cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/dmgcvm7865.1
title: The codes and real-word data supporting paper "A geographically weighted autoregressive model with an adaptive spatial weights matrix: Construction, estimation, and inference".

description: This repository contains the codes and real-word data used in submitted paper "A geographically weighted autoregressive model with an adaptive spatial weights matrix: Construction, estimation, and inference".
If you would like to replicate the results in this paper, please note that,
1. Code for model estimation of the simulation study: Folder "GWAR_simulation_
estimation" (generates Tables 1–6);
Code for test of the simulation study: Folder "GWAR_simulation_test" (generates Tables 7–8 and Figure 3);
Code for the application: Folder "GWAR_application" (generates Tables 9–11 and Figures 4–5).
2. Data of the application: Folder "data", in which "boston.txt" is the Boston data (also loadable via R package "spdep"); "z.txt" is the spatial locations of Boston data; "weight.txt" is the spatial link matrix of Boston data.
DOI: 10.17632/cj9kjrhc55
title: Metrics and proof SRS

description: The results of proof and metrics the SRS to paper
DOI: 10.17632/cj9kjrhc55.1
title: Metrics and proof SRS

description: The results of proof and metrics the SRS to paper
DOI: 10.17632/98jyhy5tbm
title: data

description: Combining water supply and machine learning approaches to estimate wheat yield in the Loess Plateau of China
DOI: 10.15468/dl.2tkdbm
title: Occurrence Download

description: A dataset containing 6683 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-5.27583 36.29359,-3.65225 36.62174,-3.94902 37.88876,-5.49914 37.62937,-5.27583 36.29359))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phlomis purpurea"
 ]
}

 The dataset includes 6683 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021844-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/98jyhy5tbm.1
title: data

description: Combining water supply and machine learning approaches to estimate wheat yield in the Loess Plateau of China
DOI: 10.15468/dl.aaqv5g
title: Occurrence Download

description: A dataset containing 129 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)"
 ]
 }
 },
 "Year is greater than or equal to 1900",
 "TaxonKey is Eriachne humilis"
 ]
}

 The dataset includes 129 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021845-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bp8xhd8fpc
title: SCNet dataset

description: Dataset A represents a fundamental two-class task, requiring the model to distinguish between mental arithmetic and a resting state. Dataset B presents a similar two-class problem but involves a significantly larger cohort of subjects. Dataset C constitutes the most challenging scenario, featuring a three-class motor task.
DOI: 10.17632/bp8xhd8fpc.1
title: SCNet dataset

description: Dataset A represents a fundamental two-class task, requiring the model to distinguish between mental arithmetic and a resting state. Dataset B presents a similar two-class problem but involves a significantly larger cohort of subjects. Dataset C constitutes the most challenging scenario, featuring a three-class motor task.
DOI: 10.17632/7d27jnr2dj
title: China's New Environmental Protection Law and Green technology innovation in Resource-Based Enterprises: A Double Machine Learning Approach

description: The sample was initially sourced from the Guotaian CSMAR database. Following the classification of resource-based industries, companies belonging to the aforementioned 12 sectors were selected. Subsequently, exclude ST companies with abnormal financial conditions, PT companies waiting for delisting, and insolvent enterprises. Finally, to obtain data for the control variables, the stock codes of these companies were matched with the CSMAR database. The final sample comprises 834 firms, yielding a total of 9491 firm-year observations.
DOI: 10.17632/7d27jnr2dj.1
title: China's New Environmental Protection Law and Green technology innovation in Resource-Based Enterprises: A Double Machine Learning Approach

description: The sample was initially sourced from the Guotaian CSMAR database. Following the classification of resource-based industries, companies belonging to the aforementioned 12 sectors were selected. Subsequently, exclude ST companies with abnormal financial conditions, PT companies waiting for delisting, and insolvent enterprises. Finally, to obtain data for the control variables, the stock codes of these companies were matched with the CSMAR database. The final sample comprises 834 firms, yielding a total of 9491 firm-year observations.
DOI: 10.15468/dl.5q9egu
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is BRA.21.68_2",
 "OccurrenceStatus is Present",
 "TaxonKey is Amphibia"
 ]
}

 The dataset includes 68 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021843-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ysk546n9np
title: An Augmented Image Dataset of Six Major Agricultural Pests for Deep Learning

description: This dataset consists of images representing six distinct categories of common agricultural pests. The classes included in this dataset are:
Cotton Bollworm
Oriental Fruit Fly
Peach Aphid
Silverleaf Whitefly
Thrips
Two-spotted Spider Mite
Data Collection and Processing:
The dataset is a compilation of original images curated from open-access repositories and web scraping. A significant portion of the base images was derived from existing Mendeley Data repositories (cited below) and Google Images. To enhance the dataset's utility for Deep Learning and Computer Vision tasks, data augmentation techniques were applied. The augmentation process involved operations such as rotation (90°, 180°, 270°), horizontal and vertical flipping, and scaling to increase the sample size and diversity of the data.
Organization:
The dataset is organized into six folders, each corresponding to a specific pest category. This dataset is suitable for training and testing machine learning models in the field of precision agriculture and automated pest detection.

References / Data Sources:
We acknowledge the authors of the original datasets used in this compilation:

[A database of eight common tomato pest images] - Available at: [https://data.mendeley.com/datasets/s62zm6djd2/1]
[TOM2024] - Available at: [https://data.mendeley.com/datasets/3d4yg89rtr/1]
DOI: 10.17632/ysk546n9np.1
title: An Augmented Image Dataset of Six Major Agricultural Pests for Deep Learning

description: This dataset consists of images representing six distinct categories of common agricultural pests. The classes included in this dataset are:
Cotton Bollworm
Oriental Fruit Fly
Peach Aphid
Silverleaf Whitefly
Thrips
Two-spotted Spider Mite
Data Collection and Processing:
The dataset is a compilation of original images curated from open-access repositories and web scraping. A significant portion of the base images was derived from existing Mendeley Data repositories (cited below) and Google Images. To enhance the dataset's utility for Deep Learning and Computer Vision tasks, data augmentation techniques were applied. The augmentation process involved operations such as rotation (90°, 180°, 270°), horizontal and vertical flipping, and scaling to increase the sample size and diversity of the data.
Organization:
The dataset is organized into six folders, each corresponding to a specific pest category. This dataset is suitable for training and testing machine learning models in the field of precision agriculture and automated pest detection.

References / Data Sources:
We acknowledge the authors of the original datasets used in this compilation:

[A database of eight common tomato pest images] - Available at: [https://data.mendeley.com/datasets/s62zm6djd2/1]
[TOM2024] - Available at: [https://data.mendeley.com/datasets/3d4yg89rtr/1]
DOI: 10.17632/dhmvfw9b2r
title: TSSOT

description: Source data files of TSSOT
DOI: 10.15468/dl.hqnb4a
title: Occurrence Download

description: A dataset containing 347 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crocidura pullata"
 ]
}

 The dataset includes 347 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021847-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/dhmvfw9b2r.1
title: TSSOT

description: Source data files of TSSOT
DOI: 10.17632/322bdrmbtv
title: data for Calcination-Free 3D printing of CuO-ZnO-ZrO2 Structured Catalysts for Enhanced CO2 Hydrogenation to Methanol

description: data for Calcination-Free 3D printing of CuO-ZnO-ZrO2 Structured Catalysts for Enhanced CO2 Hydrogenation to Methanol
DOI: 10.6084/m9.figshare.31346884
title: <b>Eukaryotic secreted proteins are encoded in repeat-rich genomic regions.</b>

description: Secretion signals are ancient and functionally conserved sequence motifs that orchestrate function and intended destination of cleaved encoded proteins <sup>1–3</sup>. To investigate the genomic landscape of secreted proteins, 4,694 annotated eukaryotic genome assemblies were analysed.
DOI: 10.6084/m9.figshare.31346884.v1
title: <b>Eukaryotic secreted proteins are encoded in repeat-rich genomic regions.</b>

description: Secretion signals are ancient and functionally conserved sequence motifs that orchestrate function and intended destination of cleaved encoded proteins <sup>1–3</sup>. To investigate the genomic landscape of secreted proteins, 4,694 annotated eukaryotic genome assemblies were analysed.
DOI: 10.17632/322bdrmbtv.1
title: data for Calcination-Free 3D printing of CuO-ZnO-ZrO2 Structured Catalysts for Enhanced CO2 Hydrogenation to Methanol

description: data for Calcination-Free 3D printing of CuO-ZnO-ZrO2 Structured Catalysts for Enhanced CO2 Hydrogenation to Methanol
DOI: 10.15468/dl.f4bg3b
title: Occurrence Download

description: A dataset containing 8852 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crocidura suaveolens"
 ]
}

 The dataset includes 8852 records from 252 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021848-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/tp95mytsyy
title: Employee Social Sustainability and Intention to Join

description: The study aims to explore how specific employee-related social sustainability dimensions impact job seekers' intentions to join an organisation, with generational membership as a moderator and person-organisation fit as a mediator. Utilising a quantitative cross-sectional design with 900 Czech respondents across Generations X, Y, and Z, the study identifies Employee Participation and Employee Development as the primary drivers of attraction, mediated by person-organisation fit. Conversely, it was found that dimensions Health and Safety and Equal Opportunities act only as baseline hygiene factors with no significant impact on the final intention to join. Furthermore, contrary to theoretical expectation, the results reveal that generational membership does not moderate the relationship between perceived social sustainability and the intention to join an organisation.
DOI: 10.17632/tp95mytsyy.1
title: Employee Social Sustainability and Intention to Join

description: The study aims to explore how specific employee-related social sustainability dimensions impact job seekers' intentions to join an organisation, with generational membership as a moderator and person-organisation fit as a mediator. Utilising a quantitative cross-sectional design with 900 Czech respondents across Generations X, Y, and Z, the study identifies Employee Participation and Employee Development as the primary drivers of attraction, mediated by person-organisation fit. Conversely, it was found that dimensions Health and Safety and Equal Opportunities act only as baseline hygiene factors with no significant impact on the final intention to join. Furthermore, contrary to theoretical expectation, the results reveal that generational membership does not moderate the relationship between perceived social sustainability and the intention to join an organisation.
DOI: 10.17632/89dz6w8c4n
title: State-Owned Capital Participation and Deleveraging of Over-Indebted Private-Owned Enterprises: Evidence from China

description: This folder contains Stata code and associated data files for conducting descriptive statistics and regression analysis in the paper, " State-Owned Capital Participation and Deleveraging of Over-Indebted Private-Owned Enterprises: Evidence from China." The data and code are compatible with Stata 15.
DOI: 10.17632/89dz6w8c4n.1
title: State-Owned Capital Participation and Deleveraging of Over-Indebted Private-Owned Enterprises: Evidence from China

description: This folder contains Stata code and associated data files for conducting descriptive statistics and regression analysis in the paper, " State-Owned Capital Participation and Deleveraging of Over-Indebted Private-Owned Enterprises: Evidence from China." The data and code are compatible with Stata 15.
DOI: 10.17632/ydx7v73t3k
title: Screening Protocol and Study Selection Data for: Artificial Intelligence for Women’s Reproductive Health: A Systematic Review of Global Diagnostic Trends and a Translational Informatics Framework for Low-Resource Settings

description: This repository contains the screening protocol and structured study-selection metadata supporting the systematic review titled Artificial Intelligence for Women’s Reproductive and Hormonal Disorders: A Systematic Review of Global Approaches and a Translational Framework for Low-Resource Settings.

Search and Screening Overview :
A total of 273 records were identified through systematic searches across multiple academic databases. Screening was performed in two stages: (i) title and abstract screening (First Pass), followed by (ii) full-text eligibility assessment (Second Pass). Explicit exclusion rationales were documented at each stage.

Final Inclusion :
Of the 273 screened records, 116 studies met the predefined inclusion criteria and were included in the final qualitative synthesis.

Taxonomy Classification :
Each included study was assigned to a predefined taxonomy category reflecting its primary methodological focus, clinical application, and translational relevance to low-resource healthcare settings.

Purpose of Data Sharing :
This dataset is shared to promote transparency, reproducibility, and methodological rigor in accordance with PRISMA 2020 guidelines.
DOI: 10.17632/ydx7v73t3k.2
title: Screening Protocol and Study Selection Data for: Artificial Intelligence for Women’s Reproductive Health: A Systematic Review of Global Diagnostic Trends and a Translational Informatics Framework for Low-Resource Settings

description: This repository contains the screening protocol and structured study-selection metadata supporting the systematic review titled Artificial Intelligence for Women’s Reproductive and Hormonal Disorders: A Systematic Review of Global Approaches and a Translational Framework for Low-Resource Settings.

Search and Screening Overview :
A total of 273 records were identified through systematic searches across multiple academic databases. Screening was performed in two stages: (i) title and abstract screening (First Pass), followed by (ii) full-text eligibility assessment (Second Pass). Explicit exclusion rationales were documented at each stage.

Final Inclusion :
Of the 273 screened records, 116 studies met the predefined inclusion criteria and were included in the final qualitative synthesis.

Taxonomy Classification :
Each included study was assigned to a predefined taxonomy category reflecting its primary methodological focus, clinical application, and translational relevance to low-resource healthcare settings.

Purpose of Data Sharing :
This dataset is shared to promote transparency, reproducibility, and methodological rigor in accordance with PRISMA 2020 guidelines.
DOI: 10.17632/5g5x7dhfzj
title: Mixed Method Study_CHBR

description: This dataset supports the study examining the determinants of e-waste technology adoption in Sri Lanka using a Task–Technology–User Fit (TTUF) framework. The research tests the hypotheses that specific task–technology characteristics (interoperability, usefulness, scalability, flexibility, and innovativeness) influence employee attitudes toward e-waste technologies, and that user competence moderates the relationship between attitude and technology adoption.

Data were collected using a sequential mixed-methods design. The qualitative phase included semi-structured interviews and focus group discussions to identify key task–technology characteristics. The quantitative phase involved a cross-sectional survey of 279 employees from formal and informal e-waste sector businesses in Sri Lanka. All constructs were measured using validated multi-item scales on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Structural Equation Modelling (CB-SEM, lavaan 0.6-19 in R) was used to test the hypotheses.

The data show that usefulness and scalability significantly predict attitude, and user competence strengthens the translation of positive attitudes into technology adoption. The dataset includes the validated survey instrument, interview protocols, labelled dataset, and full SEM scripts to enable replication. Researchers may use these materials to replicate the analysis, extend the TTUF framework, conduct cross-country comparisons, or test alternative theoretical models in technology adoption research.
DOI: 10.17632/5g5x7dhfzj.1
title: Mixed Method Study_CHBR

description: This dataset supports the study examining the determinants of e-waste technology adoption in Sri Lanka using a Task–Technology–User Fit (TTUF) framework. The research tests the hypotheses that specific task–technology characteristics (interoperability, usefulness, scalability, flexibility, and innovativeness) influence employee attitudes toward e-waste technologies, and that user competence moderates the relationship between attitude and technology adoption.

Data were collected using a sequential mixed-methods design. The qualitative phase included semi-structured interviews and focus group discussions to identify key task–technology characteristics. The quantitative phase involved a cross-sectional survey of 279 employees from formal and informal e-waste sector businesses in Sri Lanka. All constructs were measured using validated multi-item scales on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Structural Equation Modelling (CB-SEM, lavaan 0.6-19 in R) was used to test the hypotheses.

The data show that usefulness and scalability significantly predict attitude, and user competence strengthens the translation of positive attitudes into technology adoption. The dataset includes the validated survey instrument, interview protocols, labelled dataset, and full SEM scripts to enable replication. Researchers may use these materials to replicate the analysis, extend the TTUF framework, conduct cross-country comparisons, or test alternative theoretical models in technology adoption research.
DOI: 10.17632/jm378tjnrt
title: Preprocessed Image Dataset of Indian Tea Leaves for Plant Disease Recognition: Helopeltis and Red Spider based

description: Present dataset contains a curated and preprocessed dataset of images of Indian tea leaves developed to facilitate research work in the field of plant disease recognition and automated classification using computer vision and machine learning techniques. The current dataset is dedicated to two important diseases of tea plants, i.e. Helopeltis infection and Red Spider and the healthy tea leaves, which constitute a three class classification problem under real field conditions. The dataset contains RGB images of the tea leaves from Indian regions of tea growing. Images were shot under natural light and background variations in order to maintain the complexity that occurs in the real world and between leaves in terms of leaf orientations, textures, and surface appearance. To improve the robustness and usability of deep learning models, extensive image preprocessing procedures were implemented and, as a result, several transformed versions of the original images were obtained. Three different classes are represented in the dataset: healthy tea leaves which have no known symptoms, tea leaves infested by Helopeltis with evident signs of puncture marks and localised necrotic areas, and tea leaves infested by Red Spider Mite with signs of bronzing, discoloration and surface degradation. Each class is stored as its own directory to enable the easy loading and labeling of models to be trained on the data.
A total of ten preprocessing techniques were implemented on each of the original images to produce a variety of feature representations. These include conversing the image to grayscale, converting the image to binary, Gaussian blurring with kernel sizes of 3x3, 5x5, 7x7, and 9x9, adding Gaussian noise with intensities of 2% and 4%, and rotating the images with an angle of +15deg and -15deg. The inclusion of multiple preprocessing variants means that the influence of noise, texture, intensity and geometrical transformation on disease recognition performance can be studied. All of the images are standard formatted and arranged to ensure consistency between experiments. The dataset can be used for starting tasks such as image classification, impact analysis of preprocessing, feature extraction comparison and benchmarking of deep learning architectures. By providing both original visual features and a variety of preprocessed features, the dataset is favourable for the establishment of robust and generalizable models for plant disease detection. This dataset is published for academic and research purposes and is intended to contribute on precision agriculture, smart farming system/sustainable tea crop management based on data-driven disease diagnosis contribution.