Data | November 2021 - Browse Articles

9 pages, 941 KiB

Open AccessData Descriptor

Determination of Specific IgG to Identify Possible Food Intolerance in Athletes Using ELISA

by Kristina Malsagova, Alexander Stepanov, Alexandra A. Sinitsyna, Alexander Izotov, Mikhail S. Klyuchnikov, Arthur T. Kopylov and Anna L. Kaysheva

Data 2021, 6(11), 122; https://doi.org/10.3390/data6110122 - 21 Nov 2021

Viewed by 3031

Abstract

Nutrition is considered one of the foundations of athletic performance, and post-workout nutritional recommendations are fundamental to the effectiveness of the recovery and adaptive processes. Therefore, at present, new directions in dietetics are being formed, focused on the creation of personalized diets. To [...] Read more.

Nutrition is considered one of the foundations of athletic performance, and post-workout nutritional recommendations are fundamental to the effectiveness of the recovery and adaptive processes. Therefore, at present, new directions in dietetics are being formed, focused on the creation of personalized diets. To identify the probable risk of somatic and allergic reactions upon contact with food antigens, we used the method of enzyme-linked immunosorbent assay (ELISA) for the quantitative determination of IgG antibodies in the blood plasma of athletes against protein–peptide antigens accommodated in food. The study enrolled 40 athletes of boating and fighting sport disciplines. We found that the majority of the studied participants were characterized by an elevated IgG level against one or two food allergens (barley, almond, strawberry, etc.). Comparative analysis of the semiquantitative levels of IgG antibodies in athletes engaged in boating and fighting did not reveal significant differences between these groups. As a result, foods that are likely to cause the most pronounced immune response amongst the studied participants can be identified, which may indicate the presence of food intolerances. An athlete’s diet is influenced by both external and internal factors that can reduce or worsen the symptoms of a food intolerance/allergy associated with exercise. The range of foods is wide, and the effectiveness of a diet depends on the time, the place, and environmental factors. Therefore, during the recovery period (the post-competition period), athletes are advised to follow the instructions of doctors and nutritionists. An effective, comprehensive recovery strategy during the recovery period may enhance the adaptive response to fatigue, improving muscle function and increasing exercise tolerance. The data obtained may be useful for guiding the development of a new personalized approach and dietary recommendations covering the composition of athletes’ diet and the prevalence of food intolerance. Full article

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11 pages, 2049 KiB

Open AccessData Descriptor

Bicycle Mobility Data: Current Use and Future Potential. An International Survey of Domain Professionals

by Christian Werner and Martin Loidl

Data 2021, 6(11), 121; https://doi.org/10.3390/data6110121 - 18 Nov 2021

Cited by 1 | Viewed by 2449

Abstract

Active mobility, especially cycling, is an essential building block for sustainable urban mobility. Public and private stakeholders are striving to improve conditions for cycling and subsequently increase its modal share. Data are regarded as key for different measures to become efficient and targeted. [...] Read more.

Active mobility, especially cycling, is an essential building block for sustainable urban mobility. Public and private stakeholders are striving to improve conditions for cycling and subsequently increase its modal share. Data are regarded as key for different measures to become efficient and targeted. There is extensive evidence for an increasing amount of mobility data, availability of new data sources and potential usage scenarios for such data. However, little is known about the current use of these data in policy making, planning and related fields. To the best of our knowledge, it has not been investigated yet to which degree professionals in the broader field of cycling promotion benefit from an increasing amount of cycling-related data. Thus, we conducted a multi-lingual online survey among domain professionals and acquired data on their perspectives on current data availability, use and suitability as well as the potential they see for the use of cycling data in the future. In total, we received 325 complete responses from 32 countries, with the vast majority of 241 valid responses originating from Germany, Austria and Italy. Key findings are: 84% of domain professionals attribute high importance to data, and 89% state that they currently cannot or only partly solve their tasks with the data available to them. Results emphasize the need for making more and better suited data available to professionals in cycling-related positions, in both the private and public sector. Full article

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9 pages, 654 KiB

Open AccessData Descriptor

COVID-19 Lockdown Effects on Academic Functioning, Mood, and Health Correlates: Data from Dutch Pharmacy Students, PhD Candidates and Postdocs

by Pauline A. Hendriksen, Agnese Merlo, Elisabeth Y. Bijlsma, Ferdi Engels, Johan Garssen, Gillian Bruce and Joris C. Verster

Data 2021, 6(11), 120; https://doi.org/10.3390/data6110120 - 17 Nov 2021

Cited by 12 | Viewed by 3678

Abstract

Mixed results have been published on the impact of the 2019 coronavirus (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates such as alcohol consumption. Whereas a number of students report an impaired academic performance and increased alcohol [...] Read more.

Mixed results have been published on the impact of the 2019 coronavirus (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates such as alcohol consumption. Whereas a number of students report an impaired academic performance and increased alcohol intake during lockdown periods, other students report no change or an improvement in academic functioning and a reduced alcohol consumption. This data descriptor article describes the dataset of a study investigating the impact of the COVID-19 pandemic on academic functioning. To investigate this, an online survey was conducted among Dutch pharmacy students, PhD candidates and postdoctoral researchers (postdocs) of Utrecht University, the Netherlands. Compared to before the COVID-19 pandemic, the survey assessed possible changes in self-reported academic functioning, mood and health correlates such as alcohol consumption, perceived immune functioning and sleep quality. Retrospective assessments were made for four periods, including (1) the year 2019 (the period before COVID-19), (2) the first lockdown period (15 March–11 May 2020), (3) summer 2020 (no lockdown) and (4) the second lockdown (November 2020–April 2021). This article describes the content of the survey and corresponding dataset. The survey had a response rate of 24.3% and was completed by 345 participants. Full article

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25 pages, 1821 KiB

Open AccessReview

Deep Reinforcement Learning for Trading—A Critical Survey

by Adrian Millea

Data 2021, 6(11), 119; https://doi.org/10.3390/data6110119 - 16 Nov 2021

Cited by 17 | Viewed by 8867

Abstract

Deep reinforcement learning (DRL) has achieved significant results in many machine learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to trading on financial markets with the purpose of unravelling common structures used in the trading community using [...] Read more.

Deep reinforcement learning (DRL) has achieved significant results in many machine learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to trading on financial markets with the purpose of unravelling common structures used in the trading community using DRL, as well as discovering common issues and limitations of such approaches. We include also a short corpus summarization using Google Scholar. Moreover, we discuss how one can use hierarchy for dividing the problem space, as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market; the results of this survey are two-fold: firstly, to find the most promising directions for further research and secondly, to show how a lack of consistency in the community can significantly impede research and the development of DRL agents for trading. Full article

(This article belongs to the Section Featured Reviews of Data Science Research)

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9 pages, 1139 KiB

Open AccessData Descriptor

Transmission Electron Microscopy Tilt-Series Data from In-Situ Chondrocyte Primary Cilia

by Michael J. Jennings, Timothy C. A. Molteno, Robert J. Walker, Jennifer J. Bedford, John P. Leader and Tony Poole

Data 2021, 6(11), 118; https://doi.org/10.3390/data6110118 - 15 Nov 2021

Viewed by 2363

Abstract

The primary cilium has recently become the focus of intensive investigations into understanding the physical structure and processes of eukaryotic cells. This paper describes two tilt-series image datasets, acquired by transmission electron microscopy, of in situ chick-embryo sternal-cartilage primary cilia. These data have [...] Read more.

The primary cilium has recently become the focus of intensive investigations into understanding the physical structure and processes of eukaryotic cells. This paper describes two tilt-series image datasets, acquired by transmission electron microscopy, of in situ chick-embryo sternal-cartilage primary cilia. These data have been released under an open-access licence, and are well suited to tomographic reconstruction and modelling of the cilium. Full article

(This article belongs to the Section Computational Biology, Bioinformatics, and Biomedical Data Science)

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15 pages, 2096 KiB

Open AccessData Descriptor

Multi-Ideology ISIS/Jihadist White Supremacist (MIWS) Dataset for Multi-Class Extremism Text Classification

by Mayur Gaikwad, Swati Ahirrao, Shraddha Phansalkar and Ketan Kotecha

Data 2021, 6(11), 117; https://doi.org/10.3390/data6110117 - 15 Nov 2021

Cited by 4 | Viewed by 2942

Abstract

Social media platforms are a popular choice for extremist organizations to disseminate their perceptions, beliefs, and ideologies. This information is generally based on selective reporting and is subjective in content. However, the radical presentation of this disinformation and its outreach on social media [...] Read more.

Social media platforms are a popular choice for extremist organizations to disseminate their perceptions, beliefs, and ideologies. This information is generally based on selective reporting and is subjective in content. However, the radical presentation of this disinformation and its outreach on social media leads to an increased number of susceptible audiences. Hence, detection of extremist text on social media platforms is a significant area of research. The unavailability of extremism text datasets is a challenge in online extremism research. The lack of emphasis on classifying extremism text into propaganda, radicalization, and recruitment classes is a challenge. The lack of data validation methods also challenges the accuracy of extremism detection. This research addresses these challenges and presents a seed dataset with a multi-ideology and multi-class extremism text dataset. This research presents the construction of a multi-ideology ISIS/Jihadist White supremacist (MIWS) dataset with recent tweets collected from Twitter. The presented dataset can be employed effectively and importantly to classify extremist text into popular types like propaganda, radicalization, and recruitment. Additionally, the seed dataset is statistically validated with a coherence score of Latent Dirichlet Allocation (LDA) and word mover’s distance using a pretrained Google News vector. The dataset shows effectiveness in its construction with good coherence scores within a topic and appropriate distance measures between topics. This dataset is the first publicly accessible multi-ideology, multi-class extremism text dataset to reinforce research on extremism text detection on social media platforms. Full article

(This article belongs to the Special Issue Automatic Disinformation Detection on Social Media Platforms)

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17 pages, 377 KiB

Open AccessArticle

A Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya

by Nelson Kemboi Yego, Juma Kasozi and Joseph Nkurunziza

Data 2021, 6(11), 116; https://doi.org/10.3390/data6110116 - 15 Nov 2021

Cited by 7 | Viewed by 4088

Abstract

The role of insurance in financial inclusion and economic growth, in general, is immense and is increasingly being recognized. However, low uptake impedes the growth of the sector, hence the need for a model that robustly predicts insurance uptake among potential clients. This [...] Read more.

The role of insurance in financial inclusion and economic growth, in general, is immense and is increasingly being recognized. However, low uptake impedes the growth of the sector, hence the need for a model that robustly predicts insurance uptake among potential clients. This study undertook a two phase comparison of machine learning classifiers. Phase I had eight machine learning models compared for their performance in predicting the insurance uptake using 2016 Kenya FinAccessHousehold Survey data. Taking Phase I as a base in Phase II, random forest and XGBoost were compared with four deep learning classifiers using 2019 Kenya FinAccess Household Survey data. The random forest model trained on oversampled data showed the highest F1-score, accuracy, and precision. The area under the receiver operating characteristic curve was furthermore highest for random forest; hence, it could be construed as the most robust model for predicting the insurance uptake. Finally, the most important features in predicting insurance uptake as extracted from the random forest model were income, bank usage, and ability and willingness to support others. Hence, there is a need for a design and distribution of low income based products, and bancassurance could be said to be a plausible channel for the distribution of insurance products. Full article

(This article belongs to the Special Issue Data Analysis for Financial Markets)

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30 pages, 5027 KiB

Open AccessReview

Innovation Trajectories for a Society 5.0

by Fabio De Felice, Marta Travaglioni and Antonella Petrillo

Data 2021, 6(11), 115; https://doi.org/10.3390/data6110115 - 10 Nov 2021

Cited by 18 | Viewed by 4119

Abstract

Big Data, the Internet of Things, and robotic and augmented realities are just some of the technologies that belong to Industry 4.0. These technologies improve working conditions and increase productivity and the quality of industry production. However, they can also improve life and [...] Read more.

Big Data, the Internet of Things, and robotic and augmented realities are just some of the technologies that belong to Industry 4.0. These technologies improve working conditions and increase productivity and the quality of industry production. However, they can also improve life and society as a whole. A new perspective is oriented towards social well-being and it is called Society 5.0. Industry 4.0 supports the transition to the new society, but other drivers are also needed. To guide the transition, it is necessary to identify the enabling factors that integrate Industry 4.0. A conceptual framework was developed in which these factors were identified through a literature review and the analytical hierarchy process (AHP) methodology. Furthermore, the way in which they relate was evaluated with the help of the interpretive structural modeling (ISM) methodology. The proposed framework fills a research gap, which has not yet consolidated a strategy that includes all aspects of Society 5.0. As a result, the main driver, in addition to technology, is international politics. Full article

(This article belongs to the Special Issue Development of a Smart Future under Society 5.0)

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16 pages, 1045 KiB

Open AccessArticle

Metadata Schema for Managing Digital Data and Images of Thai Human Skulls

by Satapon Yosakonkun, Panya Tuamsuk, Wirapong Chansanam and Kulthida Tuamsuk

Data 2021, 6(11), 114; https://doi.org/10.3390/data6110114 - 10 Nov 2021

Viewed by 2562

Abstract

This research was aimed at developing metadata that meets international standards for the purpose of managing digital data and images of Thai human skulls for medical studies. The research was conducted by applying the Metadata Lifecycle Model of the Metadata Architecture and Application [...] Read more.

This research was aimed at developing metadata that meets international standards for the purpose of managing digital data and images of Thai human skulls for medical studies. The research was conducted by applying the Metadata Lifecycle Model of the Metadata Architecture and Application Team. The model comprises four steps: requirement assessment and content analysis, identification of metadata requirements, metadata schema development, and metadata service and evaluation. The research outcome was a metadata schema composed of four modules, seven data element sets, and 29 pieces of data, each of which had six sets of property descriptions. Metadata evaluation conducted by three specialists in the field of anatomy and forensic medicine and three experts in the field of information science and metadata through free retrieval based on the Continuum of Metadata Quality in four aspects revealed that the experts were satisfied with the quality of metadata at a very high level: 100% for completeness, accuracy, and accessibility, and 94% for conformance to expectations. The developed metadata contain details that can be used to describe the characteristics of human skulls, with consideration taken in the development of the language used, retrieval, access, data exchange, and sharing. Thus, this novel metadata schema can be of use in management of digital data and images of human skulls for the purposes of medical studies, i.e., human anatomy and forensic anthropology. Full article

(This article belongs to the Section Information Systems and Data Management)

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15 pages, 2285 KiB

Open AccessArticle

Nowcasting India Economic Growth Using a Mixed-Data Sampling (MIDAS) Model (Empirical Study with Economic Policy Uncertainty–Consumer Prices Index)

by Pradeep Mishra, Khder Alakkari, Mostafa Abotaleb, Pankaj Kumar Singh, Shilpi Singh, Monika Ray, Soumitra Sankar Das, Umme Habibah Rahman, Ali J. Othman, Nazirya Alexandrovna Ibragimova, Gulfishan Firdose Ahmed, Fozia Homa, Pushpika Tiwari and Ritisha Balloo

Data 2021, 6(11), 113; https://doi.org/10.3390/data6110113 - 02 Nov 2021

Cited by 1 | Viewed by 3208

Abstract

Economics suffers from a blurred view of the economy due to the delay in the official publication of macroeconomic variables and, essentially, of the most important variable of real GDP. Therefore, this paper aimed at nowcasting GDP in India based on high-frequency data [...] Read more.

Economics suffers from a blurred view of the economy due to the delay in the official publication of macroeconomic variables and, essentially, of the most important variable of real GDP. Therefore, this paper aimed at nowcasting GDP in India based on high-frequency data released early. Instead of using a large set of data thus increasing statistical complexity, two main indicators of the Indian economy (economic policy uncertainty and consumer price index) were relied on. The paper followed the MIDAS–Almon (PDL) weighting approach, which allowed us to successfully capture structural breaks and predict Indian GDP for the second quarter of 2021, after evaluating the accuracy of the nowcasting and out-of-sample prediction. Our results indicated low values of the RMSE in the sample and when predicting the out-of-sample1- and 4-quarter horizon, but RMSE increased when predicting the 10-quarter horizon. Due to the effect of the short-term structural break, we found that RMSE values decreased for the last prediction point. Full article

(This article belongs to the Section Information Systems and Data Management)

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18 pages, 2940 KiB

Open AccessArticle

Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier

by Syed Ali Jafar Zaidi, Saad Tariq and Samir Brahim Belhaouari

Data 2021, 6(11), 112; https://doi.org/10.3390/data6110112 - 02 Nov 2021

Cited by 7 | Viewed by 3524

Abstract

Machine learning (ML)-based prediction is considered an important technique for improving decision making during the planning process. Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. This study [...] Read more.

Machine learning (ML)-based prediction is considered an important technique for improving decision making during the planning process. Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. This study focuses on the future prediction of the effectiveness of the COVID-19 vaccine effectiveness which has been presented as a light in the dark. People bear several reservations, including concerns about the efficacy of the COVID-19 vaccine. Under these presumptions, the COVID-19 vaccine would either lower the risk of developing the malady after injection, or the vaccine would impose side effects, affecting their existing health condition. In this regard, people have publicly expressed their concerns regarding the vaccine. This study intends to estimate what perception the masses will establish about the role of the COVID-19 vaccine in the future. Specifically, this study exhibits people’s predilection toward the COVID-19 vaccine and its results based on the reviews. Five models, e.g., random forest (RF), a support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and an artificial neural network (ANN), were used for forecasting the overall predilection toward the COVID-19 vaccine. A voting classifier was used at the end of this study to determine the accuracy of all the classifiers. The results prove that the SVM produces the best forecasting results and that artificial neural networks (ANNs) produce the worst prediction toward the individual aptitude to be vaccinated by the COVID-19 vaccine. When using the voting classifier, the proposed system provided an overall accuracy of 89.9% for the random dataset and 45.7% for the date-wise dataset. Thus, the results show that the studied prediction technique is a promising and encouraging procedure for studying the future trends of the COVID-19 vaccine. Full article

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15 pages, 23389 KiB

Open AccessData Descriptor

King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD)

by Asmaa S. Alsolami, Wafaa Shalash, Wafaa Alsaggaf, Sawsan Ashoor, Haneen Refaat and Mohammed Elmogy

Data 2021, 6(11), 111; https://doi.org/10.3390/data6110111 - 25 Oct 2021

Cited by 12 | Viewed by 10884

Abstract

The current era is characterized by the rapidly increasing use of computer-aided diagnosis (CAD) systems in the medical field. These systems need a variety of datasets to help develop, evaluate, and compare their performances fairly. Physicians indicated that breast anatomy, especially dense ones, [...] Read more.

The current era is characterized by the rapidly increasing use of computer-aided diagnosis (CAD) systems in the medical field. These systems need a variety of datasets to help develop, evaluate, and compare their performances fairly. Physicians indicated that breast anatomy, especially dense ones, and the probability of breast cancer and tumor development, vary highly depending on race. Researchers reported that breast cancer risk factors are related to culture and society. Thus, there is a massive need for a local dataset representing breast cancer in our region to help develop and evaluate automatic breast cancer CAD systems. This paper presents a public mammogram dataset called King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD) version 1. To our knowledge, KAU-BCMD is the first dataset in Saudi Arabia that deals with a large number of mammogram scans. The dataset was collected from the Sheikh Mohammed Hussein Al-Amoudi Center of Excellence in Breast Cancer at King Abdulaziz University. It contains 1416 cases. Each case has two views for both the right and left breasts, resulting in 5662 images based on the breast imaging reporting and data system. It also contains 205 ultrasound cases corresponding to a part of the mammogram cases, with 405 images as a total. The dataset was annotated and reviewed by three different radiologists. Our dataset is a promising dataset that contains different imaging modalities for breast cancer with different cancer grades for Saudi women. Full article

(This article belongs to the Special Issue Machine Learning in Image Analysis and Pattern Recognition)

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10 pages, 714 KiB

Open AccessData Descriptor

Dataset of Students’ Performance Using Student Information System, Moodle and the Mobile Application “eDify”

by Raza Hasan, Sellappan Palaniappan, Salman Mahmood, Ali Abbas and Kamal Uddin Sarker

Data 2021, 6(11), 110; https://doi.org/10.3390/data6110110 - 22 Oct 2021

Cited by 8 | Viewed by 7970

Abstract

The data presented in this article comprise an educational dataset collected from the student information system (SIS), the learning management system (LMS) called Moodle, and video interactions from the mobile application called “eDify.” The dataset, from the higher educational institution (HEI) in Sultanate [...] Read more.

The data presented in this article comprise an educational dataset collected from the student information system (SIS), the learning management system (LMS) called Moodle, and video interactions from the mobile application called “eDify.” The dataset, from the higher educational institution (HEI) in Sultanate of Oman, comprises five modules of data from Spring 2017 to Spring 2021. The dataset consists of 326 student records with 40 features in total, including the students’ academic information from SIS (which has 24 features), the students’ activities performed on Moodle within and outside the campus (comprising 10 features), and the students’ video interactions collected from eDify (consisting of six features). The dataset is useful for researchers who want to explore students’ academic performance in online learning environments, and will help them to model their educational datamining models. Moreover, it can serve as an input for predicting students’ academic performance within the module for educational datamining and learning analytics. Furthermore, researchers are highly recommended to refer to the original papers for more details. Full article

(This article belongs to the Special Issue Education Data Mining)

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12 pages, 2383 KiB

Open AccessArticle

Neglected Theories of Business Cycle—Alternative Ways of Explaining Economic Fluctuations

by Klára Čermáková, Michal Bejček, Jan Vorlíček and Helena Mitwallyová

Data 2021, 6(11), 109; https://doi.org/10.3390/data6110109 - 20 Oct 2021

Cited by 10 | Viewed by 2263

Abstract

The business cycle is a frequent topic in economic research; however, the approach based on individual strategies often remains neglected. The aspiration of this study is to prove that the behavior of individuals can originate and fuel an economic cycle. For this purpose, [...] Read more.

The business cycle is a frequent topic in economic research; however, the approach based on individual strategies often remains neglected. The aspiration of this study is to prove that the behavior of individuals can originate and fuel an economic cycle. For this purpose, we are using an algorithm based on a repeated dove–hawk game. The results reveal that the sum of output in a society is affected by the ratio of individual strategies. Cyclical changes in this ratio will be translated into fluctuations of the total product of society. We present game theory modelling of a strategic behavioral approach as a valid theoretical foundation for explaining economic fluctuations. This article offers an unusual insight into the business cycle’s causes and growth theories. Full article

(This article belongs to the Special Issue Big Data Analytics in Bankruptcy Prediction)

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