cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10848532
title: Benchmark for classifying presence of coral and camera motion in underwater

description: Benchmark for classifying presence of coral in underwater videos, and camera motion that would be necessary for 3d reconstruction of coral. Videos are collected from the YouTube-8M dataset.
DOI: 10.5281/zenodo.10848533
title: Benchmark for classifying presence of coral and camera motion in underwater

description: Benchmark for classifying presence of coral in underwater videos, and camera motion that would be necessary for 3d reconstruction of coral. Videos are collected from the YouTube-8M dataset.
DOI: 10.1285/i20705948v16n3p722
title: Using t-distribution for Robust ‎Hierarchical Bayesian Small Area Estimation under Measurement Error in Covariates

description: Small area estimation often suffers from imprecise direct estimators due to small sample sizes. One method for giving direct estimators more strength is to use models.‎ Models ‎employ area effects and ‎include supplementary information from extra sources as covariates to increase the accuracy of direct estimators. ‎The valid covariates are the basis of ‎the ‎small ‎area ‎estimation.‎ Therefore, measurement error (ME) in covariates can produce contradictory results, i.e., even reduce the precision of direct estimators. The Gaussian distribution with known variance is generally apply as a distribution of ME. ‏‎ ‎However‎, ‎in real problem, ‎‎there might be situations in which the normality assumption fo MEs does not hold‎. In addition, the assumption of known ME variance is restricted. To address these issues and obtain a more robust model, ‎‎we propose modeling ME using a t-distribution with known and unknown degrees of freedom. Model parameters are estimated using a fully Bayesian framework based on MCMC methods. We validate our proposed model using simulated data and apply it to well-known crop data and the cost and income of households living in Kurdistan province of ‎Iran.
DOI: 10.5281/zenodo.10829906
title: IPMWORKS Resource Toolbox - database extraction March 2024

description: The IPMWORKS IPM Resource Toolbox (Toolbox) has been developed as an interactive, online repository of integrated pest management (IPM) resources. Populated with high priority resources for farmers and their advisors during the project, its structure enables additional resources added over time. The repository is a public interactive website, available to anyone looking to access, understand, and implement IPM. Built on an open-source content management system, the toolbox is designed to require minimal post-production site maintenance and support, while being easily expanded to integrate resources from future initiatives.At the core of the Toolbox lies MongoDB, a powerful NoSQL database management system. The schema-less nature of MongoDB allows for flexible data modeling, crucial for accommodating the diverse array of materials within the IPMWORKS ecosystem. Additionally, the integration of GridFS, a feature of MongoDB, facilitates the storage and retrieval of large files like images, PDFs, and documents. This architectural choice ensures optimal performance and efficiency in handling a wide range of materials. We here make available all content uploaded to the IPMWORKS Resource Toolbox up to 18 March 2024. Materials are available in two formats, first as MongoDB files which require users to open them as a Mongo database file, and second as JSON files. Note that the JSON format does not include access to any pdfs attached to Toolbox content, only the associated metadata.
DOI: 10.5281/zenodo.10829907
title: IPMWORKS Resource Toolbox - database extraction March 2024

description: The IPMWORKS IPM Resource Toolbox (Toolbox) has been developed as an interactive, online repository of integrated pest management (IPM) resources. Populated with high priority resources for farmers and their advisors during the project, its structure enables additional resources added over time. The repository is a public interactive website, available to anyone looking to access, understand, and implement IPM. Built on an open-source content management system, the toolbox is designed to require minimal post-production site maintenance and support, while being easily expanded to integrate resources from future initiatives.At the core of the Toolbox lies MongoDB, a powerful NoSQL database management system. The schema-less nature of MongoDB allows for flexible data modeling, crucial for accommodating the diverse array of materials within the IPMWORKS ecosystem. Additionally, the integration of GridFS, a feature of MongoDB, facilitates the storage and retrieval of large files like images, PDFs, and documents. This architectural choice ensures optimal performance and efficiency in handling a wide range of materials. We here make available all content uploaded to the IPMWORKS Resource Toolbox up to 18 March 2024. Materials are available in two formats, first as MongoDB files which require users to open them as a Mongo database file, and second as JSON files. Note that the JSON format does not include access to any pdfs attached to Toolbox content, only the associated metadata.
DOI: 10.1285/i20705948v16n3p740
title: The Extended Kumaraswamy Generated Family: Properties, Inference and Applications in Applied Fields

description: In this paper, a new Kumaraswamy generalized family is proposed. The special sub-models of the family accommodate symmetrical, left-skewed, bimodal, right-skewed unimodal, and reverse-J densities, as well as increasing, modified bathtub, decreasing, bathtub, upside down bathtub, reverse J and J shaped hazard rates. The fundamental properties of the family are derived. The maximum likelihood method and seven other methods are used for estimating the model parameters. Numerical simulations are performed to explore the performance of these estimation methods. Three real-life data sets from medicine, agriculture and engineering are fitted to illustrate the flexibility of the proposed family. The proposed family is a good alternative to the Kumaraswamy-G, beta-G, and Topp-Leone-G families.
DOI: 10.1285/i20705948v16n3p764
title: An application of Poisson hidden Markov model to forecast endometrial cancer cases in Odisha, India

description: Background: Time series model often used for forecasting, but inadequate when the data series is unbounded and over dispersed in nature. Moreover, if the observations are serially dependent, then Markov dependent mixture model i.e., Poisson Hidden Markov model can be used. The objective of this study was to apply Poisson Hidden Markov model to forecast month wise new endometrial cancer cases. Materials and Methods: Month-wise total number of registered endometrial cancer cases has been collected from a local cancer hospital in Odisha from January, 2017 to December, 2021. In this paper we have applied the Poisson hidden Markov model to forecast, the number of endometrial cancer cases for next 12 months. Results: Three state Poisson hidden Markov model was found as best fitted model and used to forecast the endometrial cancer cases for the next 12 months. The results showed that the number of endometrial cancer cases most likely to lie in state 2 in January and in state 3 in rest of the months in 2022. The monthly forecasted mean of endometrial cancer cases varies between 34 to 38 for the year 2022. Conclusion: This study reveals that the average endometrial cancer cases will increase in future months. It is also suggested that, the three-state hidden Markov model can be used to fit and forecast the distribution of the number of endometrial cancer cases.
DOI: 10.1285/i20705948v16n3p784
title: A Hybrid Regression Model for improving prediction accuracy

description: The main disadvantage of Regression tree is that it assigns the same predicted value, average value, for all the tuples which satisfies the same corresponding splitting criterion. K-Nearest Neighbors (KNN) is sensitive to irrelevant or redundant features because all features contribute to the similarity. In this paper a hybrid regression model based on Regression tree (RT) and KNN is proposed which overcomes the above two problems. The performance of proposed model is compared with KNN for 10 types of distance measures. The performance of proposed model is also compared with RT, K-Nearest neighbour regression (KNN), Support Vector Regression (SVR) through a Monte Carlo simulation study. The simulation result indicates that hybrid model outperforms all other regression model irrespective of sample size when the observations are from normal distributions as well as t-distributions. The working of the proposed model is illustrated for a real-life application on global warming data of Delhi.
DOI: 10.35097/1872
title: Research Data of the Publication "Origin of Performance Improvements in Lithium-Ion Cells after Fast Formation"

description: The data of each figure is generally structured in a way that i) the linestyle is used to differentiate multiple curves within the same plot and ii) the color is used to subdivide the plot data into multiple files in case of ambiguity. For instance, data corresponding to a solid line is given by the two columns "xData_solid" and "yData_solid". The same naming structure is used for dashed lines, i.e., "xData_dashed" and "yData_dashed", and dotted lines, i.e., "xData_dotted" and "yData_dotted". To differentiate lines or bars with the same color, the data files are explicitly labeled with the respective legend entry. For example, a C-rate test comprising C/10, 1C and 2C discharge is organized into three distinct files, each file corresponding to one of the C-rates. The values in each data file are given in the units specified in the corresponding figure of the manuscript. In labels, a C-rate of C/10 and C/2 is described as "C10" and "C2", respectively. C-rates above 1C are used verbatim. For details on the performed experimental study, see the Experimental Section in the manuscript. For information about the SEI-extended cell model and its parameterization, see Computational Methods in the manuscript.
DOI: 10.5281/zenodo.10843595
title: Additional Tables for Defining Factors Important During Colonization And Invasion By The Pediatric Pathogen Kingella kingae
DOI: 10.6084/m9.figshare.24171774
title: The chromosomal-level genome of <i>Oryzias sinensis </i>reveals chromosome fusion among ricefish

description: zhonghuaqingjiang.chr.fasta: The chromosome assembly file of Oryzias sinensis.<i>Oryzias</i>_<i>sinensis</i>_atac_raw_1.fq and <i>Oryzias</i>_<i>sinensis</i>_atac_raw_2.fq are raw data from ATAC sequencing.
DOI: 10.6084/m9.figshare.24171774.v2
title: The chromosomal-level genome of <i>Oryzias sinensis </i>reveals chromosome fusion among ricefish

description: zhonghuaqingjiang.chr.fasta: The chromosome assembly file of Oryzias sinensis.<i>Oryzias</i>_<i>sinensis</i>_atac_raw_1.fq and <i>Oryzias</i>_<i>sinensis</i>_atac_raw_2.fq are raw data from ATAC sequencing.
DOI: 10.6084/m9.figshare.24498652
title: Supplementary data of "Improving Temporal and Spatial Coverage of UK Black Carbon Measurementsby Applying a Machine Learning Approach"

description: These two csv files include the actual monitoring and estimated BC data. 10 urban background sites data: UB_actual_&_estimated_BC10 rural background sites data: RB_actual_&_estimated_BC
DOI: 10.6084/m9.figshare.24498652.v1
title: Supplementary data of "Improving Temporal and Spatial Coverage of UK Black Carbon Measurementsby Applying a Machine Learning Approach"

description: These two csv files include the actual monitoring and estimated BC data. 10 urban background sites data: UB_actual_&_estimated_BC10 rural background sites data: RB_actual_&_estimated_BC
DOI: 10.5281/zenodo.10846875
title: MicroRes Dataset

description: The Python pickle files contain the preprocessed monitoring metrics of the following microservices during normal and fault injection periods.



Train Ticket (https://github.com/FudanSELab/train-ticket/)

Social Network (https://github.com/delimitrou/DeathStarBench)

A cloud service provider's microservice system


The dataset was used in "MicroRes: Versatile Resilience Profiling in Microservices via Degradation Dissemination Indexing".

Please note that the data has been sanitized to remove confidential information.
DOI: 10.5281/zenodo.10846876
title: MicroRes Dataset

description: The Python pickle files contain the preprocessed monitoring metrics of the following microservices during normal and fault injection periods.



Train Ticket (https://github.com/FudanSELab/train-ticket/)

Social Network (https://github.com/delimitrou/DeathStarBench)

A cloud service provider's microservice system


The dataset was used in "MicroRes: Versatile Resilience Profiling in Microservices via Degradation Dissemination Indexing".

Please note that the data has been sanitized to remove confidential information.
DOI: 10.5281/zenodo.5638237
title: AID Dataset

description: The CSV file contains the preprocessed status series of all microservices of a production cloud system. The dataset was used in "AID: Efficient Prediction of Aggregated Intensity of Dependency in Large-scale Cloud Systems". Please note that we only release one day's data due to the security policy of the industrial collaborator.
DOI: 10.5281/zenodo.5638238
title: AID Dataset

description: The CSV file contains the preprocessed status series of all microservices of a production cloud system. The dataset was used in "AID: Efficient Prediction of Aggregated Intensity of Dependency in Large-scale Cloud Systems". Please note that we only release one day's data due to the security policy of the industrial collaborator.
DOI: 10.6084/m9.figshare.25450810.v1
title: Supplementary Material for: Sleep disorders and sleep disturbances in persons with multiple sclerosis: A population-based matched case-control study in Denmark

description: Introduction Adverse sleep is common in multiple sclerosis (MS). Population-based studies including adequate control groups are lacking. We hypothesized that the prevalence of sleep disorders and other sleep disturbances would be higher in persons with MS than in controls.
Methods We conducted a population-based study linking individual-level data from the Danish MS Registry (n=21,943 persons with MS) and the Danish Population Registry (n=109,715 matched controls) with information on sleep disorders from the Danish National Patient Registry and other sleep disturbances assessed by dispensed prescription drugs from the Danish National Prescription Registry.
Results Prevalence of diagnosed sleep disorders in terms of central hypersomnia (0.15% vs. 0.06%), sleep disturbances (1.05% vs. 0.70%), and sleep movements (0.22% vs. 0.13%) and other sleep disturbances identified by dispensed central acting (10.73% vs. 1.10%) and hypnotic use (30.65% vs. 20.13%) medication was statistically significantly higher among persons with MS when compared to controls. We found no statistically significant difference in the prevalence of sleep apnea and parasomnia between groups. Stratified by sex and age at MS diagnosis, results for differences between persons with MS and controls were similar.
Conclusion In this registry-based study, we found that the prevalence of several diagnosed sleep disorders was higher persons with MS than in controls, that is those reflecting insomnia and daytime symptoms including hypersomnia. Other sleep disturbances identified by dispensed prescription medication was markedly higher in persons with MS than controls.
DOI: 10.5281/zenodo.7920148
title: Data from: ChatGPT to generate clinical vignettes for teaching and multiple-choice questions for assessment: A randomized controlled experiment

description: Dataset Info

 

This dataset is based on the study published in Medical Teacher: Coşkun, Ö., Kıyak, Y. S., & Budakoğlu, I. İ. (2024). ChatGPT to generate clinical vignettes for teaching and multiple-choice questions for assessment: A randomized controlled experiment. Medical teacher, 1-7. https://www.tandfonline.com/doi/full/10.1080/0142159X.2024.2327477

1) Case Evaluation

# The first row of the dataset identifies the columns.# The first column represents the participant ids in the case evaluation.# The second column represents the participants' groups (1: ChatGPT group, 0: Control (Human-Written) Group)# The third column and the following nine columns reveal the responses of the medical students to the statements using a Likert scale (1: Definitely not agree, 5: Definitely agree).

2) Test Results

# The first row of the dataset identifies the columns.# The first column represents the participant ids in the exam.# The second column represents the participants' groups (1: ChatGPT group, 0: Control (Human-Written) Group)# The third column and the following 14 columns reveal the responses of the medical students to the single-best answer multiple-choice questions (1: Correct, 0: Incorrect).
DOI: 10.5281/zenodo.7920149
title: Data from: ChatGPT to generate clinical vignettes for teaching and multiple-choice questions for assessment: A randomized controlled experiment

description: Dataset Info

 

This dataset is based on the study published in Medical Teacher: Coşkun, Ö., Kıyak, Y. S., & Budakoğlu, I. İ. (2024). ChatGPT to generate clinical vignettes for teaching and multiple-choice questions for assessment: A randomized controlled experiment. Medical teacher, 1-7. https://www.tandfonline.com/doi/full/10.1080/0142159X.2024.2327477

1) Case Evaluation

# The first row of the dataset identifies the columns.# The first column represents the participant ids in the case evaluation.# The second column represents the participants' groups (1: ChatGPT group, 0: Control (Human-Written) Group)# The third column and the following nine columns reveal the responses of the medical students to the statements using a Likert scale (1: Definitely not agree, 5: Definitely agree).

2) Test Results

# The first row of the dataset identifies the columns.# The first column represents the participant ids in the exam.# The second column represents the participants' groups (1: ChatGPT group, 0: Control (Human-Written) Group)# The third column and the following 14 columns reveal the responses of the medical students to the single-best answer multiple-choice questions (1: Correct, 0: Incorrect).
DOI: 10.21252/7xzy-m138
title: Code lists used in the definition of the dataset for the paper “Gabapentinoid Use and the Risk of Fractures in Patients with Inflammatory Arthritis: Nested Case-Control Study in the Clinical Practice Research Datalink Aurum”

description: These files contain the lists of CPRD medical and product codes, and ICD-10 and OPCS codes (used in HES APC data) used to define all variables in the paper “Gabapentinoid Use and the Risk of Fractures in Patients with Inflammatory Arthritis: Nested Case-Control Study in the Clinical Practice Research Datalink Aurum“ by Scott et al.
DOI: 10.5281/zenodo.10566659
title: cox7a1 function as SC assembly factor in striated muscle-Part 1

description: This Dataset contains raw data related to the manuscirpt entitled  "Cox7a1 mediated CIV dimerization impacts skeletal muscle physiology and cardiac injury response"

 

Carolina García-Poyatos1,2, Prateek Arora3,4, Enrique Calvo1, Ines J. Marques3,4, Nick Kirschke3,4, Maria Galardi-Castilla1, Carla Lembke3,4, Marco Meer3,4, Paula Rodriguez-Montes1, David Haberthür5, Ruslan Hlushchuk5, Jesús Vázquez1,6, Peter Vermathen7, José Antonio Enríquez1,2* and Nadia Mercader1

1Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain

2Centro de Investigación Biomédica en red en Fragilidad y Envejecimiento saludable (CIBERFES), Madrid, Spain

3Department of Developmental Biology and Regeneration, Institute of Anatomy, University of Bern, Bern, Switzerland

4 Department for Biomedical Research, Cardiovascular Disease Program, University of Bern, Bern, Switzerland

5MikroCT, Institute of Anatomy, University of Bern, Bern, Switzerland

6Centro de Investigación Biomédica en red en Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain

7University Institute of Diagnostic and Interventional Neuroradiology, Magnetic Resonance Methodology, University of Bern, Bern, Switzerland
DOI: 10.5281/zenodo.10575260
title: cox7a1 functions as SC assembly factor in striated muscle-Part 2

description: Raw data related to manuscript 

Cox7a1 mediated CIV dimerization impacts skeletal muscle physiology and cardiac injury response 

 

Carolina García-Poyatos1,2, Prateek Arora3,4, Enrique Calvo1, Ines J. Marques3,4, Nick Kirschke3,4, Maria Galardi-Castilla1, Carla Lembke3,4, Marco Meer3,4, Paula Rodriguez-Montes1, Alexander Ernst3, David Haberthür5, Ruslan Hlushchuk5, Jesús Vázquez1,6, Peter Vermathen7, José Antonio Enríquez1,2* and Nadia Mercader1,3,4*
DOI: 10.5281/zenodo.10569338
title: cox7a1 functions as SC assembly factor in striated muscle-Part 3

description: Raw data related to the manuscript " Cox7a1 mediated CIV dimerization impacts skeletal muscle physiology and cardiac injury response"

Carolina García-Poyatos1,2, Prateek Arora3,4, Enrique Calvo1, Ines J. Marques3,4, Nick Kirschke3,4, Maria Galardi-Castilla1, Carla Lembke3,4, Marco Meer3,4, Paula Rodriguez-Montes1, David Haberthür5, Ruslan Hlushchuk5, Jesús Vázquez1,6, Peter Vermathen7, José Antonio Enríquez1,2* and Nadia Mercader1,3,4*

 

 

1Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain

2Centro de Investigación Biomédica en red en Fragilidad y Envejecimiento saludable (CIBERFES), Madrid, Spain

3Department of Developmental Biology and Regeneration, Institute of Anatomy, University of Bern, Bern, Switzerland

4 Department for Biomedical Research, Cardiovascular Disease Program, University of Bern, Bern, Switzerland

5MikroCT, Institute of Anatomy, University of Bern, Bern, Switzerland

6Centro de Investigación Biomédica en red en Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain

7University Institute of Diagnostic and Interventional Neuroradiology, Magnetic Resonance Methodology, University of Bern, Bern, Switzerland
DOI: 10.5281/zenodo.10569339
title: cox7a1 functions as SC assembly factor in striated muscle-Part 3

description: Raw data related to the manuscript " Cox7a1 mediated CIV dimerization impacts skeletal muscle physiology and cardiac injury response"

Carolina García-Poyatos1,2, Prateek Arora3,4, Enrique Calvo1, Ines J. Marques3,4, Nick Kirschke3,4, Maria Galardi-Castilla1, Carla Lembke3,4, Marco Meer3,4, Paula Rodriguez-Montes1, David Haberthür5, Ruslan Hlushchuk5, Jesús Vázquez1,6, Peter Vermathen7, José Antonio Enríquez1,2* and Nadia Mercader1,3,4*

 

 

1Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain

2Centro de Investigación Biomédica en red en Fragilidad y Envejecimiento saludable (CIBERFES), Madrid, Spain

3Department of Developmental Biology and Regeneration, Institute of Anatomy, University of Bern, Bern, Switzerland

4 Department for Biomedical Research, Cardiovascular Disease Program, University of Bern, Bern, Switzerland

5MikroCT, Institute of Anatomy, University of Bern, Bern, Switzerland

6Centro de Investigación Biomédica en red en Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain

7University Institute of Diagnostic and Interventional Neuroradiology, Magnetic Resonance Methodology, University of Bern, Bern, Switzerland
DOI: 10.7284/157556
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise EN704 using a Hawaii UHDAS instrument system onboard the platform RV Endeavor

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/157571
title: Anemometer data collected during research cruise EN704 using a Gill Windsonic instrument system onboard the platform RV Endeavor

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/157576
title: Anemometer data collected during research cruise EN704 using a Gill Windsonic instrument system onboard the platform RV Endeavor

description: Original (unprocessed) data collected during a Field_expedition