cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/84z422f8sn.1
title: Geochemical and geochronological data from Eastern Plutonic Bodies, Peninsular Malaysia

description: This dataset compiles integrated whole-rock geochemistry, zircon U-Pb geochronology, and zircon Lu-Hf isotope data from five key plutons (Maras-Jong, Jerong, Jengai, Kijal, Kuantan) of the Eastern Plutonic Bodies (EPB), Eastern Belt granitoids, Peninsular Malaysia. The data were generated to resolve long-standing controversies regarding the petrogenesis and mantle-derived contributions to these granitoids, which formed during the critical transition from Gondwana dispersal to Pangaea assembly.

The dataset includes:

 New whole-rock major and trace element geochemistry for 39 samples from the Maras-Jong, Jerong, Jengai, and Kijal plutons, analyzed by XRF and ICP-MS.

 New zircon U-Pb geochronological data for the Jerong and Jengai plutons, obtained via LA-ICP-MS.

 Compiled and reinterpreted zircon U-Pb and Lu-Hf isotopic data from Quek et al. (2021) for the Maras-Jong, Kijal, and Kuantan plutons, ensuring methodological consistency.

Key parameters and features of the data:

 Geochemistry: The granitoids are high-K calc-alkaline, peraluminous, and highly fractionated, showing characteristic I-type trends (e.g., strong negative P₂O₅ vs. SiO₂ correlation). Trace element patterns show arc-like signatures with enrichments in LILEs (Rb, Th, U) and depletions in HFSEs (Nb, Ta, Ti).

 Geochronology: Zircon U-Pb ages define two distinct magmatic pulses: a main Permian pulse (~276–262 Ma) and a Middle Triassic pulse (~245 Ma, Kijal pluton).

 Hf Isotopes: Zircon εHf(t) values range from -9.2 to +14.6, with most plutons (Kuantan, Kijal, Jengai) showing predominantly juvenile, positive signatures (εHf(t) = -0.3 to +5.3), corresponding to Neoproterozoic depleted mantle model ages (TDM² = 0.76–0.99 Ga).

This robust, multi-proxy dataset provides critical evidence for a revised petrogenetic model. It demonstrates that the Eastern Belt granitoids are highly fractionated I-types, where S-type characteristics are the result of extreme fractional crystallization and crustal assimilation, not a metasedimentary source. The Hf isotopes quantitatively establish the mantle not only as a thermal engine but also as a fundamental material component in granite genesis through crust-mantle hybridization. The data directly link the Permian magmatism to subduction-related processes and the Triassic Kijal pluton to a post-collisional slab break-off event, offering a refined geodynamic narrative for the final assembly of Pangaea in Southeast Asia.

This dataset is essential for researchers in granite petrogenesis, crust-mantle interaction, Tethyan tectonics, and supercontinent cycles. It supports comparative studies with other orogenic belts and provides a benchmark for testing geodynamic models of subduction-to-collision transitions.
DOI: 10.17632/pz6h942z6b
title: Data

description: "The association between inhaled tire-wear particle exposure and cognitive dysfunction in rats: exploring the roles of lung-gut-brain axis" related research data
DOI: 10.17632/pz6h942z6b.1
title: Data

description: "The association between inhaled tire-wear particle exposure and cognitive dysfunction in rats: exploring the roles of lung-gut-brain axis" related research data
DOI: 10.15468/dl.2ym8ua
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "HasCoordinate is true",
 "Geometry POLYGON ((-116.8346 35.09052, -116.8318 35.09293, -116.8347 35.09461, -116.8373 35.09243, -116.8346 35.09052))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005726-251120083545085/datasets/export for details.
DOI: 10.17632/jdpnmyk696
title: Uneven Illuminated Dermatological Macro-Photographs Dataset

description: This dataset contains dermatological macro-photographs used for illumination correction research. The images include various real-world lighting artifacts such as shadows, glare, uneven illumination, and localized over /under-exposed regions. These conditions make the dataset suitable for training and evaluating deep learning–based illumination correction models. 
The original clinical images were collected from the **Skin Cancer Dataset – University of Waterloo** and the **MED-NODE (Melanoma Detection) Dataset**. All images were preprocessed, curated, and standardized to ensure consistent format and quality for experimentation.
DOI: 10.17632/jdpnmyk696.1
title: Uneven Illuminated Dermatological Macro-Photographs Dataset

description: This dataset contains dermatological macro-photographs used for illumination correction research. The images include various real-world lighting artifacts such as shadows, glare, uneven illumination, and localized over /under-exposed regions. These conditions make the dataset suitable for training and evaluating deep learning–based illumination correction models. 
The original clinical images were collected from the **Skin Cancer Dataset – University of Waterloo** and the **MED-NODE (Melanoma Detection) Dataset**. All images were preprocessed, curated, and standardized to ensure consistent format and quality for experimentation.
DOI: 10.17632/b2k52v9mkc
title: Motif-Sikka: A Curated Image Dataset of Traditional Sikka Ikat Weaving Motifs for Textile Pattern Analysis

description: This dataset was created under the hypothesis that image-based computational methods can effectively be used to classify and recognize traditional textile motifs, particularly Sikka ikat weaving patterns. Variations in lighting, device resolution, and motif structure are significant challenges in textile image analysis. By capturing these variations in structured dataset, we hypothesize that it will support the development of more robust and generalizable computer vision models for motif recognition and cultural heritage preservation.
This dataset is a subset of the Sikka-MoDiVers collection, consisting of 432 images from 24 verified classes of traditional Sikka ikat weaving motifs. Each class is represented by images captured under six natural lighting conditions:
Indoor morning, Indoor noon, Indoor night, Outdoor morning, Outdoor noon, Outdoor night
The images have been resized to 512×512 pixels to standardize them for machine learning tasks such as image enhancement, segmentation, feature extraction, and motif classification. The naming convention of image files reflects both the motif class and the lighting condition.
Each motif class is stored in its own folder, with clear labeling. The dataset is aligned with the motif classification from the Geographical Indication Certificate ID G 0000000564, issued by the Directorate General of Intellectual Property, Republic of Indonesia.
Included in this release:
1. A metadata CSV file (classes.csv) listing motif name, number of images, and lighting conditions.
2. A README file (README_v3.md) that describes the directory structure, naming format, and usage instructions.tudies involving traditional Indonesian textiles.
Potential Uses:
1. Deep learning model training for traditional textile recognition.
2. Cultural heritage digital archiving and motif classification.
3. Evaluation of enhancement techniques under varying lighting conditions.

How to Use:
Extract the dataset and refer to the metadata file for understanding the structure. Each folder contains images from a single class, suitable for direct use in supervised learning models.
DOI: 10.17632/b2k52v9mkc.3
title: Motif-Sikka: A Curated Image Dataset of Traditional Sikka Ikat Weaving Motifs for Textile Pattern Analysis

description: This dataset was created under the hypothesis that image-based computational methods can effectively be used to classify and recognize traditional textile motifs, particularly Sikka ikat weaving patterns. Variations in lighting, device resolution, and motif structure are significant challenges in textile image analysis. By capturing these variations in structured dataset, we hypothesize that it will support the development of more robust and generalizable computer vision models for motif recognition and cultural heritage preservation.
This dataset is a subset of the Sikka-MoDiVers collection, consisting of 432 images from 24 verified classes of traditional Sikka ikat weaving motifs. Each class is represented by images captured under six natural lighting conditions:
Indoor morning, Indoor noon, Indoor night, Outdoor morning, Outdoor noon, Outdoor night
The images have been resized to 512×512 pixels to standardize them for machine learning tasks such as image enhancement, segmentation, feature extraction, and motif classification. The naming convention of image files reflects both the motif class and the lighting condition.
Each motif class is stored in its own folder, with clear labeling. The dataset is aligned with the motif classification from the Geographical Indication Certificate ID G 0000000564, issued by the Directorate General of Intellectual Property, Republic of Indonesia.
Included in this release:
1. A metadata CSV file (classes.csv) listing motif name, number of images, and lighting conditions.
2. A README file (README_v3.md) that describes the directory structure, naming format, and usage instructions.tudies involving traditional Indonesian textiles.
Potential Uses:
1. Deep learning model training for traditional textile recognition.
2. Cultural heritage digital archiving and motif classification.
3. Evaluation of enhancement techniques under varying lighting conditions.

How to Use:
Extract the dataset and refer to the metadata file for understanding the structure. Each folder contains images from a single class, suitable for direct use in supervised learning models.
DOI: 10.15468/dl.tw2a29
title: Occurrence Download

description: A dataset containing 5689 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Agriades glandon",
 "Geometry POLYGON((-5.2 43.7, 4 41.9, 8.5 41.9, 8.5 41.2, 10 41.2, 10 43.4, 8.5 43.4, 8.5 49, 2.5 51.2, -5.2 49.5, -5.2 43.7))"
 ]
}

 The dataset includes 5689 records from 151 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005727-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/tm3v4zmh7c
title: Chilli Leaf Disease Image Dataset for Classification and Early Diagnosis in Agriculture

description: This dataset contains a total of 8,814 high-resolution images (1000×1000) of chilli leaves collected from agricultural fields located in (Ashulia, Narsingdi, Cumilla, Feni, Noakhali, and Laksham) Bangladesh. The dataset is designed to support research in plant disease classification, computer vision, and deep learning-based agricultural AI.

The images are categorized into 6 distinct classes:
class and description are given below
1. Bacterial (Leaf lesions caused by bacterial infection)
2. Cercospora (Fungal disease with circular spots)
3. Curl Virus (Virus-infected leaves showing curling symptoms)
4. Healthy Leaf (Fresh and disease-free leaves) 
5. Nutrient Deficiency (Yellowing and discoloration from poor nutrient supply)
6. Powdery Mildew (White fungal growth on leaf surfaces)

No. of Images:
1. Bacterial (1,629)
2. Cercospora (1,898)
3. Curl Virus (1,590)
4. Healthy Leaf (1,647) 
5. Nutrient Deficiency (1,207)
6. Powdery Mildew (843)

📌 Total Images: 8,814
📌 Format: JPG and PNG
📌 Resolution: 1000 × 1000 pixels
📌 Color space: RGB
📌 Capture Device: Smartphone 
📌 Environment: Real farm conditions — variable lighting, angles & white backgrounds


Applications:
Image Classification
Disease Detection & Monitoring
Transfer Learning
Deep Learning research in Agriculture
Dataset Benchmarking for Vision Models

Folder Structure is given below:
Chilli_Leaf_Dataset/
 ├── Bacterial_Spot/
 ├── Cercospora_Leaf_Spot/
 ├── Curl_Virus/
 ├── Healthy_Leaf/
 ├── Nutrient_Deficiency/
 └── Powdery_Mildew/
DOI: 10.17632/tm3v4zmh7c.1
title: Chilli Leaf Disease Image Dataset for Classification and Early Diagnosis in Agriculture

description: This dataset contains a total of 8,814 high-resolution images (1000×1000) of chilli leaves collected from agricultural fields located in (Ashulia, Narsingdi, Cumilla, Feni, Noakhali, and Laksham) Bangladesh. The dataset is designed to support research in plant disease classification, computer vision, and deep learning-based agricultural AI.

The images are categorized into 6 distinct classes:
class and description are given below
1. Bacterial (Leaf lesions caused by bacterial infection)
2. Cercospora (Fungal disease with circular spots)
3. Curl Virus (Virus-infected leaves showing curling symptoms)
4. Healthy Leaf (Fresh and disease-free leaves) 
5. Nutrient Deficiency (Yellowing and discoloration from poor nutrient supply)
6. Powdery Mildew (White fungal growth on leaf surfaces)

No. of Images:
1. Bacterial (1,629)
2. Cercospora (1,898)
3. Curl Virus (1,590)
4. Healthy Leaf (1,647) 
5. Nutrient Deficiency (1,207)
6. Powdery Mildew (843)

📌 Total Images: 8,814
📌 Format: JPG and PNG
📌 Resolution: 1000 × 1000 pixels
📌 Color space: RGB
📌 Capture Device: Smartphone 
📌 Environment: Real farm conditions — variable lighting, angles & white backgrounds


Applications:
Image Classification
Disease Detection & Monitoring
Transfer Learning
Deep Learning research in Agriculture
Dataset Benchmarking for Vision Models

Folder Structure is given below:
Chilli_Leaf_Dataset/
 ├── Bacterial_Spot/
 ├── Cercospora_Leaf_Spot/
 ├── Curl_Virus/
 ├── Healthy_Leaf/
 ├── Nutrient_Deficiency/
 └── Powdery_Mildew/
DOI: 10.17632/k4pdksd5dw
title: Role of Jet Drops from Sub-100 μm Bubbles in Sea Spray Aerosol Production

description: The uploaded dataset includes the raw measurements used in this study, including high-speed imaging records of jet drop production, bubble size distributions for sub-100 μm bubbles, and repeated measurements of temperature-dependent jet drop yields. All data are provided in their original format with basic metadata to allow reproduction of the analysis.
DOI: 10.17632/k4pdksd5dw.1
title: Role of Jet Drops from Sub-100 μm Bubbles in Sea Spray Aerosol Production

description: The uploaded dataset includes the raw measurements used in this study, including high-speed imaging records of jet drop production, bubble size distributions for sub-100 μm bubbles, and repeated measurements of temperature-dependent jet drop yields. All data are provided in their original format with basic metadata to allow reproduction of the analysis.
DOI: 10.17632/b467446mfy
title: Source data for Ecosystem Engineering paper

description: EXCEL file with source data
Python scripts
DOI: 10.17632/b467446mfy.1
title: Source data for Ecosystem Engineering paper

description: EXCEL file with source data
Python scripts
DOI: 10.17632/tky66tdjrz
title: Code

description: This is the code of whole prototype
DOI: 10.17632/tky66tdjrz.1
title: Code

description: This is the code of whole prototype
DOI: 10.17632/shj94nrczy.2
title: Behavior Analysis Dataset for Social Engineering Attacks

description: This data includes spam e-mail and phishing detection dataset for cybersecurity research projects
DOI: 10.6084/m9.figshare.30694541
title: Data from Dehydration Unifies Mineralization and Nano-deposition Bonding Strategies

description: Data
DOI: 10.6084/m9.figshare.30694541.v1
title: Data from Dehydration Unifies Mineralization and Nano-deposition Bonding Strategies

description: Data
DOI: 10.17632/d39frxtgd3.1
title: Replication Code and Sample Synthetic Data : For Does the thin capitalization rule harm investment?

description: This is only 30 randomly a synthetic dataset generated for replication purposes. While the variable names and structure are identical to the original data, all identifying information has been removed and numerical values have been replaced with randomly generated data that preserves the original dataset's summary statistics (mean, standard deviation, and number of observations). Access to the underlying tax administration data is restricted due to confidentiality agreements with the tax authority, as is standard in this field of research. Therefore, the original datasets cannot be publicly shared.
DOI: 10.17632/xbmcvmj7zn
title: a monthly dataset for the Cangshan region

description: This is a dataset of monthly temperature, precipitation, potential evapotranspiration, SPEI, precipitation isotopes, soil water isotopes, and plant xylem isotopes for the Cangshan Mountain area (Dali, Yunnan, China).
DOI: 10.17632/xbmcvmj7zn.1
title: a monthly dataset for the Cangshan region

description: This is a dataset of monthly temperature, precipitation, potential evapotranspiration, SPEI, precipitation isotopes, soil water isotopes, and plant xylem isotopes for the Cangshan Mountain area (Dali, Yunnan, China).
DOI: 10.17632/zn7j5jnkyb
title: Distribution of soybean stations in China

description: Soybean occurrence records were obtained through a comprehensive literature search using the China National Knowledge Infrastructure (CNKI) database, including key information such as geographic location, cultivation years, and yield.
DOI: 10.17632/zn7j5jnkyb.1
title: Distribution of soybean stations in China

description: Soybean occurrence records were obtained through a comprehensive literature search using the China National Knowledge Infrastructure (CNKI) database, including key information such as geographic location, cultivation years, and yield.
DOI: 10.17632/tyrxb4j3rd
title: Oxidation state and accumulation of uranium in hydrothermal REE minerals

description: Thermodynamic dataset in GWB 11 format (text)
DOI: 10.17632/tyrxb4j3rd.1
title: Oxidation state and accumulation of uranium in hydrothermal REE minerals

description: Thermodynamic dataset in GWB 11 format (text)
DOI: 10.25574/41933
title: Chandra X-ray Observatory ObsId 41933
DOI: 10.15468/dl.cwq8bb
title: Occurrence Download

description: A dataset containing 86 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-7.092540560642471 38.81259499201562, -7.093370271227072 38.81511068153123, -7.094811449046063 38.817451391109124, -7.096808763948406 38.81952713767342, -7.099285483993186 38.82125811344868, -7.102146420590393 38.822577758713955, -7.105281588005362 38.823435325639466, -7.10857043637354 38.823797833767706, -7.111886494590525 38.82365134118745, -7.115102243095061 38.82300148197813, -7.118094027370277 38.82187324901729, -7.120746821358341 38.820310030600574, -7.122958655989165 38.81837193834202, -7.124644541396998 38.81613349132963, -7.125739731549073 38.81368074641582, -7.126202206057005 38.81110798583494, -7.126014274765056 38.80851409024251, -7.125183245012181 38.805998737128625, -7.123741127838955 38.803658570949786, -7.1217433964045735 38.80158349205435, -7.11926684606323 38.7998532065712, -7.116406639569132 38.79853416913057, -7.113272651505406 38.79767703502645, -7.109985252191038 38.79731471882316, -7.106670692125116 38.79746113321316, -7.103456262788924 38.79811065602073, -7.100465417867918 38.799238345582495, -7.097813040411754 38.800800896330315, -7.09560103607735 38.802738298296745, -7.0939144205663265 38.80497613748453, -7.092818051063049 38.80742844957699, -7.092354127501425 38.810001018232896, -7.092540560642471 38.81259499201562))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 86 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005730-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xwsbka
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "HasCoordinate is true",
 "Geometry POLYGON ((-117.9805 35.02039, -117.9806 35.01956, -117.9797 35.0195, -117.9796 35.0203, -117.9805 35.02039))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005731-251120083545085/datasets/export for details.
DOI: 10.17632/rsmm8dwbjz
title: Image datasets for automated bus fault detection

description: Automated Bus Fault Detection Dataset was created due to the need to study the common visual defects that are typically noticed on buses that run in Bangladesh. The photographs were taken during the time of 40 days, between July and August 2025, in Dhaka City, and were captured mostly with personal smartphones of digital nature. The gadgets that we will use in the collection of our data will include Motorola Edge 20, Xiaomi Note 13 Pro Plus, Redmi Note 12 Pro Max, Redmi Note 10, Samsung A22, and Redmi 12.

All the faces and vehicle license plates seen in the pictures were blurred to avoid revealing confidential information to guarantee privacy and data security. The dataset continued to center on the visible faults of buses and had a curated dataset of around 973 images. These images have six different types of bus conditions that include Wornout_tire, Wornout_body, Broken_glass, Broken_mirror, Emitting_smoke, and Standard_bus. The Standard_bus category was needed in order to compare the defective and the non-defective states.

The image formats are all JPG with the color space being RGB and have different resolutions, with a file size of 150 KB to 300 KB. The given dataset can serve as a good background to the design and testing of the algorithms designed to categorize the visible highway faults based on their evaluation of the deviations in the appearance of the usual standard buses.
DOI: 10.17632/rsmm8dwbjz.1
title: Image datasets for automated bus fault detection

description: Automated Bus Fault Detection Dataset was created due to the need to study the common visual defects that are typically noticed on buses that run in Bangladesh. The photographs were taken during the time of 40 days, between July and August 2025, in Dhaka City, and were captured mostly with personal smartphones of digital nature. The gadgets that we will use in the collection of our data will include Motorola Edge 20, Xiaomi Note 13 Pro Plus, Redmi Note 12 Pro Max, Redmi Note 10, Samsung A22, and Redmi 12.

All the faces and vehicle license plates seen in the pictures were blurred to avoid revealing confidential information to guarantee privacy and data security. The dataset continued to center on the visible faults of buses and had a curated dataset of around 973 images. These images have six different types of bus conditions that include Wornout_tire, Wornout_body, Broken_glass, Broken_mirror, Emitting_smoke, and Standard_bus. The Standard_bus category was needed in order to compare the defective and the non-defective states.

The image formats are all JPG with the color space being RGB and have different resolutions, with a file size of 150 KB to 300 KB. The given dataset can serve as a good background to the design and testing of the algorithms designed to categorize the visible highway faults based on their evaluation of the deviations in the appearance of the usual standard buses.