cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/yvrdpdv58z.1
title: Construct validity of the Arabic version of the Trait Emotional Intelligence Questionnaire (TEIQue-SF) among community-dwelling adults and students

description: We examined the construct validity of the Arabic version of the Trait Emotional Intelligence Questionnaire (TEIQue-SF) among community-dwelling adults and students. For this purpose, clustering analysis as well as exploratory and confirmatory factor analyses were used to examine various structures of the Arabic TEIQue-SF. Interesting findings are discovered.
We used a public dataset originally created by Dr. Khaled Dassean. We excluded 33 outlier cases from the dataset based on Mahalanobis distance metric, and we are sharing the modified dataset for transparency.
DOI: 10.17632/d3hph8kvcz.1
title: Indicators of the co-created destination image of island destinations on social media – a comparison of DMOs, UGC and influencers (2025)

description: This dataset provides an overview of quantitative and qualitative indicators in the context of the co-created destination image of the archipelagos of Hawaii, the Canary Islands and the Seychelles. A total of 180 image posts on the social media platform Instagram were analysed during the second half of 2025, comparing posts from destination management organisations (DMOs), user-generated content (UCG) and posts from influencers. 

The indicators provide insights into the destination image elements depicted in the image posts, as well as contributing elements and associated emotions in a comparison of different island destinations and stakeholders. On this basis, comparative research can be conducted in the context of the digital destination image and the influence of various actors on it. This dataset can thus contribute to comparative vertical and horizontal analysis and thus to studies in the field of destination management, destination image, social media and technology.
DOI: 10.5281/zenodo.18375595
title: Pro-regenerative fingerprints identified in a sub-population of adult mouse cardiomyocytes by integrative single-cell proteomics_Raw_data_2

description: Raw_data_2:

Includes the relation list for cellular compartment integration: Q2CC_RELATION_TABLE.txt

Figure4: Figure4 in adobe illustrator, png and pdf formats. Additional folder: includes VIP proteins extracted out of the PLS-DA analysis, differential expression analysis (volcano analysis) and the graphpad file including the representation of Figure4e section with the correspondant statistical values. Input_files folder: includes the input files for Metaboanalyst of the studied cases. Software folder: includes the softwares used for the analysis.

 

Figure5: Figure5 in adobe illustrator, png and pdf formats. Additional folder: includes VIP proteins extracted out of the PLS-DA analysis, differential expression analysis (volcano analysis) and the graphpad file including the representation of Figure5e section with the correspondant statistical values. Input_files folder: includes the input files for Metaboanalyst of the studied cases. Software folder: includes the softwares used for the analysis.

 

Figure6: Figure6 in adobe illustrator, png and pdf formats. Input_file folder: includes the input file for Metaboanalyst of the studied case.

 

Figure7: Figure7 in adobe illustrator, png and pdf formats. Additional folder: includes csv files with the markers of each of the clusters of the UMAP representation. It also includes the metadata file for computing the dependence of each cluster respect to TMT labelling. Input_files folder: includes the input file for SEURAT of the studied case. Software folder: includes the software used for the analysis.
DOI: 10.17632/36y9n4dcpf
title: Zarei et al article data

description: The attached files show the raw data used in each figure of the manuscript as the GraphPad Prism (version 9) files. Each fileincludes the results of the statistical analyses and the relatedgraph(s).
DOI: 10.17632/36y9n4dcpf.1
title: Zarei et al article data

description: The attached files show the raw data used in each figure of the manuscript as the GraphPad Prism (version 9) files. Each fileincludes the results of the statistical analyses and the relatedgraph(s).
DOI: 10.17632/ysh4m8wf88
title: Geographical and Seasonal Influences in Calea urticifolia (Mill.) DC. Leaf Chemical Characterization: Application for Quality Control of Commercial samples

description: All HPLC-DAD measurements for acacetin dosage, HPLC-MS data, LF-NMR binning, and statistical analyses
DOI: 10.17632/ysh4m8wf88.1
title: Geographical and Seasonal Influences in Calea urticifolia (Mill.) DC. Leaf Chemical Characterization: Application for Quality Control of Commercial samples

description: All HPLC-DAD measurements for acacetin dosage, HPLC-MS data, LF-NMR binning, and statistical analyses
DOI: 10.17632/wp6zwb73y6
title: Gender Equality: The Role of Organisational Culture, Stereo-types, and Self-bullying

description: A cross-sectional survey was used with a stratified random sample (N = 257) of female bank employees in South Africa. The Organisational Culture Questionnaire, the Women in Management Questionnaire, the Personal Views Questionnaire, and the Capability for Gender Equality Questionnaire were administered.
DOI: 10.17632/wp6zwb73y6.1
title: Gender Equality: The Role of Organisational Culture, Stereo-types, and Self-bullying

description: A cross-sectional survey was used with a stratified random sample (N = 257) of female bank employees in South Africa. The Organisational Culture Questionnaire, the Women in Management Questionnaire, the Personal Views Questionnaire, and the Capability for Gender Equality Questionnaire were administered.
DOI: 10.17632/yrbwrv7xfk
title: Mg isotopes of weathering profile

description: Mg isotope, major elemental concentration, mineral compositions of three weathering profiles from China
DOI: 10.17632/yrbwrv7xfk.1
title: Mg isotopes of weathering profile

description: Mg isotope, major elemental concentration, mineral compositions of three weathering profiles from China
DOI: 10.17632/dswvwjhrhn
title: Data for "The Teammate Trap: How Anthropomorphic AI Framing Induces Social Loafing in Human-AI Collaboration"

description: This repository contains the data and analysis scripts for replicating the findings reported in "The Teammate Trap: How Anthropomorphic AI Framing Induces Social Loafing in Human-AI Collaboration."
The study investigates whether framing AI systems as collaborative teammates (versus analytical tools) increases social loafing in human-AI collaboration, and whether organizational accountability structures can remedy this effect.
Contents:
Study 1 (N = 400): Venture capital screening task with a 2-condition design (AI Framing: Tool vs. Teammate). Examines the main effect of AI framing on social loafing, mediation through perceived AI agency, and moderation by task subjectivity.
Study 2 (N = 400): Content moderation task with a 2×2 factorial design (AI Framing: Tool vs. Teammate × Accountability: Low vs. High). Replicates the main effect and tests accountability as an organizational intervention
DOI: 10.17632/dswvwjhrhn.1
title: Data for "The Teammate Trap: How Anthropomorphic AI Framing Induces Social Loafing in Human-AI Collaboration"

description: This repository contains the data and analysis scripts for replicating the findings reported in "The Teammate Trap: How Anthropomorphic AI Framing Induces Social Loafing in Human-AI Collaboration."
The study investigates whether framing AI systems as collaborative teammates (versus analytical tools) increases social loafing in human-AI collaboration, and whether organizational accountability structures can remedy this effect.
Contents:
Study 1 (N = 400): Venture capital screening task with a 2-condition design (AI Framing: Tool vs. Teammate). Examines the main effect of AI framing on social loafing, mediation through perceived AI agency, and moderation by task subjectivity.
Study 2 (N = 400): Content moderation task with a 2×2 factorial design (AI Framing: Tool vs. Teammate × Accountability: Low vs. High). Replicates the main effect and tests accountability as an organizational intervention
DOI: 10.5281/zenodo.18375583
title: compositer fermion dispersions for the fractional Chern insulator in twisted bilayer MoTe2
DOI: 10.5281/zenodo.18375582
title: compositer fermion dispersions for the fractional Chern insulator in twisted bilayer MoTe2
DOI: 10.17632/4wkd3b9ngc
title: Dataset supporting a mixed-methods evaluation of a peer-led induction programme during transition into specialty training

description: This repository contains the anonymised quantitative and qualitative data underpinning the study “Reframing induction as an educational intervention: a mixed-methods evaluation of a peer-led programme during transition into specialty training.”

The dataset comprises learner-reported questionnaire responses collected across four iterative induction cycles within a UK hospital trauma and orthopaedics department. Quantitative data include ordinal Likert-scale responses relating to perceived preparedness, perceived support, confidence in managing specialty-specific and post-operative issues, overall induction quality, and inclusion of predefined induction components. Qualitative data consist of free-text responses describing participants’ experiences of departmental induction.

All data have been fully anonymised prior to sharing. No personally identifiable information is included. The dataset is provided to support transparency, reproducibility, and secondary analysis of the descriptive findings reported in the associated manuscript. Data are presented as collected; no inferential statistical analyses are included.
DOI: 10.17632/4wkd3b9ngc.1
title: Dataset supporting a mixed-methods evaluation of a peer-led induction programme during transition into specialty training

description: This repository contains the anonymised quantitative and qualitative data underpinning the study “Reframing induction as an educational intervention: a mixed-methods evaluation of a peer-led programme during transition into specialty training.”

The dataset comprises learner-reported questionnaire responses collected across four iterative induction cycles within a UK hospital trauma and orthopaedics department. Quantitative data include ordinal Likert-scale responses relating to perceived preparedness, perceived support, confidence in managing specialty-specific and post-operative issues, overall induction quality, and inclusion of predefined induction components. Qualitative data consist of free-text responses describing participants’ experiences of departmental induction.

All data have been fully anonymised prior to sharing. No personally identifiable information is included. The dataset is provided to support transparency, reproducibility, and secondary analysis of the descriptive findings reported in the associated manuscript. Data are presented as collected; no inferential statistical analyses are included.
DOI: 10.5518/1551
title: A Geometric Model of Impingement During Dislocation Prone Activities in Dual Mobility, Lipped Liner and Standard Acetabular Cups

description: This dataset relates to the geometric modelling of impingement during dislocation-prone activities of daily living (ADLs) and walking in standard (STD), dual mobility (DM) and lipped liner (LL) acetabular cup systems.
DOI: 10.5281/zenodo.18345667
title: Hologenomic structure of bacterial and fungal community composition in the West Nile virus vector Culex tarsalis

description: Hologenome_BioSample_NCBIsubmission260123:Metadata table deposited to NCBI SRA, linking BioSample accessions to the sample IDs used in the manuscript.

Microbiome_Rscript_metadata:R scripts for Mantel tests (Bray–Curtis dissimilarity vs geographic distance) and associated metadata, including normalized abundance tables and geographic coordinates, for bacterial (16S), fungal (ITS), and combined bacterial–fungal (16S_ITS) analyses.

Popgen_Rscript_metadata:R scripts for Mantel tests (FST vs geographic distance) and pairwise FST matrix.
DOI: 10.5281/zenodo.18375632
title: Hologenomic structure of bacterial and fungal community composition in the West Nile virus vector Culex tarsalis

description: Hologenome_BioSample_NCBIsubmission260123:Metadata table deposited to NCBI SRA, linking BioSample accessions to the sample IDs used in the manuscript.

Microbiome_Rscript_metadata:R scripts for Mantel tests (Bray–Curtis dissimilarity vs geographic distance) and associated metadata, including normalized abundance tables and geographic coordinates, for bacterial (16S), fungal (ITS), and combined bacterial–fungal (16S_ITS) analyses.

Popgen_Rscript_metadata:R scripts for Mantel tests (FST vs geographic distance) and pairwise FST matrix.
DOI: 10.71636/e2x9-dv89
title: Integrated Climate, Hydrological, and Economic Modelling Dataset for the Douro River Basin [Dataset]

description: [EN]This paper develops an actionable interdisci-plinary model that quantifies and assesses uncertainties in water resource allocation under climate change. To achieve this objective, we develop an innovative socio-ecological grand ensemble that combines climate, hydrological, and mi-croeconomic ensemble experiments with a widely used deci-sion support system for water resource planning and manage-ment. Each system is populated with multiple models (multi-model), which we use to evaluate the impacts of multiple cli-mate scenarios and policies (multi-scenario, multi-forcing) across systems so as to identify plausible futures where wa-ter management policies meet or miss their objectives and to explore potential tipping points. The application of the meth-ods is exemplified by a study conducted in the Douro River basin (DRB), an agricultural basin located in central Spain. Our results show how marginal climate changes can trigger non-linear water allocation changes in the decision support systems (DSSs) and/or non-linear adaptive responses of ir-rigators to water shortages. For example, while some irriga-tors barely experience economic losses (average profit and employment fall by &lt; 0.5 %) under mild water allocation re-ductions of 5 % or lower, profit and employment fall by up to 12 % (∼ 24 ×) when water allocation is reduced by 10 %or less (∼ 2×). This substantiates the relevance of informing the potential natural and socio-economic impacts of adapta-tion strategies and related uncertainties for identifying robust decisions.
DOI: 10.17632/dwygtjfxpw
title: Fusion of SAR and Optical Data with Flood Susceptibility Model for Mapping Floods and Assessing Impacts in Rural Area

description: Remote rural regions frequently experience severe flooding due to the high frequency and intensity of rainfall events. Prompt evaluation of these effects is essential for initiating relief efforts and reducing risks in susceptible areas. Nonetheless, the limited availability of data and the inherent constraints of conventional assessment methods present substantial obstacles. Typically, traditional change detection workflows are intricate and demand considerable human involvement. In contrast, deep learning presents an alternative that necessitates minimal human input and a reduced amount of labelled data. This study aims to evaluate and quantify the impacts of flooding by amalgamating Sentinel-1 synthetic aperture radar (SAR) data and Sentinel-2 optical imagery with a flood susceptibility model. The methodology encompasses a three-phase approach: delineating the extent of flooding, comparing classification accuracy through a novel deep learning-based change detection classifier, and evaluating flood vulnerability via Geographic Information Systems (GIS)-based modelling. Two attention mechanisms, namely the Pyramid spatial-temporal attention module (PAM) and Basic spatial-temporal attention module (BAM), were trained and evaluated using ResNet architectures. Findings indicate that PAM combined with ResNet 34 exceeded the performance of BAM with ResNet 34, achieving a remarkable overall accuracy of 98.80% and a Kappa Coefficient of 96.80%. In a separate trial utilizing a ResNet 18 backbone, the PAM attention mechanism yielded superior overall accuracy, attaining Kappa coefficients of 98.40% and 96.20%, respectively. Notably, the performance of the deep learning model exceeded that of traditional change detection methods, which recorded accuracies of 92.5% and 90.80%. To assess the vulnerability of the region and evaluate flood impacts, a flood susceptibility model was generated using a GIS-based Multi-Criteria Decision Model Analysis (MCDA). The weighted overlay technique in ArcGIS Pro 3.5 was employed to create flood susceptibility maps, categorised into five levels: very low, low, moderate, high, and very high. A significant portion of the area was identified within the high to very high susceptibility categories, with 453.37 km² (26.79%) classified as very high susceptibility and 488.93 km² (28.89%) as high susceptibility. The flood risk maps demonstrated a 97.44% agreement with the flood extent derived from Sentinel-1 imagery. The synergistic application of the developed flood risk map alongside flood extent mapping provided critical insights for spatial analysis and evaluation of flood impacts in Ingham and throughout the Shire of Hinchinbrook, Queensland, Australia.
DOI: 10.17632/dwygtjfxpw.1
title: Fusion of SAR and Optical Data with Flood Susceptibility Model for Mapping Floods and Assessing Impacts in Rural Area

description: Remote rural regions frequently experience severe flooding due to the high frequency and intensity of rainfall events. Prompt evaluation of these effects is essential for initiating relief efforts and reducing risks in susceptible areas. Nonetheless, the limited availability of data and the inherent constraints of conventional assessment methods present substantial obstacles. Typically, traditional change detection workflows are intricate and demand considerable human involvement. In contrast, deep learning presents an alternative that necessitates minimal human input and a reduced amount of labelled data. This study aims to evaluate and quantify the impacts of flooding by amalgamating Sentinel-1 synthetic aperture radar (SAR) data and Sentinel-2 optical imagery with a flood susceptibility model. The methodology encompasses a three-phase approach: delineating the extent of flooding, comparing classification accuracy through a novel deep learning-based change detection classifier, and evaluating flood vulnerability via Geographic Information Systems (GIS)-based modelling. Two attention mechanisms, namely the Pyramid spatial-temporal attention module (PAM) and Basic spatial-temporal attention module (BAM), were trained and evaluated using ResNet architectures. Findings indicate that PAM combined with ResNet 34 exceeded the performance of BAM with ResNet 34, achieving a remarkable overall accuracy of 98.80% and a Kappa Coefficient of 96.80%. In a separate trial utilizing a ResNet 18 backbone, the PAM attention mechanism yielded superior overall accuracy, attaining Kappa coefficients of 98.40% and 96.20%, respectively. Notably, the performance of the deep learning model exceeded that of traditional change detection methods, which recorded accuracies of 92.5% and 90.80%. To assess the vulnerability of the region and evaluate flood impacts, a flood susceptibility model was generated using a GIS-based Multi-Criteria Decision Model Analysis (MCDA). The weighted overlay technique in ArcGIS Pro 3.5 was employed to create flood susceptibility maps, categorised into five levels: very low, low, moderate, high, and very high. A significant portion of the area was identified within the high to very high susceptibility categories, with 453.37 km² (26.79%) classified as very high susceptibility and 488.93 km² (28.89%) as high susceptibility. The flood risk maps demonstrated a 97.44% agreement with the flood extent derived from Sentinel-1 imagery. The synergistic application of the developed flood risk map alongside flood extent mapping provided critical insights for spatial analysis and evaluation of flood impacts in Ingham and throughout the Shire of Hinchinbrook, Queensland, Australia.
DOI: 10.6084/m9.figshare.19168709
title: Quantitative proteomics reveal three potential biomarkers for risk assessment of acute myocardial infarction

description: Acute myocardial infarction (AMI) is the one of the main cause of death worldwide. Exosomes carry important information about intercellular communication and could be diagnostic marker for many diseases. Here, we aimed to find potential key proteins for the early diagnosis of AMI. A label free proteomics strategy was used to identify the differentially expressed proteins (DEPs) of AMI patients’ plasma exosome. By bioinformatics analysis and enzyme-linked immunosorbent assay to validate the candidate proteins. Compared to healthy control plasma exosome, we totally identified 72 differentially expressed proteins (DEPs) in AMI patients. Also, we found that complement and coagulation cascades was activated by KEGG analysis and GSEA. PLG, C8B and F2 were selected as candidate molecules for further study, and then validated another 40 plasma samples using enzyme-linked immunosorbent assay. Finally, we found that the expression levels of these three proteins (PLG, C8B and F2) were significantly higher than those of healthy controls (P &lt; 0.05). ROC analysis revealed that PLG, C8B and F2 had potential value for AMI early diagnosis. In conclusion, our study identified three potential biomarkers for AMI diagnosis. But there remains a need to further study the mechanism of the biomarkers.
DOI: 10.6084/m9.figshare.19168709.v1
title: Quantitative proteomics reveal three potential biomarkers for risk assessment of acute myocardial infarction

description: Acute myocardial infarction (AMI) is the one of the main cause of death worldwide. Exosomes carry important information about intercellular communication and could be diagnostic marker for many diseases. Here, we aimed to find potential key proteins for the early diagnosis of AMI. A label free proteomics strategy was used to identify the differentially expressed proteins (DEPs) of AMI patients’ plasma exosome. By bioinformatics analysis and enzyme-linked immunosorbent assay to validate the candidate proteins. Compared to healthy control plasma exosome, we totally identified 72 differentially expressed proteins (DEPs) in AMI patients. Also, we found that complement and coagulation cascades was activated by KEGG analysis and GSEA. PLG, C8B and F2 were selected as candidate molecules for further study, and then validated another 40 plasma samples using enzyme-linked immunosorbent assay. Finally, we found that the expression levels of these three proteins (PLG, C8B and F2) were significantly higher than those of healthy controls (P &lt; 0.05). ROC analysis revealed that PLG, C8B and F2 had potential value for AMI early diagnosis. In conclusion, our study identified three potential biomarkers for AMI diagnosis. But there remains a need to further study the mechanism of the biomarkers.