Data

26 pages, 3763 KiB

Open AccessArticle

Tracking Religious Freedom Violations with the Violent Incidents Database: A Methodological Approach and Comparative Analysis

by Dennis P. Petri, Kyle J. Wisdom and John T. Bainbridge

Data 2025, 10(5), 72; https://doi.org/10.3390/data10050072 (registering DOI) - 10 May 2025

Abstract

Measuring and comparing religious freedom across countries and over time requires reliable and valid data sources. Existing religious freedom datasets are either based on the coding of qualitative data (such as the Religion and State Project or the Pew Research Center), on expert [...] Read more.

Measuring and comparing religious freedom across countries and over time requires reliable and valid data sources. Existing religious freedom datasets are either based on the coding of qualitative data (such as the Religion and State Project or the Pew Research Center), on expert opinions (V-Dem or the World Watch List) or on surveys (Anti-Defamation League). Each of these approaches has its strengths and limitations. In this study, we present the Violent Incidents Database (VID), a complementary tool designed to collect, record, and analyze violent incidents related to violations of religious freedom based on media reports and other public sources. We critically describe the criteria and process for selecting, coding and verifying the incidents, as well as the categories and indicators used to classify them. We also compare the VID with other existing religious freedom datasets and show how the VID provides a complementary picture of the nature and dynamics of religious freedom violations. We offer a preliminary analysis of the data collected through the end of 2024 with selected figures for data visualization. We conclude by discussing anticipated improvements for the VID as well as its potential applications for policy makers, advocates, and practitioners. Full article

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

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10 pages, 1880 KiB

Open AccessData Descriptor

Historical Bolide Infrasound Dataset (1960–1972)

by Elizabeth A. Silber and Rodney W. Whitaker

Data 2025, 10(5), 71; https://doi.org/10.3390/data10050071 - 9 May 2025

Abstract

We present the first fully curated, publicly accessible archive of infrasonic records from ten large bolide events documented by the U.S. Air Force Technical Applications Center’s global microbarometer network between 1960 and 1972. Captured on analog strip-chart paper, these waveforms predate modern digital [...] Read more.

We present the first fully curated, publicly accessible archive of infrasonic records from ten large bolide events documented by the U.S. Air Force Technical Applications Center’s global microbarometer network between 1960 and 1972. Captured on analog strip-chart paper, these waveforms predate modern digital arrays and space-based sensors, making them a unique window on meteoroid activity in the mid-twentieth century. Prior studies drew important scientific conclusions from the records but released only limited artifacts, chiefly period–amplitude tables and unprocessed scans, leaving the underlying data inaccessible for independent study. The present release transforms those limited excerpts into a research-ready resource. By capturing ten large events in the mid-20th century, the dataset constitutes a critical reference point for assessing bolide activity before the advent of modern space-based and digital ground-based monitoring. The multi-year coverage and worldwide distribution of events provide a valuable reference for comparing past and more recent detections, facilitating assessments of long-term flux and the dynamics of acoustic wave propagation in Earth’s atmosphere. The dataset’s availability in a consolidated format ensures straightforward access to waveforms and derived measurements, supporting a wide range of scientific inquiries into bolide physics and infrasound monitoring. By preserving these historical acoustic observations, the collection maintains a significant record of mid-20th-century meteoroid entries. It thereby establishes a basis for further refinement of impact hazard evaluations, contributes to historical continuity in atmospheric observation, and enriches the study of meteoroid-generated infrasound signals on a global scale. Full article

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21 pages, 360 KiB

Open AccessArticle

Linear Dimensionality Reduction: What Is Better?

by Mohit Baliyan and Evgeny M. Mirkes

Data 2025, 10(5), 70; https://doi.org/10.3390/data10050070 - 6 May 2025

Abstract

This research paper focuses on dimensionality reduction, which is a major subproblem in any data processing operation. Dimensionality reduction based on principal components is the most used methodology. Our paper examines three heuristics, namely Kaiser’s rule, the broken stick, and the conditional number [...] Read more.

This research paper focuses on dimensionality reduction, which is a major subproblem in any data processing operation. Dimensionality reduction based on principal components is the most used methodology. Our paper examines three heuristics, namely Kaiser’s rule, the broken stick, and the conditional number rule, for selecting informative principal components when using principal component analysis to reduce high-dimensional data to lower dimensions. This study uses 22 classification datasets and three classifiers, namely Fisher’s discriminant classifier, logistic regression, and K nearest neighbors, to test the effectiveness of the three heuristics. The results show that there is no universal answer to the best intrinsic dimension, but the conditional number heuristic performs better, on average. This means that the conditional number heuristic is the best candidate for automatic data pre-processing. Full article

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

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17 pages, 6804 KiB

Open AccessData Descriptor

Mineralogical and Geochemical Compositions of Sedimentary Rocks in the Gosau Group (Late Cretaceous), Grünbach–Neue Welt Area, Austria

by Xinxuan Xiang, Eun Young Lee, Erich Draganits and Michael Wagreich

Data 2025, 10(5), 69; https://doi.org/10.3390/data10050069 - 6 May 2025

Abstract

Sedimentary rocks of the Gosau Group in the Grünbach–Neue Welt area (Eastern Alps, Austria) were analyzed to determine their mineralogical and geochemical compositions. This study includes the following: (1) the identification of major minerals using X-ray diffraction (XRD), (2) the analysis of major, [...] Read more.

Sedimentary rocks of the Gosau Group in the Grünbach–Neue Welt area (Eastern Alps, Austria) were analyzed to determine their mineralogical and geochemical compositions. This study includes the following: (1) the identification of major minerals using X-ray diffraction (XRD), (2) the analysis of major, minor, and trace elements via X-ray fluorescence spectroscopy (XRF) and inductively coupled plasma mass spectrometry (ICP-MS), and (3) the quantification of total organic carbon (TOC), total nitrogen (TN), and total sulfur (TS) using an Elementar Unicube analyzer. Samples were collected from four artificial trenches and one outcrop in Maiersdorf, spanning the Grünbach and Piesting formations deposited during a terrestrial-to-marine transition in the upper Santonian to Campanian (Late Cretaceous). The dominant minerals—quartz, muscovite, illite, and calcite—exhibit relative abundances corresponding with variations in major oxide concentrations. Minor elements show variability but generally follow consistent trends. Trace and rare earth elements display greater variability but similar patterns, with a broader distribution in the Grünbach Formation. Elevated TOC, TN, and TS values are observed near the formation boundary and in the Piesting formation. These results offer the mineralogical and geochemical characterization of the strata, and lay a foundation for further investigations into the paleoenvironmental and basin evolution of the Gosau Group in the region, providing a comparative framework for Gosau basins across the Eastern Alps. Full article

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22 pages, 687 KiB

Open AccessArticle

Performance and Scalability of Data Cleaning and Preprocessing Tools: A Benchmark on Large Real-World Datasets

by Pedro Martins, Filipe Cardoso, Paulo Váz, José Silva and Maryam Abbasi

Data 2025, 10(5), 68; https://doi.org/10.3390/data10050068 - 5 May 2025

Abstract

Data cleaning remains one of the most time-consuming and critical steps in modern data science, directly influencing the reliability and accuracy of downstream analytics. In this paper, we present a comprehensive evaluation of five widely used data cleaning tools—OpenRefine, Dedupe, Great Expectations, TidyData [...] Read more.

Data cleaning remains one of the most time-consuming and critical steps in modern data science, directly influencing the reliability and accuracy of downstream analytics. In this paper, we present a comprehensive evaluation of five widely used data cleaning tools—OpenRefine, Dedupe, Great Expectations, TidyData (PyJanitor), and a baseline Pandas pipeline—applied to large-scale, messy datasets spanning three domains (healthcare, finance, and industrial telemetry). We benchmark each tool on dataset sizes ranging from 1 million to 100 million records, measuring execution time, memory usage, error detection accuracy, and scalability under increasing data volumes. Additionally, we assess qualitative aspects such as usability and ease of integration, reflecting real-world adoption concerns. We incorporate recent findings on parallelized data cleaning and highlight how domain-specific anomalies (e.g., negative amounts in finance, sensor corruption in industrial telemetry) can significantly impact tool choice. Our findings reveal that no single solution excels across all metrics; while Dedupe provides robust duplicate detection and Great Expectations offers in-depth rule-based validation, tools like TidyData and baseline Pandas pipelines demonstrate strong scalability and flexibility under chunk-based ingestion. The choice of tool ultimately depends on domain-specific requirements (e.g., approximate matching in finance and strict auditing in healthcare) and the magnitude of available computational resources. By highlighting each framework’s strengths and limitations, this study offers data practitioners clear, evidence-driven guidance for selecting and combining tools to tackle large-scale data cleaning challenges. Full article

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

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15 pages, 14645 KiB

Open AccessData Descriptor

Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024)

by Antonio Rangel, Juan Terven, Diana-Margarita Córdova-Esparza, Julio-Alejandro Romero-González, Alfonso Ramírez-Pedraza, Edgar A. Chávez-Urbiola, Francisco. J. Willars-Rodríguez and Gendry Alfonso-Francia

Data 2025, 10(5), 67; https://doi.org/10.3390/data10050067 - 4 May 2025

Abstract

Monitoring land cover changes is crucial for understanding how natural processes and human activities such as deforestation, urbanization, and agriculture reshape the environment. We introduce a publicly available dataset covering the entire United States from 2016 to 2024, integrating six spectral bands (Red, [...] Read more.

Monitoring land cover changes is crucial for understanding how natural processes and human activities such as deforestation, urbanization, and agriculture reshape the environment. We introduce a publicly available dataset covering the entire United States from 2016 to 2024, integrating six spectral bands (Red, Green, Blue, NIR, SWIR1, and SWIR2) from Sentinel-2 imagery with pixel-level land cover annotations from the Dynamic World dataset. This combined resource provides a consistent, high-resolution view of the nation’s landscapes, enabling detailed analysis of both short- and long-term changes. To ease the complexities of remote sensing data handling, we supply comprehensive code for data loading, basic analysis, and visualization. We also demonstrate an example application—semantic segmentation with state-of-the-art models—to evaluate dataset quality and reveal challenges associated with minority classes. The dataset and accompanying tools facilitate research in environmental monitoring, urban planning, and climate adaptation, offering a valuable asset for understanding evolving land cover dynamics over time. Full article

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4 pages, 482 KiB

Open AccessData Descriptor

Zooplankton Standing Stock Biomass and Population Density: Data from Long-Term Studies Covering Changes in Trophy and Climate Impacts in a Deep Subalpine Lake (Lake Maggiore, Italy)

by Roberta Piscia, Rossana Caroni and Marina Manca

Data 2025, 10(5), 66; https://doi.org/10.3390/data10050066 - 2 May 2025

Abstract

Lake Maggiore is a deep subalpine lake that has been well studied since the last century thanks to a monitoring program funded by the International Commission for the Protection of Italian–Swiss Waters. The monitoring program comprises both abiotic and biotic parameters, including zooplankton [...] Read more.

Lake Maggiore is a deep subalpine lake that has been well studied since the last century thanks to a monitoring program funded by the International Commission for the Protection of Italian–Swiss Waters. The monitoring program comprises both abiotic and biotic parameters, including zooplankton pelagic organisms. In this study, we present a dataset of 15,563 records of population densities and standing stock biomass for zooplankton pelagic taxa recorded over 43 years (1981–2023). The long-term dataset is valuable for tracing changes in trophic conditions experienced by the lake during the last century (eutrophication and its reversal) and the impact of global warming. Zooplankton samples (Crustacea and Rotifera Monogononta) were collected within 0–50 m depth by vertical hauls with an 80 µm light plankton sampler. The sampling frequency was monthly, with the exception of the 2009–2012 period, which employed seasonal frequency. The estimation of zooplankton taxon abundance and of its standing stock biomass is crucial in order to quantify the flux of matter, energy, and pollutants up to the upper trophic levels of the food web. The dataset provided is also suitable for food web analysis because the zooplankton taxa have been classified according to their ecological roles (microphagous organisms; primary and secondary consumers). Full article

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8 pages, 372 KiB

Open AccessData Descriptor

Dataset on Food Waste in Households: The Case of Latvia

by Ilze Beitane, Sandra Iriste, Martins Sabovics, Gita Krumina-Zemture and Janis Jenzis

Data 2025, 10(5), 65; https://doi.org/10.3390/data10050065 - 30 Apr 2025

Abstract

This publication presents raw data from an online survey in Latvia that reflects households’ practices, opinions, attitudes, and social responsibility regarding food waste. A total of 1336 respondents (households) participated in the survey. The questionnaire consisted of three parts, with the first part [...] Read more.

This publication presents raw data from an online survey in Latvia that reflects households’ practices, opinions, attitudes, and social responsibility regarding food waste. A total of 1336 respondents (households) participated in the survey. The questionnaire consisted of three parts, with the first part focusing on daily food habits and shopping habits, the second part focusing on respondents’ opinions and social responsibility on food waste management, and the third part containing questions on the frequency of shopping for different product groups. The dataset presented in the publication includes survey questions and response options, as well as raw survey data that can be used to compare households’ food waste behavior across countries. The data can help policy makers make data-driven decisions or serve as the basis for further research. Full article

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14 pages, 4526 KiB

Open AccessData Descriptor

A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG

by Aimilia Ntetska, Andreas Miltiadous, Markos G. Tsipouras, Katerina D. Tzimourta, Theodora Afrantou, Panagiotis Ioannidis, Dimitrios G. Tsalikakis, Konstantinos Sakkas, Emmanouil D. Oikonomou, Nikolaos Grigoriadis, Pantelis Angelidis, Nikolaos Giannakeas and Alexandros T. Tzallas

Data 2025, 10(5), 64; https://doi.org/10.3390/data10050064 - 29 Apr 2025

Abstract

Research interest in the application of electroencephalogram (EEG) as a non-invasive diagnostic tool for the automated detection of neurodegenerative diseases is growing. Open-access datasets have become crucial for researchers developing such methodologies. Our previously published open-access dataset of resting-state (eyes-closed) EEG recordings from [...] Read more.

Research interest in the application of electroencephalogram (EEG) as a non-invasive diagnostic tool for the automated detection of neurodegenerative diseases is growing. Open-access datasets have become crucial for researchers developing such methodologies. Our previously published open-access dataset of resting-state (eyes-closed) EEG recordings from patients with Alzheimer’s disease (AD), frontotemporal dementia (FTD), and cognitively normal (CN) controls has attracted significant attention. In this paper, we present a complementary dataset consisting of eyes-open photic stimulation recordings from the same cohort. The dataset includes recordings from 88 participants (36 AD, 23 FTD, and 29 CN) and is provided in Brain Imaging Data Structure (BIDS) format, promoting consistency and ease of use across research groups. Additionally, a fully preprocessed version is included, using EEGLAB-based pipelines that involve filtering, artifact removal, and Independent Component Analysis, preparing the data for machine learning applications. This new dataset enables the study of brain responses to visual stimulation across different cognitive states and supports the development and validation of automated classification algorithms for dementia detection. It offers a valuable benchmark for both methodological comparisons and biological investigations, and it is expected to significantly contribute to the fields of neurodegenerative disease research, biomarker discovery, and EEG-based diagnostics. Full article

(This article belongs to the Special Issue Benchmarking Datasets in Bioinformatics, 2nd Edition)

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53 pages, 1551 KiB

Open AccessArticle

From Crisis to Algorithm: Credit Delinquency Prediction in Peru Under Critical External Factors Using Machine Learning

by Jomark Noriega, Luis Rivera, Jorge Castañeda and José Herrera

Data 2025, 10(5), 63; https://doi.org/10.3390/data10050063 - 28 Apr 2025

Abstract

Robust credit risk prediction in emerging economies increasingly demands the integration of external factors (EFs) beyond borrowers’ control. This study introduces a scenario-based methodology to incorporate EF—namely COVID-19 severity (mortality and confirmed cases), climate anomalies (temperature deviations, weather-induced road blockages), and social unrest—into [...] Read more.

Robust credit risk prediction in emerging economies increasingly demands the integration of external factors (EFs) beyond borrowers’ control. This study introduces a scenario-based methodology to incorporate EF—namely COVID-19 severity (mortality and confirmed cases), climate anomalies (temperature deviations, weather-induced road blockages), and social unrest—into machine learning (ML) models for credit delinquency prediction. The approach is grounded in a CRISP-DM framework, combining stationarity testing (Dickey–Fuller), causality analysis (Granger), and post hoc explainability (SHAP, LIME), along with performance evaluation via AUC, ACC, KS, and F1 metrics. The empirical analysis uses nearly 8.2 million records compiled from multiple sources, including 367,000 credit operations granted to individuals and microbusiness owners by a regulated Peruvian financial institution (FMOD) between January 2020 and September 2023. These data also include time series of delinquency by economic activity, external factor indicators (e.g., mortality, climate disruptions, and protest events), and their dynamic interactions assessed through Granger causality to evaluate both the intensity and propagation of external shocks. The results confirm that EF inclusion significantly enhances model performance and robustness. Time-lagged mortality (COVID MOV) emerges as the most powerful single predictor of delinquency, while compound crises (climate and unrest) further intensify default risk—particularly in portfolios without public support. Among the evaluated models, CNN and XGB consistently demonstrate superior adaptability, defined as their ability to maintain strong predictive performance across diverse stress scenarios—including pandemic, climate, and unrest contexts—and to dynamically adjust to varying input distributions and portfolio conditions. Post hoc analyses reveal that EF effects dynamically interact with borrower income, indebtedness, and behavioral traits. This study provides a scalable, explainable framework for integrating systemic shocks into credit risk modeling. The findings contribute to more informed, adaptive, and transparent lending decisions in volatile economic contexts, relevant to financial institutions, regulators, and risk practitioners in emerging markets. Full article

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

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10 pages, 1175 KiB

Open AccessData Descriptor

A Dataset for Examining the Problem of the Use of Accounting Semi-Identity-Based Models in Econometrics

by Francisco Javier Sánchez-Vidal

Data 2025, 10(5), 62; https://doi.org/10.3390/data10050062 - 28 Apr 2025

Abstract

The problem of using accounting semi-identity-based (ASI) models in Econometrics can be severe in certain circumstances, and estimations from OLS regressions in such models may not accurately reflect causal relationships. This dataset was generated through Monte Carlo simulations, which allowed for the precise [...] Read more.

The problem of using accounting semi-identity-based (ASI) models in Econometrics can be severe in certain circumstances, and estimations from OLS regressions in such models may not accurately reflect causal relationships. This dataset was generated through Monte Carlo simulations, which allowed for the precise control of a causal relationship. The problem of an ASI cannot be directly demonstrated in real samples, as researchers lack insight into the specific factors driving each company’s investment policy. Consequently, it is impossible to distinguish whether regression results in such datasets stem from actual causality or are merely a byproduct of arithmetic distortions introduced by the ASI. The strategy of addressing this issue through simulations allows researchers to determine the true value of any estimator with certainty. The selected model for testing the influence of the ASI problem is the investment-cash flow sensitivity model (Fazzari, Hubbard and Petersen (FHP hereinafter) (1988)), which seeks to establish a relationship between a company’s investments and its cash flows and which is an ASI as well. The dataset included randomly generated independent variables (cash flows and Tobin’s Q) to analyze how they influence the dependent variable (cash flows). The Monte Carlo methodology in Stata enabled repeated sampling to assess how ASIs affect regression models, highlighting their impact on variable relationships and the unreliability of estimated coefficients. The purpose of this paper is twofold: its first goal is to provide a deeper explanation of the syntax in the related article, offering more insights into the ASI problem. The openly available dataset supports replication and further research on ASIs’ effects in economic models and can be adapted for other ASI-based analyses, as the information comprised in the reusability examples prove. Second, our aim is to encourage research supported by Monte Carlo simulations, as they enable the modeling of a comprehensive ecosystem of economic relationships between variables. This allows researchers to address a variety of issues, such as partial correlations, heteroskedasticity, multicollinearity, autocorrelation, endogeneity, and more, while testing their impact on the true value of coefficients. Full article

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12 pages, 2485 KiB

Open AccessData Descriptor

Time-Course Transcriptomic Dataset of Gallic Acid-Induced Human Cervical Carcinoma HeLa Cell Death

by Ho Man Tang and Peter Chi Keung Cheung

Data 2025, 10(5), 61; https://doi.org/10.3390/data10050061 - 28 Apr 2025

Abstract

Gallic acid is a natural phenolic acid that displays potent anti-cancer activity in a large variety of cell types and rodent cancer xenograft models. Although research has focused on determining the efficacy of gallic acid against various types of human cancer cells, the [...] Read more.

Gallic acid is a natural phenolic acid that displays potent anti-cancer activity in a large variety of cell types and rodent cancer xenograft models. Although research has focused on determining the efficacy of gallic acid against various types of human cancer cells, the molecular mechanisms governing the anti-cancer properties of gallic acid remain largely unclear, and a transcriptomic study of gallic acid-induced cancer cell death has rarely been reported. Therefore, we applied time-course bulk RNA-sequencing to elucidate the molecular signature of gallic acid-induced cell death in human cervical cancer HeLa cells, as this is a widely used in vitro model in the field. Our RNA-sequencing dataset covers the early (2^nd hour), middle (4^th, 6^th hour), and late (9^th hour) stages of the cell death process after exposure of HeLa cells to gallic acid, and the untreated (0^th hour) cells served as controls. Differential expression of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) was identified at each time point in the dataset. In summary, this dataset is a unique and valuable resource with which the scientific community can explore the molecular mechanisms and identify druggable regulators of the gallic acid-induced cell death process in cancer. Full article

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22 pages, 2020 KiB

Open AccessArticle

A Synergistic Bridge Between Human–Computer Interaction and Data Management Within CDSS

by Ali Azadi and Francisco José García-Peñalvo

Data 2025, 10(5), 60; https://doi.org/10.3390/data10050060 - 26 Apr 2025

Cited by 1

Abstract

Clinical Decision Support Systems (CDSSs) have become indispensable in medical decision-making. The heterogeneity and vast volume of medical data require firm attention to data management and integration strategies. On the other hand, CDSS functionality must be enhanced through improved human–computer interaction (HCI) principles. [...] Read more.

Clinical Decision Support Systems (CDSSs) have become indispensable in medical decision-making. The heterogeneity and vast volume of medical data require firm attention to data management and integration strategies. On the other hand, CDSS functionality must be enhanced through improved human–computer interaction (HCI) principles. This study investigates the bidirectional relationship between data management practices (specifically data entry management, data transformation, and data integration) and HCI principles within CDSSs. Through a novel framework and practical case studies, we demonstrate how high-quality data entry, driven by controlled workflows and automated technologies, is crucial for system usability and reliability. We explore the transformative positive impact of robust data management techniques, including standardization, normalization, and advanced integration solutions, on the HCI elements and overall system performance. Conversely, we illustrate how effective HCI design improves data quality by reducing cognitive load, minimizing errors, and fostering user engagement. The findings reveal a synergistic relationship between HCI and data science, providing actionable insights for designing intuitive and efficient CDSSs. This research bridges the gap between technical and human-centric approaches, advancing CDSS usability, decision accuracy, and clinician trust for better patient outcomes. Full article

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

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28 pages, 11666 KiB

Open AccessData Descriptor

Introducing UWF-ZeekData24: An Enterprise MITRE ATT&CK Labeled Network Attack Traffic Dataset for Machine Learning/AI

by Marshall Elam, Dustin Mink, Sikha S. Bagui, Russell Plenkers and Subhash C. Bagui

Data 2025, 10(5), 59; https://doi.org/10.3390/data10050059 - 25 Apr 2025

Abstract

This paper describes the creation of a new dataset, UWF-ZeekData24, aligned with the Enterprise MITRE ATT&CK Framework, that addresses critical shortcomings in existing network security datasets. Controlling the construction of attacks and meticulously labeling the data provides a more accurate and dynamic environment [...] Read more.

This paper describes the creation of a new dataset, UWF-ZeekData24, aligned with the Enterprise MITRE ATT&CK Framework, that addresses critical shortcomings in existing network security datasets. Controlling the construction of attacks and meticulously labeling the data provides a more accurate and dynamic environment for testing of IDS/IPS systems and their machine learning algorithms. The outcomes of this research will assist in the development of cybersecurity solutions as well as increase the robustness and adaptability towards modern day cybersecurity threats. This new carefully engineered dataset will enhance cyber defense mechanisms that are responsible for safeguarding critical infrastructures and digital assets. Finally, this paper discusses the differences between crowd-sourced data and data collected in a more controlled environment. Full article

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25 pages, 14600 KiB

Open AccessArticle

Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification

by Edgar Acuña and Roxana Aparicio

Data 2025, 10(5), 58; https://doi.org/10.3390/data10050058 - 24 Apr 2025

Abstract

In this paper, we use visualization tools to give insight into the performance of six classifiers on multivariate time series data. Five of these classifiers are deep learning models, while the Rocket classifier represents a non-deep learning approach. Our comparison is conducted across [...] Read more.

In this paper, we use visualization tools to give insight into the performance of six classifiers on multivariate time series data. Five of these classifiers are deep learning models, while the Rocket classifier represents a non-deep learning approach. Our comparison is conducted across fifteen datasets from the UEA repository. Additionally, we apply data engineering techniques to each dataset, allowing us to assess classifier performance concerning the available features and channels within the time series. The results of our experiments indicate that the ROCKET classifier consistently achieves strong performance across most datasets, while the Transformer model underperforms, likely due to the limited number of instances per class in certain datasets. Full article

(This article belongs to the Topic Future Trends and Challenges in Data Mining Technology)

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10 pages, 1490 KiB

Open AccessData Descriptor

The Long-Term Annual Datasets for Azov Sea Basin Ecosystems for 1925–2024 and Russian Sturgeon Occurrences in 2000–2024

by Mikhail M. Piatinskii, Dmitrii G. Bitiutskii, Arsen V. Mirzoyan, Valerii A. Luzhniak, Vladimir N. Belousov, Dmitry F. Afanasyev, Svetlana V. Zhukova, Sergey N. Kulba, Lyubov A. Zhivoglyadova, Dmitrii V. Hrenkin, Tatjana I. Podmareva, Polina M. Cherniavksaia, Dmitrii S. Burlachko, Nadejda S. Elfimova, Olga V. Kirichenko and Inna D. Kozobrod

Data 2025, 10(5), 57; https://doi.org/10.3390/data10050057 - 24 Apr 2025

Abstract

The abundance of the Russian sturgeon population in the Sea of Azov declined many times in the XX–XXI centuries. This paper presents long-term annual and spatial occurrence datasets to create statistical and machine learning models to better understand the distribution patterns as well [...] Read more.

The abundance of the Russian sturgeon population in the Sea of Azov declined many times in the XX–XXI centuries. This paper presents long-term annual and spatial occurrence datasets to create statistical and machine learning models to better understand the distribution patterns as well as biological and ecological features. The annual dataset provides annually averaged results of environmental and biotic population estimates obtained by in situ observations for 1925–2024. The spatial occurrence dataset contains raw survey observations with a bottom trawl over the period of 2000–2024. Preliminary diagnostics of the annual dataset reveal no evidence of non-stationarity or significant outliers that cannot be explained by biological parameters. The published dataset allows any researcher to perform statistical and machine learning-based analysis in order to compare and describe the population abundance or spatial occurrence of Russian sturgeon in the Sea of Azov. Full article

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8 pages, 1783 KiB

Open AccessData Descriptor

Orange Leaves Images Dataset for the Detection of Huanglongbing

by Juan Carlos Torres-Galván, Paul Hernández Herrera, Juan Antonio Obispo, Xocoyotzin Guadalupe Ávila Cruz, Liliana Montserrat Camacho Ibarra, Paula Magaldi Morales Orosco, Alfonso Alba, Edgar R. Arce-Santana, Valdemar Arce-Guevara, J. S. Murguía, Edgar Guevara and Miguel G. Ramírez-Elías

Data 2025, 10(5), 56; https://doi.org/10.3390/data10050056 - 23 Apr 2025

Abstract

In agriculture, machine learning (ML) and deep learning (DL) have increased significantly in the last few years. The use of ML and DL for image classification in plant disease has generated significant interest due to their cost, automatization, scalability, and early detection. However, [...] Read more.

In agriculture, machine learning (ML) and deep learning (DL) have increased significantly in the last few years. The use of ML and DL for image classification in plant disease has generated significant interest due to their cost, automatization, scalability, and early detection. However, high-quality image datasets are required to train robust classifier models for plant disease detection. In this work, we have created an image dataset of 649 orange leaves divided into two groups: control (n = 379) and huanglongbing (HLB) disease (n = 270). The images were acquired with several smartphone cameras of high resolution and processed to remove the background. The dataset enriches the information on characteristics and symptoms of citrus leaves with HLB and healthy leaves. This enhancement makes the dataset potentially valuable for disease identification through leaf segmentation and abnormality detection, particularly when applying ML and DL models. Full article

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22 pages, 1339 KiB

Open AccessArticle

A Comprehensive Data Maturity Model for Data Pre-Analysis

by Lukas Knoflach, Lin Shao and Torsten Ullrich

Data 2025, 10(4), 55; https://doi.org/10.3390/data10040055 - 19 Apr 2025

Abstract

Data analysis is widely used in research and industry where there is a need to extract information from data. A significant amount of time within a data analysis project is required to prepare the data for subsequent analysis. This paper presents a comprehensive [...] Read more.

Data analysis is widely used in research and industry where there is a need to extract information from data. A significant amount of time within a data analysis project is required to prepare the data for subsequent analysis. This paper presents a comprehensive weighted maturity model to estimate the readiness of data for subsequent data analysis, with the goal of avoiding delays due to data quality problems. The maturity model uses a questionnaire with nine criteria to determine the maturity level of data preparation. The maturity model is integrated into a web application that provides an automated evaluation of maturity and a novel visualization approach that combines a modified spider chart and augmented chord diagrams. The comprehensive weighted maturity model is a ready-to-use application that provides prospective users with an easy and quick way to check their data for maturity for subsequent data analysis, with the goal of improving the data preparation process. The weighted maturity model is applicable to all types of data analysis, regardless of the domain of the data. Full article

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27 pages, 5139 KiB

Open AccessArticle

A Partition-Based Hybrid Algorithm for Effective Imbalanced Classification

by Kittipong Theephoowiang and Anantaporn Hanskunatai

Data 2025, 10(4), 54; https://doi.org/10.3390/data10040054 - 18 Apr 2025

Abstract

Imbalanced classification presents a significant challenge in real-world datasets, requiring innovative solutions to enhance performance. This study introduces a hybrid binary classification algorithm designed to effectively address this challenge. The algorithm identifies different data types, pairs them, and trains multiple models, which then [...] Read more.

Imbalanced classification presents a significant challenge in real-world datasets, requiring innovative solutions to enhance performance. This study introduces a hybrid binary classification algorithm designed to effectively address this challenge. The algorithm identifies different data types, pairs them, and trains multiple models, which then vote on predictions using weighted strategies to ensure stable performance and minimize overfitting. Unlike some methods, it is designed to work consistently with both noisy and noise-free datasets, prioritizing overall stability rather than specific noise adjustments. The algorithm’s effectiveness is evaluated using Recall, G-Mean, and AUC, measuring its ability to detect the minority class while maintaining balance. The results reveal notable improvements in minority class detection, with Recall outperforming other methods in 16 out of 22 datasets, supported by paired t-tests. The algorithm also shows promising improvements in G-Mean and AUC, ranking first in 17 and 18 datasets, respectively. To further evaluate its performance, the study compares the proposed algorithm with previous methods using G-Mean. The comparison confirms that the proposed algorithm also exhibits strong performance, further highlighting its potential. These findings emphasize the algorithm’s versatility in handling diverse datasets and its ability to balance minority class detection with overall accuracy. Full article

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

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