Data

11 pages, 697 KiB

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 KiB

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 KiB

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 KiB

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 KiB

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 KiB

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 KiB

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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27 pages, 4973 KiB

Open AccessArticle

LSTM-Based River Discharge Forecasting Using Spatially Gridded Input Data

by Kamilla Rakhymbek, Balgaisha Mukanova, Andrey Bondarovich, Dmitry Chernykh, Almas Alzhanov, Dauren Nurekenov, Anatoliy Pavlenko and Aliya Nugumanova

Data 2025, 10(8), 122; https://doi.org/10.3390/data10080122 - 27 Jul 2025

Abstract

Accurate river discharge forecasting remains a critical challenge in hydrology, particularly in data-scarce mountainous regions where in situ observations are limited. This study investigated the potential of long short-term memory (LSTM) networks to improve discharge prediction by leveraging spatially distributed reanalysis data. Using [...] Read more.

Accurate river discharge forecasting remains a critical challenge in hydrology, particularly in data-scarce mountainous regions where in situ observations are limited. This study investigated the potential of long short-term memory (LSTM) networks to improve discharge prediction by leveraging spatially distributed reanalysis data. Using the ERA5-Land dataset, we developed an LSTM model that integrates grid-based meteorological inputs and assesses their relative importance. We conducted experiments on two snow-dominated basins with contrasting physiographic characteristics, the Uba River basin in Kazakhstan and the Flathead River basin in the USA, to answer three research questions: (1) whether full-grid input outperforms reduced configurations and models trained on Caravan, (2) the impact of spatial resolution on accuracy and efficiency, and (3) the effect of partial spatial coverage on prediction reliability. Specifically, we compared the full-grid LSTM with a single-cell LSTM, a basin-average LSTM, a Caravan-trained LSTM, and coarser cell aggregations. The results demonstrate that the full-grid LSTM consistently yields the highest forecasting performance, achieving a median Nash–Sutcliffe efficiency of 0.905 for Uba and 0.93 for Middle Fork Flathead, while using coarser grids and random subsets reduces performance. Our findings highlight the critical importance of spatial input richness and provide a reproducible framework for grid selection in flood-prone basins lacking dense observation networks. Full article

(This article belongs to the Special Issue New Progress in Big Earth Data)

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9 pages, 2733 KiB

Open AccessData Descriptor

Investigating Mid-Latitude Lower Ionospheric Responses to Energetic Electron Precipitation: A Case Study

by Aleksandra Kolarski, Vladimir A. Srećković, Zoran R. Mijić and Filip Arnaut

Data 2025, 10(8), 121; https://doi.org/10.3390/data10080121 - 26 Jul 2025

Abstract

Localized ionization enhancements (LIEs) in altitude range corresponding to the D-region ionosphere, disrupting Very-Low-Frequency (VLF) signal propagation. This case study focuses on Lightning-induced Electron Precipitation (LEP), analyzing amplitude and phase variations in VLF signals recorded in Belgrade, Serbia, from worldwide transmitters. Due to [...] Read more.

Localized ionization enhancements (LIEs) in altitude range corresponding to the D-region ionosphere, disrupting Very-Low-Frequency (VLF) signal propagation. This case study focuses on Lightning-induced Electron Precipitation (LEP), analyzing amplitude and phase variations in VLF signals recorded in Belgrade, Serbia, from worldwide transmitters. Due to the localized, transient nature of Energetic Electron Precipitation (EEP) events and the path-dependence of VLF responses, research relies on event-specific case studies to model reflection height and sharpness via numerical simulations. Findings show LIEs are typically under 1000 × 500 km, with varying internal structure. Accumulated case studies and corresponding data across diverse conditions contribute to a broader understanding of ionospheric dynamics and space weather effects. These findings enhance regional modeling, support aerosol–electricity climate research, and underscore the value of VLF-based ionospheric monitoring and collaboration in Europe. Full article

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

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12 pages, 249 KiB

Open AccessData Descriptor

Time Series Dataset of Phenology, Biomass, and Chemical Composition of Cassava (Manihot esculenta Crantz) as Affected by Time of Planting and Variety Interactions in Field Trials at Koronivia, Fiji

by Poasa Nauluvula, Bruce L. Webber, Roslyn M. Gleadow, William Aalbersberg, John N. G. Hargreaves, Bianca T. Das, Diogenes L. Antille and Steven J. Crimp

Data 2025, 10(8), 120; https://doi.org/10.3390/data10080120 - 23 Jul 2025

Abstract

Cassava is the sixth most important food crop and is cultivated in more than 100 countries. The crop tolerates low soil fertility and drought, enabling it to play a role in climate adaptation strategies. Cassava generally requires careful preparation to remove toxic hydrogen [...] Read more.

Cassava is the sixth most important food crop and is cultivated in more than 100 countries. The crop tolerates low soil fertility and drought, enabling it to play a role in climate adaptation strategies. Cassava generally requires careful preparation to remove toxic hydrogen cyanide (HCN) before its consumption, but HCN concentrations can vary considerably between varieties. Climate change and low inputs, particularly carbon and nutrients, affect agriculture in Pacific Island countries where cassava is commonly grown alongside traditional crops (e.g., taro). Despite increasing popularity in this region, there is limited experimental data about cassava crop management for different local varieties, their relative toxicity and nutritional value for human consumption, and their interaction with changing climate conditions. To help address this knowledge gap, three field experiments were conducted at the Koronivia Research Station of the Fiji Ministry of Agriculture. Two varieties of cassava with contrasting HCN content were planted at three different times coinciding with the start of the wet (September-October) or dry (April) seasons. A time series of measurements was conducted during the full 18-month or differing 6-month durations of each crop, based on destructive harvests and phenological observations. The former included determination of total biomass, HCN potential, carbon isotopes (δ¹³C), and elemental composition. Yield and nutritional value were significantly affected by variety and time of planting, and there were interactions between the two factors. Findings from this work will improve cassava management locally and will provide a valuable dataset for agronomic and biophysical model testing. Full article

16 pages, 5175 KiB

Open AccessData Descriptor

From Raw GPS to GTFS: A Real-World Open Dataset for Bus Travel Time Prediction

by Aigerim Mansurova, Aigerim Mussina, Sanzhar Aubakirov, Aliya Nugumanova and Didar Yedilkhan

Data 2025, 10(8), 119; https://doi.org/10.3390/data10080119 - 23 Jul 2025

Abstract

The data descriptor introduces an open, high-resolution dataset of real-world bus operations in Astana, Kazakhstan, captured from GPS trajectories between July and September 2024. The data covers three high-frequency routes and have been processed into a GTFS format, enabling direct use with existing [...] Read more.

The data descriptor introduces an open, high-resolution dataset of real-world bus operations in Astana, Kazakhstan, captured from GPS trajectories between July and September 2024. The data covers three high-frequency routes and have been processed into a GTFS format, enabling direct use with existing transit modeling tools. Unlike typical static GTFS feeds, this dataset provides empirically observed dwell times, run times, and travel times, offering a detailed snapshot of operational variability in urban bus systems. The dataset supports applications in machine learning–based travel time prediction, timetable optimization, and transit reliability analysis, especially in settings where live feeds are unavailable. By releasing this dataset publicly, we aim to promote transparent, data-driven transport research in emerging urban contexts. Full article

(This article belongs to the Special Issue IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues)

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7 pages, 4461 KiB

Open AccessData Descriptor

Dataset on Environmental Parameters and Greenhouse Gases in Port and Harbor Seawaters of Jeju Island, Korea

by Jae-Hyun Lim, Ju-Hyoung Kim, Hyo-Ryeon Kim, Seo-Young Kim and Il-Nam Kim

Data 2025, 10(7), 118; https://doi.org/10.3390/data10070118 - 19 Jul 2025

Abstract

This dataset presents environmental observations collected in August 2021 from 18 port and harbor sites located around Jeju Island, Korea. It includes physical, biogeochemical, and greenhouse gas (GHG) variables measured in surface seawater, such as temperature, salinity, dissolved oxygen, nutrients, chlorophyll-a, [...] Read more.

This dataset presents environmental observations collected in August 2021 from 18 port and harbor sites located around Jeju Island, Korea. It includes physical, biogeochemical, and greenhouse gas (GHG) variables measured in surface seawater, such as temperature, salinity, dissolved oxygen, nutrients, chlorophyll-a, pH, total alkalinity, and dissolved inorganic carbon. Concentrations and air–sea fluxes of nitrous oxide (N₂O), methane (CH₄), and carbon dioxide (CO₂) were also quantified. All measurements were conducted following standardized analytical protocols, and certified reference materials and duplicate analyses were used to ensure data accuracy. Consequently, the dataset revealed that elevated nutrient accumulation in port and harbor waters and GHG concentrations tended to be higher at sites with stronger land-based influence. During August 2021, most sites functioned as sources of N₂O, CH₄, and CO₂ to the atmosphere. This integrated dataset offers valuable insights into the influence of anthropogenic and hydrological factors on coastal GHG dynamics and provides a foundation for future studies across diverse semi-enclosed marine systems. Full article

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19 pages, 1906 KiB

Open AccessArticle

LADOS: Aerial Imagery Dataset for Oil Spill Detection, Classification, and Localization Using Semantic Segmentation

by Konstantinos Gkountakos, Maria Melitou, Konstantinos Ioannidis, Konstantinos Demestichas, Stefanos Vrochidis and Ioannis Kompatsiaris

Data 2025, 10(7), 117; https://doi.org/10.3390/data10070117 - 14 Jul 2025

Abstract

Oil spills on the water surface pose a significant environmental hazard, underscoring the critical need for developing Artificial Intelligence (AI) detection methods. Utilizing Unmanned Aerial Vehicles (UAVs) can significantly improve the efficiency of oil spill detection at early stages, reducing environmental damage; however, [...] Read more.

Oil spills on the water surface pose a significant environmental hazard, underscoring the critical need for developing Artificial Intelligence (AI) detection methods. Utilizing Unmanned Aerial Vehicles (UAVs) can significantly improve the efficiency of oil spill detection at early stages, reducing environmental damage; however, there is a lack of training datasets in the domain. In this paper, LADOS is introduced, an aeriaL imAgery Dataset for Oil Spill detection, classification, and localization by incorporating both liquid and solid classes of low-altitude images. LADOS comprises 3388 images annotated at the pixel level across six distinct classes, including the background. In addition to including a general oil class describing various oil spill appearances, LADOS provides a detailed categorization by including emulsions and sheens. Detailed examination of both instance and semantic segmentation approaches is illustrated to validate the dataset’s performance and significance to the domain. The results on the test set demonstrate an overall performance exceeding 66% mean Intersection over Union (mIoU), with specific classes such as oil and emulsion to surpass 74% of IoU part of the experiments. Full article

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29 pages, 2211 KiB

Open AccessArticle

Big Data Analytics Framework for Decision-Making in Sports Performance Optimization

by Dan Cristian Mănescu

Data 2025, 10(7), 116; https://doi.org/10.3390/data10070116 - 14 Jul 2025

Abstract

The rapid proliferation of wearable sensors and advanced tracking technologies has revolutionized data collection in elite sports, enabling continuous monitoring of athletes’ physiological and biomechanical states. This study proposes a comprehensive big data analytics framework that integrates data acquisition, processing, analytics, and decision [...] Read more.

The rapid proliferation of wearable sensors and advanced tracking technologies has revolutionized data collection in elite sports, enabling continuous monitoring of athletes’ physiological and biomechanical states. This study proposes a comprehensive big data analytics framework that integrates data acquisition, processing, analytics, and decision support, demonstrated through synthetic datasets in football, basketball, and athletics case scenarios, modeled to represent typical data patterns and decision-making workflows observed in elite sport environments. Analytical methods, including gradient boosting classifiers, logistic regression, and multilayer perceptron models, were employed to predict injury risk, optimize in-game tactical decisions, and personalize sprint mechanics training. Key results include a 12% reduction in hamstring injury rates in football, a 16% improvement in clutch decision-making accuracy in basketball, and an 8% decrease in 100 m sprint times among athletes. The framework’s visualization tools and alert systems supported actionable insights for coaches and medical staff. Challenges such as data quality, privacy compliance, and model interpretability are addressed, with future research focusing on edge computing, federated learning, and augmented reality integration for enhanced real-time feedback. This study demonstrates the potential of integrated big data analytics to transform sports performance optimization, offering a reproducible and ethically sound platform for advancing personalized, data-driven athlete management. Full article

(This article belongs to the Special Issue Big Data and Data-Driven Research in Sports)

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6 pages, 171 KiB

Open AccessData Descriptor

A Combined HF Radar and Drifter Dataset for Analysis of Highly Variable Surface Currents

by Bartolomeo Doronzo, Michele Bendoni, Stefano Taddei, Angelo Boccacci and Carlo Brandini

Data 2025, 10(7), 115; https://doi.org/10.3390/data10070115 - 12 Jul 2025

Abstract

This data descriptor presents the HF radar and drifter datasets, along with the methods used to process and apply them in a previously published study on the validation of surface current measurements in a region characterized by highly variable coastal dynamics. The data [...] Read more.

This data descriptor presents the HF radar and drifter datasets, along with the methods used to process and apply them in a previously published study on the validation of surface current measurements in a region characterized by highly variable coastal dynamics. The data were collected in the framework of a large-scale Lagrangian experiment, which included extensive drifter deployment and the generation of virtual trajectories based on HF radar-derived flow fields. Both Eulerian and Lagrangian approaches were used to assess radar performance through correlation and RMSE metrics, with additional refinement achieved via Kriging interpolation. The validation results, published in Remote Sensing, demonstrated good agreement between HF radar and drifter observations, particularly when quality control parameters were optimized. The datasets and associated methodologies described here support ongoing efforts to enhance HF radar tuning strategies and improve surface current monitoring in complex marine environments. Full article

(This article belongs to the Special Issue Data in Astrophysics and Geophysics: Research and Applications, 3rd Edition)

15 pages, 2054 KiB

Open AccessData Descriptor

Data on Brazilian Powdered Milk Formulations for Infants of Various Age Groups: 0–6 Months, 6–12 Months, and 12–36 Months

by Francisco José Mendes dos Reis, Antonio Marcos Jacques Barbosa, Elaine Silva de Pádua Melo, Marta Aratuza Pereira Ancel, Rita de Cássia Avellaneda Guimarães, Priscila Aiko Hiane, Flavio Santana Michels, Daniele Bogo, Karine de Cássia Freitas Gielow, Diego Azevedo Zoccal Garcia, Geovanna Vilalva Freire, João Batista Gomes de Souza and Valter Aragão do Nascimento

Data 2025, 10(7), 114; https://doi.org/10.3390/data10070114 - 9 Jul 2025

Abstract

Milk powder is a key nutritional alternative to breastfeeding, but its thermal properties, which vary with temperature, can affect its quality and shelf life. However, there is little information about the physical and chemical properties of powdered milk in several countries. This dataset [...] Read more.

Milk powder is a key nutritional alternative to breastfeeding, but its thermal properties, which vary with temperature, can affect its quality and shelf life. However, there is little information about the physical and chemical properties of powdered milk in several countries. This dataset contains the result of an analysis of the aflatoxins, macroelement and microelement concentrations, oxidative stability, and fatty acid profile of infant formula milk powder. The concentrations of Al, As, Ba, Cd, Co, Cr, Cu, Fe, Mg, Mn, Mo, Ni, Pb, Se, V, and Zn in digested powdered milk samples were quantified through inductively coupled plasma optical emission spectrometry (ICP OES). Thermogravimetry (TG) and differential scanning calorimetry (DSC) were used to estimate the oxidative stability of infant formula milk powder, while the methyl esters of the fatty acids were analyzed by gas chromatography. Most milk samples showed significant concentrations of As (0.5583–1.3101 mg/kg) and Pb (0.2588–0.0847 mg/kg). The concentrations of aflatoxins G2 and B2 are below the limits established by Brazilian regulatory agencies. The thermal degradation behavior of the samples is not the same due to their fatty acid compositions. The data presented may be useful in identifying compounds present in infant milk powder used as a substitute for breast milk and understanding the mechanism of thermal stability and degradation, ensuring food safety for those who consume them. Full article

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10 pages, 4821 KiB

Open AccessData Descriptor

Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach

by Robert Rettig, Felix Becker, Alexander Berghoff, Tobias Binkele, Wolfram Michael Butter, Tilman Floehr, Martin Kumm, Carolin Leluschko, Florian Littau, Elmar Reinders, Eike Rodenbäck, Tobias Schmid, Sabine Schründer, Sören Schweigert, Michael Sinhuber, Jens Wellhausen, Frederic Stahl and Christoph Tholen

Data 2025, 10(7), 113; https://doi.org/10.3390/data10070113 - 9 Jul 2025

Abstract

The dataset developed within the PlasticObs+ project aims to facilitate a multi-resolution approach for detecting and quantifying anthropogenic litter through areal images. Traditional detection methods often suffer from narrow, use-case-specific limitations, reducing their transferability. To address this, an image dataset was created featuring [...] Read more.

The dataset developed within the PlasticObs+ project aims to facilitate a multi-resolution approach for detecting and quantifying anthropogenic litter through areal images. Traditional detection methods often suffer from narrow, use-case-specific limitations, reducing their transferability. To address this, an image dataset was created featuring various spatial and spectral resolutions. The highest spatial resolution images (ground sampling distance = 0.2 cm) were used to generate a labeled dataset, which was georeferenced for mapping onto coarser-resolution images. Full article

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

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9 pages, 16281 KiB

Open AccessData Descriptor

Advancements in Regional Weather Modeling for South Asia Through the High Impact Weather Assessment Toolkit (HIWAT) Archive

by Timothy Mayer, Jonathan L. Case, Jayanthi Srikishen, Kiran Shakya, Deepak Kumar Shah, Francisco Delgado Olivares, Lance Gilliland, Patrick Gatlin, Birendra Bajracharya and Rajesh Bahadur Thapa

Data 2025, 10(7), 112; https://doi.org/10.3390/data10070112 - 9 Jul 2025

Abstract

Some of the most intense thunderstorms and extreme weather events on Earth occur in the Hindu Kush Himalaya (HKH) region of Southern Asia. The need to provide end users, stakeholders, and decision makers with accurate forecasts and alerts of extreme weather is critical. [...] Read more.

Some of the most intense thunderstorms and extreme weather events on Earth occur in the Hindu Kush Himalaya (HKH) region of Southern Asia. The need to provide end users, stakeholders, and decision makers with accurate forecasts and alerts of extreme weather is critical. To that end, a cutting edge weather modeling framework coined the High Impact Weather Assessment Toolkit (HIWAT) was created through the National Aeronautics and Space Administration (NASA) SERVIR Applied Sciences Team (AST) effort, which consists of a suite of varied numerical weather prediction (NWP) model runs to provide probabilities of straight-line damaging winds, hail, frequent lightning, and intense rainfall as part of a daily 54 h forecast tool. The HIWAT system was first deployed in 2018, and the recently released model archive hosted by the Global Hydrometeorology Resource Center (GHRC) Distributed Active Archive Center (DAAC) provides daily model outputs for the years of 2018–2022. With a nested modeling domain covering Nepal, Bangladesh, Bhutan, and Northeast India, the HIWAT archive spans the critical pre-monsoon and monsoon months of March–October when severe weather and flooding are most frequent. As part of NASA’s Transformation To Open Science (TOPS), this data archive is freely available to practitioners and researchers. Full article

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

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12 pages, 659 KiB

Open AccessArticle

PlantDRs: A Database of Dispersed Repeats in Plant Genomes Identified by the Iterative Procedure Method

by Valentina Rudenko, Eugene Korotkov and Dmitrii Kostenko

Data 2025, 10(7), 111; https://doi.org/10.3390/data10070111 - 9 Jul 2025

Abstract

In this work, we searched for and analyzed highly divergent dispersed repeats (DRs) in the genomes of four plants: Arabidopsis thaliana, Capsicum annuum, Daucus carota, and Zea mays. DRs were detected using the iterative procedure method which has shown [...] Read more.

In this work, we searched for and analyzed highly divergent dispersed repeats (DRs) in the genomes of four plants: Arabidopsis thaliana, Capsicum annuum, Daucus carota, and Zea mays. DRs were detected using the iterative procedure method which has shown efficacy in searches for highly divergent repeats in bacteria and algae. The results indicated that the number of DRs in the plant genomes depended on the genome size, whereas the number of repeat families did not. The DRs covered from 36 to 50% of the studied genomes. The shortest repeats were observed in the D. carota genome, but their consensus lengths were similar to those in the other species. Analysis of periodicity in various DR families showed that most periods were 3 bp long. We created a database of the detected DRs, which contains 5,392,216 DRs grouped in 150 families and which can be accessed on the Research Center of Biotechnology RAS server. The server makes it possible to search for repeats based on various criteria and to download the obtained data. Full article

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

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