cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/677y6kh3s2.1
title: Visualizing and Optimizing Operating Room Nurses’ Workload Using Generative AI: A Cross-Sectional Study

description: Description:
This dataset contains raw data collected for a cross-sectional study on the workload of operating room nurses at a tertiary care hospital in Japan (Nagasaki Medical Center). The survey quantified the time allocation of perioperative nurses during different shifts (weekday daytime, weekday nighttime, weekend/holiday shifts), covering both nursing tasks (e.g., direct patient care, preparation, intraoperative support) and non-nursing tasks (e.g., documentation, supply management, administrative work).

The dataset was obtained through a structured workload survey and time-motion style observational records between 2023 and 2024. It includes aggregated time distribution across task categories, shift type, and nurse demographics. The data were used for analysis in the manuscript “Visualizing and Optimizing Operating Room Nurses’ Workload Using Generative AI: A Cross-Sectional Study”.

File contents:

Excel spreadsheets summarizing observed task times by category and shift.

Supplementary tables for task classification (nursing vs. non-nursing).

Anonymized records ensuring no individual nurse identifiers are present.

Ethics:
The study was approved by the Ethics Committee of Kumamoto University (Approval No. 2024015). Participation was voluntary, and all participants provided informed consent.

Potential use:
The dataset can support further research in:

Nursing workload measurement and benchmarking.

Workforce planning and staffing optimization in perioperative settings.

Application of AI and digital tools for task visualization and efficiency improvement.

Limitations:
The dataset is from a single institution and may not be generalizable to all healthcare contexts. Task classification reflects the local practice environment.
DOI: 10.7910/dvn/bwoebf
title: Replication Data for: Defining Democracy: Citizen Priorities and Regime Context

description: This is the replication file for the manuscript with the working title: "Defining Democracy: Citizen Priorities and Regime Context"
DOI: 10.5281/zenodo.13834494
title: Building height and roof material dataset - Dominica, Pichelin and Roseau, 2018, 2024

description: Science Case Name



 Multi-hazards in the Caribbean SIDS





Dataset Title



Building height and roof material dataset - Dominica, Pichelin and Roseau, 2018, 2024





Dataset Description



Building height predictions of a monocular depth estimation network trained on UAV imageries of Pichelin (2024) and Roseau (2018) trained on Google’s Open Building 2.5D Temporal dataset ( https://sites.research.google/gr/open-buildings/temporal/) and roof material classification using the model provided in GFDRR’s Caribbean Rooftop Classification (https://github.com/GFDRR/caribbean-rooftop-classification):

·        pichelin_network_height_rooftype_predictions.gpkg – Geopackage containing building height estimation and roof material classification of Pichelin, Dominica (2024).

·        roseau_network_predicitons.gpkg – Geopackage containing building height estimation and roof material classification of Roseau, Dominica (2018).

 





Key Methodologies



A monocular depth estimation network called MiDaS (https://pytorch.org/hub/intelisl_midas_v2/) and the roof type and material classification network of GFDRR based on ResNet-50.





Temporal Domain



2018 and 2024





Spatial Domain



Roseau and Pichelin, Dominica





Key Indicators



Building height and roof material





Data Format



A vector as a Geopackage (.gpkg)





Source Data



Openaerialmap UAV image of Roseau (2018), UAV images of Pichelin (2024) captured using a DJI drone, and Google’s Open Building 2.5D Temporal dataset





Accessibility



10.5281/zenodo.17100331

Disclaimer – This dataset is provided solely for research purposes. It is based on available data and has not been verified or approved by any local authority. The dataset represents the outcome of the applied methodology and research judgments, and does not reflect the official policies or positions of the authorities of Dominica. 





Stakeholder Relevance



The building height and roof material dataset can be used for hazard mapping and risk management. For example, they can be integrated into the digital twin environment of buildings-at-risk.





Limitations/Assumptions



Temporal alignment of images used and building height dataset the network was trained on.





Additional Information

NA




Contact Information



Bergado, John Ray (UT-ITC)
DOI: 10.5281/zenodo.17100331
title: Building height and roof material dataset - Dominica, Pichelin and Roseau, 2018, 2024

description: Science Case Name



 Multi-hazards in the Caribbean SIDS





Dataset Title



Building height and roof material dataset - Dominica, Pichelin and Roseau, 2018, 2024





Dataset Description



Building height predictions of a monocular depth estimation network trained on UAV imageries of Pichelin (2024) and Roseau (2018) trained on Google’s Open Building 2.5D Temporal dataset ( https://sites.research.google/gr/open-buildings/temporal/) and roof material classification using the model provided in GFDRR’s Caribbean Rooftop Classification (https://github.com/GFDRR/caribbean-rooftop-classification):

·        pichelin_network_height_rooftype_predictions.gpkg – Geopackage containing building height estimation and roof material classification of Pichelin, Dominica (2024).

·        roseau_network_predicitons.gpkg – Geopackage containing building height estimation and roof material classification of Roseau, Dominica (2018).

 





Key Methodologies



A monocular depth estimation network called MiDaS (https://pytorch.org/hub/intelisl_midas_v2/) and the roof type and material classification network of GFDRR based on ResNet-50.





Temporal Domain



2018 and 2024





Spatial Domain



Roseau and Pichelin, Dominica





Key Indicators



Building height and roof material





Data Format



A vector as a Geopackage (.gpkg)





Source Data



Openaerialmap UAV image of Roseau (2018), UAV images of Pichelin (2024) captured using a DJI drone, and Google’s Open Building 2.5D Temporal dataset





Accessibility



10.5281/zenodo.17100331

Disclaimer – This dataset is provided solely for research purposes. It is based on available data and has not been verified or approved by any local authority. The dataset represents the outcome of the applied methodology and research judgments, and does not reflect the official policies or positions of the authorities of Dominica. 





Stakeholder Relevance



The building height and roof material dataset can be used for hazard mapping and risk management. For example, they can be integrated into the digital twin environment of buildings-at-risk.





Limitations/Assumptions



Temporal alignment of images used and building height dataset the network was trained on.





Additional Information

NA




Contact Information



Bergado, John Ray (UT-ITC)
DOI: 10.17632/b4m7jgnzf4.1
title: Crotonylation of PB2 Protein at Lysine 482 Hampers Replication and Adaptive Fitness of H9N2 Avian Influenza Virus in Mammals

description: Source Data for figures
DOI: 10.17909/8pvd-9663
title: Visit 8, GJ 3929 Lya observations

description: Two exposures of the Rocky Worlds DDT program to measure the Lyman-alpha emission of GJ 3929.
DOI: 10.15468/dl.cztazc
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bromus lepidus",
 "Country is Poland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008047-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ywjm6x2665.1
title: Digital technology and regional income inequality: are better institutions the solution

description: The do file (ABRP_dofile) and the related dataset (ABRP_db) describe how to obtain the tables of our paper "Digital technology and regional income inequality: are better institutions the solution".
The aim of the paper was to examine the effect of regional digital technology (including computing, communication equipment, software, and databases) on income distribution at the regional level. We aim to fill a gap in existing research by exploring the moderating role of formal and informal institutions —such as bonding and bridging social capital— in shaping how digital technology affects income inequality across European NUTS2 regions from 2006 to 2016. The results indicate that regions with greater access to digital technology are prone to higher levels of income inequality. However, this negative link is mitigated by strong formal and informal institutions, particularly through improved government effectiveness and bridging social capital. The findings are robust to potential endogeneity concerns, as demonstrated by the instrumental variable approach adopted.
DOI: 10.15468/dl.ke44ep
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bromus lepidus",
 "Country is Slovenia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008049-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/vb9mfszpxr
title: African large carnivores in space and time: insights from camera-traps on the fine-scale spatio-temporal dynamics of lions, spotted hyaenas and leopards

description: This dataset is for the publication of the same name, here is the abstract: Coexistence of sympatric large carnivores is common in many ecosystems. On the one hand, the potential for competitive interactions, from kleptoparasitism to intraguild predation, is high, so species are expected to avoid each other. On the other hand, positive interactions, such as scavenging opportunities, also exist and may explain the attraction of some carnivores to others. In this study, we assess the patterns of spatio-temporal co-occurrence at different scales of the three most common large carnivores of African savannas: the spotted hyaena (Crocuta crocuta), the African lion (Panthera leo) and the leopard (Panthera pardus). We use data from a camera-trapping survey (285 stations; 14316 camera trap days; 2446 independent detection pictures) covering the ~ 15 000 km² of Hwange National Park, Zimbabwe, a key ecosystem of the world’s largest trans-frontier conservation area (Kavango-Zambezi). After corroborating the important overlap in the species diel activity pattern and their use of the same areas of the landscape, we further explore their finer spatio-temporal co-occurrence patterns. Time-to-event analyses suggest that hyaenas tend to use areas previously used by lions and leopards closely in time (in the 2 hours following the use of the area by the other two species) but also during consecutive nights. We show that lion fine-scale spatio-temporal use is not affected by hyaenas, and that lions and leopards use the same areas of the landscapes but not close in time. We finally discuss the potential mechanisms (kleptoparasitism, scavenging opportunities, interference competition and indirect interactions through prey catchability) that can underlie the patterns observed, but caution against quick inference from patterns to mechanisms, as illustrated with our results on hyaenas, for which the time-to-event analyses supported the “follower hypothesis” but the picture inspection showed that hyaenas were going in the same direction as the other carnivore in only 42.4% of the cases.
DOI: 10.17632/vb9mfszpxr.1
title: African large carnivores in space and time: insights from camera-traps on the fine-scale spatio-temporal dynamics of lions, spotted hyaenas and leopards

description: This dataset is for the publication of the same name, here is the abstract: Coexistence of sympatric large carnivores is common in many ecosystems. On the one hand, the potential for competitive interactions, from kleptoparasitism to intraguild predation, is high, so species are expected to avoid each other. On the other hand, positive interactions, such as scavenging opportunities, also exist and may explain the attraction of some carnivores to others. In this study, we assess the patterns of spatio-temporal co-occurrence at different scales of the three most common large carnivores of African savannas: the spotted hyaena (Crocuta crocuta), the African lion (Panthera leo) and the leopard (Panthera pardus). We use data from a camera-trapping survey (285 stations; 14316 camera trap days; 2446 independent detection pictures) covering the ~ 15 000 km² of Hwange National Park, Zimbabwe, a key ecosystem of the world’s largest trans-frontier conservation area (Kavango-Zambezi). After corroborating the important overlap in the species diel activity pattern and their use of the same areas of the landscape, we further explore their finer spatio-temporal co-occurrence patterns. Time-to-event analyses suggest that hyaenas tend to use areas previously used by lions and leopards closely in time (in the 2 hours following the use of the area by the other two species) but also during consecutive nights. We show that lion fine-scale spatio-temporal use is not affected by hyaenas, and that lions and leopards use the same areas of the landscapes but not close in time. We finally discuss the potential mechanisms (kleptoparasitism, scavenging opportunities, interference competition and indirect interactions through prey catchability) that can underlie the patterns observed, but caution against quick inference from patterns to mechanisms, as illustrated with our results on hyaenas, for which the time-to-event analyses supported the “follower hypothesis” but the picture inspection showed that hyaenas were going in the same direction as the other carnivore in only 42.4% of the cases.
DOI: 10.15468/dl.h4qgj3
title: Occurrence Download

description: A dataset containing 172 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bromus lepidus",
 "Country is Sweden",
 "HasCoordinate is true"
 ]
}

 The dataset includes 172 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008050-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/c4kt7xn2b5.1
title: Velocity datasets of an asymmetric dairy barn model by LDA in BLWT

description: The datasets present high-resolution airflow and turbulence measurements obtained in and around a 1:50 scaled asymmetric dairy barn model using a boundary layer wind tunnel (BLWT), including characterization data of boundary layer profile and airflow measurements at a cross-sectional plane. All instantaneous air velocities were acquired using a two-dimensional Laser Doppler Anemometry (LDA) system. The dataset of 233 point-dataset is primarily designed for validation and refinement of computational fluid dynamics (CFD) models, and includes pointwise instantaneous velocity fluctuations that is ideal for comparing time-series turbulence statistics and transient structures. Researchers in livestock housing design can use this dataset to evaluate and improve ventilation strategies for complex geometries. The spatially resolved measurement points allow for detailed comparisons between experimental and simulated results, supporting robust model calibration and validation efforts. Additionally, the datasets can be employed as references in indoor air quality and emission research, such as assessing ventilation efficacy and studying contaminant transport. The datasets can also serve as a reference for experimental planning and sensor placement optimization in scaled or in-situ airflow studies, enhancing the design of measurement campaigns. In summary, our datasets provide critical references for numerical modelling, and is valuable for further investigations of wind-driven ventilation performance as well as emission characterization in naturally-ventilated livestock buildings.
DOI: 10.17632/cgs9ry34fr
title: Frequency_Shift_Plot

description: This file provides the raw data for the frequency shift plots in Figure 1b and Figure 1c.
DOI: 10.17632/cgs9ry34fr.1
title: Frequency_Shift_Plot

description: This file provides the raw data for the frequency shift plots in Figure 1b and Figure 1c.
DOI: 10.17632/sy62n95yzw
title: A549 SLC25A20-OE Transcriptome and metabolome

description: The transcriptome and metabolome datasets of the A549 cell line with controlled and overexpressed SLC25A20
DOI: 10.17632/sy62n95yzw.1
title: A549 SLC25A20-OE Transcriptome and metabolome

description: The transcriptome and metabolome datasets of the A549 cell line with controlled and overexpressed SLC25A20
DOI: 10.17632/h5fwm9nc2c.1
title: The Environmental Administrative Penalties and Firm Digital Transformation: A Reexamination of Porter Hypothesis

description: This is the STATA data set and STATA code for empirical part of The Environmental Administrative Penalties and Firm Digital Transformation: A Reexamination of Porter Hypothesis
DOI: 10.5281/zenodo.15224857
title: Resilience of road segments - Dominica, 2015-2025

description:  






Science Case Name



Multi-hazards in the Caribbean SIDS





Dataset Name/Title



Resilience of road segments - Dominica, 2015-2025





Dataset Description



The dataset described the resilience of the main road segments to floods and landslides. 





Key Methodologies



Calculating the resilience of road segments by analysing the impact (and the robustness) and the recovery time using classes, which were combined into a resilience score. 





Temporal Domain



Erika: 2015, Maria: 2017, other maps are for the situation in 2025





Spatial Domain



Dominica, UTM WGS 84 Zone 20N





Key Variables/Indicators



Resilience of road segments to floods and landslides. The dataset is made by combining robustness with recovery time indicators. 





Data Format



The data are all in Zip format, and the zip files contain ESRI Shapefiles.





Source Data



Openstreetmap, Google OpenBuildings, Dominica Road Asset Management System





Accessibility



https://zenodo.org/uploads/15366616

Only freely accessible data is used.

Disclaimer – This dataset is provided solely for research purposes. It is based on available data and has not been verified or approved by any local authority. The dataset represents the outcome of the applied methodology and research judgments, and does not reflect the official policies or positions of the authorities of Dominica. 





Stakeholder Relevance



Used for determining which roads are most endangered for floods and landslides, as a basis for early warning and risk-informed planning 





Limitations/Assumptions



Flood maps were made with a 5-meter pixel size. 





Additional Outputs/Information

 




Contact Information



C.J. Van Westen (UT-ITC)
DOI: 10.5281/zenodo.15366616
title: Resilience of road segments - Dominica, 2015-2025

description:  






Science Case Name



Multi-hazards in the Caribbean SIDS





Dataset Name/Title



Resilience of road segments - Dominica, 2015-2025





Dataset Description



The dataset described the resilience of the main road segments to floods and landslides. 





Key Methodologies



Calculating the resilience of road segments by analysing the impact (and the robustness) and the recovery time using classes, which were combined into a resilience score. 





Temporal Domain



Erika: 2015, Maria: 2017, other maps are for the situation in 2025





Spatial Domain



Dominica, UTM WGS 84 Zone 20N





Key Variables/Indicators



Resilience of road segments to floods and landslides. The dataset is made by combining robustness with recovery time indicators. 





Data Format



The data are all in Zip format, and the zip files contain ESRI Shapefiles.





Source Data



Openstreetmap, Google OpenBuildings, Dominica Road Asset Management System





Accessibility



https://zenodo.org/uploads/15366616

Only freely accessible data is used.

Disclaimer – This dataset is provided solely for research purposes. It is based on available data and has not been verified or approved by any local authority. The dataset represents the outcome of the applied methodology and research judgments, and does not reflect the official policies or positions of the authorities of Dominica. 





Stakeholder Relevance



Used for determining which roads are most endangered for floods and landslides, as a basis for early warning and risk-informed planning 





Limitations/Assumptions



Flood maps were made with a 5-meter pixel size. 





Additional Outputs/Information

 




Contact Information



C.J. Van Westen (UT-ITC)
DOI: 10.15468/dl.mvmxsr
title: Occurrence Download

description: A dataset containing 357 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Randia obcordata"
 ]
}

 The dataset includes 357 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008051-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/s34dh5nwp7
title: Exploring the usability of graphic medicine as an educational tool among psychology students

description: This study explores the use of graphic medicine—which includes comics, graphic novels, and illustrations—as an innovative educational tool for psychology students learning about mental health. Traditional teaching methods, such as lectures and textbooks, are effective for delivering theoretical knowledge but may not fully capture the complexity of mental health experiences or encourage the empathy needed for future clinical practice. Graphic medicine offers a visual and narrative approach that can make abstract concepts more tangible, encourage active participation in learning, and foster emotional connection.

The primary aim of the study is to evaluate the usability of mental health–themed graphic novels among psychology students. Usability is assessed using the User Experience Questionnaire (UEQ), which measures aspects such as attractiveness, clarity, efficiency, reliability, stimulation, and novelty. Secondary aims include examining changes in students’ emotional responses (positive and negative affect), motivation to learn about mental health, enjoyment of reading graphic novels, attitudes toward their use in education, willingness to use them in the future, and perceptions of their validity.

This is a pre-post quasi-experimental study involving psychology students from the University of Málaga. During the intervention, participants read selected excerpts from graphic novels that portray real-life experiences of individuals with mental health conditions, their families, and professionals involved in their care. These narratives aim to bridge the gap between theory and real-life application and evoke empathy.

Before and after the reading activity, students complete self-report questionnaires assessing usability and the secondary variables. Data analysis involves Wilcoxon signed-rank test for paired samples, with Bonferroni correction applied to control for multiple comparisons, and a thematic analysis with Atlas.ti.
DOI: 10.15468/dl.fme2ch
title: Occurrence Download

description: A dataset containing 166 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hippodamia arctica"
 ]
}

 The dataset includes 166 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008052-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.