cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/y7x39rvrwm.3
title: Dataset :Computational thinking related in educational robotic-supported programming activities

description: Research Hypothesis:
This study hypothesizes that students’ computational thinking (CT) development in educational robotics-supported programming learning environments is influenced by both internal psychological factors (learning motivation, cognitive engagement, cognitive load, and prior programming knowledge) and external technology-related factors (task-technology fit, perceived usefulness, and perceived ease of use). The study integrates the Cognitive-Affective Theory of Multimedia Learning (CATLM), the Technology Acceptance Model (TAM), and the Task-Technology Fit (TTF) model into a unified theoretical framework.

Data Summary:
The dataset includes responses to 54 items measuring seven constructs:

Computational Thinking (CT)

Learning Motivation

Cognitive Load

Cognitive Engagement

Prior Programming Knowledge (measured via a Python programming test)

Perceived Usefulness (PU)

Perceived Ease of Use (PEU)

Task-Technology Fit (TTF)

All Likert-scale responses were rated on a 5-point scale (1 = strongly disagree, 5 = strongly agree). The Python programming test consists of 10 multiple-choice questions assessing basic programming skills (e.g., loops, conditions), scored from 0 to 10.

Data Collection Method:
The data were collected online using the Wenjuanxing platform in March 2025. Participants completed a structured questionnaire and a Python fundamentals test. All participants had prior experience with educational robotics in programming learning. The instruments used were adapted from validated scales and reviewed by experts. Participation was anonymous and voluntary, with informed consent obtained from all participants and guardians.

Notable Findings:

The proposed model explains 54.1% of the variance in computational thinking.

Learning motivation and task-technology fit are the strongest predictors of computational thinking.

Task-technology fit significantly influences perceived usefulness and ease of use, enhancing technology acceptance.

Prior programming knowledge reduces cognitive load and positively impacts computational thinking.

Cognitive engagement positively mediates learning outcomes, while cognitive load exerts a negative effect on engagement.

How to Interpret the Data:

Each row represents an individual student’s complete response.

Items are grouped by construct and follow a consistent coding scheme.

Higher scores reflect more positive perceptions or stronger abilities (e.g., higher CT or motivation).

Factor scores can be used to compute latent variables or replicate the structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses.

Potential Uses:

Reproducing or extending the SEM/ANN analyses presented in the study.

Exploring the relationships between cognitive, motivational, and technological factors in programming education.

Informing instructional design and technology integration strategies in STEM or robotics education contexts.
DOI: 10.15468/dl.gtm6ww
title: Occurrence Download

description: A dataset containing 15912 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Androsace alpina"
 ]
}

 The dataset includes 15912 records from 187 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021226-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16260991
title: Feature-Enhanced Graph Neural Networks for Classification of Synthetic Graph Generative Models: A Benchmarking Study

description: Dataset associated with the study: 'Feature-Enhanced Graph Neural Networks for Classification of Synthetic Graph Generative Models: A Benchmarking Study'.

Dataset consists of two folders: 'graph_features.zip' and 'graph_objects.zip'. These should be downloaded to the data/ folder within the project folder, and extracted.
DOI: 10.5281/zenodo.16260992
title: Feature-Enhanced Graph Neural Networks for Classification of Synthetic Graph Generative Models: A Benchmarking Study

description: Dataset associated with the study: 'Feature-Enhanced Graph Neural Networks for Classification of Synthetic Graph Generative Models: A Benchmarking Study'.

Dataset consists of two folders: 'graph_features.zip' and 'graph_objects.zip'. These should be downloaded to the data/ folder within the project folder, and extracted.
DOI: 10.17632/zw955wpv82.1
title: Toenail matrix graft reconstruction for pediatric fingernail longitudinal melanonychia &gt; 3 mm:Evaluation of the method in a series of 7 cases

description: Supplementary Figure 1. Nail matrix transplantation for the treatment of longitudinal melanonychia (LM). 1A, LM on the fingernail. 1B, Surgical avulsion of the nail plate and complete excision of the pigmented lesion down to the periosteum of the distal phalanx. 1C, Harvesting a size-matched composite graft (nail matrix or matrix-bed) from the hallux or second toe. 1D, Microsurgical implantation of the toenail matrix or matrix-bed graft into the surgical defect after lesion excision. E, Reattach the avulsed fingernail plate to its anatomical position using absorbable sutures(8-0 polyglactin), and then apply a protective dressing. F, Postoperatively, immobilize using a sterile composite dressing system to promote graft adherence and survival.
Supplementary table 1. Patients demographic and clinical characteristics.
Supplementary Fig 2A-G. Toenail matrix-bed transplantation for the treatment of longitudinal melanonychia (LM). 2A. Preoperative photograph of the LM on left ring finger. 2B. Intraoperative findings revealed the pigment involved both the nail matrix and the nail bed. 2C. Complete excision of the pigmented nail matrix-bed from the finger. 2D. A size-matched nail matrix-bed graft was harvested from the right great toe. 2E. The harvested composite graft of the great toe nail matrix-bed was transplanted to the surgical defect after resection. 2F-2G. Postoperative outcomes demonstrating no recurrence of melanonychia or nail dystrophy in the reconstructed fingernail, with preserved toe donor-site morphology at 30-month postoperative follow-up.
Supplementary Fig 3A-G. Toenail matrix transplantation for the treatment of longitudinal melanonychia (LM). 3A. Preoperative photograph of the LM on right middle finger. 3B. Intraoperative findings revealed the pigment involved both the nail matrix and the nail bed. 3C. Complete excision of the pigmented nail matrix-bed from the finger. 3D. A size-matched nail matrix-bed graft was harvested from the right great toe. 3E. The harvested composite graft of the great toe nail matrix-bed was transplanted to the surgical defect after resection. 3F-3G. Postoperative outcomes demonstrating no recurrence of melanonychia but nail longitudinal ridges in the reconstructed fingernail, with preserved toe donor-site morphology at 18-month postoperative follow-up.
DOI: 10.15468/dl.nbqzey
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Buteogallus anthracinus)",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021233-250717081556266/datasets/export for details.
DOI: 10.17632/z4rw52g5nn
title: Relative Importance of Self-Disgust as a Predictor of Self-Injurious Thoughts and Behaviors among Young Adults

description: Introduction: Self-injurious thoughts and behaviors (SITBs) are prevalent among young adults. This study aimed to explore self-disgust as a potential correlate and predictor of SITBs. Methods: Participants were recruited on a college campus. One hundred and sixteen participants (62% female-identifying, 51% Caucasian, 40% LGBTQ+, Mage = 19.68, SD = 1.41) completed assessments. Results: Participants with a history of SITBs had significantly higher self-disgust scores than those without. Self-disgust was significantly associated with other demographic (e.g., LGBTQ+ identity), psychological (e.g., experiential avoidance), and clinical (e.g., history of psychopathology) variables. Multivariable logistic regression analyses showed that even when adjusting for other strong correlates, self-disgust remained a significant predictor of SITB history (yes/no). Further, self-disgust outperformed all other variables in the model on multiple metrics of relative importance. Conclusions: Findings suggest the significance of self-disgust as an important emotion in further understanding, and potentially predicting, SITBs among young adults.
DOI: 10.17632/z4rw52g5nn.1
title: Relative Importance of Self-Disgust as a Predictor of Self-Injurious Thoughts and Behaviors among Young Adults

description: Introduction: Self-injurious thoughts and behaviors (SITBs) are prevalent among young adults. This study aimed to explore self-disgust as a potential correlate and predictor of SITBs. Methods: Participants were recruited on a college campus. One hundred and sixteen participants (62% female-identifying, 51% Caucasian, 40% LGBTQ+, Mage = 19.68, SD = 1.41) completed assessments. Results: Participants with a history of SITBs had significantly higher self-disgust scores than those without. Self-disgust was significantly associated with other demographic (e.g., LGBTQ+ identity), psychological (e.g., experiential avoidance), and clinical (e.g., history of psychopathology) variables. Multivariable logistic regression analyses showed that even when adjusting for other strong correlates, self-disgust remained a significant predictor of SITB history (yes/no). Further, self-disgust outperformed all other variables in the model on multiple metrics of relative importance. Conclusions: Findings suggest the significance of self-disgust as an important emotion in further understanding, and potentially predicting, SITBs among young adults.
DOI: 10.17632/wxmyj2sy42
title: Houston Meyerland Street-Level Image Dataset (HMSLID)

description: This dataset comprises a novel collection of 92,725 street-level images sourced from Google Street View (GSV), meticulously curated to serve as a high-precision reference database for disaster image geolocalization research. The dataset covers approximately 15 square miles (39 square km) of the Meyerland district in Houston, Texas, a region historically prone to significant flooding, including major events like the Memorial Day flood (2015), Tax Day flood (2016), and Hurricane Harvey (2017).

Images were systematically collected by first extracting road data from OpenStreetMap within a defined boundary box (Latitude: 29.70344981°N to 29.6527425°S; Longitude: -95.43105637°E to -95.5026166°W). Points were then generated along these roads at 11-meter intervals, yielding 24,201 distinct GPS coordinates. For each coordinate, four 640x640 pixel GSV images were downloaded via the GSV Image API, capturing headings of 0°, 90°, 180°, and 270°. A temporal filter was applied to include only images from 2017 and prior, ensuring relevance to the Hurricane Harvey disaster context.

Unlike broader, less specific datasets, this Meyerland reference database offers a localized scope with known geopositions for robust quantitative evaluation of image geolocation techniques in challenging, flood-affected urban environments. It aims to address the limitations of existing datasets and accelerate advancements in disaster informatics and computer vision applications for real-time emergency response.
DOI: 10.17632/wxmyj2sy42.2
title: Houston Meyerland Street-Level Image Dataset (HMSLID)

description: This dataset comprises a novel collection of 92,725 street-level images sourced from Google Street View (GSV), meticulously curated to serve as a high-precision reference database for disaster image geolocalization research. The dataset covers approximately 15 square miles (39 square km) of the Meyerland district in Houston, Texas, a region historically prone to significant flooding, including major events like the Memorial Day flood (2015), Tax Day flood (2016), and Hurricane Harvey (2017).

Images were systematically collected by first extracting road data from OpenStreetMap within a defined boundary box (Latitude: 29.70344981°N to 29.6527425°S; Longitude: -95.43105637°E to -95.5026166°W). Points were then generated along these roads at 11-meter intervals, yielding 24,201 distinct GPS coordinates. For each coordinate, four 640x640 pixel GSV images were downloaded via the GSV Image API, capturing headings of 0°, 90°, 180°, and 270°. A temporal filter was applied to include only images from 2017 and prior, ensuring relevance to the Hurricane Harvey disaster context.

Unlike broader, less specific datasets, this Meyerland reference database offers a localized scope with known geopositions for robust quantitative evaluation of image geolocation techniques in challenging, flood-affected urban environments. It aims to address the limitations of existing datasets and accelerate advancements in disaster informatics and computer vision applications for real-time emergency response.
DOI: 10.17632/wxzyxnmsw7
title: Dataset for Trans-scale Interphase Engineering for Enhanced Interfacial Reinforcement Between ‘Rigid’ HMX Crystals and ‘Soft’ Binders

description: This dataset quantifies interfacial parameters governing mechanical enhancement in octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX)/fluoropolymer F2314 composites through: (1) nanoscale topography of surface-patterned HMX via tapping-mode atomic force microscopy (AFM; Horiba XploRA Nano, 3 × 3 μm² scan fields), (2) modulus distribution across interfaces mapped by PeakForce QNM AFM (Bruker Dimension Icon, 512 × 512 pixels, 5 × 5 μm² fields), and (3) interfacial failure modes analyzed via cryofractured surface morphology (SEM; Zeiss Ultra 55, 2 kV). The compiled dataset comprises 10 topography scans, 10 modulus maps, and 10 high-resolution SEM images, collectively validating a trans-scale interphase architecture that establishes continuous modulus gradients between HMX and F2314 phases. These data enable calibration of multiscale failure models, design of biomimetic interfaces with mechanical property gradients, and optimization of energetic materials balancing detonation performance with structural integrity.
DOI: 10.17632/wxzyxnmsw7.1
title: Dataset for Trans-scale Interphase Engineering for Enhanced Interfacial Reinforcement Between ‘Rigid’ HMX Crystals and ‘Soft’ Binders

description: This dataset quantifies interfacial parameters governing mechanical enhancement in octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX)/fluoropolymer F2314 composites through: (1) nanoscale topography of surface-patterned HMX via tapping-mode atomic force microscopy (AFM; Horiba XploRA Nano, 3 × 3 μm² scan fields), (2) modulus distribution across interfaces mapped by PeakForce QNM AFM (Bruker Dimension Icon, 512 × 512 pixels, 5 × 5 μm² fields), and (3) interfacial failure modes analyzed via cryofractured surface morphology (SEM; Zeiss Ultra 55, 2 kV). The compiled dataset comprises 10 topography scans, 10 modulus maps, and 10 high-resolution SEM images, collectively validating a trans-scale interphase architecture that establishes continuous modulus gradients between HMX and F2314 phases. These data enable calibration of multiscale failure models, design of biomimetic interfaces with mechanical property gradients, and optimization of energetic materials balancing detonation performance with structural integrity.
DOI: 10.25387/g3.31158451
title: Supplementary Material for Marconcini et al., 2026

description: <b>File S3.</b> Raw data for octanoic acid resistance GWAS.<b>File S5.</b> Plink input files for GWAS analyses.
DOI: 10.25387/g3.31158451.v1
title: Supplementary Material for Marconcini et al., 2026

description: <b>File S3.</b> Raw data for octanoic acid resistance GWAS.<b>File S5.</b> Plink input files for GWAS analyses.
DOI: 10.5281/zenodo.17523714
title: Metadata-driven identification of high-temperature superconductor candidates

description: A dataset containing information on 1,060,019 potential quaternary hydride superconductors. It belongs to the publication "Metadata-driven identification ofhigh-temperature superconductor candidates" - Sci. Rep. 15, 44858 (2025).
DOI: 10.5281/zenodo.17092127
title: Metadata-driven identification of high-temperature superconductor candidates

description: A dataset containing information on 1,060,019 potential quaternary hydride superconductors. It belongs to the publication "Metadata-driven identification ofhigh-temperature superconductor candidates" - Sci. Rep. 15, 44858 (2025).
DOI: 10.15468/dl.knprmg
title: Occurrence Download

description: A dataset containing 4603419 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= -37.42857426825602
 AND decimallatitude &lt;= -33.06103686093294
 AND decimallongitude &gt;= 144.71903012755376
 AND decimallongitude &lt;= 155.42312884394758
 AND GBIF_Within('POLYGON ((155.423129 -37.428574, 155.423129 -33.061037, 144.719030 -33.061037, 144.719030 -37.428574, 155.423129 -37.428574))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 4603419 records were derived from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016592-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18507484
title: PREDICTIVE ANALYTICS IN CARDIOVASCULAR HEALTH: A MACHINE LEARNING APPROACH TO DIAGNOSTIC OPTIMIZATION

description: Early detection of cardiovascular disease (CVD) is critical for reducing global mortality rates. This study evaluates the comparative efficacy of K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Random Forest classifiers in a clinical diagnostic setting. A central focus of this research was the optimization of the data preprocessing pipeline. We demonstrate that the selection of imputation strategies significantly alters model performance. Specifically, replacing Median Imputation with Mean Imputation resulted in a critical accuracy improvement from 86.00% to 88.00% in the best-performing model. While KNN showed superior performance during validation, the Naive Bayes model demonstrated the highest robustness on unseen test data, establishing it as the most reliable architecture for this specific domain.
DOI: 10.5281/zenodo.18507485
title: PREDICTIVE ANALYTICS IN CARDIOVASCULAR HEALTH: A MACHINE LEARNING APPROACH TO DIAGNOSTIC OPTIMIZATION

description: Early detection of cardiovascular disease (CVD) is critical for reducing global mortality rates. This study evaluates the comparative efficacy of K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Random Forest classifiers in a clinical diagnostic setting. A central focus of this research was the optimization of the data preprocessing pipeline. We demonstrate that the selection of imputation strategies significantly alters model performance. Specifically, replacing Median Imputation with Mean Imputation resulted in a critical accuracy improvement from 86.00% to 88.00% in the best-performing model. While KNN showed superior performance during validation, the Naive Bayes model demonstrated the highest robustness on unseen test data, establishing it as the most reliable architecture for this specific domain.
DOI: 10.15468/dl.v2su7r
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-26.83673817979313 -89.99999991246035, -26.842850860862104 -89.99999991245015, -26.850148672601023 -89.99999991244194, -26.85835014434436 -89.99999991243608, -26.86714093102249 -89.99999991243278, -26.87618492572805 -89.99999991243217, -26.88513546652492 -89.99999991243423, -26.893648261937305 -89.99999991243895, -26.901396575141256 -89.99999991244611, -26.90808403737545 -89.99999991245545, -26.913453589737998 -89.99999991246658, -26.917298306510816 -89.99999991247911, -26.91947057433236 -89.99999991249254, -26.919885144213715 -89.99999991250637, -26.918526089827264 -89.99999991252004, -26.91544405007308 -89.99999991253306, -26.910758096047473 -89.9999999125449, -26.904646721588513 -89.99999991255511, -26.89734457638201 -89.99999991256333, -26.889133784100505 -89.99999991256918, -26.880330113541667 -89.99999991257249, -26.87127163408045 -89.99999991257312, -26.862307207938496 -89.99999991257103, -26.853783243853908 -89.99999991256632, -26.84602818376791 -89.99999991255915, -26.839339417227606 -89.99999991254981, -26.83397420188978 -89.99999991253866, -26.830138806262394 -89.9999999125261, -26.827979416711997 -89.99999991251269, -26.82757933458548 -89.99999991249885, -26.828952269908434 -89.99999991248518, -26.83204548001173 -89.99999991247218, -26.83673817979313 -89.99999991246035))",
 "KingdomKey is one of (Animalia, Fungi, Plantae)",
 "Year is one of (2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016601-260129131611470/datasets/export for details.
DOI: 10.15468/dl.6abmza
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phymatolithon lusitanicum"
 ]
}

 The dataset includes 70 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016602-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18507461
title: LINF_320032500

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18507462
title: LINF_320032500

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)