Journal Description
Data
Data
is a peer-reviewed, open access journal on data in science, with the aim of enhancing data transparency and reusability. The journal publishes in two sections: a section on the collection, treatment and analysis methods of data in science; a section publishing descriptions of scientific and scholarly datasets (one dataset per paper). The journal is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, Inspec, RePEc, and other databases.
- Journal Rank: JCR - Q2 (Multidisciplinary Sciences) / CiteScore - Q2 (Information Systems and Management)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 27.7 days after submission; acceptance to publication is undertaken in 3.5 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.2 (2023);
5-Year Impact Factor:
2.4 (2023)
Latest Articles
Bootstrap Method as a Tool for Analyzing Data with Atypical Distributions Deviating from Parametric Assumptions: Critique and Effectiveness Evaluation
Data 2024, 9(8), 95; https://doi.org/10.3390/data9080095 - 26 Jul 2024
Abstract
In today’s research environment characterized by exponential data growth and increasing complexity, the selection of appropriate statistical tests, tailored to research objectives and data distributions, is paramount for rigorous analysis and accurate interpretation. This article explores the growing prominence of bootstrapping, an advanced
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In today’s research environment characterized by exponential data growth and increasing complexity, the selection of appropriate statistical tests, tailored to research objectives and data distributions, is paramount for rigorous analysis and accurate interpretation. This article explores the growing prominence of bootstrapping, an advanced statistical technique for multiple comparisons analysis, offering flexibility and customization by estimating sample distributions without assuming population distributions, thus serving as a valuable alternative to traditional methods in various data scenarios. Computer simulations were conducted using data from cardiovascular disease patients. Two approaches, spontaneous partly controlled simulation and fully constrained simulation using self-written R scripts, were utilized to generate datasets with specified distributions and analyze the data using tests for comparing more than two groups. The utilization of the bootstrap method greatly improves statistical analysis, especially in overcoming the constraints of conventional parametric tests. Our research showcased its effectiveness in comparing multiple scenarios, yielding strong findings across diverse distributions, even with minor inflation in p values. Serving as a valuable substitute for parametric approaches, bootstrap promotes careful consideration when rejecting hypotheses, thus fostering a deeper understanding of statistical nuances and bolstering analytical rigor.
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Open AccessData Descriptor
SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants
by
Bernd Accou, Lies Bollens, Marlies Gillis, Wendy Verheijen, Hugo Van hamme and Tom Francart
Data 2024, 9(8), 94; https://doi.org/10.3390/data9080094 - 26 Jul 2024
Abstract
Researchers investigating the neural mechanisms underlying speech perception often employ electroencephalography (EEG) to record brain activity while participants listen to spoken language. The high temporal resolution of EEG enables the study of neural responses to fast and dynamic speech signals. Previous studies have
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Researchers investigating the neural mechanisms underlying speech perception often employ electroencephalography (EEG) to record brain activity while participants listen to spoken language. The high temporal resolution of EEG enables the study of neural responses to fast and dynamic speech signals. Previous studies have successfully extracted speech characteristics from EEG data and, conversely, predicted EEG activity from speech features. Machine learning techniques are generally employed to construct encoding and decoding models, which necessitate a substantial quantity of data. We present SparrKULee, a Speech-evoked Auditory Repository of EEG data, measured at KU Leuven, comprising 64-channel EEG recordings from 85 young individuals with normal hearing, each of whom listened to 90–150 min of natural speech. This dataset is more extensive than any currently available dataset in terms of both the number of participants and the quantity of data per participant. It is suitable for training larger machine learning models. We evaluate the dataset using linear and state-of-the-art non-linear models in a speech encoding/decoding and match/mismatch paradigm, providing benchmark scores for future research.
Full article
Open AccessArticle
Optimizing Database Performance in Complex Event Processing through Indexing Strategies
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Maryam Abbasi, Marco V. Bernardo, Paulo Váz, José Silva and Pedro Martins
Data 2024, 9(8), 93; https://doi.org/10.3390/data9080093 - 24 Jul 2024
Abstract
Complex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data continue to grow, optimizing the performance of
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Complex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data continue to grow, optimizing the performance of CEP systems becomes a critical challenge. This paper investigates the impact of indexing strategies on the performance of databases handling complex event processing. We propose a novel indexing technique, called Hierarchical Temporal Indexing (HTI), specifically designed for the efficient processing of complex event queries. HTI leverages the temporal nature of event data and employs a multi-level indexing approach to optimize query execution. By combining temporal indexing with spatial- and attribute-based indexing, HTI aims to accelerate the retrieval and processing of relevant events, thereby improving overall query performance. In this study, we evaluate the effectiveness of HTI by implementing complex event queries on various CEP systems with different indexing strategies. We conduct a comprehensive performance analysis, measuring the query execution times and resource utilization (CPU, memory, etc.), and analyzing the execution plans and query optimization techniques employed by each system. Our experimental results demonstrate that the proposed HTI indexing strategy outperforms traditional indexing approaches, particularly for complex event queries involving temporal constraints and multi-dimensional event attributes. We provide insights into the strengths and weaknesses of each indexing strategy, identifying the factors that influence performance, such as data volume, query complexity, and event characteristics. Furthermore, we discuss the implications of our findings for the design and optimization of CEP systems, offering recommendations for indexing strategy selection based on the specific requirements and workload characteristics. Finally, we outline the potential limitations of our study and suggest future research directions in this domain.
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Open AccessData Descriptor
BELMASK—An Audiovisual Dataset of Adversely Produced Speech for Auditory Cognition Research
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Cleopatra Christina Moshona, Frederic Rudawski, André Fiebig and Ennes Sarradj
Data 2024, 9(8), 92; https://doi.org/10.3390/data9080092 - 24 Jul 2024
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In this article, we introduce the Berlin Dataset of Lombard and Masked Speech (BELMASK), a phonetically controlled audiovisual dataset of speech produced in adverse speaking conditions, and describe the development of the related speech task. The dataset contains in total 128 min of
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In this article, we introduce the Berlin Dataset of Lombard and Masked Speech (BELMASK), a phonetically controlled audiovisual dataset of speech produced in adverse speaking conditions, and describe the development of the related speech task. The dataset contains in total 128 min of audio and video recordings of 10 German native speakers (4 female, 6 male) with a mean age of 30.2 years (SD: 6.3 years), uttering matrix sentences in cued, uninstructed speech in four conditions: (i) with a Filtering Facepiece P2 (FFP2) mask in silence, (ii) without an FFP2 mask in silence, (iii) with an FFP2 mask while exposed to noise, iv) without an FFP2 mask while exposed to noise. Noise consisted of mixed-gender six-talker babble played over headphones to the speakers, triggering the Lombard effect. All conditions are readily available in face-and-voice and voice-only formats. The speech material is annotated, employing a multi-layer architecture, and was originally conceptualized to be used for the administration of a working memory task. The dataset is stored in a restricted-access Zenodo repository and is available for academic research in the area of speech communication, acoustics, psychology and related disciplines upon request, after signing an End User License Agreement (EULA).
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Open AccessData Descriptor
Data Descriptor of Snakebites in Brazil from 2007 to 2020
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Alexandre Vilhena Silva-Neto, Gabriel Santos Mouta, Antônio Alcirley Silva Balieiro, Jady Shayenne Mota Cordeiro, Patricia Carvalho Silva Balieiro, Tatyana Costa Amorin Ramos, Djane Clarys Baia-da-Silva, Élisson Silva Rocha, Patricia Takako Endo, Theo Lynn, Wuelton Marcelo Monteiro and Vanderson Souza Sampaio
Data 2024, 9(8), 91; https://doi.org/10.3390/data9080091 - 24 Jul 2024
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Snakebite envenomations (SBE) are a significant global public health threat due to their morbidity and mortality. This is a neglected public health issue in many tropical and subtropical countries. Brazil is in the top ten countries affected by SBE, with 32,160 cases reported
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Snakebite envenomations (SBE) are a significant global public health threat due to their morbidity and mortality. This is a neglected public health issue in many tropical and subtropical countries. Brazil is in the top ten countries affected by SBE, with 32,160 cases reported only in 2020, posing a high burden for this population. In this paper, we describe the data structure of snakebite records from 2007 to 2020 in the Notifiable Disease Information System (SINAN), made available by the Brazilian Ministry of Health (MoH). In addition, we also provide R scripts that allow a quick and automatic updating of data from the SINAN according to its availability. The data presented in this work are related to clinical and demographic information on SBE cases. Also, data on outcomes, laboratory results, and treatment are available. The dataset is available and freely accessible; however, preprocessing, adjustments, and standardization are necessary due to incompleteness and inconsistencies. Regardless of these limitations, it provides a solid basis for assessing different aspects and the national burden of envenoming.
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Open AccessData Descriptor
SaBi3d—A LiDAR Point Cloud Data Set of Car-to-Bicycle Overtaking Maneuvers
by
Christian Odenwald and Moritz Beeking
Data 2024, 9(8), 90; https://doi.org/10.3390/data9080090 - 24 Jul 2024
Abstract
While cycling presents environmental benefits and promotes a healthy lifestyle, the risks associated with overtaking maneuvers by motorized vehicles represent a significant barrier for many potential cyclists. A large-scale analysis of overtaking maneuvers could inform traffic researchers and city planners how to reduce
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While cycling presents environmental benefits and promotes a healthy lifestyle, the risks associated with overtaking maneuvers by motorized vehicles represent a significant barrier for many potential cyclists. A large-scale analysis of overtaking maneuvers could inform traffic researchers and city planners how to reduce these risks by better understanding these maneuvers. Drawing from the fields of sensor-based cycling research and from LiDAR-based traffic data sets, this paper provides a step towards addressing these safety concerns by introducing the Salzburg Bicycle 3d (SaBi3d) data set, which consists of LiDAR point clouds capturing car-to-bicycle overtaking maneuvers. The data set, collected using a LiDAR-equipped bicycle, facilitates the detailed analysis of a large quantity of overtaking maneuvers without the need for manual annotation through enabling automatic labeling by a neural network. Additionally, a benchmark result for 3D object detection using a competitive neural network is provided as a baseline for future research. The SaBi3d data set is structured identically to the nuScenes data set, and therefore offers compatibility with numerous existing object detection systems. This work provides valuable resources for future researchers to better understand cycling infrastructure and mitigate risks, thus promoting cycling as a viable mode of transportation.
Full article
(This article belongs to the Section Spatial Data Science and Digital Earth)
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Open AccessReview
Literature-Based Inventory of Chemical Substance Concentrations Measured in Organic Food Consumed in Europe
by
Joanna Choueiri, Pascal Petit, Franck Balducci, Dominique J. Bicout and Christine Demeilliers
Data 2024, 9(7), 89; https://doi.org/10.3390/data9070089 - 3 Jul 2024
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Populations are exposed daily to numerous environmental pollutants, particularly through food. To address environmental issues, many agricultural production methods have been developed, including organic farming. To date, there is no exhaustive inventory of the contamination of organic foods as there is for conventional
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Populations are exposed daily to numerous environmental pollutants, particularly through food. To address environmental issues, many agricultural production methods have been developed, including organic farming. To date, there is no exhaustive inventory of the contamination of organic foods as there is for conventional foods. The main objective of this work was to construct a growing and updatable database on chemical substances and their levels in organic foods consumed in Europe. To this end, a literature search was conducted, resulting in a total of 1207 concentration values from 823 food–substances pairs involving 166 food matrices and 209 chemical substances, among which 95% were not authorized in organic farming and 80% were pesticides. The most encountered substance groups are “inorganic contaminants” and “organophosphate”, and the most studied food groups are “fruit used as fruit” and “Cereals and cereal primary derivatives”. Further studies are needed to continue updating the database with robust and comprehensive data on organic food contamination. This database could be used to study the health risks associated with these contaminants.
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Open AccessData Descriptor
Multi-Scale Earthquake Damaged Building Feature Set
by
Guorui Gao, Futao Wang, Zhenqing Wang, Qing Zhao, Litao Wang, Jinfeng Zhu, Wenliang Liu, Gang Qin and Yanfang Hou
Data 2024, 9(7), 88; https://doi.org/10.3390/data9070088 - 28 Jun 2024
Abstract
Earthquake disasters are marked by their unpredictability and potential for extreme destructiveness. Accurate information on building damage, captured in post-earthquake remote sensing images, is critical for an effective post-disaster emergency response. The foundational features within these images are essential for the accurate extraction
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Earthquake disasters are marked by their unpredictability and potential for extreme destructiveness. Accurate information on building damage, captured in post-earthquake remote sensing images, is critical for an effective post-disaster emergency response. The foundational features within these images are essential for the accurate extraction of building damage data following seismic events. Presently, the availability of publicly accessible datasets tailored specifically to earthquake-damaged buildings is limited, and existing collections of post-earthquake building damage characteristics are insufficient. To address this gap and foster research advancement in this domain, this paper introduces a new, large-scale, publicly available dataset named the Major Earthquake Damage Building Feature Set (MEDBFS). This dataset comprises image data sourced from five significant global earthquakes and captured by various optical remote sensing satellites, featuring diverse scale characteristics and multiple spatial resolutions. It includes over 7000 images of buildings pre- and post-disaster, each subjected to stringent quality control and expert validation. The images are categorized into three primary groups: intact/slightly damaged, severely damaged, and completely collapsed. This paper develops a comprehensive feature set encompassing five dimensions: spectral, texture, edge detection, building index, and temporal sequencing, resulting in 16 distinct classes of feature images. This dataset is poised to significantly enhance the capabilities for data-driven identification and analysis of earthquake-induced building damage, thereby supporting the advancement of scientific and technological efforts for emergency earthquake response.
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(This article belongs to the Section Spatial Data Science and Digital Earth)
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Open AccessData Descriptor
A Point Cloud Dataset of Vehicles Passing through a Toll Station for Use in Training Classification Algorithms
by
Alexander Campo-Ramírez, Eduardo F. Caicedo-Bravo and Eval B. Bacca-Cortes
Data 2024, 9(7), 87; https://doi.org/10.3390/data9070087 - 27 Jun 2024
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This work presents a point cloud dataset of vehicles passing through a toll station in Colombia to be used to train artificial vision and computational intelligence algorithms. This article details the process of creating the dataset, covering initial data acquisition, range information preprocessing,
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This work presents a point cloud dataset of vehicles passing through a toll station in Colombia to be used to train artificial vision and computational intelligence algorithms. This article details the process of creating the dataset, covering initial data acquisition, range information preprocessing, point cloud validation, and vehicle labeling. Additionally, a detailed description of the structure and content of the dataset is provided, along with some potential applications of its use. The dataset consists of 36,026 total objects divided into 6 classes: 31,432 cars, campers, vans and 2-axle trucks with a single tire on the rear axle, 452 minibuses with a single tire on the rear axle, 1158 buses, 1179 2-axle small trucks, 797 2-axle large trucks, and 1008 trucks with 3 or more axles. The point clouds were captured using a LiDAR sensor and Doppler effect speed sensors. The dataset can be used to train and evaluate algorithms for range data processing, vehicle classification, vehicle counting, and traffic flow analysis. The dataset can also be used to develop new applications for intelligent transportation systems.
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Open AccessArticle
Tuning Data Mining Models to Predict Secondary School Academic Performance
by
William Hoyos and Isaac Caicedo-Castro
Data 2024, 9(7), 86; https://doi.org/10.3390/data9070086 - 26 Jun 2024
Abstract
In recent years, educational data mining has emerged as a growing discipline focused on developing models for predicting academic performance. The primary objective of this research was to tune classification models to predict academic performance in secondary school. The dataset employed for this
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In recent years, educational data mining has emerged as a growing discipline focused on developing models for predicting academic performance. The primary objective of this research was to tune classification models to predict academic performance in secondary school. The dataset employed for this study encompassed information from 19,545 high school students. We used descriptive statistics to characterise information contained in personal, school, and socioeconomic variables. We implemented two data mining techniques, namely artificial neural networks (ANN) and support vector machines (SVM). Parameter optimisation was conducted through five–fold cross–validation, and model performance was assessed using accuracy and –Score. The results indicate a functional dependence between predictor variables and academic performance. The algorithms demonstrated an average performance exceeding 80% accuracy. Notably, ANN outperformed SVM in the dataset analysed. This type of methodology could help educational institutions to predict academic underachievement and thus generate strategies to improve students’ academic performance.
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(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
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Open AccessData Descriptor
Evaluation of Online Inquiry Competencies of Chilean Elementary School Students: A Dataset
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Luz Chourio-Acevedo and Roberto González-Ibañez
Data 2024, 9(7), 85; https://doi.org/10.3390/data9070085 - 25 Jun 2024
Abstract
In the age of abundant digital content, children and adolescents face the challenge of developing new information literacy competencies, particularly those pertaining to online inquiry, in order to thrive academically and personally. This article addresses the challenge encountered by Chilean students in developing
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In the age of abundant digital content, children and adolescents face the challenge of developing new information literacy competencies, particularly those pertaining to online inquiry, in order to thrive academically and personally. This article addresses the challenge encountered by Chilean students in developing online inquiry competencies (OICs) essential for completing school assignments, particularly in natural science education. A diagnostic study was conducted with 279 elementary school students (from fourth to eighth grade) from four educational institutions in Chile, representing diverse socioeconomic backgrounds. An instrument aligned with the national curriculum, featuring questions related to natural sciences, was administered through a game named NEURONE-Trivia, which integrates a search engine and a logging component to record students’ search behavior. The primary outcome of this study is a dataset comprising demographic information, self-perception, and information-seeking behaviors data collected during students’ online search sessions for natural science research tasks. This dataset serves as a valuable resource for researchers, educators, and practitioners interested in investigating the interplay between demographic characteristics, self-perception, and information-seeking behaviors among elementary students within the context of OIC development. Furthermore, it enables further examination of students’ search behaviors concerning source evaluation, information retrieval, and information utilization.
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(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
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Open AccessData Descriptor
Gender Distribution of Scientific Prizes Is Associated with Naming of Awards after Men, Women or Neutral
by
Katja Gehmlich and Stefan Krause
Data 2024, 9(7), 84; https://doi.org/10.3390/data9070084 - 25 Jun 2024
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Woman scientists have for long been under-represented as recipients of academic prizes. The reasons for this lack of recognition are manifold, including potential gender bias amongst award panels and nomination practices. This dataset of the gender distribution of 8747 recipients of 345 scientific
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Woman scientists have for long been under-represented as recipients of academic prizes. The reasons for this lack of recognition are manifold, including potential gender bias amongst award panels and nomination practices. This dataset of the gender distribution of 8747 recipients of 345 scientific medals and prizes awarded by 11 General Scientific Societies as well as subject-specific societies in the Earth and Environmental Sciences and in Cardiology between 1731 and 2021 explores the magnitude, temporal trends and potential drivers of observed gender imbalances. Our analysis revealed women were particularly underrepresented in awards named after men with awards not named after a person or named after a woman being more frequently awarded to woman scientists. Time-series analysis confirmed persisting trends that are only starting to change since the early 2000s, indicating that a lot remains to be accomplished to achieve true equity. We encourage the scientific community to extend our data and analysis, as they represent important evidence of the recognition of academic achievements towards other under-represented groups and including also nomination information.
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Open AccessArticle
Leveraging Sports Analytics and Association Rule Mining to Uncover Recovery and Economic Impacts in NBA Basketball
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Vangelis Sarlis, George Papageorgiou and Christos Tjortjis
Data 2024, 9(7), 83; https://doi.org/10.3390/data9070083 - 24 Jun 2024
Abstract
This study examines the multifaceted field of injuries and their impacts on performance in the National Basketball Association (NBA), leveraging a blend of Data Science, Data Mining, and Sports Analytics. Our research is driven by three pivotal questions: Firstly, we explore how Association
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This study examines the multifaceted field of injuries and their impacts on performance in the National Basketball Association (NBA), leveraging a blend of Data Science, Data Mining, and Sports Analytics. Our research is driven by three pivotal questions: Firstly, we explore how Association Rule Mining can elucidate the complex interplay between players’ salaries, physical attributes, and health conditions and their influence on team performance, including team losses and recovery times. Secondly, we investigate the relationship between players’ recovery times and their teams’ financial performance, probing interdependencies with players’ salaries and career trajectories. Lastly, we examine how insights gleaned from Data Mining and Sports Analytics on player recovery times and financial influence can inform strategic financial management and salary negotiations in basketball. Harnessing extensive datasets detailing player demographics, injuries, and contracts, we employ advanced analytic techniques to categorize injuries and transform contract data into a format conducive to deep analytical scrutiny. Our anomaly detection methodologies, an ensemble combination of DBSCAN, isolation forest, and Z-score algorithms, spotlight patterns and outliers in recovery times, unveiling the intricate dance between player health, performance, and financial outcomes. This nuanced understanding emphasizes the economic stakes of sports injuries. The findings of this study provide a rich, data-driven foundation for teams and stakeholders, advocating for more effective injury management and strategic planning. By addressing these research questions, our work not only contributes to the academic discourse in Sports Analytics but also offers practical frameworks for enhancing player welfare and team financial health, thereby shaping the future of strategic decisions in professional sports.
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(This article belongs to the Special Issue Machine Learning and Data Mining in Exercise, Sports and Health Research)
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Open AccessData Descriptor
Hardware Trojan Dataset of RISC-V and Web3 Generated with ChatGPT-4
by
Victor Takashi Hayashi and Wilson Vicente Ruggiero
Data 2024, 9(6), 82; https://doi.org/10.3390/data9060082 - 19 Jun 2024
Abstract
Although hardware trojans impose a relevant threat to the hardware security of RISC-V and Web3 applications, existing datasets have a limited set of examples, as the most famous hardware trojan dataset TrustHub has 106 different trojans. RISC-V specifically has study cases of three
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Although hardware trojans impose a relevant threat to the hardware security of RISC-V and Web3 applications, existing datasets have a limited set of examples, as the most famous hardware trojan dataset TrustHub has 106 different trojans. RISC-V specifically has study cases of three and four different hardware trojans, and no research was found regarding Web3 hardware trojans in modules such as a hardware wallet. This research presents a dataset of 290 Verilog examples generated with ChatGPT-4 Large Language Model (LLM) based on 29 golden models and the TrustHub taxonomy. It is expected that this dataset supports future research endeavors regarding defense mechanisms against hardware trojans in RISC-V, hardware wallet, and hardware Proof of Work (PoW) miner.
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(This article belongs to the Section Information Systems and Data Management)
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Open AccessData Descriptor
Beyond the Classroom: An Analysis of Internal and External Factors Related to Students’ Love of Learning and Educational Outcomes
by
Charles M. Burke, Lori P. Montross and Vera G. Dianova
Data 2024, 9(6), 81; https://doi.org/10.3390/data9060081 - 16 Jun 2024
Abstract
This study explores the multifaceted factors influencing student learning motivations and educational outcomes. Utilizing a diverse student body from Franklin University Switzerland, the study emphasizes the impact of internal factors, such as the psychological state of flow and a self-reported love of learning,
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This study explores the multifaceted factors influencing student learning motivations and educational outcomes. Utilizing a diverse student body from Franklin University Switzerland, the study emphasizes the impact of internal factors, such as the psychological state of flow and a self-reported love of learning, alongside GPA and student cohort influences like year of study, academic discipline, country of origin, and academic travel. Through a cross-sectional survey of 112 students, the study evaluates how these factors correlate with and diverge from each other and student GPAs, aiming to dissect the influences of intrinsic motivations, demographic variables, and educational experiences. Our analysis revealed significant correlations between students’ self-reported love of learning, experiences of flow, and academic performance. Conversely, academic travel did not show a significant direct impact, suggesting that while such experiences are enriching, they do not necessarily translate into a greater love of learning, flow, or higher academic achievement in the short term. However, demographic factors, particularly discipline of study and country of origin, significantly influenced the students’ love of learning, indicating varied motivational drives across different cultural and educational backgrounds. This study provides valuable insights for educational policymakers and institutions aiming to cultivate more engaging and fulfilling learning environments.
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(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
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Open AccessData Descriptor
Data for Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection
by
Santiago Bustamante-Mesa, Jorge W. Gonzalez-Sanchez, Sergio D. Saldarriaga-Zuluaga, Jesús M. López-Lezama and Nicolás Muñoz-Galeano
Data 2024, 9(6), 80; https://doi.org/10.3390/data9060080 - 13 Jun 2024
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The data presented in this paper are related to the paper entitled “Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection”, available in the Energies journal. Here, data are included to show the results of an Under-Frequency Load Shedding
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The data presented in this paper are related to the paper entitled “Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection”, available in the Energies journal. Here, data are included to show the results of an Under-Frequency Load Shedding (UFLS) scheme that considers the injection of virtual inertia by a VSC-HVDC link. The data obtained in six cases which were considered and analyzed are shown. In this paper, each case represents a different frequency response configuration in the event of generation loss, taking into account the presence or absence of a VSC-HVDC link, traditional and optimized UFLS schemes, as well as the injection of virtual inertia by the VSC-HVDC link. Data for each example contain the state of the relay, threshold, position in every delay, load shed, and relay configuration parameters. Data were obtained through Digsilent Power Factory and Python simulations. The purpose of this dataset is so that other researchers can reproduce the results reported in our paper.
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Open AccessData Descriptor
CrazyPAD: A Dataset for Assessing the Impact of Structural Defects on Nano-Quadcopter Performance
by
Kamil Masalimov, Tagir Muslimov, Evgeny Kozlov and Rustem Munasypov
Data 2024, 9(6), 79; https://doi.org/10.3390/data9060079 - 13 Jun 2024
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This article presents a novel dataset focused on structural damage in quadcopters, addressing a significant gap in unmanned aerial vehicle (UAV or drone) research. The dataset is called CrazyPAD (Crazyflie Propeller Anomaly Data) according to the name of the Crazyflie 2.1 nano-quadrocopter used
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This article presents a novel dataset focused on structural damage in quadcopters, addressing a significant gap in unmanned aerial vehicle (UAV or drone) research. The dataset is called CrazyPAD (Crazyflie Propeller Anomaly Data) according to the name of the Crazyflie 2.1 nano-quadrocopter used to collect the data. Despite the existence of datasets on UAV anomalies and behavior, none of them covers structural damage specifically in nano-quadrocopters. Our dataset, therefore, provides critical data for developing predictive models for defect detection in nano-quadcopters. This work details the data collection methodology, involving rigorous simulations of structural damages and their effects on UAV performance. The ultimate goal is to enhance UAV safety by enabling accurate defect diagnosis and predictive maintenance, contributing substantially to the field of UAV technology and its practical applications.
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In Vivo and In Vitro Electrochemical Impedance Spectroscopy of Acute and Chronic Intracranial Electrodes
by
Kyle P. O’Sullivan, Brian J. Philip, Jonathan L. Baker, John D. Rolston, Mark E. Orazem, Kevin J. Otto and Christopher R. Butson
Data 2024, 9(6), 78; https://doi.org/10.3390/data9060078 - 6 Jun 2024
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Invasive intracranial electrodes are used in both clinical and research applications for recording and stimulation of brain tissue, providing essential data in acute and chronic contexts. The impedance characteristics of the electrode–tissue interface (ETI) evolve over time and can change dramatically relative to
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Invasive intracranial electrodes are used in both clinical and research applications for recording and stimulation of brain tissue, providing essential data in acute and chronic contexts. The impedance characteristics of the electrode–tissue interface (ETI) evolve over time and can change dramatically relative to pre-implantation baseline. Understanding how ETI properties contribute to the recording and stimulation characteristics of an electrode can provide valuable insights for users who often do not have access to complex impedance characterizations of their devices. In contrast to the typical method of characterizing electrical impedance at a single frequency, we demonstrate a method for using electrochemical impedance spectroscopy (EIS) to investigate complex characteristics of the ETI of several commonly used acute and chronic electrodes. We also describe precise modeling strategies for verifying the accuracy of our instrumentation and understanding device–solution interactions, both in vivo and in vitro. Included with this publication is a dataset containing both in vitro and in vivo device characterizations, as well as some examples of modeling and error structure analysis results. These data can be used for more detailed interpretation of neural recordings performed on common electrode types, providing a more complete picture of their properties than is often available to users.
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Data on Stark Broadening of N VI Spectral Lines
by
Milan S. Dimitrijević, Magdalena D. Christova and Sylvie Sahal-Bréchot
Data 2024, 9(6), 77; https://doi.org/10.3390/data9060077 - 29 May 2024
Abstract
Data on Stark broadening parameters, spectral line widths, and shifts for 15 multiplets of N VI, whose spectral lines are broadened by collisions with electrons, protons, alpha particles (He III) and B III, B IV, B V and B VI ions, are presented.
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Data on Stark broadening parameters, spectral line widths, and shifts for 15 multiplets of N VI, whose spectral lines are broadened by collisions with electrons, protons, alpha particles (He III) and B III, B IV, B V and B VI ions, are presented. They have been calculated using the semiclassical perturbation theory, for temperatures from 50,000 K to 2,000,000 K, and perturber densities from 1016 cm−3 up to 1024 cm−3. The data for e, p and He III are of particular interest for the analysis and modelling of atmospheres of hot and dense stars, as, e.g., white dwarfs, and for investigation of their spectra, and data for boron ions are used for analysis and modelling of laser-driven plasma in proton–boron fusion research.
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(This article belongs to the Section Information Systems and Data Management)
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The China Historical Christian Database: A Dataset Quantifying Christianity in China from 1550 to 1950
by
Alex Mayfield, Margaret Frei, Daryl Ireland and Eugenio Menegon
Data 2024, 9(6), 76; https://doi.org/10.3390/data9060076 - 29 May 2024
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The era of digitization is revolutionizing traditional humanities research, presenting both novel methodologies and challenges. This field harnesses quantitative techniques to yield groundbreaking insights, contingent upon comprehensive datasets on historical subjects. The China Historical Christian Database (CHCD) exemplifies this trend, furnishing researchers with
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The era of digitization is revolutionizing traditional humanities research, presenting both novel methodologies and challenges. This field harnesses quantitative techniques to yield groundbreaking insights, contingent upon comprehensive datasets on historical subjects. The China Historical Christian Database (CHCD) exemplifies this trend, furnishing researchers with a rich repository of historical, relational, and geographical data about Christianity in China from 1550 to 1950. The study of Christianity in China confronts formidable obstacles, including the mobility of historical agents, fluctuating relational networks, and linguistic disparities among scattered sources. The CHCD addresses these challenges by curating an open-access database built in neo4j that records information about Christian institutions in China and those that worked inside of them. Drawing on historical sources, the CHCD contains temporal, relational, and geographic data. The database currently has over 40,000 nodes and 200,000 relationships, and continues to grow. Beyond its utility for religious studies, the CHCD encompasses broader interdisciplinary inquiries including social network analysis, geospatial visualization, and economic modeling. This article introduces the CHCD’s structure, and explains the data collection and curation process.
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