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Data, Volume 8, Issue 7 (July 2023) – 10 articles

Cover Story (view full-size image): The chemical and fermentative quality of maize silage and its aerobic stability depends on farming cultivation, harvesting and further silage bunker management. In Northeast Italy, on-field data were collected between 2016 and 2022, resulting in a dataset of 1500 records of freshly harvested plant and silage chemical and nutritional traits. The trials involved different hybrids (early and late FAO classes), agronomic input levels, bacterial inoculants, bunker densities and delays in silo sealing. Multivariate and prognostic risk score approaches applied to the collected maize data enable the assessment of the relationship between the freshly harvested chemical composition and the quality and aerobic stability of the derived ensiled forage. View this paper
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24 pages, 3472 KiB  
Article
A Wavelet-Decomposed WD-ARMA-GARCH-EVT Model Approach to Comparing the Riskiness of the BitCoin and South African Rand Exchange Rates
by Thabani Ndlovu and Delson Chikobvu
Data 2023, 8(7), 122; https://doi.org/10.3390/data8070122 - 24 Jul 2023
Viewed by 1261
Abstract
In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness [...] Read more.
In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR empirically. Maximal Overlap Discrete Wavelet Transform (MODWT) and two mother wavelet filters on the returns series are considered in this paper, viz., the Haar and Daubechies (d4). The findings show that BitCoin/USD is riskier than ZAR/USD since it has a higher VaR per unit invested in each currency. At the 99% significance level, BitCoin/USD has average values of VaR of 2.71% and 4.98% for the WD-ARMA-GARCH-GPD and WD-ARMA-GARCH-GEVD models, respectively; and this is slightly higher than the respective 2.69% and 3.59% for the ZAR/USD. The average BitCoin/USD returns of 0.001990 are higher than ZAR/USD returns of −0.000125. These findings are consistent with the mean-variance portfolio theory, which suggests a higher yield for riskier assets. Based on the p-values of the Kupiec likelihood ratio test, the hybrid model adequacy is largely accepted, as p-values are greater than 0.05, except for the WD-ARMA-GARCH-GEVD models at a 99% significance level for both currencies. The findings are helpful to financial risk practitioners and forex traders in formulating their diversification and hedging strategies and ascertaining the risk-adjusted capital requirement to be set aside as a cushion in the event of the occurrence of an actual loss. Full article
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16 pages, 5700 KiB  
Data Descriptor
Knowledge Discovery and Dataset for the Improvement of Digital Literacy Skills in Undergraduate Students
by Pongpon Nilaphruek and Pattama Charoenporn
Data 2023, 8(7), 121; https://doi.org/10.3390/data8070121 - 20 Jul 2023
Cited by 2 | Viewed by 1534
Abstract
For over two decades, scholars and practitioners have emphasized the importance of digital literacy, yet the existing datasets are insufficient for establishing learning analytics in Thailand. Learning analytics focuses on gathering and analyzing student data to optimize learning tools and activities to improve [...] Read more.
For over two decades, scholars and practitioners have emphasized the importance of digital literacy, yet the existing datasets are insufficient for establishing learning analytics in Thailand. Learning analytics focuses on gathering and analyzing student data to optimize learning tools and activities to improve students’ learning experiences. The main problem is that the ICT skill levels of the youth are rather low in Thailand. To facilitate research in this field, this study has compiled a dataset containing information from the IC3 digital literacy certification delivered at the Rajamangala University of Technology Thanyaburi (RMUTT) in Thailand between 2016 and 2023. This dataset is unique since it includes demographic and academic records about undergraduate students. The dataset was collected and underwent a preparation process, including data cleansing, anonymization, and release. This data enables the examination of student learning outcomes, represented by a dataset containing information about 45,603 records with students’ certification assessment scores. This compiled dataset provides a rich resource for researchers studying digital literacy and learning analytics. It offers researchers the opportunity to gain valuable insights, inform evidence-based educational practices, and contribute to the ongoing efforts to improve digital literacy education in Thailand and beyond. Full article
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8 pages, 24489 KiB  
Data Descriptor
PoPu-Data: A Multilayered, Simultaneously Collected Lying Position Dataset
by Luís Fonseca, Fernando Ribeiro, José Metrôlho, Adriana Santos, Rogério Dionisio, Mohammad Mohammad Amini, Arlindo F. Silva, Ahmad Reza Heravi, Davood Fanaei Sheikholeslami, Filipe Fidalgo, Francisco B. Rodrigues, Osvaldo Santos, Patrícia Coelho and Seyyed Sajjad Aemmi
Data 2023, 8(7), 120; https://doi.org/10.3390/data8070120 - 16 Jul 2023
Cited by 3 | Viewed by 1484
Abstract
This study presents a dataset containing three layers of data that are useful for body position classification and all uses related to it. The PoPu dataset contains simultaneously collected data from two different sensor sheets—one placed over and one placed under a mattress; [...] Read more.
This study presents a dataset containing three layers of data that are useful for body position classification and all uses related to it. The PoPu dataset contains simultaneously collected data from two different sensor sheets—one placed over and one placed under a mattress; furthermore, a segmentation data layer was added where different body parts are identified using the pressure data from the sensors over the mattress. The data included were gathered from 60 healthy volunteers distributed among the different gathered characteristics: namely sex, weight, and height. This dataset can be used for position classification, assessing the viability of sensors placed under a mattress, and in applications regarding bedded or lying people or sleep related disorders. Full article
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7 pages, 893 KiB  
Data Descriptor
Proteomic Shift in Mouse Embryonic Fibroblasts Pfa1 during Erastin, ML210, and BSO-Induced Ferroptosis
by Olga M. Kudryashova, Alexey M. Nesterenko, Dmitry A. Korzhenevskii, Valeriy K. Sulyagin, Vasilisa M. Tereshchuk, Vsevolod V. Belousov and Arina G. Shokhina
Data 2023, 8(7), 119; https://doi.org/10.3390/data8070119 - 12 Jul 2023
Viewed by 1263
Abstract
Ferroptosis is a unique variety of non-apoptotic cell death, driven by massive lipid oxidation in an iron-dependent manner. Since ferroptosis was introduced as a concept in 2012, it has demonstrated its essential role in the pathogenesis in neurodegenerative diseases and an important role [...] Read more.
Ferroptosis is a unique variety of non-apoptotic cell death, driven by massive lipid oxidation in an iron-dependent manner. Since ferroptosis was introduced as a concept in 2012, it has demonstrated its essential role in the pathogenesis in neurodegenerative diseases and an important role in therapy-resistant cancer cells. Thus, detailed molecular understanding of both canonical and alternative ferroptosis pathways is required. There is a set of widely used chemical agents to modulate ferroptosis using different pathway targets: erastin blocks cystine–glutamate antiporter, system xc-; ML210 directly inactivates GPX4; and L-buthionine sulfoximine (BSO) inhibits γ-glutamylcysteine synthetase, an essential enzyme for glutathione synthesis de novo. Most studies have focused on the lipidomic profiling of model systems undergoing death in a ferroptotic modality. In this study, we developed high-quality shotgun proteome sequencing during ferroptosis induction by three widely used chemical agents (erastin, ML210, and BSO) before and after 24 and 48 h of treatment. Chromato-mass spectra were registered in DDA mode and are suitable for further label-free quantification. Both processed and raw files are publicly available and could be a valuable dynamic proteome map for further ferroptosis investigation. Full article
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16 pages, 19867 KiB  
Data Descriptor
A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models
by Andreas Anael Pereira Gomes, Francisco Itamarati Secolo Ganacim, Fabiano Gustavo Silveira Magrin, Nara Bobko, Leonardo Göbel Fernandes, Anselmo Pombeiro and Eduardo Félix Ribeiro Romaneli
Data 2023, 8(7), 118; https://doi.org/10.3390/data8070118 - 5 Jul 2023
Viewed by 1684
Abstract
The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with [...] Read more.
The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes, including insulators, disconnect switches, transformers and other equipment commonly found in substation environments. The images were captured using a combination of human, fixed and AGV-mounted cameras at different times of the day, providing a diverse set of training and testing data for algorithm development. In total, 50,705 annotations were created by a team of experienced annotators, using a standardized process to ensure accuracy across the dataset. The resulting dataset provides a valuable resource for researchers and practitioners working in the fields of substation automation, substation monitoring and computer vision. Its availability has the potential to advance the state of the art in this important area. Full article
(This article belongs to the Topic Methods for Data Labelling for Intelligent Systems)
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8 pages, 251 KiB  
Data Descriptor
Assessment of Maize Silage Quality under Different Pre-Ensiling Conditions
by Lorenzo Serva, Igino Andrighetto, Severino Segato, Giorgio Marchesini, Maria Chinello and Luisa Magrin
Data 2023, 8(7), 117; https://doi.org/10.3390/data8070117 - 2 Jul 2023
Cited by 1 | Viewed by 1368
Abstract
Maize silage suffers from several factors that affect the final quality and, to some extent, pre-ensiled conditions that can be potentially tuned during harvesting. After assessing new indices for silage quality under lab-scale conditions, several trials have been conducted to find associations between [...] Read more.
Maize silage suffers from several factors that affect the final quality and, to some extent, pre-ensiled conditions that can be potentially tuned during harvesting. After assessing new indices for silage quality under lab-scale conditions, several trials have been conducted to find associations between fresh maize characteristics and silage features. Among the first, we included field input levels, FAO class, maturity stage, use of bacterial inoculants, sealing delay and chemical traits, whereas, among the latter, we assessed density and porosity, pH, fermentative profile, dry matter loss and aerobic stability. The trials were conducted using vacuum bags or mini silo buckets. More than 1500 maize samples harvested in Northeast Italy were analysed during the 2016–2022 period. Moreover, to evaluate silage aerobic stability, the fermentative profile and temperature were measured 14 days after the opening of the silo. The association between silage quality and aerobic stability was assessed, and a prognostic risk score was used to calculate the probability of aerobic instability. The dataset could provide baseline information to promote the continuous improvement of maize silage management from different botanical and crop fields, thus improving agronomic and animal farm resource allocation from a precision agriculture perspective. Full article
8 pages, 4348 KiB  
Data Descriptor
Dataset of Linkability Networks of Ethereum Accounts Involved in NFT Trading of Top 15 NFT Collections
by Aleksandar Tošić, Niki Hrovatin and Jernej Vičič
Data 2023, 8(7), 116; https://doi.org/10.3390/data8070116 - 28 Jun 2023
Viewed by 1451
Abstract
In this paper, we present subgraphs of Ethereum wallets involved in NFT trades of the top 15 ERC721 NFT collections. To obtain the subgraphs, we have extracted the Ethereum transaction graph from a live Ethereum node and filtered out exchanges, mining pools, and [...] Read more.
In this paper, we present subgraphs of Ethereum wallets involved in NFT trades of the top 15 ERC721 NFT collections. To obtain the subgraphs, we have extracted the Ethereum transaction graph from a live Ethereum node and filtered out exchanges, mining pools, and smart contracts. For each of the selected collections, we identified the set of accounts involved in NFT trading, which we used to perform a breadth-first search in the Ethereum transaction graph to obtain a subgraph. These subgraphs can offer insight into the linkability of accounts participating in NFT trading on the Ethereum blockchain. Full article
(This article belongs to the Special Issue Blockchain Applications in Data Management and Governance)
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9 pages, 2498 KiB  
Data Descriptor
Factory-Based Vibration Data for Bearing-Fault Detection
by Adam Lundström and Mattias O’Nils
Data 2023, 8(7), 115; https://doi.org/10.3390/data8070115 - 28 Jun 2023
Cited by 1 | Viewed by 2691
Abstract
The importance of preventing failures in bearings has led to a large amount of research being conducted to find methods for fault diagnostics and prognostics. Many of these solutions, such as deep learning methods, require a significant amount of data to perform well. [...] Read more.
The importance of preventing failures in bearings has led to a large amount of research being conducted to find methods for fault diagnostics and prognostics. Many of these solutions, such as deep learning methods, require a significant amount of data to perform well. This is a reason why publicly available data are important, and there currently exist several open datasets that contain different conditions and faults. However, one challenge is that almost all of these data come from a laboratory setting, where conditions might differ from those found in an industrial environment where the methods are intended to be used. This also means that there may be characteristics of the industrial data that are important to take into account. Therefore, this study describes a completely new dataset for bearing faults from a pulp mill. The analysis of the data shows that the faults vary significantly in terms of fault development, rotation speed, and the amplitude of the vibration signal. It also suggests that methods built for this environment need to consider that no historical examples of faults in the target domain exist and that external events can occur that are not related to any condition of the bearing. Full article
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9 pages, 3184 KiB  
Data Descriptor
A Survey Dataset Evaluating Perceptions of Civil Engineering Students about Building Information Modelling (BIM)
by Diego Maria Barbieri, Baowen Lou, Marco Passavanti, Aurora Barbieri and Fredrik Bjørheim
Data 2023, 8(7), 114; https://doi.org/10.3390/data8070114 - 28 Jun 2023
Cited by 1 | Viewed by 1482
Abstract
The implementation of Building Information Modelling (BIM) technologies has become increasingly central in the design, construction and maintenance of both civil structures and infrastructures. As more and more software houses develop new BIM software solutions and a wide range of private and public [...] Read more.
The implementation of Building Information Modelling (BIM) technologies has become increasingly central in the design, construction and maintenance of both civil structures and infrastructures. As more and more software houses develop new BIM software solutions and a wide range of private and public stakeholders employ them, several educational institutes across the globe strive to expand their teaching portfolio to encompass learning and teaching of BIM. This dataset deals with the perceptions expressed by all the civil engineering undergraduate students who attended an academic course specifically about BIM at University of Stavanger (UiS), Norway, during the second semester 2022. The survey was divided into five parts and collected information regarding as many overarching aspects: socio-demographic data, perceptions about BIM before and after course attendance, satisfaction about the academic course and the way it was conducted. Considering the very moderate sample size (28 students) and potential biases due to the specific context of the University of Stavanger, the dataset can provide a useful insight into teaching approaches and future curriculum development, rather than indicating major and generalized trends in BIM education. As the questionnaire responses shed light on the feedbacks and perceptions expressed by university students dealing with BIM for their first time, the formed dataset can offer a straightforward appreciation of students’ cognitive behaviour in BIM education. Full article
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12 pages, 410 KiB  
Article
VPTD: Human Face Video Dataset for Personality Traits Detection
by Kenan Kassab, Alexey Kashevnik, Alexander Mayatin and Dmitry Zubok
Data 2023, 8(7), 113; https://doi.org/10.3390/data8070113 - 22 Jun 2023
Viewed by 2557
Abstract
In this paper, we propose a dataset for personality traits detection based on human face videos. Ground truth data have been annotated using the IPIP-50 personality test that every participant is implementing. To collect the dataset, we developed a web-based platform that allows [...] Read more.
In this paper, we propose a dataset for personality traits detection based on human face videos. Ground truth data have been annotated using the IPIP-50 personality test that every participant is implementing. To collect the dataset, we developed a web-based platform that allows us to acquire spontaneous answers for predefined questions from the respondents. The website allows the participants to record an interactive interview in order to imitate the real-life interview. The dataset includes 38 videos (2 min on average) for people of different races, genders, and ages. In the paper, we propose the top five personality traits calculated based on the test, as well as the top five personality traits calculated by our own developed model that determines this information based on video analysis. We introduced a statistical analysis for the collected dataset, and we also applied a K-means clustering algorithm to cluster the data and present the clustering results. Full article
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