Data

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

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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20 pages, 819 KiB

Open AccessArticle

Sharing Research Data in Collaborative Material Science and Engineering Projects

by Tim Opatz, Kim Feldhoff, Hajo Wiemer and Steffen Ihlenfeldt

Data 2025, 10(4), 53; https://doi.org/10.3390/data10040053 - 15 Apr 2025

Abstract

The objective of this paper was to examine the potential for the automated release of research data within the context of material science consortium projects, with adherence to the stipulated rules and contractual agreements, while also considering all relevant framework conditions, including those [...] Read more.

The objective of this paper was to examine the potential for the automated release of research data within the context of material science consortium projects, with adherence to the stipulated rules and contractual agreements, while also considering all relevant framework conditions, including those pertaining to protection and confidentiality. The investigation further aimed to explore the utilisation of the release process as a means for ensuring the quality of research data, employing an integrated review procedure. The study commenced with an examination of the regulations governing the sharing and reusing of research data, and the associated benefits. The fist phase of the study involved an evaluation of current release processes in common research data infrastructures. This was followed by the development of a methodological approach to the release of research data according to the needs of researcher in collaborative projects, such as automation of the release process, quality queries, reusability of data and more. The implementation of the main functions of this methodological approach was then undertaken in Kadi4Mat, an open-source research data infrastructure originally developed in the context of materials science. This implementation took the form of a prototypical and modular plugin. Full article

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

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19 pages, 2604 KiB

Open AccessArticle

Quantifying Relational Exploration in Cultural Heritage Knowledge Graphs with LLMs: A Neuro-Symbolic Approach for Enhanced Knowledge Discovery

by Mohammed Maree

Data 2025, 10(4), 52; https://doi.org/10.3390/data10040052 - 10 Apr 2025

Abstract

This paper introduces a neuro-symbolic approach for relational exploration in cultural heritage knowledge graphs, exploiting Large Language Models (LLMs) for explanation generation and a mathematically grounded model to quantify the interestingness of relationships. We demonstrate the importance of the proposed interestingness measure through [...] Read more.

This paper introduces a neuro-symbolic approach for relational exploration in cultural heritage knowledge graphs, exploiting Large Language Models (LLMs) for explanation generation and a mathematically grounded model to quantify the interestingness of relationships. We demonstrate the importance of the proposed interestingness measure through a quantitative analysis, highlighting its significant impact on system performance, particularly in terms of precision, recall, and F1-score. Utilizing the Wikidata Cultural Heritage Linked Open Data (WCH-LOD) dataset, our approach achieves a precision of 0.70, recall of 0.68, and an F1-score of 0.69, outperforming both graph-based (precision: 0.28, recall: 0.25, F1-score: 0.26) and knowledge-based (precision: 0.45, recall: 0.42, F1-score: 0.43) baselines. Furthermore, the proposed LLM-powered explanations exhibit better quality, as evidenced by higher BLEU (0.52), ROUGE-L (0.58), and METEOR (0.63) scores compared to baseline approaches. We further demonstrate a strong correlation (0.65) between the interestingness measure and the quality of generated explanations, validating its ability to guide the system towards more relevant discoveries. This system offers more effective exploration by achieving more diverse and human-interpretable relationship explanations compared to purely knowledge-based and graph-based methods, contributing to the knowledge-based systems field by providing a personalized and adaptable relational exploration framework. Full article

(This article belongs to the Topic New Applications of Big Data Technology: Integration of Data Mining and Artificial Intelligence)

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18 pages, 4309 KiB

Open AccessArticle

Observational Monitoring Records Downstream Impacts of Beaver Dams on Water Quality and Quantity in Temperate Mixed-Land-Use Watersheds

by Erin E. Novobilsky, Jack R. Navin, Deon H. Knights and P. Zion Klos

Data 2025, 10(4), 51; https://doi.org/10.3390/data10040051 - 7 Apr 2025

Abstract

Beaver populations in the U.S. northeast are rising, increasing the number of beaver dams and ponds in suburban watersheds. These new beaver ponds may impact the way that harmful algal blooms occur by changing biogeochemical cycling and sediment characteristics. In this study, piezometers, [...] Read more.

Beaver populations in the U.S. northeast are rising, increasing the number of beaver dams and ponds in suburban watersheds. These new beaver ponds may impact the way that harmful algal blooms occur by changing biogeochemical cycling and sediment characteristics. In this study, piezometers, installed upstream and downstream of multiple dam structures were used to evaluate changes in nitrate and orthophosphate concentrations in surface and hyporheic water. Data were also collected with seepage meters, discharge measurements, lab and field-based analytical tests, and sediment samples. These were collected from beaver dams and paired non-beaver dams upstream of unimpounded reaches to look at the potential for dormant sediment-based cyanobacteria to bloom and produce toxins under ideal light and nutrient levels. Results indicate a significant increase in orthophosphate from upstream to downstream of beaver dams. Results also demonstrate that toxin potential did not increase between cyanobacteria in beaver pond sediment and the paired unimpounded sample; however, under ideal light and nutrient levels, sediment from a beaver dam led to faster cyanobacterial growth. These findings highlight that while beaver dams and impoundments function as nutrient sinks within the tributary watersheds, there are potential risks from downstream transport of bloom-inducing sediment following a dam collapse. Full article

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13 pages, 633 KiB

Open AccessArticle

Sentiment Matters for Cryptocurrencies: Evidence from Tweets

by Radu Lupu and Paul Cristian Donoiu

Data 2025, 10(4), 50; https://doi.org/10.3390/data10040050 - 1 Apr 2025

Abstract

This study provides empirical evidence that cryptocurrency market movements are influenced by sentiment extracted from social media. Using a high frequency dataset covering four major cryptocurrencies (Bitcoin, Ether, Litecoin, and Ripple) from October 2017 to September 2021, we apply state-of-the-art natural language processing [...] Read more.

This study provides empirical evidence that cryptocurrency market movements are influenced by sentiment extracted from social media. Using a high frequency dataset covering four major cryptocurrencies (Bitcoin, Ether, Litecoin, and Ripple) from October 2017 to September 2021, we apply state-of-the-art natural language processing techniques on tweets from influential Twitter accounts. We classify sentiment into positive, negative, and neutral categories and analyze its effects on log returns, liquidity, and price jumps by examining market reactions around tweet occurrences. Our findings show that tweets significantly impact trading volume and liquidity: neutral sentiment tweets enhance liquidity consistently, negative sentiments prompt immediate volatility spikes, and positive sentiments exert a delayed yet lasting influence on the market. This highlights the critical role of social media sentiment in influencing intraday market dynamics and extends the research on sentiment-driven market efficiency. Full article

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13 pages, 219 KiB

Open AccessData Descriptor

Predictors of Immune Fitness and the Alcohol Hangover: Survey Data from UK and Irish Adults

by Joris C. Verster, Agnese Merlo, Maureen N. Zijlstra, Benthe R. C. van der Weij, Anne S. Boogaard, Sanne E. Schulz, Jessica Balikji, Andy J. Kim, Sherry H. Stewart, Simon B. Sherry, Johan Garssen, Gillian Bruce and Lydia E. Devenney

Data 2025, 10(4), 49; https://doi.org/10.3390/data10040049 - 1 Apr 2025

Abstract

Immune fitness is defined as the capacity of the body to respond to health challenges (such as infections) by activating an appropriate immune response to promote health and prevent and resolve disease, which is essential for improving quality of life. Thus, immune fitness [...] Read more.

Immune fitness is defined as the capacity of the body to respond to health challenges (such as infections) by activating an appropriate immune response to promote health and prevent and resolve disease, which is essential for improving quality of life. Thus, immune fitness plays an essential role in health, and reduced immune fitness may be an important signal of increased susceptibility for disease. Lifestyle factors such as increased levels of alcohol consumption have been shown to negatively impact immune fitness. The alcohol hangover is the most frequently reported negative consequence of alcohol consumption and is defined as the combination of negative mental and physical symptoms, which can be experienced after a single episode of alcohol consumption, starting when blood alcohol concentration (BAC) approaches zero. Significant correlations have been reported between hangover severity and both immune fitness and biomarkers of systemic inflammation. The concepts of immune fitness and alcohol hangover are further linked by the fact that the inflammatory response to alcohol consumption plays an important role in the pathology of the alcohol hangover. Moreover, immune fitness has been related to the susceptibility of experiencing hangovers per se. It is therefore important to investigate the interrelationship between immune fitness and the alcohol hangover, and to identify possible predictor variables of both constructs. This data descriptor article describes a study that was conducted with adults living in the UK or Ireland, evaluating possible correlates and predictors of immune fitness and the alcohol hangover. Data on mood, personality, mental resilience, pain catastrophizing, and sleep were collected from n = 1178 participants through an online survey. Herein, the survey and corresponding dataset are described. Full article

5 pages, 638 KiB

Open AccessData Descriptor

Plankton Dataset During Austral Spring and Summer in the Valdés Biosphere Reserve, Patagonia, Argentina

by Ariadna Celina Nocera, Maité Latorre, Valeria Carina D’Agostino, Brenda Temperoni, Carla Derisio, María Sofía Dutto, Anabela Berasategui, Irene Ruth Schloss and Rodrigo Javier Gonçalves

Data 2025, 10(4), 48; https://doi.org/10.3390/data10040048 - 31 Mar 2025

Abstract

The present dataset served to evaluate the plankton community composition and abundance in Nuevo Gulf (42°42′ S, 64°30′ W), a World Heritage Site in Argentinian Patagonia and part of the Valdés Biosphere Reserve. It reports zooplankton abundance (>300 µm) and phytoplankton concentration (10–200 [...] Read more.

The present dataset served to evaluate the plankton community composition and abundance in Nuevo Gulf (42°42′ S, 64°30′ W), a World Heritage Site in Argentinian Patagonia and part of the Valdés Biosphere Reserve. It reports zooplankton abundance (>300 µm) and phytoplankton concentration (10–200 μm) during the spring and summer seasons from 2019 to 2021. Special attention was given to the taxonomic classification of zooplankton, leading to the first identification of jellyfish species within the Gulf and the detection of an unreported copepod for the area (Drepanopus forcipatus). Samples were collected at two depths—a surface and a deeper layer—to assess vertical distribution patterns of plankton communities and explore potential environmental drivers influencing their variability. This dataset provides a valuable baseline for future studies analyzing temporal variations in the Gulf’s plankton communities. Moreover, it encourages the local scientific community to contribute data and promote open access to marine biodiversity records in the region. Full article

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11 pages, 421 KiB

Open AccessData Descriptor

A Comprehensive Monte Carlo-Simulated Dataset of WAXD Patterns of Wood Cellulose Microfibrils

by Ricardo Baettig and Ben Ingram

Data 2025, 10(4), 47; https://doi.org/10.3390/data10040047 - 29 Mar 2025

Abstract

Wide-angle X-ray diffraction analysis is a powerful tool for investigating the structure and orientation of cellulose microfibrils in plant cell walls, but the complex relationship between diffraction patterns and underlying structural parameters remains challenging to both understand and validate. This study presents a [...] Read more.

Wide-angle X-ray diffraction analysis is a powerful tool for investigating the structure and orientation of cellulose microfibrils in plant cell walls, but the complex relationship between diffraction patterns and underlying structural parameters remains challenging to both understand and validate. This study presents a comprehensive dataset of 81,906 Monte Carlo-simulated wide-angle X-ray diffraction patterns for the cellulose Iβ 200 lattice. The dataset was generated using a mechanistic, physically informed simulation procedure that incorporates realistic cell wall geometries from wood anatomy, including circular and polygonal fibers, and accounts for the full range of crystallographic and anatomical parameters influencing diffraction patterns. Each simulated pattern required multiple nested Monte Carlo iterations, totaling approximately 10 million calculations per pattern. The resulting dataset pairs each diffraction pattern with its exact generating parameter set, including mean microfibril angle (MFA), MFA variability, fiber tilt angles, and cell wall cross-sectional shape. The dataset addresses a significant barrier in the field—the lack of validated reference data with known ground truth values for testing and developing new analytical methods. It enables the development, validation, and benchmarking of novel algorithms and machine learning models for MFA prediction from diffraction patterns. The simulated data also allow for systematic investigation of the effects of geometric factors on diffraction patterns and serves as an educational resource for visualizing structure–diffraction relationships. Despite some limitations, such as assuming ideal diffraction conditions and focusing primarily on the S2 cell wall layer, this dataset provides a valuable foundation for advancing X-ray diffraction analysis methods for cellulose microfibril architecture characterization in plant cell walls. Full article

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13 pages, 4706 KiB

Open AccessData Descriptor

River Restoration Units: Riverscape Units for European Freshwater Ecosystem Management

by Gonçalo Duarte, Angeliki Peponi, Pedro Segurado, Tamara Leite, Florian Borgwardt, Andrea Funk, Sebastian Birk, Maria Teresa Ferreira and Paulo Branco

Data 2025, 10(4), 46; https://doi.org/10.3390/data10040046 - 28 Mar 2025

Abstract

Freshwater habitats and biota are among the most threatened worldwide. In Europe, significant efforts are being taken to counteract detrimental human impacts on nature. In line with these efforts, the MERLIN project funded by the H2020 program focuses on mainstreaming ecosystem restoration for [...] Read more.

Freshwater habitats and biota are among the most threatened worldwide. In Europe, significant efforts are being taken to counteract detrimental human impacts on nature. In line with these efforts, the MERLIN project funded by the H2020 program focuses on mainstreaming ecosystem restoration for freshwater-related environments at the landscape scale. Additionally, the Dammed Fish project focuses on one of the main threats affecting European Networks—artificial fragmentation of the river. Meeting the objectives of both projects to work on a large, pan-European scale, we developed a novel spatial database for river units. These spatial units, named River Restoration Units (R2Us), abide by river network functioning while creating the possibility of aggregating multiple data sources with varying resolutions to size-wise comparable units. To create the R2U, we set a methodological framework that departs from the Catchment Characterization and Modelling—River and Catchment Database v2.1 (CCM2)—together with the capabilities of the River Network Toolkit (v2) software (RivTool) to implement a seven-step methodological procedure. This enabled the creation of 11,557 R2U units in European sea outlet river basins along with their attributes. Procedure outputs were associated with spatial layers and then reorganized to create a relational database with normalized data. Under the MERLIN project, R2Us have been used as the spatial analysis unit for a large-scale analysis using multiple input datasets (e.g., ecosystem services, climate, and European Directive reporting data). This database will be valuable for river management and conservation planning, being particularly well suited for large-scale restoration planning in accordance with European Nature legislation. Full article

(This article belongs to the Topic Intersection Between Macroecology and Data Science)

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23 pages, 8564 KiB

Open AccessArticle

A Benchmark Dataset for the Validation of Phase-Based Motion Magnification-Based Experimental Modal Analysis

by Pierpaolo Dragonetti, Marco Civera, Gaetano Miraglia and Rosario Ceravolo

Data 2025, 10(4), 45; https://doi.org/10.3390/data10040045 - 27 Mar 2025

Abstract

In recent years, the development of computer vision technology has led to significant implementations of non-contact structural identification. This study investigates the performance offered by the Phase-Based Motion Magnification (PBMM) algorithm, which employs video acquisitions to estimate the displacements of target pixels and [...] Read more.

In recent years, the development of computer vision technology has led to significant implementations of non-contact structural identification. This study investigates the performance offered by the Phase-Based Motion Magnification (PBMM) algorithm, which employs video acquisitions to estimate the displacements of target pixels and amplify vibrations occurring within a desired frequency band. Using low-cost acquisition setups, this technique can potentially replace the pointwise measurements provided by traditional contact sensors. The main novelty of this experimental research is the validation of PBMM-based experimental modal analyses on multi-storey frame structures with different stiffnesses, considering six structural layouts with different configurations of diagonal bracings. The PBMM results, both in terms of time series and identified modal parameters, are validated against benchmarks provided by an array of physically attached accelerometers. In addition, the influence of pixel intensity on estimates’ accuracy is investigated. Although the PBMM method shows limitations due to the low frame rates of the commercial cameras employed, along with an increase in the signal-to-noise ratio in correspondence of bracing nodes, this method turned out to be effective in modal identification for structures with modest variations in stiffness in terms of height. Moreover, the algorithm exhibits modest sensitivity to pixel intensity. An open access dataset containing video and sensor data recorded during the experiments, is available to support further research at the following https://doi.org/10.5281/zenodo.10412857. Full article

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8 pages, 3288 KiB

Open AccessData Descriptor

Experimental Dataset for Fiber Optic Specklegram Sensing Under Thermal Conditions and Use in a Deep Learning Interrogation Scheme

by Francisco J. Vélez, Juan D. Arango, Víctor H. Aristizábal, Carlos Trujillo and Jorge A. Herrera-Ramírez

Data 2025, 10(4), 44; https://doi.org/10.3390/data10040044 - 26 Mar 2025

Abstract

This dataset comprises specklegram images acquired from a multimode optical fiber subjected to varying thermal conditions. Designed for training neural networks focused on developing Fiber Optic Specklegram Sensors (FSSs), these experimental data enable the detection of changes in speckle patterns corresponding to applied [...] Read more.

This dataset comprises specklegram images acquired from a multimode optical fiber subjected to varying thermal conditions. Designed for training neural networks focused on developing Fiber Optic Specklegram Sensors (FSSs), these experimental data enable the detection of changes in speckle patterns corresponding to applied temperature variations. The dataset includes 24,528 images captured over a temperature range from 25 °C to 200 °C, with incremental steps of approximately 0.175 °C. Key acquisition parameters include a wavelength of 633 nm, a sensing zone length of 20 mm, and a multimode fiber with a core diameter of 62.5 μm. This dataset supports developing and validating temperature-sensing models using fiber optic technology and can facilitate benchmarking against other experimental or synthetic datasets. Finally, an implementation is presented for utilizing the dataset in a deep learning interrogation scheme. Full article

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22 pages, 2388 KiB

Open AccessArticle

Improved Script Identification Algorithm Using Unicode-Based Regular Expression Matching Strategy

by Mamtimin Qasim and Wushour Silamu

Data 2025, 10(4), 43; https://doi.org/10.3390/data10040043 - 25 Mar 2025

Abstract

While script identification is the first step in many natural language processing and text mining tasks, at present, there is no open-source script identification algorithm for text. For this reason, we analyze the Unicode encoding of each type of script and construct regular [...] Read more.

While script identification is the first step in many natural language processing and text mining tasks, at present, there is no open-source script identification algorithm for text. For this reason, we analyze the Unicode encoding of each type of script and construct regular expressions in this study, in order to design an improved script identification algorithm. Because some scripts share common characters, it’s impossible to count and summarize them. As a result, some extracted scripts are incomplete, which affects subsequent text processing tasks; furthermore, if a new script identification feature is required, the regular expression for each script must be re-adjusted. To improve the performance and scalability of script identification, we analyze the encoding range of each script provided on the official Unicode website and identify the shared characters, allowing us to design an improved script identification algorithm. Using this approach, we can fully consider all 169 Unicode script types. The proposed method is scalable and does not require numbers, punctuation marks, or other symbols to be filtered during script identification; furthermore, these items in the text are also included in the script identification results, thus ensuring the integrity of the provided information. The experimental results show that the proposed algorithm performs almost as well as our previous script identification algorithm while providing improvements on its basis. Full article

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

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12 pages, 2320 KiB

Open AccessData Descriptor

Linking Fungal Genomics to Thermal Growth Limits: A Dataset of 730 Sequenced Species

by William Bains

Data 2025, 10(4), 42; https://doi.org/10.3390/data10040042 - 25 Mar 2025

Abstract

The response of fungal species to changes in temperature is of theoretical and practical importance in a world of changing temperatures, ecologies and populations. Genomic sequencing to identify fungal species and their potential metabolic capabilities is well established, but linking this to growth [...] Read more.

The response of fungal species to changes in temperature is of theoretical and practical importance in a world of changing temperatures, ecologies and populations. Genomic sequencing to identify fungal species and their potential metabolic capabilities is well established, but linking this to growth temperature conditions has been limited. To that end, I describe a dataset that brings together the maximum and minimum temperature growth limits for 730 species of Fungi and Oomycetes for which genome sequences are available, together with supporting proteome and taxonomic data and literature references. The set will provide an entry for studies into how genomic structure and sequence can be used to predict the potential for growth at low or high temperatures, and hence the potential industrial use or pathogenic liability of existing or new fungal species. Full article

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

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5 pages, 1513 KiB

Open AccessData Descriptor

Terrestrial Carbon Storage Estimation in Guangdong Province (2000–2021)

by Wei Wang, Yueming Hu, Xiaoyun Mao, Ying Zhang, Liangbo Tang and Junxing Cai

Data 2025, 10(4), 41; https://doi.org/10.3390/data10040041 - 25 Mar 2025

Abstract

(1) Terrestrial ecosystems are critical carbon sinks, and the accurate assessment of their carbon storage is vital for understanding global carbon cycles and formulating climate change mitigation strategies. (2) This study integrated vegetation indices, meteorological factors, land use data, soil/vegetation types, field sampling, [...] Read more.

(1) Terrestrial ecosystems are critical carbon sinks, and the accurate assessment of their carbon storage is vital for understanding global carbon cycles and formulating climate change mitigation strategies. (2) This study integrated vegetation indices, meteorological factors, land use data, soil/vegetation types, field sampling, and a convolutional neural network (CNN) model to estimate the carbon storage of terrestrial ecosystems in Guangdong Province. (3) Total carbon storage increased by 0.11 Pg from 2000 to 2021, with vegetation carbon gains (+0.19 Pg) offsetting soil carbon losses (−0.08 Pg), with the latter primarily being driven by reduced soil carbon in forest ecosystems. (4) Northern and eastern Guangdong exhibit high potential for enhancing carbon storage capacity, which is crucial for achieving regional carbon peaking and neutrality targets. Full article

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

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10 pages, 2466 KiB

Open AccessData Descriptor

Analysis of Minerals Using Handheld Laser-Induced Breakdown Spectroscopy Technology

by Naila Mezoued, Cécile Fabre, Jean Cauzid, YongHwi Kim and Marjolène Jatteau

Data 2025, 10(3), 40; https://doi.org/10.3390/data10030040 - 20 Mar 2025

Abstract

Laser-induced breakdown spectroscopy (LIBS), a rapid and versatile analytical technique, is becoming increasingly widespread within the geoscience community. Suitable for fieldwork analyses using handheld analyzers, the elemental composition of a sample is revealed by generating plasma using a high-energy laser, providing a practical [...] Read more.

Laser-induced breakdown spectroscopy (LIBS), a rapid and versatile analytical technique, is becoming increasingly widespread within the geoscience community. Suitable for fieldwork analyses using handheld analyzers, the elemental composition of a sample is revealed by generating plasma using a high-energy laser, providing a practical solution to numerous geological challenges, including identifying and discriminating between different mineral phases. This data paper presents over 12,000 reference mineral spectra acquired using a handheld LIBS analyzer (© SciAps), including those of silicates (e.g., beryl, quartz, micas, spodumene, vesuvianite, etc.), carbonates (e.g., dolomite, magnesite, aragonite), phosphates (e.g., amblygonite, apatite, topaz), oxides (e.g., hematite, magnetite, rutile, chromite, wolframite), sulfates (e.g., baryte, gypsum), sulfides (e.g., chalcopyrite, pyrite, pyrrhotite), halides (e.g., fluorite), and native elements (e.g., sulfur and copper). The datasets were collected from 170 pure mineral samples in the form of crystals, powders, and rock specimens, during three research projects: NEXT, Labex Ressources 21, and ARTeMIS. The extensive spectral range covered by the analyzer spectrometers (190–950 nm) allowed for the detection of both major (>1 wt.%) and trace (<1 wt.%) elements, recording a unique spectral signature for each mineral. Mineral spectra can serve as reference data to (i) identify relevant emission lines and spectral ranges for specific minerals, (ii) be compared to unknown LIBS spectra for mineral identification, or (iii) constitute input data for machine learning algorithms. Full article

(This article belongs to the Topic Techniques and Science Exploitations for Earth Observation and Planetary Exploration)

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