cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5ph4d654pr.1
title: Data for: Scale-habitat differentiation effect of fragmentation on multiple ecosystem functions in urban remnant forests

description: This data set contains all data used for analyzing the effect of fragmentation on multiple ecosystem functions in urban remnant forests. It includes plant information, soil physiochemical and microbial data and ecosystem functional data in 60 interior and edge samples in 30 remnant forest patches.
DOI: 10.17632/jtk6sxvjjd
title: Supplementary data for "Risk of Cutaneous T-cell Lymphoma in Patients with Autoimmune Diseases: A Propensity Score-Matched Retrospective Cohort-Study of Patients Using the Trinetx Network"

description: We present additional data in Supplementary Table 1 ("Additional autoimmune diseases examined") and Supplementary Table 2 "Pre-PSM data on all AIDs" for "Risk of Cutaneous T-cell Lymphoma in Patients with Autoimmune Diseases: A Propensity Score-Matched Retrospective Cohort-Study of Patients Using the Trinetx Network." Data were extracted from electronic medical records in the United States from patients with autoimmune diseases and a comparison group of individuals without autoimmune diseases between April 6th 2004 and April 6th 2024 using the TriNetX platform.
DOI: 10.17632/jtk6sxvjjd.1
title: Supplementary data for "Risk of Cutaneous T-cell Lymphoma in Patients with Autoimmune Diseases: A Propensity Score-Matched Retrospective Cohort-Study of Patients Using the Trinetx Network"

description: We present additional data in Supplementary Table 1 ("Additional autoimmune diseases examined") and Supplementary Table 2 "Pre-PSM data on all AIDs" for "Risk of Cutaneous T-cell Lymphoma in Patients with Autoimmune Diseases: A Propensity Score-Matched Retrospective Cohort-Study of Patients Using the Trinetx Network." Data were extracted from electronic medical records in the United States from patients with autoimmune diseases and a comparison group of individuals without autoimmune diseases between April 6th 2004 and April 6th 2024 using the TriNetX platform.
DOI: 10.17632/kntjzw468p
title: Framework for automatic composition of videos.

description: In this Python code, we are taking Input Script ( Love Letters) and processing this file into meaningful sentences/chunks so that a computational scene can be constructed and a well-formatted meta- JSON file is generated. The JSON file acts as a structure for building the sequence of video composition and at the same time it conducts a Timeline analysis.



The python app is made in the Flask library and is used for evaluating the quality of SQL queries generated. These SQL queries are generated at runtime and gets their input parameter/ search arguments from the LLM. Basically, the LLM gives the extracted keywords from the input Love Letters script. The DAR system helps us evaluate and compute its performance parameters automatically as we go through the evaluation process.
DOI: 10.17632/kntjzw468p.9
title: Framework for automatic composition of videos.

description: In this Python code, we are taking Input Script ( Love Letters) and processing this file into meaningful sentences/chunks so that a computational scene can be constructed and a well-formatted meta- JSON file is generated. The JSON file acts as a structure for building the sequence of video composition and at the same time it conducts a Timeline analysis.



The python app is made in the Flask library and is used for evaluating the quality of SQL queries generated. These SQL queries are generated at runtime and gets their input parameter/ search arguments from the LLM. Basically, the LLM gives the extracted keywords from the input Love Letters script. The DAR system helps us evaluate and compute its performance parameters automatically as we go through the evaluation process.
DOI: 10.17632/bswxgfdjxy
title: Zn isotope insights into the Archean continental crust stabilization

description: This supplementary material presents additional information on Zn isotope fractionation during partial melting of TTG and the generation of WA potassic granites, including: 1) assessment of data reliability and post-magmatic effects; 2) thermodynamic modeling framework; and 3) tables containing whole-rock geochemistry and modelling results.
DOI: 10.17632/bswxgfdjxy.1
title: Zn isotope insights into the Archean continental crust stabilization

description: This supplementary material presents additional information on Zn isotope fractionation during partial melting of TTG and the generation of WA potassic granites, including: 1) assessment of data reliability and post-magmatic effects; 2) thermodynamic modeling framework; and 3) tables containing whole-rock geochemistry and modelling results.
DOI: 10.17632/997k4x4fyf
title: Evaluating cardiometabolic risk in morphea a case-control study with carotid imaging and metabolic assessment

description: Detailed methodology of Evaluating cardiometabolic risk in morphea a case-control study with carotid imaging and metabolic assessment.
DOI: 10.17632/997k4x4fyf.1
title: Evaluating cardiometabolic risk in morphea a case-control study with carotid imaging and metabolic assessment

description: Detailed methodology of Evaluating cardiometabolic risk in morphea a case-control study with carotid imaging and metabolic assessment.
DOI: 10.17632/9g668bfhy5
title: RoseLeafSet: Real-World Leaf Image Dataset for AI-Based Agricultural Solutions

description: This RoseLeafSet dataset has been meticulously curated collected images (Primary 3,113, and Expanded 10,000) to support research in machine learning (ML), deep learning (DL), and computer vision (CV) for plant health assessment and disease classification. Collected from Amin Model Town, Khagan, Ashuliya, Dhaka, the dataset is a reflection of real-world agricultural conditions, capturing a variety of rose leaf health states under natural environmental lighting and backgrounds. The dataset consists of 3,113 annotated images, systematically categorized into four major classes:

Primary Images (3,113):
1. Healthy Leaf – 818 images
2. Black Spot – 1,288 images
3. Leaf Hole – 683 images
4. Dry Leaf – 324 images

Preprocessing Steps:
1. Background Remove
2. Add White Background
3. Cropping Image
4. Brightness Enhance
5. Contrast Enhance 
6. Resize into 2024 * 2024

Augmented Images (10,000):
1. Healthy Leaf – 2,500 images
2. Black Spot – 2,500 images
3. Leaf Hole – 2,500 images
4. Dry Leaf – 2,500 images

The classification reflects some of the most common rose plant conditions seen in Bangladeshi gardens and nurseries. Each class represents unique visual features—such as color changes, textural differences, and structural anomalies—that are vital for building accurate and robust classification models.

Image Collection Methodology:
Images were captured using high-resolution smartphone cameras at various angles and distances to ensure diversity. Different times of the day were considered to include a range of natural lighting conditions (e.g., direct sunlight, overcast, and shade), making the dataset suitable for real-world deployment scenarios. The data was further reviewed manually to eliminate duplicates, ensure class consistency, and maintain quality.

Use Cases and Research Scope, and dataset is highly applicable for:

- Supervised learning models: Training CNNs (e.g., DenseNet201, VGG16, InceptionV3, ResNet50) to classify leaf diseases.
- Transfer learning: Fine-tuning pre-trained architectures on this domain-specific dataset.
- Data augmentation studies: Investigating the impact of rotation, flipping, zooming, and brightness normalization to improve model generalization.
- Explainable AI (XAI): Developing interpretable models to assist agronomists and farmers in understanding model predictions.
- Mobile and edge computing deployment: Enabling real-time disease detection in low-resource settings using lightweight DL models.

Agricultural and Societal Impact:
Rose cultivation is economically and culturally significant in many parts of Bangladesh. Early detection of leaf diseases can substantially reduce the use of harmful pesticides, improve plant health, and boost productivity. This dataset serves as a foundation for precision agriculture systems and intelligent farming applications, promoting sustainability, cost-effectiveness, and technological integration in agriculture.
DOI: 10.17632/9g668bfhy5.3
title: RoseLeafSet: Real-World Leaf Image Dataset for AI-Based Agricultural Solutions

description: This RoseLeafSet dataset has been meticulously curated collected images (Primary 3,113, and Expanded 10,000) to support research in machine learning (ML), deep learning (DL), and computer vision (CV) for plant health assessment and disease classification. Collected from Amin Model Town, Khagan, Ashuliya, Dhaka, the dataset is a reflection of real-world agricultural conditions, capturing a variety of rose leaf health states under natural environmental lighting and backgrounds. The dataset consists of 3,113 annotated images, systematically categorized into four major classes:

Primary Images (3,113):
1. Healthy Leaf – 818 images
2. Black Spot – 1,288 images
3. Leaf Hole – 683 images
4. Dry Leaf – 324 images

Preprocessing Steps:
1. Background Remove
2. Add White Background
3. Cropping Image
4. Brightness Enhance
5. Contrast Enhance 
6. Resize into 2024 * 2024

Augmented Images (10,000):
1. Healthy Leaf – 2,500 images
2. Black Spot – 2,500 images
3. Leaf Hole – 2,500 images
4. Dry Leaf – 2,500 images

The classification reflects some of the most common rose plant conditions seen in Bangladeshi gardens and nurseries. Each class represents unique visual features—such as color changes, textural differences, and structural anomalies—that are vital for building accurate and robust classification models.

Image Collection Methodology:
Images were captured using high-resolution smartphone cameras at various angles and distances to ensure diversity. Different times of the day were considered to include a range of natural lighting conditions (e.g., direct sunlight, overcast, and shade), making the dataset suitable for real-world deployment scenarios. The data was further reviewed manually to eliminate duplicates, ensure class consistency, and maintain quality.

Use Cases and Research Scope, and dataset is highly applicable for:

- Supervised learning models: Training CNNs (e.g., DenseNet201, VGG16, InceptionV3, ResNet50) to classify leaf diseases.
- Transfer learning: Fine-tuning pre-trained architectures on this domain-specific dataset.
- Data augmentation studies: Investigating the impact of rotation, flipping, zooming, and brightness normalization to improve model generalization.
- Explainable AI (XAI): Developing interpretable models to assist agronomists and farmers in understanding model predictions.
- Mobile and edge computing deployment: Enabling real-time disease detection in low-resource settings using lightweight DL models.

Agricultural and Societal Impact:
Rose cultivation is economically and culturally significant in many parts of Bangladesh. Early detection of leaf diseases can substantially reduce the use of harmful pesticides, improve plant health, and boost productivity. This dataset serves as a foundation for precision agriculture systems and intelligent farming applications, promoting sustainability, cost-effectiveness, and technological integration in agriculture.
DOI: 10.15468/dl.5grj6u
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Capra hircus",
 "Country is Fiji",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026637-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2b6j8b2gb6
title: Lipidomics revealed the geographical origin and breed variability of sheep milk

description: Figure S1. PCA score plot of test samples and quality control (QC) samples.
Figure S2. Total ion chromatograms of QC samples in positive ion mode (A) and negative ion mode (B).
Figure S3. Total ion chromatograms of blank sample In positive ion mode (A) and negative ion mode (B).
Figure S4. One dimensional distribution diagram of PCA-X for quality control (QC) samples in PCA scores plot.
Figure S5. The peak areas of lipid classes among different breeds and different geographical origins.
Figure S6. Permutation test of OPLS-DA model for East Friesian (China) vs. DairyMeade (China) (A), and East Friesian (China) vs. East Friesian (Netherlands) (B).
Figure S7. FDA scores of two breeds and two geographical origins.
Table S1. The identification of lipids in positive and negative ion mode.
Table S2. The distribution of lipids in sheep milk from three sources.
Table S3. Lipids with significant differentials between two breeds and two origins.
Table S4. Results of stepwise canonical discriminant analysis for breed (DairyMeade vs. East Friesian) and origin (China vs. the Netherlands) comparisons.
Discriminant formulas
DOI: 10.17632/2b6j8b2gb6.2
title: Lipidomics revealed the geographical origin and breed variability of sheep milk

description: Figure S1. PCA score plot of test samples and quality control (QC) samples.
Figure S2. Total ion chromatograms of QC samples in positive ion mode (A) and negative ion mode (B).
Figure S3. Total ion chromatograms of blank sample In positive ion mode (A) and negative ion mode (B).
Figure S4. One dimensional distribution diagram of PCA-X for quality control (QC) samples in PCA scores plot.
Figure S5. The peak areas of lipid classes among different breeds and different geographical origins.
Figure S6. Permutation test of OPLS-DA model for East Friesian (China) vs. DairyMeade (China) (A), and East Friesian (China) vs. East Friesian (Netherlands) (B).
Figure S7. FDA scores of two breeds and two geographical origins.
Table S1. The identification of lipids in positive and negative ion mode.
Table S2. The distribution of lipids in sheep milk from three sources.
Table S3. Lipids with significant differentials between two breeds and two origins.
Table S4. Results of stepwise canonical discriminant analysis for breed (DairyMeade vs. East Friesian) and origin (China vs. the Netherlands) comparisons.
Discriminant formulas
DOI: 10.17632/ppncbf8s25
title: Data & Code: Public data availability and bond credit spreads: Evidence from China

description: This dataset contains Stata code and associated data files for conducting regression analysis of "Public data availability and bond credit spreads: Evidence from China". The data used in this paper are publicly available, and the code is compatible with Stata version MP17.
DOI: 10.15468/dl.tnmcr7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Scolopendra morsitans",
 "OccurrenceStatus is Absent"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026636-250811113504898/datasets/export for details.
DOI: 10.17632/ppncbf8s25.1
title: Data & Code: Public data availability and bond credit spreads: Evidence from China

description: This dataset contains Stata code and associated data files for conducting regression analysis of "Public data availability and bond credit spreads: Evidence from China". The data used in this paper are publicly available, and the code is compatible with Stata version MP17.
DOI: 10.17632/k4r9hvbtjd
title: Raw data for "Sodium alginate supplementation can alleviate non-alcoholic fatty liver disease in rats via restoring hepatic lipid metabolism and gut microbiota"

description: The dataset includes rat weight, abdominal and epididymal fat indexfat, serum lipid level, liver biochemical indicator, liver NAS score, 16s rRNA sequenceing analysis data, fecal SCFAs analysis data, liver metabolomics sequenceing analysis data, and IHC analysis data .
DOI: 10.17632/k4r9hvbtjd.1
title: Raw data for "Sodium alginate supplementation can alleviate non-alcoholic fatty liver disease in rats via restoring hepatic lipid metabolism and gut microbiota"

description: The dataset includes rat weight, abdominal and epididymal fat indexfat, serum lipid level, liver biochemical indicator, liver NAS score, 16s rRNA sequenceing analysis data, fecal SCFAs analysis data, liver metabolomics sequenceing analysis data, and IHC analysis data .
DOI: 10.17632/7j665x4vmd
title: Stiffness degradation virtual samples

description: This is the FE generated stiffness degradation samples for GFRP laminates.
DOI: 10.17632/7j665x4vmd.1
title: Stiffness degradation virtual samples

description: This is the FE generated stiffness degradation samples for GFRP laminates.
DOI: 10.17632/nv8k6y8mrb
title: Re-roof_it: A Multi-Criteria Framework for Rooftop retrofit in Low-Rise Residential Buildings, Warm-Humid climate of India.

description: Using 21 low-rise residential archetypes from Bhubaneswar, Odisha, as representative cases, the assessment evaluated 23 MCAs through three major performance criteria: thermal transmittance (Uroof); energy use intensity reductions (EUI); and annual electric cost (EC).
DOI: 10.17632/nv8k6y8mrb.1
title: Re-roof_it: A Multi-Criteria Framework for Rooftop retrofit in Low-Rise Residential Buildings, Warm-Humid climate of India.

description: Using 21 low-rise residential archetypes from Bhubaneswar, Odisha, as representative cases, the assessment evaluated 23 MCAs through three major performance criteria: thermal transmittance (Uroof); energy use intensity reductions (EUI); and annual electric cost (EC).
DOI: 10.17632/bt56zyfdnk
title: DATA & CODE

description: Data and code for the paper titled as "Downside risk: The dark side of carbon emission trading？".
DOI: 10.15468/dl.r2amf7
title: Occurrence Download

description: A dataset containing 54269 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Procambarus clarkii",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 54269 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026585-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bt56zyfdnk.1
title: DATA & CODE

description: Data and code for the paper titled as "Downside risk: The dark side of carbon emission trading？".
DOI: 10.17632/2xrkfnxccy
title: Static and Dynamic Water Contact Angles of Electrospun pure DegraPol® Fiber Meshes and from Emulsion Electrospun DegraPol® Fiber Meshes with Incorporated Secretome Harvested from Rabbit Adipose-Derived Stem Cell: Rabbit Achilles Tenocyte Co-Culture (3:1)

description: Electrospun Tubes were characterized by static and dynamic water contact angles. We provide raw data for pure DegraPol® tubes and for DegraPol® tubes that were emulsion electrospun (water in oil emulsion) where in the small water droplets secretome was dissolved. The secretome had been previously harvested from a co-culture of rabbit adipose-derived stem cells and rabbit Achilles tenocytes in a ratio of 3:1.
DOI: 10.17632/2xrkfnxccy.1
title: Static and Dynamic Water Contact Angles of Electrospun pure DegraPol® Fiber Meshes and from Emulsion Electrospun DegraPol® Fiber Meshes with Incorporated Secretome Harvested from Rabbit Adipose-Derived Stem Cell: Rabbit Achilles Tenocyte Co-Culture (3:1)

description: Electrospun Tubes were characterized by static and dynamic water contact angles. We provide raw data for pure DegraPol® tubes and for DegraPol® tubes that were emulsion electrospun (water in oil emulsion) where in the small water droplets secretome was dissolved. The secretome had been previously harvested from a co-culture of rabbit adipose-derived stem cells and rabbit Achilles tenocytes in a ratio of 3:1.
DOI: 10.17632/pc33ybs8wn
title: Murray Law-Derived Quantitative Flow Ratio (μ-QFR) -Guided Risk Stratification and Personalized Pharmacotherapy in Patients with Angiographically Confirmed Myocardial Bridging

description: Raw Data
DOI: 10.17632/pc33ybs8wn.1
title: Murray Law-Derived Quantitative Flow Ratio (μ-QFR) -Guided Risk Stratification and Personalized Pharmacotherapy in Patients with Angiographically Confirmed Myocardial Bridging

description: Raw Data
DOI: 10.17632/kfvft5gb6v
title: Targeting eEF1A: A Novel Broad-Spectrum Strategy Combat Avian Leukosis Virus by Disrupting Conserved Reverse Transcription

description: original data
DOI: 10.17632/kfvft5gb6v.1
title: Targeting eEF1A: A Novel Broad-Spectrum Strategy Combat Avian Leukosis Virus by Disrupting Conserved Reverse Transcription

description: original data
DOI: 10.15468/dl.ekj76f
title: Occurrence Download

description: A dataset containing 807 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Capra hircus",
 "Country is France",
 "HasCoordinate is true"
 ]
}

 The dataset includes 807 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026638-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/56fssg3hk3
title: GCI-FDI data

description: GCI and FDI data of CPTPP members
DOI: 10.17632/56fssg3hk3.1
title: GCI-FDI data

description: GCI and FDI data of CPTPP members
DOI: 10.17632/yjj9rhds3z.1
title: Supplementary material

description: An automated machine learning (AutoML) framework integrating Auto-Sklearn and Auto-Keras algorithms was implemented to develop predictive models for the unexplored parameter spaces of the BaTiO3–Bi(Mg1/2Ti1/2)O3 (BT–BMT) material system. The Auto-Sklearn algorithm yielded a high-precision model with an value of 0.9929, significantly surpassing the performance of manually tuned models. This model facilitated the identification of parameter configurations that satisfy X9R temperature stability standards for the dielectric constant across the entire parameter space. Furthermore, the model was employed to prioritize experimental parameters impacting the dielectric properties of the BT-BMT system during the solid-state reaction process. Critical factors identified included sintering temperature, coarse grinding time, post-calcination grinding time, and sintering time. These findings highlight the scalability and adaptability of the AutoML framework, emphasizing its potential to accelerate material discovery and optimization in various domains of materials science.
DOI: 10.17632/f66mp68zzk.1
title: Comparative analysis of high-resolution GCMs and RCMs ensembles in simulating and projecting compound extreme events in China

description: The final mapping data of the paper “Comparative analysis of high-resolution GCMs and RCMs ensembles in simulating and projecting compound extreme events in China"