAR (CAPABL) that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This LiDAR points near-vertically and measures backscatter in three polarization states. Parameters include: backscatter in the parallel channel, the depolarization ratio, and the diattenuation. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The LiDAR is owned by the National Oceanic and Atmospheric Administration's Earth System Research Laboratory. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.15468/dl.eza7s2
title: Occurrence Download

description: A dataset containing 141 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)"
 ]
 }
 },
 "TaxonKey is one of (Miricacalia makineana (Yatabe) Kitam.)"
 ]
}

 The dataset includes 141 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027654-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18739/a2448t
title: Cloud and aerosol polarization and backscatter LiDAR measurements at Summit Station, Greenland, 2012

description: This data set contains measurements from a Cloud and Aerosol Polarization and Backscatter LiDAR (CAPABL) that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This LiDAR points near-vertically and measures backscatter in three polarization states. Parameters include: backscatter in the parallel channel, the depolarization ratio, and the diattenuation. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The LiDAR is owned by the National Oceanic and Atmospheric Administration's Earth System Research Laboratory. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.18739/a2m786
title: Cloud and aerosol polarization and backscatter LiDAR measurements at Summit Station, Greenland, 2013

description: This data set contains measurements from a Cloud and Aerosol Polarization and Backscatter LiDAR (CAPABL) that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This LiDAR points near-vertically and measures backscatter in three polarization states. Parameters include: backscatter in the parallel channel, the depolarization ratio, and the diattenuation. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The LiDAR is owned by the National Oceanic and Atmospheric Administration's Earth System Research Laboratory. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.17632/r9sw9bjts9.1
title: HPEO code and benchmark study

description: This document includes the HPEO algorithm and scripts necessary for generating all plots featured in the article titled 'Heuristic Particle Elimination Optimization (HPEO) Algorithm for Efficient Structural Design: A Comprehensive Benchmark Study'.
DOI: 10.18739/a2tr3b
title: Cloud and aerosol polarization and backscatter LiDAR measurements at Summit Station, Greenland, 2015

description: This data set contains measurements from a Cloud and Aerosol Polarization and Backscatter LiDAR (CAPABL) that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This LiDAR points near-vertically and measures backscatter in three polarization states. Parameters include: backscatter in the parallel channel, the depolarization ratio, and the diattenuation. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The LiDAR is owned by the National Oceanic and Atmospheric Administration's Earth System Research Laboratory. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.17632/pnnhy57kg9.1
title: Validation of STEM-CIS in Indonesian Context

description: This dataset contains questionnaires and results of analyses
DOI: 10.15468/dl.3wq8gh
title: Occurrence Download

description: A dataset containing 304 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Africa",
 "Country is South Africa",
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Helichrysum kraussii Sch.Bip."
 ]
}

 The dataset includes 304 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027655-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18739/a2pz01
title: Cloud and Aerosol Polarization and Backscatter LiDAR measurements taken at Summit Station, Greenland, 2016

description: This data set contains measurements from a Cloud and Aerosol Polarization and Backscatter LiDAR (CAPABL) that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This LiDAR points near-vertically and measures backscatter in three polarization states. Parameters include: backscatter in the parallel channel, the depolarization ratio, and the diattenuation. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The LiDAR is owned by the National Oceanic and Atmospheric Administration's Earth System Research Laboratory. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.18739/a2sr4d
title: Ceilometer Cloud Base Height Measurements at Summit Station, Greenland, 2010

description: This data set contains measurements from a Vaisala ceilometer that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This ceilometer points near-vertically and provides estimates of cloud base height (for up to three layers) and the total visibility, based on Vaisala's proprietary algorithm. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The ceilometer is owned by the Department of Energy Atmospheric Radiation Measurement Program. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.18739/a2j79z
title: Ceilometer Cloud Base Height Measurements at Summit Station, Greenland, 2011

description: This data set contains measurements from a Vaisala ceilometer that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This ceilometer points near-vertically and provides estimates of cloud base height (for up to three layers) and the total visibility, based on Vaisala's proprietary algorithm. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The ceilometer is owned by the Department of Energy Atmospheric Radiation Measurement Program. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.18739/a2qv22
title: Ceilometer Cloud Base Height Measurements at Summit Station, Greenland, 2012

description: This data set contains measurements from a Vaisala ceilometer that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This ceilometer points near-vertically and provides estimates of cloud base height (for up to three layers) and the total visibility, based on Vaisala's proprietary algorithm. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The ceilometer is owned by the Department of Energy Atmospheric Radiation Measurement Program. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.18739/a2fb8p
title: Ceilometer Cloud Base Height Measurements at Summit Station, Greenland, 2013

description: This data set contains measurements from a Vaisala ceilometer that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This ceilometer points near-vertically and provides estimates of cloud base height (for up to three layers) and the total visibility, based on Vaisala's proprietary algorithm. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The ceilometer is owned by the Department of Energy Atmospheric Radiation Measurement Program. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.18739/a2221v
title: Ceilometer Cloud Base Height Measurements at Summit Station, Greenland, 2014

description: This data set contains measurements from a Vaisala ceilometer that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This ceilometer points near-vertically and provides estimates of cloud base height (for up to three layers) and the total visibility, based on Vaisala's proprietary algorithm. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The ceilometer is owned by the Department of Energy Atmospheric Radiation Measurement Program. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.18739/a2sn4c
title: Ceilometer Cloud Base Height Measurements at Summit Station, Greenland, 2015

description: This data set contains measurements from a Vaisala ceilometer that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This ceilometer points near-vertically and provides estimates of cloud base height (for up to three layers) and the total visibility, based on Vaisala's proprietary algorithm. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The ceilometer is owned by the Department of Energy Atmospheric Radiation Measurement Program. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.18739/a2mv3h
title: Ceilometer Cloud Base Height Measurements at Summit Station, Greenland, 2016

description: This data set contains measurements from a Vaisala ceilometer that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This ceilometer points near-vertically and provides estimates of cloud base height (for up to three layers) and the total visibility, based on Vaisala's proprietary algorithm. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file. The ceilometer is owned by the Department of Energy Atmospheric Radiation Measurement Program. Field operations support is provided by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.18739/a2622g
title: Annual Observations of the Biological and Physical Marine Environment in the Chukchi and Nearshore Beaufort Seas near Barrow, AK

description: The Arctic Ocean ecosystem may respond dramatically to climate change through already observed modification of the physical environment (e.g., hydrography and ice cover). A better understanding of the coupled biological-physical ocean ecosystem, and its interannual variability, is necessary to predict and understand these potential impacts of climate change on the Arctic ecosystem. The Chukchi-Beaufort Sea region near Barrow, AK, including coastal waters, has been identified as key because it lies at the junction of several ocean regions and downstream of the Pacific Arctic gateway and hence is influenced physically and ecologically by input from Bering Strait. Researchers have conducted oceanographic sampling of the coupled physical and biological marine environment in the Chukchi and near-shore Beaufort Seas for five years (2005 - 2009), including coastal waters, using boat-based surveys during late summer and short-term (month) and year-long bottom-mounted moorings, and have observed significant interannual variability. They will now establish annual surveys of the pelagic marine environment and deploy a year-round mooring in Barrow Canyon as components of the Arctic Observing Network (AON). The surveys will measure a range of biological and physical parameters including hydrography, currents, plankton standing stocks, nutrients, and plankton genetics. The mooring will measure ocean currents, near bottom temperature and salinity, and acoustic backscatter (zooplankton proxy) in Barrow Canyon at a location typically within or beneath the Alaska Coastal Current. Results from this AON component will provide a 10-year record and enable a better understanding of inter-annual variability in the physical and biological characteristics of the Barrow area ocean confluence. Based on these data, changes in biological characteristics that respond to larger climate change can be detected. This proposed AON component includes measurements of key oceanic biological and chemical parameters, and their coupling to physical parameters from a region sensitive to climate change. The researchers will actively communicate plans and results with the community of Barrow through formal and informal discussions and presentations. They will also support the participation of a teacher in fieldwork for two of the years. Sponsor:University of Rhode Island, Research Office, Kingston, RI 02881-1967
DOI: 10.5281/zenodo.13985597
title: Primary MS data and secondary data for: "Post-proline cleaving enzymes also show specificity to reduced cysteine" Part2

description: raw LC-MS/MS data (Bruker Daltonics - tims TOF SCP) and related PEAKS search results (*.csv) plus corresponding custom database (*.fasta). Protein identifications were performed using PEAKS programs.

Data were used to:



extract cleavage preferences of Clarity Ferm AnPEP. The target sample was HEK cell lysate.


These data are related to https://doi.org/10.5281/zenodo.13938580.