cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.wv66tz
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Peru",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018922-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/w5hg2yc3ww.1
title: Effect of collective action on women's empowerment

description: The data entails the raw data collected from 532 households in Central Kenya and its associated do file.
DOI: 10.17632/x52kt9p5sj.1
title: Dataset on Stability Analysis of High Biomass and Yield in Maize Across Multiple Genotypes and Environments under Normal and Intercropping Systems

description: This dataset contains raw data collected from field experiments involving 20 maize genotypes grown in various environments with replicated blocks. The primary aim of the study was to evaluate biomass production and grain yield performance under two cropping systems: sole cropping and intercropping.

The dataset includes genotype codes, environmental identifiers, replication numbers, grain yield (tons per hectare), and total biomass (kilograms per hectare). These data support in-depth stability analyses, including biplot visualizations and the Genotype Stability Index (GSI), facilitating the assessment of adaptability and consistency of maize genotypes across diverse agro-ecological contexts.

Researchers in plant genetics, breeding, and agronomy can leverage this dataset to identify superior genotypes and implement sustainable cultivation strategies. Additionally, the open availability of this dataset promotes scientific transparency, reproducibility, and enables further meta-analytical research on crop stability.
DOI: 10.17632/g4pncgjt58
title: Peel-and-Swiss Roll Biopsy: A Practical Technique for Tissue Sampling in Fragile Blistering Skin Conditions

description: 1. Video demonstration of the peel-and-Swiss roll biopsy technique in a representative patient with pemphigus vulgaris, illustrating the atraumatic retrieval and preparation of a detached epidermal sheet for histopathological examination
2. Supplementary Fig 1. (A) Representative tissue sections from a patient with Staphylococcal scalded skin syndrome (SSSS), which shows a rolled-up, detached horny layer with mounds of parakeratosis (Hematoxylin and eosin, 100x), with (B) focal cluster of acantholytic cells (Hematoxylin and eosin, 400x).
3. Supplementary Fig 2. (A) Representative tissue sections from a patient with pemphigus vulgaris, showing “Swiss-roll” configuration at low-power magnification (Hematoxylin and eosin, 100x). (B) Intraepidermal separation with a blister roof and distinct acantholysis can be seen at high power view (Hematoxylin and eosin, 400x).
DOI: 10.17632/g4pncgjt58.2
title: Peel-and-Swiss Roll Biopsy: A Practical Technique for Tissue Sampling in Fragile Blistering Skin Conditions

description: 1. Video demonstration of the peel-and-Swiss roll biopsy technique in a representative patient with pemphigus vulgaris, illustrating the atraumatic retrieval and preparation of a detached epidermal sheet for histopathological examination
2. Supplementary Fig 1. (A) Representative tissue sections from a patient with Staphylococcal scalded skin syndrome (SSSS), which shows a rolled-up, detached horny layer with mounds of parakeratosis (Hematoxylin and eosin, 100x), with (B) focal cluster of acantholytic cells (Hematoxylin and eosin, 400x).
3. Supplementary Fig 2. (A) Representative tissue sections from a patient with pemphigus vulgaris, showing “Swiss-roll” configuration at low-power magnification (Hematoxylin and eosin, 100x). (B) Intraepidermal separation with a blister roof and distinct acantholysis can be seen at high power view (Hematoxylin and eosin, 400x).
DOI: 10.17632/9mpntm2xth
title: Entrepreneurial, AI Adoption, and Capabilities

description: The data consists of a profile of entrepreneur students who adopt AI for their business
DOI: 10.17632/9mpntm2xth.1
title: Entrepreneurial, AI Adoption, and Capabilities

description: The data consists of a profile of entrepreneur students who adopt AI for their business
DOI: 10.17632/gxmcxmghdt
title: Optimizing Green Fashion Through AI-Driven Engineering: Sustainable Marketing and Circular Economy Innovations

description: Optimizing Green Fashion Through AI-Driven Engineering: Sustainable Marketing and Circular Economy Innovations Data
DOI: 10.17632/gxmcxmghdt.1
title: Optimizing Green Fashion Through AI-Driven Engineering: Sustainable Marketing and Circular Economy Innovations

description: Optimizing Green Fashion Through AI-Driven Engineering: Sustainable Marketing and Circular Economy Innovations Data
DOI: 10.15468/dl.n4gk8s
title: Occurrence Download

description: A dataset containing 290 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Isatis glauca"
 ]
}

 The dataset includes 290 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018924-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rmdy4rhn7y
title: Do macroprudential policies influence FinTech credit growth: Quarterly Panel Dataset (2005q1–2018q4)

description: Data Description
This dataset comprises an unbalanced panel of quarterly data for 25 advanced and emerging economies, spanning the period 2005q1 to 2018q4. It is constructed to empirically to examine the impact of macroprudential policy (MaPP) actions on FinTech credit growth, using a wide range of macroeconomic and financial control variables control variables. We hypothesize that MaPP positively influences FinTech credit growth. Employing fixed effects (FE) and feasible generalized least squares (FGLS) models, the analysis reveals that an aggregate macroprudential policy action positively influences FinTech credit growth. The analysis further uncovers that the tightening action of macroprudential policies largely drives this effect, suggesting regulatory arbitrage. 

Purpose of the Dataset
The dataset was compiled to support the author's PhD research at Bournemouth University, focusing on how macroprudential policies shape the evolution of FinTech credit. It serves multiple purposes:
1. Peer reviewers – for verifying the accuracy and robustness of the study.
2. Researchers – to replicate, validate, or extend the analysis.
3. Journal editors – to ensure data transparency and compliance with data-sharing policies.
4. Policymakers and regulators – to extract insights for informed decisions on FinTech oversight and macroprudential governance.

Data Creation 
• FinTech Credit Data represented by FinTech credit volumes (in USD) comprising balance sheet lending, P2P/marketplace lending and invoice trading. It was sourced from various sources using Web scraping of loan-level origination data from FinTech lending platforms (website) loan books. The data was supplemented with data from: national P2P associations, platform annual reports, private databases (e.g., S&P Global Market Intelligence, CIEC and Brismo). FinTech credit data were cross-checked using multiple sources (publicly available datasets, industry reports, and private databases).

• Macroprudential Policy Data was sourced from the Integrated Macroprudential Policy (iMaPP) Database by Alam et al. (2019). The dataset covers 17 macroprudential tools implemented by central banks and regulators. Policy action indicators were coded as +1 (tightening), -1 (loosening), 0 (no action). 

• Control variables comprise Real GDP growth rate (RGDPG), monetary policy rate (MPR), real effective exchange rate (REER), Domestic credit to private sector (DCPS), financial openness index (FINOP), Regulation quality index (REGQ), crisis dummy (Crisis). Data for control variables were sourced from IMF Financial Soundness Indicators (FSI), World Bank (GFDD, WBI), OECD, Bank of International Settlement (BIS), and BankScope. 

Contributors
Onneetse L. Sikalao-Lekobane (PhD)

Citation
Sikalao-Lekobane, O. L. (2025). Do macroprudential policies influence FinTech credit growth: Quarterly Panel Dataset (2005q1–2018Q4) [Data set]. Mendeley Data.
DOI: 10.17632/rmdy4rhn7y.1
title: Do macroprudential policies influence FinTech credit growth: Quarterly Panel Dataset (2005q1–2018q4)

description: Data Description
This dataset comprises an unbalanced panel of quarterly data for 25 advanced and emerging economies, spanning the period 2005q1 to 2018q4. It is constructed to empirically to examine the impact of macroprudential policy (MaPP) actions on FinTech credit growth, using a wide range of macroeconomic and financial control variables control variables. We hypothesize that MaPP positively influences FinTech credit growth. Employing fixed effects (FE) and feasible generalized least squares (FGLS) models, the analysis reveals that an aggregate macroprudential policy action positively influences FinTech credit growth. The analysis further uncovers that the tightening action of macroprudential policies largely drives this effect, suggesting regulatory arbitrage. 

Purpose of the Dataset
The dataset was compiled to support the author's PhD research at Bournemouth University, focusing on how macroprudential policies shape the evolution of FinTech credit. It serves multiple purposes:
1. Peer reviewers – for verifying the accuracy and robustness of the study.
2. Researchers – to replicate, validate, or extend the analysis.
3. Journal editors – to ensure data transparency and compliance with data-sharing policies.
4. Policymakers and regulators – to extract insights for informed decisions on FinTech oversight and macroprudential governance.

Data Creation 
• FinTech Credit Data represented by FinTech credit volumes (in USD) comprising balance sheet lending, P2P/marketplace lending and invoice trading. It was sourced from various sources using Web scraping of loan-level origination data from FinTech lending platforms (website) loan books. The data was supplemented with data from: national P2P associations, platform annual reports, private databases (e.g., S&P Global Market Intelligence, CIEC and Brismo). FinTech credit data were cross-checked using multiple sources (publicly available datasets, industry reports, and private databases).

• Macroprudential Policy Data was sourced from the Integrated Macroprudential Policy (iMaPP) Database by Alam et al. (2019). The dataset covers 17 macroprudential tools implemented by central banks and regulators. Policy action indicators were coded as +1 (tightening), -1 (loosening), 0 (no action). 

• Control variables comprise Real GDP growth rate (RGDPG), monetary policy rate (MPR), real effective exchange rate (REER), Domestic credit to private sector (DCPS), financial openness index (FINOP), Regulation quality index (REGQ), crisis dummy (Crisis). Data for control variables were sourced from IMF Financial Soundness Indicators (FSI), World Bank (GFDD, WBI), OECD, Bank of International Settlement (BIS), and BankScope. 

Contributors
Onneetse L. Sikalao-Lekobane (PhD)

Citation
Sikalao-Lekobane, O. L. (2025). Do macroprudential policies influence FinTech credit growth: Quarterly Panel Dataset (2005q1–2018Q4) [Data set]. Mendeley Data.
DOI: 10.17632/6vttpjr7xw
title: Jujube Diseases Identification Image Dataset

description: The Jujube Diseases Identification Image Dataset provides high-resolution images and precise measurements of various jujube diseases, capturing key attributes such as size, color, and texture. This dataset serves as a valuable resource for agricultural research, offering detailed morphological and spectral data to aid in disease detection, classification, and management. Researchers can leverage these insights to refine cultivation techniques and enhance resistance to pests and diseases.
Dataset Overview
• Total Images: 1000 original images, captured in real-world field conditions
• Augmented Images: 6000 (generated using data augmentation techniques)
Augmentation Pipeline
To enhance dataset diversity and improve model generalization, the following augmentation techniques were applied:
1. Horizontal flipping (50% probability)
2. Vertical flipping (30% probability)
3. Random brightness and contrast adjustments
4. Rotation within a range of -30° to +30°
5. Shear transformation along both X and Y axes
6. Addition of random noise
This enriched dataset provides a robust foundation for developing deep learning models for automated jujube disease identification, contributing to precision agriculture and improved disease management strategies.
DOI: 10.17632/6vttpjr7xw.2
title: Jujube Diseases Identification Image Dataset

description: The Jujube Diseases Identification Image Dataset provides high-resolution images and precise measurements of various jujube diseases, capturing key attributes such as size, color, and texture. This dataset serves as a valuable resource for agricultural research, offering detailed morphological and spectral data to aid in disease detection, classification, and management. Researchers can leverage these insights to refine cultivation techniques and enhance resistance to pests and diseases.
Dataset Overview
• Total Images: 1000 original images, captured in real-world field conditions
• Augmented Images: 6000 (generated using data augmentation techniques)
Augmentation Pipeline
To enhance dataset diversity and improve model generalization, the following augmentation techniques were applied:
1. Horizontal flipping (50% probability)
2. Vertical flipping (30% probability)
3. Random brightness and contrast adjustments
4. Rotation within a range of -30° to +30°
5. Shear transformation along both X and Y axes
6. Addition of random noise
This enriched dataset provides a robust foundation for developing deep learning models for automated jujube disease identification, contributing to precision agriculture and improved disease management strategies.
DOI: 10.17632/4kjz78m7x5
title: BDRubberLeaf: A Comprehensive Dataset of Rubber Tree Leaf Diseases from Bangladesh for Agricultural Research

description: The Rubber Leaf Disease Dataset is a comprehensive collection of high-quality images of rubber tree leaves, systematically gathered from a Rubber Garden in Sreemangal Upazila, Moulvibazar District, Bangladesh, between December 23, 2024, and July 10, 2025. The dataset consists of eight distinct classes: Abnormal, Anthracnose, Black_Spot, Dry_Leaf, Healthy_Leaf, Leaf_Blight, Leaf_Spot and Powdery_Mildew which represent various stages of both diseased and healthy conditions in rubber tree leaves. The total dataset contains 4,066 images, with class-wise distribution as follows: Abnormal (561 images), Anthracnose (300 images), Healthy Leaf (506 images), Dry Leaf (450 images), Leaf_Blight(414 images), Powdery_Mildew (480 images), Black_Spot (450 images), and Leaf Spot (595 images). The images were captured using the Realme 6i smartphone in natural lighting conditions, ensuring high resolution and clarity. The original images were captured in jpg format at a resolution of 4000 × 3000 pixels, later resized to 480 × 640 pixels with a resolution of 72 dpi to standardize the dataset. Although the dataset does not include annotations, it serves as a valuable resource for researchers and machine learning practitioners focused on plant disease classification and early disease detection in rubber trees. This dataset is particularly relevant for applications in precision agriculture and automated plant pathology analysis, where understanding leaf conditions can play a critical role in improving crop health management strategies.
DOI: 10.15468/dl.gdfxqt
title: Occurrence Download

description: A dataset containing 157 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Norway",
 "HasCoordinate is true"
 ]
}

 The dataset includes 157 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018920-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/4kjz78m7x5.4
title: BDRubberLeaf: A Comprehensive Dataset of Rubber Tree Leaf Diseases from Bangladesh for Agricultural Research

description: The Rubber Leaf Disease Dataset is a comprehensive collection of high-quality images of rubber tree leaves, systematically gathered from a Rubber Garden in Sreemangal Upazila, Moulvibazar District, Bangladesh, between December 23, 2024, and July 10, 2025. The dataset consists of eight distinct classes: Abnormal, Anthracnose, Black_Spot, Dry_Leaf, Healthy_Leaf, Leaf_Blight, Leaf_Spot and Powdery_Mildew which represent various stages of both diseased and healthy conditions in rubber tree leaves. The total dataset contains 4,066 images, with class-wise distribution as follows: Abnormal (561 images), Anthracnose (300 images), Healthy Leaf (506 images), Dry Leaf (450 images), Leaf_Blight(414 images), Powdery_Mildew (480 images), Black_Spot (450 images), and Leaf Spot (595 images). The images were captured using the Realme 6i smartphone in natural lighting conditions, ensuring high resolution and clarity. The original images were captured in jpg format at a resolution of 4000 × 3000 pixels, later resized to 480 × 640 pixels with a resolution of 72 dpi to standardize the dataset. Although the dataset does not include annotations, it serves as a valuable resource for researchers and machine learning practitioners focused on plant disease classification and early disease detection in rubber trees. This dataset is particularly relevant for applications in precision agriculture and automated plant pathology analysis, where understanding leaf conditions can play a critical role in improving crop health management strategies.
DOI: 10.15468/dl.2f3wa5
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ziziphus mauritiana",
 "Country is Venezuela (Bolivarian Republic of)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 22 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018921-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25581/ornlsfa.017/1579907
title: Eddy Flux and Meteorology over Deciduous Forest, Prairie, and Soybean Ecosystems in Missouri, USA, during the Total Solar Eclipse of 2017

description: Eddy fluxes and meteorology data are reported at high temporal resolution for three flux tower sites in Mid-Missouri before, during, and after the Total Solar Eclipse of August 21, 2017. Mid-Missouri experienced up to 2 minutes and 40 seconds of totality at around solar noon during the eclipse (Fig. 1). The sites are located in deciduous oak-hickory forest, native prairie, and soybean ecosystems (Table 1 and Fig. 3). Eddy fluxes were computed using a wavelet-based approach that permitted the calculation of two-minute mean fluxes without losing low frequency flux contributions (Fig. 2). During the eclipse, standard meteorological variables were sampled and recorded at 5 second intervals. The two-minute means of fluxes and meteorology are reported. This data product includes three data files in NetCDF (*.nc) format, three data dictionary files and a file-level metadata file in comma separate (*.csv) format, and a user guide in PDF (*.pdf) format.
DOI: 10.17632/3n9vv2x4xt.1
title: Bangladesh Labor Act, 2006 Dataset For NLP

description: This dataset consists of a collection of question-and-answer pairs based on the Bangladesh Labor Act, 2006, designed for Natural Language Processing (NLP) tasks. It serves as a resource for training and evaluating machine learning models in understanding labor law concepts, regulations, and legal frameworks specific to Bangladesh. The dataset includes a variety of labor-related queries, providing valuable insights for legal automation, AI-powered legal assistance, and policy analysis in the context of Bangladeshi labor laws.
DOI: 10.17632/d25rbttsvr
title: The Impact of Psychiatric Comorbidities on Health-related Quality of Life in Patients with Melanoma: A Retrospective Cohort Study

description: Supplemental Table 1 detailing the association between patient demographics, medical comorbidities, and psychiatric comorbidities and FACT-M Subscales in patients with melanoma.
DOI: 10.17632/d25rbttsvr.1
title: The Impact of Psychiatric Comorbidities on Health-related Quality of Life in Patients with Melanoma: A Retrospective Cohort Study

description: Supplemental Table 1 detailing the association between patient demographics, medical comorbidities, and psychiatric comorbidities and FACT-M Subscales in patients with melanoma.
DOI: 10.17632/yfw8hx79kx
title: Impact of Climate Change on Protected Area Effectiveness in the Middle and Lower Yangtze River Basin: Implications for Adaptive Management

description: In the *Biodiversity Hotspots* folder, I have included species distribution data obtained from the IUCN, as well as environmental data used in MegaSDM to generate baseline conditions and species richness maps under the SSP2-4.5 and SSP5-8.5 scenarios for the 2050s, 2070s, and 2090s.
In the *Conservation Gaps* folder, I have included baseline and future conservation gap maps (for the 2050s, 2070s, and 2090s under SSP2-4.5 and SSP5-8.5 scenarios), which were derived by identifying areas outside protected areas where species richness exceeds the 75th percentile for each time period but falls below the low human impact (LHI) threshold.
DOI: 10.17632/yfw8hx79kx.1
title: Impact of Climate Change on Protected Area Effectiveness in the Middle and Lower Yangtze River Basin: Implications for Adaptive Management

description: In the *Biodiversity Hotspots* folder, I have included species distribution data obtained from the IUCN, as well as environmental data used in MegaSDM to generate baseline conditions and species richness maps under the SSP2-4.5 and SSP5-8.5 scenarios for the 2050s, 2070s, and 2090s.
In the *Conservation Gaps* folder, I have included baseline and future conservation gap maps (for the 2050s, 2070s, and 2090s under SSP2-4.5 and SSP5-8.5 scenarios), which were derived by identifying areas outside protected areas where species richness exceeds the 75th percentile for each time period but falls below the low human impact (LHI) threshold.
DOI: 10.17632/cp5bnct9yp
title: SE Australia springtime rainfall forecast

description: Forecasts for 2025 springtime rainfall in SE Australia using deep learning neural networks with Indian ocean and the Pacific ocean sea surface temperatures. 
References:
arXiv:2408.10550
Wolfram editor’s pick: https://community.wolfram.com/groups/-/m/t/3282530
S. Marčelja: Forecasting springtime rainfall in SE Australia using Empirical Orthogonal Functions and Neural Networks, Advances in Statistical Climatology, Meteorology and Oceanography (in press).
DOI: 10.17632/cp5bnct9yp.1
title: SE Australia springtime rainfall forecast

description: Forecasts for 2025 springtime rainfall in SE Australia using deep learning neural networks with Indian ocean and the Pacific ocean sea surface temperatures. 
References:
arXiv:2408.10550
Wolfram editor’s pick: https://community.wolfram.com/groups/-/m/t/3282530
S. Marčelja: Forecasting springtime rainfall in SE Australia using Empirical Orthogonal Functions and Neural Networks, Advances in Statistical Climatology, Meteorology and Oceanography (in press).
DOI: 10.15468/dl.tc8ssu
title: Occurrence Download

description: A dataset containing 1299 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Cameroon",
 "TaxonKey is Chelonia mydas"
 ]
}

 The dataset includes 1299 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018925-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.