cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34943/274587d9-aa4f-46a9-bff8-5bcd7169f0e0
title: Conception Bay Conductivity Temperature Depth Deployed 2023-12-13

description: The Sea-Bird Microcat SBE37SIP 5691 was deployed on 2023-12-13 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Conductivity Temperature Depth. Conductivity Temperature Depth (CTD) is an instrument package that contains sensors for measuring the conductivity, temperature, and pressure of seawater. Salinity, sound velocity, depth and density are variables that can be derived from sensor measurements. CTDs can carry additional instruments and sensors such as oxygen sensors, turbidity sensors and fluorometers. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.10475292
title: Micro-Ultrasound Prostate Segmentation Dataset

description: This dataset comprises micro-ultrasound scans and human prostate annotations of 75 patients who underwent micro-ultrasound guided prostate biopsy at the University of Florida. All images and segmentations have been fully de-identified in the NIFTI format.

Under the "train" folder, you'll find three subfolders:



"micro_ultrasound_scans" contains micro-ultrasound images from 55 patients for training.

"expert_annotations" contains ground truth prostate segmentations annotated by our expert urologist.

"non_expert_annotations" contains prostate segmentations annotated by a graduate student.


In the "test" folder, there are five subfolders:



"micro_ultrasound_scans" contains micro-ultrasound images from 20 patients for testing.

"expert_annotations" contains ground truth prostate segmentations by the expert urologist.

"master_student_annotations" contains segmentations by a master's student.

"medical_student_annotations" contains segmentations by a medical student.

"clinician_annotations" contains segmentations by a urologist with limited experience in reading micro-ultrasound images.


If you use this dataset, please cite our paper: Jiang, Hongxu, et al. "MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images." Computerized Medical Imaging and Graphics (2024): 102326. DOI: https://doi.org/10.1016/j.compmedimag.2024.102326.

For any dataset-related queries, please reach out to Dr. Wei Shao:  weishao@ufl.edu.
DOI: 10.5281/zenodo.10475293
title: Micro-Ultrasound Prostate Segmentation Dataset

description: This dataset comprises micro-ultrasound scans and human prostate annotations of 75 patients who underwent micro-ultrasound guided prostate biopsy at the University of Florida. All images and segmentations have been fully de-identified in the NIFTI format.

Under the "train" folder, you'll find three subfolders:



"micro_ultrasound_scans" contains micro-ultrasound images from 55 patients for training.

"expert_annotations" contains ground truth prostate segmentations annotated by our expert urologist.

"non_expert_annotations" contains prostate segmentations annotated by a graduate student.


In the "test" folder, there are five subfolders:



"micro_ultrasound_scans" contains micro-ultrasound images from 20 patients for testing.

"expert_annotations" contains ground truth prostate segmentations by the expert urologist.

"master_student_annotations" contains segmentations by a master's student.

"medical_student_annotations" contains segmentations by a medical student.

"clinician_annotations" contains segmentations by a urologist with limited experience in reading micro-ultrasound images.


If you use this dataset, please cite our paper: Jiang, Hongxu, et al. "MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images." Computerized Medical Imaging and Graphics (2024): 102326. DOI: https://doi.org/10.1016/j.compmedimag.2024.102326.

For any dataset-related queries, please reach out to Dr. Wei Shao:  weishao@ufl.edu.
DOI: 10.5281/zenodo.10475658
title: Multimodal Brain-Computer Interface Emotion Dataset (MBCI-ED)

description: The Multimodal Brain-Computer Interface Emotion Dataset (MBCIED) is a unique and invaluable resource that focuses on capturing the neural and ocular responses of participants as they are exposed to various emotion-eliciting videos. The EEG recordings and eye-tracking data, primarily gathered using Tobii eye-tracking glasses, are combined to provide a comprehensive, multimodal dataset that allows researchers to explore the complex interplay between emotional processing and visual attention.

In this dataset, participants were asked to watch a series of videos that were designed to induce different emotional states. The high-density EEG setup (64 electrodes) captures the whole-brain response, while the Tobii eye-tracking glasses monitor participants’ gaze patterns and eye movements, providing an additional layer of information regarding their visual focus and attention during the emotion-evoking tasks.Please see: https://mbci-ed.wearlab.net
DOI: 10.5281/zenodo.10475657
title: Multimodal Brain-Computer Interface Emotion Dataset (MBCI-ED)

description: The Multimodal Brain-Computer Interface Emotion Dataset (MBCIED) is a unique and invaluable resource that focuses on capturing the neural and ocular responses of participants as they are exposed to various emotion-eliciting videos. The EEG recordings and eye-tracking data, primarily gathered using Tobii eye-tracking glasses, are combined to provide a comprehensive, multimodal dataset that allows researchers to explore the complex interplay between emotional processing and visual attention.

In this dataset, participants were asked to watch a series of videos that were designed to induce different emotional states. The high-density EEG setup (64 electrodes) captures the whole-brain response, while the Tobii eye-tracking glasses monitor participants’ gaze patterns and eye movements, providing an additional layer of information regarding their visual focus and attention during the emotion-evoking tasks.Please see: https://mbci-ed.wearlab.net
DOI: 10.25810/jck1-hf50.68
title: US Newspaper Coverage of Climate Change or Global Warming, 2000-2023 - November 2023

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.10472251
title: Citizens living in localities close to a petrochemical site in Castellón (Spain)

description: The dataset was made up with responses from citizens living in localities close to a petrochemical site in Castellón (Spain). We received 992 valid responses by means of simple random sampling.
DOI: 10.5281/zenodo.10472252
title: Citizens living in localities close to a petrochemical site in Castellón (Spain)

description: The dataset was made up with responses from citizens living in localities close to a petrochemical site in Castellón (Spain). We received 992 valid responses by means of simple random sampling.
DOI: 10.35077/ace-cab-ear
title: Whole-brain LSFM imaging of a E15.5 mouse for DevCCF project Subject: E15.5_BB0428 Background

description: This dataset is part of a BICCN project to create developmental CCFswith associated ontology and true 3D anatomical labels while also demonstrating the application of our CCFsby generating quantitative mappings of GABa Ergic neurons in the developing mouse brain. This sample consists of LSFM imaging of a E15.5 mouse. Subject: E15.5_BB0428 Technique: Background Autofluorescence.
description: Total processed cells: NA
 DATASET METADATA:
 title:'Whole-brain LSFM imaging of a E15.5 mouse for DevCCF project Subject: E15.5_BB0428 Background',
 GeneralModality:'population imaging',
 Technique:'lightsheet'
 SPECIMEN METADATA:
 LocalID:'E15.5_BB0428',
 Species:'mouse',
 NCBITaxonomy:'NCBI:txid10090',
 Age:'E15.5',
 Ageunit:'Embryonic Days',
 Sex:'Male',
 Genotype:'Strain_Name=C57BL6 Cre_Driver_Line=Vasoactive_intestinal_peptide Reporter_Name=Ai14C',
 OrganName:'Brain',
 Locations:'brain'
 INSTRUMENT METADATA:
 MicroscopeType:'Light sheet microscope (LSFM)'
 IMAGE METADATA:
 yAxis:'inferior-to-superior',
 zAxis:'anterior-to-posterior',
 Number:'Channels: 0 1, Wavelength nm: 488 561',
 displayColor:'488 561',
 stepSizeX:'1.8',
 stepSizeY:'1.8',
 stepSizeZ:'5'
DOI: 10.25810/c862-0e81.68
title: US Television Coverage of Climate Change or Global Warming, 2000-2023 - November 2023

description: The Media and Climate Change Observatory Data monitors 130 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.6084/m9.figshare.24969531
title: Data files (Akeren & Akeren).csv

description: <b>Objective:</b><b> </b>The present study aimed to examine the relationships between loneliness, perceived support and fear of old age, to determine how they differ according to some socio-demographic variables and finally to reveal the mediating role of perceived social support in the effect of loneliness on fear of old age.<b>Materials</b><b> </b><b>and</b><b> </b><b>Method:</b><b> </b>In the descriptive cross-sectional study conducted between November 2023 and January 2024, the Loneliness Orientation Scale (LOS), Multidimensional Perceived Social Support fear of aging, loneliness, social support.fear of aging, loneliness, social support.Scale (MSPSS) and Fear of Old Age Scale (FOS) were used. Correlation analysis was used in the analysis of the data collected from 611 participants aged 18-65 with two-stage sampling, T Test and ANOVA were used in the comparison of averages, and the Process integrated into the SPSS Program was used to determine the mediating role of perceived social support.<b>Findings: </b>Women, single, young people (10-29 years old), those with low education and income fear old age more. There is a positive correlation between loneliness and fear of old age and a negative correlation between these two and perceived social support.<b>Conclusions</b>: As a result, perceived social support is a partial mediator in the effect of loneliness on fear of old age, and it should be taken into consideration that it is an effective source in reducing fear of old age.
DOI: 10.6084/m9.figshare.24969531.v1
title: Data files (Akeren & Akeren).csv

description: <b>Objective:</b><b> </b>The present study aimed to examine the relationships between loneliness, perceived support and fear of old age, to determine how they differ according to some socio-demographic variables and finally to reveal the mediating role of perceived social support in the effect of loneliness on fear of old age.<b>Materials</b><b> </b><b>and</b><b> </b><b>Method:</b><b> </b>In the descriptive cross-sectional study conducted between November 2023 and January 2024, the Loneliness Orientation Scale (LOS), Multidimensional Perceived Social Support fear of aging, loneliness, social support.fear of aging, loneliness, social support.Scale (MSPSS) and Fear of Old Age Scale (FOS) were used. Correlation analysis was used in the analysis of the data collected from 611 participants aged 18-65 with two-stage sampling, T Test and ANOVA were used in the comparison of averages, and the Process integrated into the SPSS Program was used to determine the mediating role of perceived social support.<b>Findings: </b>Women, single, young people (10-29 years old), those with low education and income fear old age more. There is a positive correlation between loneliness and fear of old age and a negative correlation between these two and perceived social support.<b>Conclusions</b>: As a result, perceived social support is a partial mediator in the effect of loneliness on fear of old age, and it should be taken into consideration that it is an effective source in reducing fear of old age.
DOI: 10.5281/zenodo.10475741
title: DETERMINANTS OF PROJECT IMPLEMENTATION DELAYS: CASE OF OIL AND GAS DEPOTS UPGRADE AT RUBiS ENERGY RWANDA

description: ABSTRACT: The study investigated determinants of project implementation delays: case of OIL and Gas Depots Upgrade at RUBiS energy Rwanda. The specific objectives were to examine how financial implications affects the oil and gas project implementation in Rwanda; the influence of infrastructure to the oil and gas project implementation delays in Rwanda; to explore the influence of regulatory framework to OIL and Gas Depots Upgrade projects; and the effectiveness of current project management practices in addressing delays to OIL and Gas Depots Upgrade project implemented at RUBiS energy Rwanda. Target population was 60 participants the projects of OIL and Gas Depots Upgrade at RUBiS energy Rwanda. The questionnaires were given to 52 respondents as sample size. Interview and documentary techniques have been used to collect data.  The analysis methods were descriptive statistic and multiple regression models.  The results confirmed a significant and strong correlation between financial Implications and Oil and Gas projects implementation delays by r= 0.894. The results revealed very strong correlation between infrastructure factors and Oil and Gas projects implementation delays as shown by r= .917. Findings revealed that there is a significant very strong correlation between Regulatory features and Oil and Gas projects implementation delays as confirmed by r= .972. The results also show that there is very strong correlation between Project management practices and Oil and Gas projects implementation delays as stated by r= .952, with a p-value <0.01. The findings displayed those determinants of project implementation represented by project management practices, financial implications, infrastructure factors, regulatory features have contributed R=.974a of the variation in Oil and Gas projects implementation delays as explained by r2 of .949 indicates 94.9% in the model as very strong, as the independent variable very highly explained the dependent variable and show that the model is a very good prediction. 

Key words: project implementation; delays; financial implications; infrastructure; project management practices
DOI: 10.5281/zenodo.10475742
title: DETERMINANTS OF PROJECT IMPLEMENTATION DELAYS: CASE OF OIL AND GAS DEPOTS UPGRADE AT RUBiS ENERGY RWANDA

description: ABSTRACT: The study investigated determinants of project implementation delays: case of OIL and Gas Depots Upgrade at RUBiS energy Rwanda. The specific objectives were to examine how financial implications affects the oil and gas project implementation in Rwanda; the influence of infrastructure to the oil and gas project implementation delays in Rwanda; to explore the influence of regulatory framework to OIL and Gas Depots Upgrade projects; and the effectiveness of current project management practices in addressing delays to OIL and Gas Depots Upgrade project implemented at RUBiS energy Rwanda. Target population was 60 participants the projects of OIL and Gas Depots Upgrade at RUBiS energy Rwanda. The questionnaires were given to 52 respondents as sample size. Interview and documentary techniques have been used to collect data.  The analysis methods were descriptive statistic and multiple regression models.  The results confirmed a significant and strong correlation between financial Implications and Oil and Gas projects implementation delays by r= 0.894. The results revealed very strong correlation between infrastructure factors and Oil and Gas projects implementation delays as shown by r= .917. Findings revealed that there is a significant very strong correlation between Regulatory features and Oil and Gas projects implementation delays as confirmed by r= .972. The results also show that there is very strong correlation between Project management practices and Oil and Gas projects implementation delays as stated by r= .952, with a p-value <0.01. The findings displayed those determinants of project implementation represented by project management practices, financial implications, infrastructure factors, regulatory features have contributed R=.974a of the variation in Oil and Gas projects implementation delays as explained by r2 of .949 indicates 94.9% in the model as very strong, as the independent variable very highly explained the dependent variable and show that the model is a very good prediction. 

Key words: project implementation; delays; financial implications; infrastructure; project management practices
DOI: 10.5519/qd.bh5xz6n0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (100122 records)
DOI: 10.18715/ipgp.2024.lr68kbwy
title: Avalanches catalogue associated to the s1222a Marsquake

description: This is the catalogue of dust avalanches mapped around the s1222a marsquake. The repository contains the avalanche catalogue and the avalanches that have been detected on the post-marskquake images. Both data bases are delivered into a pickle file. In addition, the repository contains a Jupyter notebook that read the pickle files and the full resolution of the oblique view of the mapping generated with the Generic Mapping Tools (GMT) available from https://www.generic-mapping-tools.org. See the file "requirements.txt" for instructions on how to install the required Python environment.
DOI: 10.25810/4c3b-b819.68
title: World Newspaper Coverage of Climate Change or Global Warming, 2004-2023 - November 2023

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.10475752
title: DETERMINANTS OF PROJECT IMPLEMENTATION DELAYS: CASE OF OIL AND GAS DEPOTS UPGRADE AT RUBiS ENERGY RWANDA

description: ABSTRACT: The study investigated determinants of project implementation delays: case of OIL and Gas Depots Upgrade at RUBiS energy Rwanda. The specific objectives were to examine how financial implications affects the oil and gas project implementation in Rwanda; the influence of infrastructure to the oil and gas project implementation delays in Rwanda; to explore the influence of regulatory framework to OIL and Gas Depots Upgrade projects; and the effectiveness of current project management practices in addressing delays to OIL and Gas Depots Upgrade project implemented at RUBiS energy Rwanda. Target population was 60 participants the projects of OIL and Gas Depots Upgrade at RUBiS energy Rwanda. The questionnaires were given to 52 respondents as sample size. Interview and documentary techniques have been used to collect data.  The analysis methods were descriptive statistic and multiple regression models.  The results confirmed a significant and strong correlation between financial Implications and Oil and Gas projects implementation delays by r= 0.894. The results revealed very strong correlation between infrastructure factors and Oil and Gas projects implementation delays as shown by r= .917. Findings revealed that there is a significant very strong correlation between Regulatory features and Oil and Gas projects implementation delays as confirmed by r= .972. The results also show that there is very strong correlation between Project management practices and Oil and Gas projects implementation delays as stated by r= .952, with a p-value <0.01. The findings displayed those determinants of project implementation represented by project management practices, financial implications, infrastructure factors, regulatory features have contributed R=.974a of the variation in Oil and Gas projects implementation delays as explained by r2 of .949 indicates 94.9% in the model as very strong, as the independent variable very highly explained the dependent variable and show that the model is a very good prediction. 

Key words: project implementation; delays; financial implications; infrastructure; project management practices
DOI: 10.5281/zenodo.10475753
title: DETERMINANTS OF PROJECT IMPLEMENTATION DELAYS: CASE OF OIL AND GAS DEPOTS UPGRADE AT RUBiS ENERGY RWANDA

description: ABSTRACT: The study investigated determinants of project implementation delays: case of OIL and Gas Depots Upgrade at RUBiS energy Rwanda. The specific objectives were to examine how financial implications affects the oil and gas project implementation in Rwanda; the influence of infrastructure to the oil and gas project implementation delays in Rwanda; to explore the influence of regulatory framework to OIL and Gas Depots Upgrade projects; and the effectiveness of current project management practices in addressing delays to OIL and Gas Depots Upgrade project implemented at RUBiS energy Rwanda. Target population was 60 participants the projects of OIL and Gas Depots Upgrade at RUBiS energy Rwanda. The questionnaires were given to 52 respondents as sample size. Interview and documentary techniques have been used to collect data.  The analysis methods were descriptive statistic and multiple regression models.  The results confirmed a significant and strong correlation between financial Implications and Oil and Gas projects implementation delays by r= 0.894. The results revealed very strong correlation between infrastructure factors and Oil and Gas projects implementation delays as shown by r= .917. Findings revealed that there is a significant very strong correlation between Regulatory features and Oil and Gas projects implementation delays as confirmed by r= .972. The results also show that there is very strong correlation between Project management practices and Oil and Gas projects implementation delays as stated by r= .952, with a p-value <0.01. The findings displayed those determinants of project implementation represented by project management practices, financial implications, infrastructure factors, regulatory features have contributed R=.974a of the variation in Oil and Gas projects implementation delays as explained by r2 of .949 indicates 94.9% in the model as very strong, as the independent variable very highly explained the dependent variable and show that the model is a very good prediction. 

Key words: project implementation; delays; financial implications; infrastructure; project management practices
DOI: 10.35077/ace-ban-ear
title: fMOST_182681

description: This dataset comes from combining genetic labeling with fMOST to achieve a genetically targeted single neuron saturation screen of
 major classes of cortical projection neurons in the sensorimotor system.
description: Total processed cells: NA
 DATASET METADATA:
 title:'fMOST_182681',
 GeneralModality:'cell morphology',
 Technique:'fMOST'
 SPECIMEN METADATA:
 LocalID:'182681',
 Species:'mouse',
 NCBITaxonomy:'NCBI:txid10090',
 Age:'13',
 Ageunit:'Weeks',
 Sex:'Male',
 Genotype:'Fezf2-creER+LSL-flp',
 OrganName:'Brain',
 Locations:'brain'
 INSTRUMENT METADATA:
 MicroscopeType:'fMOST'
 IMAGE METADATA:
 yAxis:'inferior-to-superior',
 zAxis:'anterior-to-posterior',
 Number:'1, gray',
 displayColor:'(0.8, 0.8, 0.8)',
 stepSizeX:'0.35 micron/pixel',
 stepSizeY:'0.35 micron/pixel',
 stepSizeZ:'1 micron/pixel'
DOI: 10.25810/k55w-7a04.68
title: World Radio Coverage of Climate Change or Global Warming, 2000-2023 - November 2023

description: The Media and Climate Change Observatory Data monitors 130 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.