Data

13 pages, 293 KB

Open AccessArticle

Scalable Model-Based Diagnosis with FastDiag: A Dataset and Parallel Benchmark Framework

by Delia Isabel Carrión León, Cristian Vidal-Silva and Nicolás Márquez

Data 2025, 10(9), 141; https://doi.org/10.3390/data10090141 - 3 Sep 2025

FastDiag is a widely used algorithm for model-based diagnosis, computing minimal subsets of constraints whose removal restores consistency in knowledge-based systems. As applications grow in complexity, researchers have proposed parallel extensions such as FastDiagP and FastDiagP++ to accelerate diagnosis through speculative and multiprocessing [...] Read more.

FastDiag is a widely used algorithm for model-based diagnosis, computing minimal subsets of constraints whose removal restores consistency in knowledge-based systems. As applications grow in complexity, researchers have proposed parallel extensions such as FastDiagP and FastDiagP++ to accelerate diagnosis through speculative and multiprocessing strategies. This paper presents a reproducible and extensible framework for evaluating FastDiag and its parallel variants across a benchmark suite of feature models and ontology-like constraints. We analyze each variant in terms of recursion structure, runtime performance, and diagnostic correctness. Tracking mechanisms and structured logs enable the fine-grained comparison of recursive behavior and branching strategies. Technical validation confirms that parallel execution preserves minimality and structural soundness, while benchmark results show runtime improvements of up to 4× with FastDiagP++. The accompanying dataset, available as open source, supports educational use, algorithmic benchmarking, and integration into interactive configuration environments. The framework is primarily intended for reproducible benchmarking and teaching with open-source implementations that facilitate analysis and extension. Full article

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5 pages, 205 KB

Open AccessData Descriptor

Data on Stark Broadening of N V Spectral Lines

by Milan S. Dimitrijević, Magdalena D. Christova and Sylvie Sahal-Bréchot

Data 2025, 10(9), 140; https://doi.org/10.3390/data10090140 - 31 Aug 2025

Abstract

A data set on Stark broadening parameters defining the Lorentzian line profile (spectral line widths and shifts) for 31 multiplets of four-times-charged nitrogen ion (N V), with lines broadened by impacts with electrons (e), protons (p), He II ions,

α

particles (He III), [...] Read more.

A data set on Stark broadening parameters defining the Lorentzian line profile (spectral line widths and shifts) for 31 multiplets of four-times-charged nitrogen ion (N V), with lines broadened by impacts with electrons (e), protons (p), He II ions,

α

particles (He III), and B III, B IV, B V, and B VI ions, is given. The above-mentioned data have been calculated within the frame of the semiclassical perturbation theory, for temperatures from 50,000 K to 1,000,000 K, and densities of perturbers from 10¹⁵ cm⁻³ up to 10²¹ cm⁻³. These data are, first of all, of interest for diagnostics and modeling of laser-driven plasma in experiments and investigations of proton–boron fusion, especially when the target is boron nitride (BN). Data on Stark broadening by collisions with e, p, He II ions, and

α

particles are useful for the investigation of stellar plasma, in particular for white dwarf atmospheres and subphotospheric layer modeling. Full article

(This article belongs to the Section Spatial Data Science and Digital Earth)

14 pages, 1043 KB

Open AccessArticle

A Dataset and Experimental Evaluation of a Parallel Conflict Detection Solution for Model-Based Diagnosis

by Jessica Janina Cabezas-Quinto, Cristian Vidal-Silva, Jorge Serrano-Malebrán and Nicolás Márquez

Data 2025, 10(9), 139; https://doi.org/10.3390/data10090139 - 29 Aug 2025

Abstract

This article presents a dataset and experimental evaluation of a parallelized variant of Junker’s QuickXPlain algorithm, designed to efficiently compute minimal conflict sets in constraint-based diagnosis tasks. The dataset includes performance benchmarks, conflict traces, and solution metadata for a wide range of configurable [...] Read more.

This article presents a dataset and experimental evaluation of a parallelized variant of Junker’s QuickXPlain algorithm, designed to efficiently compute minimal conflict sets in constraint-based diagnosis tasks. The dataset includes performance benchmarks, conflict traces, and solution metadata for a wide range of configurable diagnosis problems based on real-world and synthetic CSP instances. Our parallel variant leverages multicore architectures to reduce computation time while preserving the completeness and minimality guarantees of QuickXPlain. All evaluations were conducted using reproducible scripts and parameter configurations, enabling comparison across different algorithmic strategies. The provided dataset can be used to replicate experiments, analyze scalability under varying problem sizes, and serve as a baseline for future improvements in conflict explanation algorithms. The full dataset, codebase, and benchmarking scripts are openly available and documented to promote transparency and reusability in constraint-based diagnostic systems research. Full article

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15 pages, 2075 KB

Open AccessData Descriptor

A Curated Dataset of Regional Meteor Events with Simultaneous Optical and Infrasound Observations (2006–2011)

by Elizabeth A. Silber, Emerson Brown, Andrea R. Thompson and Vedant Sawal

Data 2025, 10(9), 138; https://doi.org/10.3390/data10090138 - 28 Aug 2025

Abstract

We present a curated, openly accessible dataset of 71 regional meteor events simultaneously recorded by optical and infrasound instrumentation between 2006 and 2011. These events were captured during an observational campaign using the all-sky cameras of the Southern Ontario Meteor Network and the [...] Read more.

We present a curated, openly accessible dataset of 71 regional meteor events simultaneously recorded by optical and infrasound instrumentation between 2006 and 2011. These events were captured during an observational campaign using the all-sky cameras of the Southern Ontario Meteor Network and the co-located Elginfield Infrasound Array. Each entry provides optical trajectory measurements, infrasound waveforms, and atmospheric specification profiles. The integration of optical and acoustic data enables robust linkage between observed acoustic signals and specific points along meteor trajectories, offering new opportunities to examine shock wave generation, propagation, and energy deposition processes. This release fills a critical observational gap by providing the first validated, openly accessible archive of simultaneous optical–infrasound meteor observations that supports trajectory reconstruction, acoustic propagation modeling, and energy deposition analyses. By making these data openly available in a structured format, this work establishes a durable reference resource that advances reproducibility, fosters cross-disciplinary research, and underpins future developments in meteor physics, atmospheric acoustics, and planetary defense. Full article

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18 pages, 712 KB

Open AccessArticle

The Discussions of Monkeypox Misinformation on Social Media

by Or Elroy and Abraham Yosipof

Data 2025, 10(9), 137; https://doi.org/10.3390/data10090137 - 25 Aug 2025

Abstract

The global outbreak of the monkeypox virus was declared a health emergency by the World Health Organization (WHO). During such emergencies, misinformation about health suggestions can spread rapidly, leading to serious consequences. This study investigates the relationships between tweet readability, user engagement, and [...] Read more.

The global outbreak of the monkeypox virus was declared a health emergency by the World Health Organization (WHO). During such emergencies, misinformation about health suggestions can spread rapidly, leading to serious consequences. This study investigates the relationships between tweet readability, user engagement, and susceptibility to misinformation. Our conceptual model posits that tweet readability influences user engagement, which in turn affects the spread of misinformation. Specifically, we hypothesize that tweets with higher readability and grammatical correctness garner more user engagement and that misinformation tweets tend to be less readable than accurate information tweets. To test these hypotheses, we collected over 1.4 million tweets related to monkeypox discussions on X (formerly Twitter) and trained a semi-supervised learning classifier to categorize them as misinformation or not-misinformation. We analyzed the readability and grammar levels of these tweets using established metrics. Our findings indicate that readability and grammatical correctness significantly boost user engagement with accurate information, thereby enhancing its dissemination. Conversely, misinformation tweets are generally less readable, which reduces their spread. This study contributes to the advancement of knowledge by elucidating the role of readability in combating misinformation. Practically, it suggests that improving the readability and grammatical correctness of accurate information can enhance user engagement and consequently mitigate the spread of misinformation during health emergencies. These insights offer valuable strategies for public health communication and social media platforms to more effectively address misinformation. Full article

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22 pages, 1506 KB

Open AccessArticle

A FAIR Perspective on Data Quality Frameworks

by Nicholas Nicholson, Raquel Negrao Carvalho and Iztok Štotl

Data 2025, 10(9), 136; https://doi.org/10.3390/data10090136 - 23 Aug 2025

Abstract

Despite considerable effort and analysis over the last two to three decades, no integrated scenario yet exists for data quality frameworks. Currently, the choice is between several frameworks dependent upon the type and use of data. While the frameworks are appropriate to their [...] Read more.

Despite considerable effort and analysis over the last two to three decades, no integrated scenario yet exists for data quality frameworks. Currently, the choice is between several frameworks dependent upon the type and use of data. While the frameworks are appropriate to their specific purposes, they are generally prescriptive of the quality dimensions they prescribe. We reappraise the basis for measuring data quality by laying out a concept for a framework that addresses data quality from the foundational basis of the FAIR data guiding principles. We advocate for a federated data contextualisation framework able to handle the FAIR-related quality dimensions in the general data contextualisation descriptions and the remaining intrinsic data quality dimensions in associated dedicated context spaces without being overly prescriptive. A framework designed along these lines provides several advantages, not least of which is its ability to encapsulate most other data quality frameworks. Moreover, by contextualising data according to the FAIR data principles, many subjective quality measures are managed automatically and can even be quantified to a degree, whereas objective intrinsic quality measures can be handled to any level of granularity for any data type. This serves to avoid blurring quality dimensions between the data and the data application perspectives as well as to support data quality provenance by providing traceability over a chain of data processing operations. We show by example how some of these concepts can be implemented at a practical level. Full article

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15 pages, 1905 KB

Open AccessArticle

Predicting Real Estate Prices Using Machine Learning in Bosnia and Herzegovina

by Zvezdan Stojanović, Dario Galić and Hava Kahrić

Data 2025, 10(9), 135; https://doi.org/10.3390/data10090135 - 23 Aug 2025

Abstract

The real estate market has a major impact on the economy and everyday life. Accurate real estate valuation is essential for buyers, sellers, investors, and government institutions. Traditionally, valuation has been conducted using various estimation models. However, recent advancements in information technology, particularly [...] Read more.

The real estate market has a major impact on the economy and everyday life. Accurate real estate valuation is essential for buyers, sellers, investors, and government institutions. Traditionally, valuation has been conducted using various estimation models. However, recent advancements in information technology, particularly in artificial intelligence and machine learning, have enabled more precise predictions of real estate prices. Machine learning allows computers to recognize patterns in data and create models that can predict prices based on the characteristics of the property, such as location, square footage, number of rooms, age of the building, and similar features. The aim of this paper is to investigate how the application of machine learning can be used to predict real estate prices. A machine learning model was developed using four algorithms: Linear Regression, Random Forest Regression, XGBoost, and K-Nearest Neighbors. The dataset used in this study was collected from major online real estate listing portals in Bosnia and Herzegovina. The performance of each model was evaluated using the R² score, Root Mean Squared Error (RMSE), scatter plots, and error distributions. Based on this evaluation, the most accurate model was selected. Additionally, a simple web interface was created to allow for non-experts to easily obtain property price estimates. Full article

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8 pages, 2781 KB

Open AccessData Descriptor

Experimental Dataset of Greenhouse Gas Emissions from Laboratory Biocover Experiment

by Kristaps Siltumens, Inga Grinfelde and Juris Burlakovs

Data 2025, 10(8), 134; https://doi.org/10.3390/data10080134 - 21 Aug 2025

Abstract

The dataset presented in this manuscript consists of three distinct sets of data collected during a laboratory experiment aimed at quantifying the emissions of greenhouse gases (GHGs), specifically methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O). [...] Read more.

The dataset presented in this manuscript consists of three distinct sets of data collected during a laboratory experiment aimed at quantifying the emissions of greenhouse gases (GHGs), specifically methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O). The experiment was conducted in three phases, each initiated at different times. The first phase began on 6 June 2022, using a biocover composed of 60% fine-fraction waste, 20% clay soil, and 20% stabilized compost. The second phase commenced on 26 August 2022, with two biocover variants: one composed of 50% fine-fraction waste and 50% clay soil, and the other consisting of 40% fine-fraction waste, 40% clay soil, and 20% shredded paper. The final phase started on 27 October 2022, introducing two biocovers: one containing 25% dried algae, 25% fine-fraction waste, 25% gravel (0–20 mm), and 25% ash, and the other composed of 40% fine-fraction waste, 40% dried algae, and 20% chernozem. Emission assessments were conducted three weeks after the biocover installation to allow for settling and stabilization, followed by weekly measurements two to three days before irrigation with 250 mL of water to simulate field conditions. GHG emission quantification was carried out using the Cavity Ring-Down Spectroscopy gas measurement device, Picarro G2508. This dataset offers substantial scientific value for advancing biocover technologies aimed at reducing GHG emissions in landfill environments, particularly for mitigating methane emissions. In addition to initial experimental use, the dataset offers a wide range of possibilities for reuse, including modeling landfill gas emissions, validating gas flow measurement methods, developing machine learning models, and performing meta-analyses. Its detailed structure facilitates multi-faceted environmental research and supports optimization of landfill management. Full article

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29 pages, 991 KB

Open AccessArticle

GroupView: A Visual Framework for Exploring Group Membership Dynamics over Time

by Mithilesh Kumar Singh and Klaus Mueller

Data 2025, 10(8), 133; https://doi.org/10.3390/data10080133 - 21 Aug 2025

Abstract

Tracking group membership dynamics over time is a persistent challenge in visual analytics, particularly when dealing with complex, multidimensional datasets. Existing tools often struggle to visualize dynamic group transitions while preserving attribute relationships and maintaining consistent group definitions. We present GroupView, a visual [...] Read more.

Tracking group membership dynamics over time is a persistent challenge in visual analytics, particularly when dealing with complex, multidimensional datasets. Existing tools often struggle to visualize dynamic group transitions while preserving attribute relationships and maintaining consistent group definitions. We present GroupView, a visual framework designed to explore temporal data and group dynamics to address this. GroupView enables users to slice data into time-based segments and create dynamic groupings, facilitating the identification of trends and patterns that may otherwise remain hidden. Its features include automated grouping based on data similarities, combinatorial grouping for richer insights, and custom grouping for tailored analysis. A heuristic user study involving visualization experts provided feedback on usability and analytical value, highlighting the strengths of GroupView in intuitive exploration and insight discovery. These features position GroupView as a valuable tool for analysts and researchers working with evolving datasets, offering new avenues for uncovering trends and tracking group-level changes over time. Full article

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19 pages, 1398 KB

Open AccessSystematic Review

Data Science Project Barriers—A Systematic Review

by Natan Labarrère, Lino Costa and Rui M. Lima

Data 2025, 10(8), 132; https://doi.org/10.3390/data10080132 - 20 Aug 2025

Abstract

This study aims to identify and categorize barriers to the success of Data Science (DS) projects through a systematic literature review combined with quantitative methods of analysis. PRISMA is used to conduct a literature review to identify the barriers in the existing literature. [...] Read more.

This study aims to identify and categorize barriers to the success of Data Science (DS) projects through a systematic literature review combined with quantitative methods of analysis. PRISMA is used to conduct a literature review to identify the barriers in the existing literature. With techniques from bibliometrics and network science, the barriers are hierarchically clustered using the Jaccard distance as a measure of dissimilarity. The review identified 27 barriers to the success of DS projects from 26 studies. These barriers were grouped into six thematic clusters: people, data and technology, management, economic, project, and external barriers. The barrier “insufficient skills” is the most frequently cited in the literature and the most frequently considered critical. From the quantitative analysis, the barriers “insufficient skills”, “poor data quality”, “data privacy and security”, “lack of support from top management”, “insufficient funding”, “insufficient ROI or justification”, “government policies and regulation”, and “inadequate, immature or inconsistent methodology” were identified as the most central in their cluster. Full article

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10 pages, 1572 KB

Open AccessData Descriptor

Simultaneous EEG-fNIRS Data on Learning Capability via Implicit Learning Induced by Cognitive Tasks

by Chayapol Chaiyanan, Thanate Angsuwatanakul, Keiji Iramina and Boonserm Kaewkamnerdpong

Data 2025, 10(8), 131; https://doi.org/10.3390/data10080131 - 18 Aug 2025

Abstract

The development of real-time learning assessment tools is hindered by an incomplete understanding of the underlying neural mechanisms. To address this gap, this study aimed to identify the specific neural correlates of implicit learning, a foundational process crucial for skill acquisition. We collected [...] Read more.

The development of real-time learning assessment tools is hindered by an incomplete understanding of the underlying neural mechanisms. To address this gap, this study aimed to identify the specific neural correlates of implicit learning, a foundational process crucial for skill acquisition. We collected simultaneous electroencephalography and functional near-infrared spectroscopy data from thirty healthy adults (ages 21–29) performing a serial reaction time task designed to induce implicit learning. By capturing both electrophysiological and hemodynamic responses concurrently at shared locations, this dataset offers a unique opportunity to investigate neurovascular coupling during implicit learning and gain deeper insights into the neural mechanisms of learning. The dataset is categorized into two groups: participants who demonstrated implicit learning (based on post-experiment interviews) and those who did not. This dataset enables the identification of prominent brain regions, features, and temporal patterns associated with successful implicit learning. This identification will form the basis for future real-time learning assessment tools. Full article

(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—3rd Edition)

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8 pages, 529 KB

Open AccessData Descriptor

An Extended Dataset of Educational Quality Across Countries (1970–2023)

by Hanol Lee and Jong-Wha Lee

Data 2025, 10(8), 130; https://doi.org/10.3390/data10080130 - 15 Aug 2025

Abstract

This study presents an extended dataset on educational quality covering 101 countries, from 1970 to 2023. While existing international assessments, such as the Programme for International Student Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS), offer valuable snapshots of student [...] Read more.

This study presents an extended dataset on educational quality covering 101 countries, from 1970 to 2023. While existing international assessments, such as the Programme for International Student Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS), offer valuable snapshots of student performance, their limited coverage across countries and years constrains broader analyses. To address this limitation, we harmonized observed test scores across assessments and imputed missing values using both linear interpolation and machine learning (Least Absolute Shrinkage and Selection Operator (LASSO) regression). The dataset included (i) harmonized test scores for 15 year olds, (ii) annual educational quality indicators for the 15–19 age group, and (iii) educational quality indexes for the working-age population (15–64). These measures are provided in machine-readable formats and support empirical research on human capital, economic development, and global education inequalities across economies. Full article

(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—3rd Edition)

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11 pages, 697 KB

Open AccessData Descriptor

A Multi-Sensor Dataset for Human Activity Recognition Using Inertial and Orientation Data

by Jhonathan L. Rivas-Caicedo, Laura Saldaña-Aristizabal, Kevin Niño-Tejada and Juan F. Patarroyo-Montenegro

Data 2025, 10(8), 129; https://doi.org/10.3390/data10080129 - 14 Aug 2025

Abstract

Human Activity Recognition (HAR) using wearable sensors is an increasingly relevant area for applications in healthcare, rehabilitation, and human–computer interaction. However, publicly available datasets that provide multi-sensor, synchronized data combining inertial and orientation measurements are still limited. This work introduces a publicly available [...] Read more.

Human Activity Recognition (HAR) using wearable sensors is an increasingly relevant area for applications in healthcare, rehabilitation, and human–computer interaction. However, publicly available datasets that provide multi-sensor, synchronized data combining inertial and orientation measurements are still limited. This work introduces a publicly available dataset for Human Activity Recognition, captured using wearable sensors placed on the chest, hands, and knees. Each device recorded inertial and orientation data during controlled activity sessions involving participants aged 20 to 70. A standardized acquisition protocol ensured consistent temporal alignment across all signals. The dataset was preprocessed and segmented using a sliding window approach. An initial baseline classification experiment, employing a Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) model, demonstrated an average accuracy of 93.5% in classifying activities. The dataset is publicly available in CSV format and includes raw sensor signals, activity labels, and metadata. This dataset offers a valuable resource for evaluating machine learning models, studying distributed HAR approaches, and developing robust activity recognition pipelines utilizing wearable technologies. Full article

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27 pages, 9197 KB

Open AccessData Descriptor

A Six-Year, Spatiotemporally Comprehensive Dataset and Data Retrieval Tool for Analyzing Chlorophyll-a, Turbidity, and Temperature in Utah Lake Using Sentinel and MODIS Imagery

by Kaylee B. Tanner, Anna C. Cardall and Gustavious P. Williams

Data 2025, 10(8), 128; https://doi.org/10.3390/data10080128 - 13 Aug 2025

Abstract

Data from earth observation satellites provide unique and valuable information about water quality conditions in freshwater lakes but require significant processing before they can be used, even with the use of tools like Google Earth Engine. We use imagery from Sentinel 2 and [...] Read more.

Data from earth observation satellites provide unique and valuable information about water quality conditions in freshwater lakes but require significant processing before they can be used, even with the use of tools like Google Earth Engine. We use imagery from Sentinel 2 and MODIS and in situ data from the State of Utah Ambient Water Quality Management System (AQWMS) database to develop models and to generate a highly accessible, easy-to-use CSV file of chlorophyll-a (which is an indicator of algal biomass), turbidity, and water temperature measurements on Utah Lake. From a collection of 937 Sentinel 2 images spanning the period from January 2019 to May 2025, we generated 262,081 estimates each of chlorophyll-a and turbidity, with an additional 1,140,777 data points interpolated from those estimates to provide a dataset with a consistent time step. From a collection of 2333 MODIS images spanning the same time period, we extracted 1,390,800 measurements each of daytime water surface temperature and nighttime water surface temperature and interpolated or imputed an additional 12,058 data points from those estimates. We interpolated the data using piecewise cubic Hermite interpolation polynomials to preserve the original distribution of the data and provide the most accurate estimates of measurements between observations. We demonstrate the processing steps required to extract usable, accurate estimates of these three water quality parameters from satellite imagery and format them for analysis. We include summary statistics and charts for the resulting dataset, which show the usefulness of this data for informing Utah Lake management issues. We include the Jupyter Notebook with the implemented processing steps and the formatted CSV file of data as ^{supplemental materials}. The Jupyter Notebook can be used to update the Utah Lake data or can be easily modified to generate similar data for other waterbodies. We provide this method, tool set, and data to make remotely sensed water quality data more accessible to researchers, water managers, and others interested in Utah Lake and to facilitate the use of satellite data for those interested in applying remote sensing techniques to other waterbodies. Full article

(This article belongs to the Collection Modern Geophysical and Climate Data Analysis: Tools and Methods)

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Graphical abstract

16 pages, 2323 KB

Open AccessArticle

Limitations of Influence-Based Dataset Compression for Waste Classification

by Julian Aberger, Lena Brensberger, Gerald Koinig, Benedikt Häcker, Jesús Pestana and Renato Sarc

Data 2025, 10(8), 127; https://doi.org/10.3390/data10080127 - 7 Aug 2025

Abstract

Influence-based data selection methods, such as TracIn, aim to estimate the impact of individual training samples on model predictions and are increasingly used for dataset curation and reduction. This study investigates whether selecting the most positively influential training examples can be used to [...] Read more.

Influence-based data selection methods, such as TracIn, aim to estimate the impact of individual training samples on model predictions and are increasingly used for dataset curation and reduction. This study investigates whether selecting the most positively influential training examples can be used to create compressed yet effective training datasets for transfer learning in plastic waste classification. Using a ResNet-18 model trained on a custom dataset of plastic waste images, TracIn was applied to compute influence scores across multiple training checkpoints. The top 50 influential samples per class were extracted and used to train a new model. Contrary to expectations, models trained on these highly influential subsets significantly underperformed compared to models trained on either the full dataset or an equally sized random sample. Further analysis revealed that many top-ranked influential images originated from different classes, indicating model biases and potential label confusion. These findings highlight the limitations of using influence scores for dataset compression. However, TracIn proved valuable for identifying problematic or ambiguous samples, class imbalance issues, and issues with fuzzy class boundaries. Based on the results, the utilized TracIn approach is recommended as a diagnostic instrument rather than for dataset curation. Full article

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37 pages, 2744 KB

Open AccessArticle

Synergistic Evolution or Competitive Disruption? Analysing the Dynamic Interaction Between Digital and Real Economies in Henan, China, Based on Panel Data

by Yaping Zhu, Qingwei Xu, Chutong Hao, Shuaishuai Geng and Bingjun Li

Data 2025, 10(8), 126; https://doi.org/10.3390/data10080126 - 4 Aug 2025

Abstract

In the digital transformation era, understanding the relationship between digital and real economies is vital for regional development. This study analyses the interaction between these two economies in Henan Province using panel data from 18 cities (2011–2023). It incorporates policy support intensity through [...] Read more.

In the digital transformation era, understanding the relationship between digital and real economies is vital for regional development. This study analyses the interaction between these two economies in Henan Province using panel data from 18 cities (2011–2023). It incorporates policy support intensity through fuzzy set theory, applies an integrated weighting method to measure development levels, and uses regression models to assess the digital economy’s impact on the real economy. The coupling coordination degree model, kernel density estimation, and Gini coefficient reveal the coordination status and spatial distribution, while the ecological Lotka–Volterra model identifies the symbiotic patterns. The key findings are as follows: (1) The digital economy does not directly determine the state of the real economy. For example, cities such as Zhoukou and Zhumadian have low digital economy levels but high real economy levels. However, the development of the digital economy promotes the real economy without signs of diminishing returns. (2) The two economies are generally coordinated but differ spatially, with greater coordination in the Central Plains urban agglomeration. (3) The digital and real economies exhibit both collaboration and competition, with reciprocal mutualism as the dominant mode of integration. These insights provide guidance for policymakers and offer a new perspective on the integration of both economies. Full article

(This article belongs to the Special Issue Data in Behavioral and Experimental Research: Datasets and Applications)

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11 pages, 3192 KB

Open AccessData Descriptor

Carbon Monoxide (CO) and Ozone (O₃) Concentrations in an Industrial Area: A Dataset at the Neighborhood Level

by Jailene Marlen Jaramillo-Perez, Bárbara A. Macías-Hernández, Edgar Tello-Leal and René Ventura-Houle

Data 2025, 10(8), 125; https://doi.org/10.3390/data10080125 - 1 Aug 2025

Abstract

The growth of urban and industrial areas is accompanied by an increase in vehicle traffic, resulting in rising concentrations of various air pollutants. This is a global issue that causes environmental damage and risks to human health. The dataset presented in this research [...] Read more.

The growth of urban and industrial areas is accompanied by an increase in vehicle traffic, resulting in rising concentrations of various air pollutants. This is a global issue that causes environmental damage and risks to human health. The dataset presented in this research contains records with measurements of the air pollutants ozone (O₃) and carbon monoxide (CO), as well as meteorological parameters such as temperature (T), relative humidity (RH), and barometric pressure (BP). This dataset was collected using a set of low-cost sensors over a four-month study period (March to June) in 2024. The monitoring of air pollutants and meteorological parameters was conducted in a city with high industrial activity, heavy traffic, and close proximity to a petrochemical refinery plant. The data were subjected to a series of statistical analyses for visualization using plots that allow for the identification of their behavior. Finally, the dataset can be utilized for air quality studies, public health research, and the development of prediction models based on mathematical approaches or artificial intelligence algorithms. Full article

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7 pages, 1048 KB

Open AccessData Descriptor

Dataset of Morphometry and Metal Concentrations in Coptodon rendalli and Oreochromis mossambicus from the Shongweni Dam, South Africa

by Smangele Ncayiyana, Neo Mashila Maleka and Jeffrey Lebepe

Data 2025, 10(8), 124; https://doi.org/10.3390/data10080124 - 1 Aug 2025

Abstract

The uMlazi River receives effluents from wastewater work before feeding the Shongweni Dam. However, local communities are consuming fish from this dam for protein supplements. This study was undertaken to investigate the metal concentrations in the water and sediment, the general health of [...] Read more.

The uMlazi River receives effluents from wastewater work before feeding the Shongweni Dam. However, local communities are consuming fish from this dam for protein supplements. This study was undertaken to investigate the metal concentrations in the water and sediment, the general health of Coptodon rendalli and Oreochromis mossambicus, and metal bioaccumulation. Sampling was conducted during the dry (July–August) and wet seasons (November and December) in 2021. Water was sampled using acid-pre-treated sampling bottles, whereas sediment was collected using the Van Veen grab at the inflow, middle, and dam wall. Fish were collected, and their tissues were digested using aqua regia. Metal concentrations were measured using inductively coupled plasma optical emission spectroscopy (ICP-OES). This data manuscript reports the physical parameters of the water and concentrations of antimony, arsenic, cadmium, copper, iron, manganese, lead, selenium, and strontium in the water and sediment from the Shongweni Dam. Moreover, the fish morphometric data and metal concentrations observed in the muscle are also presented. This data could be used as baseline information on metal concentrations in the Shongweni Dam. Moreover, it provides insight into the potential impact of wastewater effluents on metal increases in freshwater bodies. Full article

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21 pages, 22884 KB

Open AccessData Descriptor

An Open-Source Clinical Case Dataset for Medical Image Classification and Multimodal AI Applications

by Mauro Nievas Offidani, Facundo Roffet, María Carolina González Galtier, Miguel Massiris and Claudio Delrieux

Data 2025, 10(8), 123; https://doi.org/10.3390/data10080123 - 31 Jul 2025

Abstract

High-quality, openly accessible clinical datasets remain a significant bottleneck in advancing both research and clinical applications within medical artificial intelligence. Case reports, often rich in multimodal clinical data, represent an underutilized resource for developing medical AI applications. We present an enhanced version of [...] Read more.

High-quality, openly accessible clinical datasets remain a significant bottleneck in advancing both research and clinical applications within medical artificial intelligence. Case reports, often rich in multimodal clinical data, represent an underutilized resource for developing medical AI applications. We present an enhanced version of MultiCaRe, a dataset derived from open-access case reports on PubMed Central. This new version addresses the limitations identified in the previous release and incorporates newly added clinical cases and images (totaling 93,816 and 130,791, respectively), along with a refined hierarchical taxonomy featuring over 140 categories. Image labels have been meticulously curated using a combination of manual and machine learning-based label generation and validation, ensuring a higher quality for image classification tasks and the fine-tuning of multimodal models. To facilitate its use, we also provide a Python package for dataset manipulation, pretrained models for medical image classification, and two dedicated websites. The updated MultiCaRe dataset expands the resources available for multimodal AI research in medicine. Its scale, quality, and accessibility make it a valuable tool for developing medical AI systems, as well as for educational purposes in clinical and computational fields. Full article

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