cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31222708
title: <b>StitchingNet-Seg</b>, A semantic segmentation dataset for industrial sewing stitch defects

description: <b>StitchingNet-Seg</b> is a large-scale dataset for semantic segmentation, comprising 10,836 images with pixel-level annotations, designed to advance automated quality inspection in the textile and apparel industry.<b>Motivation</b>Adopting Industry 4.0 technologies in the textile and apparel industry has been historically slow due to the labor-intensive nature of production and the inherent deformability of non-rigid textile materials. While automated quality inspection is well-established for raw fabric defects, sewing stitch defects, which occur dynamically during assembly, still rely heavily on manual visual checks, leading to a defect leakage rate of 5–10% and significant production bottlenecks. Existing datasets often provide only image-level labels or bounding boxes, which are insufficient for the precise geometric analysis needed for high-end quality control. To bridge this gap, we introduce StitchingNet-Seg, a large-scale semantic segmentation dataset comprising 10,836 images with meticulous pixel-level masks across 11 fabric types and 7 defect categories, providing a reliable foundation for developing intelligent Automated Optical Inspection (AOI) systems in the global garment industry.<b>Dataset description</b>The dataset includes diverse sewing conditions and precise semantic masks to ensure model robustness.Total images: 10,836 images (derived and filtered from the original StitchingNet)Fabric varieties: 11 representative fabric types with various textures and colors<br>: A. Cotton-Poly, B. Linen-Poly, C. Denim-Poly, D. Velveteen-Poly, E. Polyester-Poly, F. Satin-Core, G. Chiffon-Poly, H. Nylon-Core, I. Jacquard-Poly, J. Oxford-Core, and K. Polyester (coated)-CoreThread colors: Combinations of similar and contrasting thread colors.Classes: normal and 7 defective types<br>: Normal, 1. Skipped stitch, 2. Broken stitch, 3. Pinched fabric, 4. Crooked seam, 5. Thread sagging, 7. Stain and damage, and 10. Overlapped stitchResolution: 224 × 224 pixels.<b>Creation details</b>Original source: StitchingNet (14,565 sewing stitch images)Time period (filtration and annotation): 2025.02 - 2026.01Annotation: Pixel-level semantic masks created using the Computer Vision Annotation Tool (CVAT)<b>Data records</b>The dataset is organized into a hierarchical structure containing various fabric types and sewing defects. For the convenience of the researchers, we provided original images, segmentation masks, and annotation files in the COCO format.<b>Code examples</b>We provide reference implementation codes (Python and Google Colab notebook) in the code-examples.zip file to help researchers quickly get started with StitchingNet-Seg.<b>License</b>The StitchingNet is licensed under CC BY-NC 4.0. This means it is free for research updates and non-commercial use with proper attribution.
DOI: 10.6084/m9.figshare.31222708.v2
title: <b>StitchingNet-Seg</b>, A semantic segmentation dataset for industrial sewing stitch defects

description: <b>StitchingNet-Seg</b> is a large-scale dataset for semantic segmentation, comprising 10,836 images with pixel-level annotations, designed to advance automated quality inspection in the textile and apparel industry.<b>Motivation</b>Adopting Industry 4.0 technologies in the textile and apparel industry has been historically slow due to the labor-intensive nature of production and the inherent deformability of non-rigid textile materials. While automated quality inspection is well-established for raw fabric defects, sewing stitch defects, which occur dynamically during assembly, still rely heavily on manual visual checks, leading to a defect leakage rate of 5–10% and significant production bottlenecks. Existing datasets often provide only image-level labels or bounding boxes, which are insufficient for the precise geometric analysis needed for high-end quality control. To bridge this gap, we introduce StitchingNet-Seg, a large-scale semantic segmentation dataset comprising 10,836 images with meticulous pixel-level masks across 11 fabric types and 7 defect categories, providing a reliable foundation for developing intelligent Automated Optical Inspection (AOI) systems in the global garment industry.<b>Dataset description</b>The dataset includes diverse sewing conditions and precise semantic masks to ensure model robustness.Total images: 10,836 images (derived and filtered from the original StitchingNet)Fabric varieties: 11 representative fabric types with various textures and colors<br>: A. Cotton-Poly, B. Linen-Poly, C. Denim-Poly, D. Velveteen-Poly, E. Polyester-Poly, F. Satin-Core, G. Chiffon-Poly, H. Nylon-Core, I. Jacquard-Poly, J. Oxford-Core, and K. Polyester (coated)-CoreThread colors: Combinations of similar and contrasting thread colors.Classes: normal and 7 defective types<br>: Normal, 1. Skipped stitch, 2. Broken stitch, 3. Pinched fabric, 4. Crooked seam, 5. Thread sagging, 7. Stain and damage, and 10. Overlapped stitchResolution: 224 × 224 pixels.<b>Creation details</b>Original source: StitchingNet (14,565 sewing stitch images)Time period (filtration and annotation): 2025.02 - 2026.01Annotation: Pixel-level semantic masks created using the Computer Vision Annotation Tool (CVAT)<b>Data records</b>The dataset is organized into a hierarchical structure containing various fabric types and sewing defects. For the convenience of the researchers, we provided original images, segmentation masks, and annotation files in the COCO format.<b>Code examples</b>We provide reference implementation codes (Python and Google Colab notebook) in the code-examples.zip file to help researchers quickly get started with StitchingNet-Seg.<b>License</b>The StitchingNet is licensed under CC BY-NC 4.0. This means it is free for research updates and non-commercial use with proper attribution.
DOI: 10.6084/m9.figshare.31222708.v1
title: <b>StitchingNet-Seg</b>, A semantic segmentation dataset for industrial sewing stitch defects

description: <b>StitchingNet-Seg</b> is a large-scale dataset for semantic segmentation, comprising 10,836 images with pixel-level annotations, designed to advance automated quality inspection in the textile and apparel industry.<b>Motivation</b>Adopting Industry 4.0 technologies in the textile and apparel industry has been historically slow due to the labor-intensive nature of production and the inherent deformability of non-rigid textile materials. While automated quality inspection is well-established for raw fabric defects, sewing stitch defects, which occur dynamically during assembly, still rely heavily on manual visual checks, leading to a defect leakage rate of 5–10% and significant production bottlenecks. Existing datasets often provide only image-level labels or bounding boxes, which are insufficient for the precise geometric analysis needed for high-end quality control. To bridge this gap, we introduce StitchingNet-Seg, a large-scale semantic segmentation dataset comprising 10,836 images with meticulous pixel-level masks across 11 fabric types and 7 defect categories, providing a reliable foundation for developing intelligent Automated Optical Inspection (AOI) systems in the global garment industry.<b>Dataset description</b>The dataset includes diverse sewing conditions and precise semantic masks to ensure model robustness.Total images: 10,836 images (derived and filtered from the original StitchingNet)Fabric varieties: 11 representative fabric types with various textures and colors<br>: A. Cotton-Poly, B. Linen-Poly, C. Denim-Poly, D. Velveteen-Poly, E. Polyester-Poly, F. Satin-Core, G. Chiffon-Poly, H. Nylon-Core, I. Jacquard-Poly, J. Oxford-Core, and K. Polyester (coated)-CoreThread colors: Combinations of similar and contrasting thread colors.Classes: normal and 7 defective types<br>: Normal, 1. Skipped stitch, 2. Broken stitch, 3. Pinched fabric, 4. Crooked seam, 5. Thread sagging, 7. Stain and damage, and 10. Overlapped stitchResolution: 224 × 224 pixels.<b>Creation details</b>Original source: StitchingNet (14,565 sewing stitch images)Time period (filtration and annotation): 2025.02 - 2026.01Annotation: Pixel-level semantic masks created using the Computer Vision Annotation Tool (CVAT)<b>Data records</b>The dataset is organized into a hierarchical structure containing various fabric types and sewing defects. For the convenience of the researchers, we provided original images, segmentation masks, and annotation files in the COCO format.<b>Code examples</b>We provide reference implementation codes in the code-examples.zip file to help researchers quickly get started with StitchingNet-Seg.<b>License</b>The StitchingNet is licensed under CC BY-NC 4.0. This means it is free for research updates and non-commercial use with proper attribution.
DOI: 10.15468/dl.7gvcgn
title: Occurrence Download

description: A dataset containing 19881 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Primula farinosa, Primula farinosa, Primula farinosa, Primula farinosa, Primula farinosa)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 19881 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008806-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18463965
title: Raw Data_The Association Between Coaching Behaviors and Athlete Burnout Among College Athletes: An Integrated SEM–ANN Model Based on Self-Determination Theory

description: This dataset was collected through an online questionnaire-based survey administered via the Sojump platform between November 2025 and January 2026. Participants were college student athletes enrolled in physical education and sport-related programs at universities in several regions of China. The study aimed to examine how autonomy-supportive and controlling coaching behaviors are associated with athlete burnout and to test the mediating roles of basic psychological needs (autonomy, competence, and relatedness) within the framework of Self-Determination Theory. A probability-based random sampling strategy was used to enhance representativeness and reduce selection bias. After data screening, 800 valid responses were retained for analysis.

 

The dataset includes demographic variables (e.g., gender, age, and academic grade level) and multiple validated psychometric measures. The core constructs include autonomy-supportive coaching behaviors, controlling coaching behaviors, autonomy, competence, relatedness, and athlete burnout. All variables were measured using standardized Likert-type instruments with established reliability and validity in sport and psychology research. The data were analyzed using a two-stage approach integrating partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis to support both theory testing and predictive modeling.

 

This dataset is suitable for research in sport psychology, coaching science, and athlete mental health. It is particularly useful for examining motivational climate, psychological need satisfaction, and burnout mechanisms in competitive sport contexts, and it enables both theory-driven (linear) and prediction-oriented (nonlinear) analyses.
DOI: 10.5281/zenodo.18463964
title: Raw Data_The Association Between Coaching Behaviors and Athlete Burnout Among College Athletes: An Integrated SEM–ANN Model Based on Self-Determination Theory

description: This dataset was collected through an online questionnaire-based survey administered via the Sojump platform between November 2025 and January 2026. Participants were college student athletes enrolled in physical education and sport-related programs at universities in several regions of China. The study aimed to examine how autonomy-supportive and controlling coaching behaviors are associated with athlete burnout and to test the mediating roles of basic psychological needs (autonomy, competence, and relatedness) within the framework of Self-Determination Theory. A probability-based random sampling strategy was used to enhance representativeness and reduce selection bias. After data screening, 800 valid responses were retained for analysis.

 

The dataset includes demographic variables (e.g., gender, age, and academic grade level) and multiple validated psychometric measures. The core constructs include autonomy-supportive coaching behaviors, controlling coaching behaviors, autonomy, competence, relatedness, and athlete burnout. All variables were measured using standardized Likert-type instruments with established reliability and validity in sport and psychology research. The data were analyzed using a two-stage approach integrating partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis to support both theory testing and predictive modeling.

 

This dataset is suitable for research in sport psychology, coaching science, and athlete mental health. It is particularly useful for examining motivational climate, psychological need satisfaction, and burnout mechanisms in competitive sport contexts, and it enables both theory-driven (linear) and prediction-oriented (nonlinear) analyses.
DOI: 10.15468/dl.u4zsun
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cambaroides dauricus"
 ]
}

 The dataset includes 12 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008807-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ggug5j
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Polymixiiformes, , , , , , , , , , , )",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((109.630957 19.966667,110.78571 19.966667,110.78571 21.783333,109.630957 21.783333,109.630957 19.966667))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008808-260129131611470/datasets/export for details.
DOI: 10.15468/dl.mcv2d9
title: Occurrence Download

description: A dataset containing 194 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Procambarus clarkii"
 ]
}

 The dataset includes 194 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008809-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gebnnr
title: Occurrence Download

description: A dataset containing 113 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Craseonycteris thonglongyai"
 ]
}

 The dataset includes 113 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008810-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60703/24kp-q821
title: Death Registrations (Western Australia Registry of Births, Deaths and Marriages)
title: RBDM Death Registrations (WA)

description: The Death Registration Statements contains the details prescribed by Regulation, including but not limited to: demographic details such as name at birth, Aboriginal/Torres Strait Islander status, sex, occupation, date and place of birth; age at death; date of death; place of death; cause of death; marital status. The registration of a death requires a death registration statement and either a medical report or coroner's report.
DOI: 10.15468/dl.ahyym5
title: Occurrence Download

description: A dataset containing 317 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Primula matthioli, Primula matthioli, Primula matthioli, Cortusa matthioli)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 317 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008811-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15483/3015758
title: FleetREDI: Mail Delivery Class 6 Propane Trucks

description: Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks.

Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles while producing significantly less emissions of tailpipe oxides of nitrogen (NOx). The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks.

![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)
DOI: 10.17605/osf.io/c8thj
title: A data recapitulation of 543 participants

description: A data recapitulation of 543 undergraduates who have completed the Sustainable Gratitude Based on Theistic Spiritual Values Scale (SGBTSV-45)
DOI: 10.15468/dl.cdzyhv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (, , , , , , , , , , , , , , , , , Polypteriformes, , , , , , , , , , , , , , , , , , , , , , )",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((109.630957 19.966667,110.78571 19.966667,110.78571 21.783333,109.630957 21.783333,109.630957 19.966667))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008812-260129131611470/datasets/export for details.
DOI: 10.15468/dl.v4q8zp
title: Occurrence Download

description: A dataset containing 3271 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Primula minima, Primula minima)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 3271 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008813-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.enxv7d
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , )",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((109.630957 19.966667,110.78571 19.966667,110.78571 21.783333,109.630957 21.783333,109.630957 19.966667))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008814-260129131611470/datasets/export for details.
DOI: 10.15483/3015759
title: Baseline vs. DER Scenario

description: Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).
DOI: 10.5281/zenodo.18463822
title: Experimental Dataset
DOI: 10.5281/zenodo.18463823
title: Experimental Dataset
DOI: 10.15483/3015760
title: 220EV Box Truck Telemetry Dataset

description: This dataset contains telemetry data from the 220EV box truck as provided by the manufacturer, Dana. Data are recorded in a time series interval of 1 second. Recorded data contain odometer reading (kilometers), axle speed, state of charge of the battery, and cabin heater setting.
DOI: 10.15483/3015761
title: ChargePoint Level 2 Charging Data

description: This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
DOI: 10.15483/3015762
title: SWS Building Electric Demand Profile

description: This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
DOI: 10.15468/dl.ppu4uk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (, , , , , , , , , , , , Pristiophoriformes, , , , , , , , , , , , , , , , , , , )",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((109.630957 19.966667,110.78571 19.966667,110.78571 21.783333,109.630957 21.783333,109.630957 19.966667))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008816-260129131611470/datasets/export for details.