cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15494329
title: Intermittent In-situ Uniaxial Compression of Foam Glasses under Micro-CT scan datasets - Sample D

description: Dataset Description:

This dataset contains micro-computed tomography (micro-CT) scans of porous foam glass specimens subjected to intermittent in-situ compression testing. The measurements were performed using an Xradia 510 Versa micro-CT scanner in combination with a Deben CT5000 in-situ compression cell.

Each specimen underwent five scans corresponding to different loading stages: an initial reference scan without applied compression (Stage 0), followed by four subsequent scans under increasing compressive loads.

This dataset specifically corresponds to one specimen out of a total of five scanned in the overall experimental campaign.
description: Measurement parameters:

Camera binning: 2Image size: 1024x1024Source setting: - Applied voltage: 80 kV - Current: 88 µA - Power: 7 WEffective pixel size: 10 µmOptical magnification: 0.4XExposure time: 10 s

Reconstruction settings:

Beam hardening correction: 0.05Filter: smooth - σ: 0.5Byte scaling: Min: -0.1; Max: 0.25
description: Folder and File Structure:

The dataset is organized by specimen (A, B, C, D and E).Within the folder for each particular specimen, five subfolders (labeled 0 to 4) contain reconstructed slice data for each compression stage.

Folder 0: Reference scan with no applied load.Folders 1–4: Scans acquired during successive compression stages. Each folder is named using the format:  '#COMPRESSION_IDENTER-DISPLACEMENT[µm]', indicating the stage and the corresponding indenter displacement.

In addition to the dataset itself (reconstructed image slices), each compression stage folder contains: - A .png file with acquisition parameters - A .png file with reconstruction settings - A .png preview of the 3D reconstructed volume
DOI: 10.5281/zenodo.15494330
title: Intermittent In-situ Uniaxial Compression of Foam Glasses under Micro-CT scan datasets - Sample D

description: Dataset Description:

This dataset contains micro-computed tomography (micro-CT) scans of porous foam glass specimens subjected to intermittent in-situ compression testing. The measurements were performed using an Xradia 510 Versa micro-CT scanner in combination with a Deben CT5000 in-situ compression cell.

Each specimen underwent five scans corresponding to different loading stages: an initial reference scan without applied compression (Stage 0), followed by four subsequent scans under increasing compressive loads.

This dataset specifically corresponds to one specimen out of a total of five scanned in the overall experimental campaign.
description: Measurement parameters:

Camera binning: 2Image size: 1024x1024Source setting: - Applied voltage: 80 kV - Current: 88 µA - Power: 7 WEffective pixel size: 10 µmOptical magnification: 0.4XExposure time: 10 s

Reconstruction settings:

Beam hardening correction: 0.05Filter: smooth - σ: 0.5Byte scaling: Min: -0.1; Max: 0.25
description: Folder and File Structure:

The dataset is organized by specimen (A, B, C, D and E).Within the folder for each particular specimen, five subfolders (labeled 0 to 4) contain reconstructed slice data for each compression stage.

Folder 0: Reference scan with no applied load.Folders 1–4: Scans acquired during successive compression stages. Each folder is named using the format:  '#COMPRESSION_IDENTER-DISPLACEMENT[µm]', indicating the stage and the corresponding indenter displacement.

In addition to the dataset itself (reconstructed image slices), each compression stage folder contains: - A .png file with acquisition parameters - A .png file with reconstruction settings - A .png preview of the 3D reconstructed volume
DOI: 10.17632/5rzjd96w7f
title: Mean-Mouth
DOI: 10.17632/5rzjd96w7f.1
title: Mean-Mouth
DOI: 10.5281/zenodo.15494341
title: Intermittent In-situ Uniaxial Compression of Foam Glasses under Micro-CT scan datasets - Sample E

description: Dataset Description:

This dataset contains micro-computed tomography (micro-CT) scans of porous foam glass specimens subjected to intermittent in-situ compression testing. The measurements were performed using an Xradia 510 Versa micro-CT scanner in combination with a Deben CT5000 in-situ compression cell.

Each specimen underwent five scans corresponding to different loading stages: an initial reference scan without applied compression (Stage 0), followed by four subsequent scans under increasing compressive loads.

This dataset specifically corresponds to one specimen out of a total of five scanned in the overall experimental campaign.
description: Measurement parameters:

Camera binning: 2Image size: 1024x1024Source setting: - Applied voltage: 80 kV - Current: 88 µA - Power: 7 WEffective pixel size: 10 µmOptical magnification: 0.4XExposure time: 10 s

Reconstruction settings:

Beam hardening correction: 0.05Filter: smooth - σ: 0.5Byte scaling: Min: -0.1; Max: 0.25
description: Folder and File Structure:

The dataset is organized by specimen (A, B, C, D and E).Within the folder for each particular specimen, five subfolders (labeled 0 to 4) contain reconstructed slice data for each compression stage.

Folder 0: Reference scan with no applied load.Folders 1–4: Scans acquired during successive compression stages. Each folder is named using the format:  '#COMPRESSION_IDENTER-DISPLACEMENT[µm]', indicating the stage and the corresponding indenter displacement.

In addition to the dataset itself (reconstructed image slices), each compression stage folder contains: - A .png file with acquisition parameters - A .png file with reconstruction settings - A .png preview of the 3D reconstructed volume
DOI: 10.5281/zenodo.15494342
title: Intermittent In-situ Uniaxial Compression of Foam Glasses under Micro-CT scan datasets - Sample E

description: Dataset Description:

This dataset contains micro-computed tomography (micro-CT) scans of porous foam glass specimens subjected to intermittent in-situ compression testing. The measurements were performed using an Xradia 510 Versa micro-CT scanner in combination with a Deben CT5000 in-situ compression cell.

Each specimen underwent five scans corresponding to different loading stages: an initial reference scan without applied compression (Stage 0), followed by four subsequent scans under increasing compressive loads.

This dataset specifically corresponds to one specimen out of a total of five scanned in the overall experimental campaign.
description: Measurement parameters:

Camera binning: 2Image size: 1024x1024Source setting: - Applied voltage: 80 kV - Current: 88 µA - Power: 7 WEffective pixel size: 10 µmOptical magnification: 0.4XExposure time: 10 s

Reconstruction settings:

Beam hardening correction: 0.05Filter: smooth - σ: 0.5Byte scaling: Min: -0.1; Max: 0.25
description: Folder and File Structure:

The dataset is organized by specimen (A, B, C, D and E).Within the folder for each particular specimen, five subfolders (labeled 0 to 4) contain reconstructed slice data for each compression stage.

Folder 0: Reference scan with no applied load.Folders 1–4: Scans acquired during successive compression stages. Each folder is named using the format:  '#COMPRESSION_IDENTER-DISPLACEMENT[µm]', indicating the stage and the corresponding indenter displacement.

In addition to the dataset itself (reconstructed image slices), each compression stage folder contains: - A .png file with acquisition parameters - A .png file with reconstruction settings - A .png preview of the 3D reconstructed volume
DOI: 10.21227/d18h-ya67
title: "Network attack detection datase"

description: "The datasets utilized in this experiment are sourced from CICIoMT2024 and CI-CIDS2017, which are classic experimental datasets for network attack detection [29-30].The CICIoMT2024 dataset is a IoMT dataset collected from 25 real medical devices and 15 virtual devices. The initial dataset encompasses 5 major categories and 18 sub-categories of attacks, including DDoS, DoS, scanning attacks, MQTT, and Spoofing at-tacks, among others, totaling over 8.77 million samples and comprising 44 traffic features.The initial CICIDS2017 dataset contains 7 major categories and 33 subcategories of attacks, such as DOS, brute force, web attacks, and botnets, amounting to 2.5 million samples and including 79 network flow features, such as flow duration, packet count, packet size, and flow rate.To validate the effectiveness of the algorithm proposed in this paper, we employed the Dirichlet distribution sampling method to extract a subset of samples from the complete data, serving as the training and testing sets. The sample distribution is illustrated in Tables 1-4. It is evident from the columnar data comparison that the data distribution among clients exhibits Non-IID characteristics. From a row-wise perspective, the distribution of different categories of data is highly imbalanced, with each client harboring a minority of samples. For instance, the number of class 0 data on client 1, relative to other categories, is only 71 samples, which is regarded as a minority class sample."
DOI: 10.15468/dl.p7ayne
title: Occurrence Download

description: A dataset containing 911 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ptinus tectus Boieldieu, 1856"
 ]
}

 The dataset includes 911 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015270-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29134952
title: fig、

description: Data processing
DOI: 10.6084/m9.figshare.29134952.v1
title: fig、

description: Data processing
DOI: 10.15468/dl.r8hxkq
title: Occurrence Download

description: A dataset containing 205 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Circulifer tenellus (Baker, 1896)"
 ]
}

 The dataset includes 205 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015274-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f3t3dw
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Occurrence evidence)",
 "TaxonKey is Stenoma catenifer Walsingham, 1912"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015273-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28942985
title: <b>Impacts of a Novel Multi-User Virtual Reality Platform on English Vocabulary Learning among Junior High School Students</b>

description: Virtual reality (VR)-assisted language learning has been trending in recent years, with many scholars exploring numerous possibilities of applying VR in L2 vocabulary learning. However, most previous empirical studies in VR-assisted vocabulary learning (VRVL) focused on single-user VR learning modes rather than multi-user VR (MUVR). To bridge this gap, the present study utilized a novel, high-fidelity, customizable, game-based MUVR platform on desktop computers. A quasi-experiment was first conducted with 50 Grade 8 students from a mixed-gender junior high school in Mainland China. Both the experimental and the control groups contained students at three different vocabulary proficiency levels (high, intermediate, and low level). Following the experiment, all 25 participants in the experimental group were invited to an individual semi-structured interview to share their perceptions. The results showed that both vocabulary gain and retention in the experimental group were significantly higher than those in the control group. Also, the study found no significant differences in learning outcomes between genders, whereas performance varied significantly across vocabulary proficiency levels. Qualitative analysis revealed that most students expressed positive attitudes towards the MUVR-assisted vocabulary learning, though some noted technical challenges. Furthermore, three new constructs—collaborative learning, motivation, and co-presence—emerged from the study, contributing to an expanded framework for MUVR-assisted vocabulary learning, which builds upon the existing theoretical framework of VRVL.
DOI: 10.15468/dl.knsts3
title: Occurrence Download

description: A dataset containing 5584 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ahaetulla Link, 1807"
 ]
}

 The dataset includes 5584 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015276-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25421/yorksj.28763495
title: Understanding the Behaviors and Detecting the Mental Health Symptoms Among Students Using Machine Learning Algorithms

description: This project is about the mental health of international students in the UK using Machine Learning Algorithms.
DOI: 10.25421/yorksj.28763495.v1
title: Understanding the Behaviors and Detecting the Mental Health Symptoms Among Students Using Machine Learning Algorithms

description: This project is about the mental health of international students in the UK using Machine Learning Algorithms.
DOI: 10.6084/m9.figshare.29107607
title: Innovative work behavior of community nurses: a scoping review protocol.

description: This is a protocol for a scoping review study that explores and describes research studies that examined innovative (work) behavior in the nursing context with special focus on home caring nurses.
DOI: 10.6084/m9.figshare.29069420
title: <b>Title-Detection method of PPE wearing and small target positioning for offshore operators: The improved YOLOv11 model and targets recognition</b><b>Short Title-Detection method of PPE wearing and small target positioning for offshore operators</b>

description: Focusing on the practical challenges of insufficient samples, incomplete categories, and low detection accuracy (particularly for small targets) in Personal Protective Equipment (PPE) wearing condition monitoring for operators in offshore environments, this research investigates PPE targets detection for offshore operators using an optimized YOLOv11 model. The optimized model integrates the time-frequency features enhancement module (SFEAF) into the model's backbone network, employs a statistical-driven dynamic gating attention module (TSSA) to refine attention weight distribution in the original C2PSA module, and incorporates a Normalized Wasserstein Distance (NWD) loss function. These modifications collectively enhance the model's capability to detect PPE targets for offshore operators. To mitigate missed detection problem of small targets such as earplugs and gloves, a cascadednetwork of YOLOv11 and YOLOv11-Pose models is proposedfor small targetsdetection. The solution involves extracting human key points through YOLOv11-Posemodel, constructing spatial constraint regions via two-pointarea positioningmethod, enhancing small target features through localized region cropping and normalization, and performing secondary detection on refined regions using YOLOv11model. Ablation and comparative experiments validate the optimization method's efficacy, and the superiority of the cascaded network detection is verified through secondary detection experiments on small targets.
DOI: 10.6084/m9.figshare.29069420.v3
title: <b>Title-Detection method of PPE wearing and small target positioning for offshore operators: The improved YOLOv11 model and targets recognition</b><b>Short Title-Detection method of PPE wearing and small target positioning for offshore operators</b>

description: Focusing on the practical challenges of insufficient samples, incomplete categories, and low detection accuracy (particularly for small targets) in Personal Protective Equipment (PPE) wearing condition monitoring for operators in offshore environments, this research investigates PPE targets detection for offshore operators using an optimized YOLOv11 model. The optimized model integrates the time-frequency features enhancement module (SFEAF) into the model's backbone network, employs a statistical-driven dynamic gating attention module (TSSA) to refine attention weight distribution in the original C2PSA module, and incorporates a Normalized Wasserstein Distance (NWD) loss function. These modifications collectively enhance the model's capability to detect PPE targets for offshore operators. To mitigate missed detection problem of small targets such as earplugs and gloves, a cascadednetwork of YOLOv11 and YOLOv11-Pose models is proposedfor small targetsdetection. The solution involves extracting human key points through YOLOv11-Posemodel, constructing spatial constraint regions via two-pointarea positioningmethod, enhancing small target features through localized region cropping and normalization, and performing secondary detection on refined regions using YOLOv11model. Ablation and comparative experiments validate the optimization method's efficacy, and the superiority of the cascaded network detection is verified through secondary detection experiments on small targets.
DOI: 10.6084/m9.figshare.29069420.v2
title: <b>题目-海上作业人员PPE穿戴与小目标定位检测方法：改进的YOLOv11模型及目标识别</b><b>Short Title-Detection method of PPE wearing and small target positioning for offshore operators</b>

description: 针对海上环境中作员的个人防护设备 （PPE） 佩戴状态监测中样本不足、类别不完整和检测精度低（特别是对于小目标）的实际挑战，本研究使用优化的 YOLOv11 模型调查了海上作员的 PPE 目标检测。优化后的模型将时频特征增强模块（SFEAF）集成到模型的骨干网络中，采用统计驱动的动态门控注意力模块（TSSA）来细化原始C2PSA模块中的注意力权重分布，并加入了归一化Wasserstein距离（NWD）损失函数。这些修改共同增强了该模型为海上作员检测 PPE 目标的能力。为了缓解耳塞和手套等小目标的漏检问题，该文提出一种 YOLOv11 和 YOLOv11-Pose 模型的级联网络进行小目标检测。该解决方案涉及通过 YOLOv11-Pose 模型提取人体关键点，通过两点区域定位方法构建空间约束区域，通过局部区域裁剪和归一化增强小目标特征，以及使用 YOLOv11 模型对细化区域进行二次检测。消融和对比实验验证了优化方法的有效性，并通过对小目标的二次检测实验验证了级联网络检测的优越性。
DOI: 10.15468/dl.658cqa
title: Occurrence Download

description: A dataset containing 5878 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sanicula chinensis Bunge"
 ]
}

 The dataset includes 5878 records from 165 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015275-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sx8cd8
title: Occurrence Download

description: A dataset containing 1258 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)"
 ]
 }
 },
 "TaxonKey is Poecilanthe Benth."
 ]
}

 The dataset includes 1258 records from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015281-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15438638
title: MESA: A Multi-Environment Synthetic Adaptation Dataset for visual SLAM Evaluation and Feature Learning

description: This repository contains a dataset of image sequences produced in virtual environments as part of our work in the European Conference on Mobile Robots 2025. It mainly targets the training and evaluation processes of Deep Learning architectures for visual SLAM and Path Planning architectures in dynamic and highly varying conditions.

Acknowledgment

This research is implemented in the framework of H.F.R.I call ``Basic research Financing (Horizontal support of all Sciences)'' under the National Recovery and Resilience Plan ``Greece 2.0'' funded by the European Union – NextGenerationEU (H.F.R.I. Project Number: 15339).

We encourage the utilization of our dataset under the restriction of following the above acknowledgment.

For more information, please visit the project's official website or contact us at: lbampis [at] ee.duth.gr
DOI: 10.5281/zenodo.15166310
title: Data set for letter "Coherent Interaction of 2s and 1s Exciton States in Transition-Metal Dichalcogenide Monolayers"

description: The files contain the data depicted in the figures of the article "Coherent Interaction of 2s and 1s Exciton States in Transition-Metal Dichalcogenide Monolayers", arXiv:2501.12966.

The data for each figure is given as a .csv file with a ',' as the delimiter.  The first row contains a header that specifies the variable and its unit for each column.

We include a Jupyter notebook that generates minimal versions of the figures in the article using the provided .csv files.
DOI: 10.5281/zenodo.15166311
title: Data set for letter "Coherent Interaction of 2s and 1s Exciton States in Transition-Metal Dichalcogenide Monolayers"

description: The files contain the data depicted in the figures of the article "Coherent Interaction of 2s and 1s Exciton States in Transition-Metal Dichalcogenide Monolayers", arXiv:2501.12966.

The data for each figure is given as a .csv file with a ',' as the delimiter.  The first row contains a header that specifies the variable and its unit for each column.

We include a Jupyter notebook that generates minimal versions of the figures in the article using the provided .csv files.
DOI: 10.15468/dl.myzzgn
title: Occurrence Download

description: A dataset containing 160 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sanicula astrantiifolia H.Wolff ex Kretschmer"
 ]
}

 The dataset includes 160 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015279-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s2u4vg
title: Occurrence Download

description: A dataset containing 257241 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Natural History Museum Rotterdam - Specimens",
 {
 "and" : [
 "BasisOfRecord is one of (Specimen)",
 "Continent is one of (Europe)"
 ]
 }
 ]
}

 The dataset includes 257241 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015280-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qu3cxs
title: Occurrence Download

description: A dataset containing 7030 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Sanicula lamelligera Hance, Sanicula chinensis Bunge)"
 ]
}

 The dataset includes 7030 records from 177 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015277-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.