Data, Volume 11, Issue 4 (April 2026) – 25 articles

Agriculture in the Pacific is driven primarily by small-scale private farmers, many of whom do not have access to soil testing services or advice, nor the means to interpret analytical results into soil management and agronomic recommendations. Soil degradation through the process of acidification poses a significant risk to food and income security as it directly threatens crop productivity. The nutritional quality of food crops may also be affected through sub-optimal nutrient uptake by plants and nutrient imbalances. The dataset reported here provides a useful platform for the development of a decision-support tool (DST) that will assist Fiji farmers in understanding and managing soil pH and soil acidity. The DST will enable making informed decisions about liming to help correct soil pH. To support this development, historical soil pH data available from the Pacific Soils Portal were combined with updated analyses of agricultural soils from 17 locations in Viti Levu Island (Fiji) collected during a field campaign undertaken in August 2025. The soils were sampled at two depth intervals (0–15 and 15–30 cm) and analyzed for pH using a variety of methods. These methods included direct field measurements using a portable pH-meter as well as traditional laboratory determinations. Of the soils sampled, it was found that most soils exhibited pH levels below 7, which were observed for both depth intervals. Across all samples taken in 2025, it was found that 54.3% of them had soil pH < 5, 38.6% had soil pH between 5 and 6, and 7.1% had pH > 6 (based on soil pH_1:5 soil-to-water method). Depending upon specific land uses, climate and cropping intensity, it was recommended that routine liming be built into soil fertility management programs to help farmers overcome soil acidity-related constraints to production. Liming frequency, timing of application and application rate will need to be determined for specific soil and cropping situations; however, it was suggested that soil pH was not changed by more than 1 unit each time lime was applied. Such an approach should reduce the risk of soil organic matter loss through accelerated mineralization, which would be challenging to restore in that environment if soils remained under continuous cropping. The analytical information contained in this article expanded and updated the datasets available in the Pacific Soils Portal. Furthermore, this work provided an opportunity to build analytical expertise in aspects of soil chemistry at local organizations to support academic and extension activities as well as the ongoing development of the Pacific Soils Portal. Full article

University data governance is an essential requirement for the informatization of universities and holds significant importance in advancing the modernization of university governance systems and governance capabilities. This study focuses on the data governance policies released by “Double First-Class” universities in China since 2015. Based on policy text mining and the PMC index model, the paper developed an evaluation system for university data governance policies consisting of 9 primary indicators and 43 secondary indicators and conducted quantitative assessment. The results indicate that the policies are of good quality overall, with 25% rated as excellent, 66.1% as good, and 8.9% as moderate. Many universities have made significant progress in formulating data governance policies. However, there is still considerable room for improvement. For example, while the policy objectives are clearly defined, certain aspects require further refinement; the stakeholder involvement is relatively narrow, lacking diversity; and the mix of policy instruments is imbalanced. To address these issues, it is recommended that policies be optimized by balancing regulatory priorities, establishing a multi-stakeholder collaborative governance framework, and rationalizing the policy instruments mix. Full article

The increasing adoption of Large Language Models (LLMs) in software development has enabled automatic code generation from natural language, yet the influence of communicative factors such as prompt tone remains underexplored. This work introduces PromptTone, a controlled dataset designed to investigate how variations in prompt politeness affect LLM-based code generation in web development. The dataset is constructed through a structured experimental design combining three variables: programming paradigm (Vue.js Composition API vs. Options API), LLM provider (GPT, Claude, Gemini), and prompt tone (impolite, neutral, polite), resulting in 396 generated components across 22 implementations. Data were collected in an educational setting under a single-prompt constraint to capture first-shot model behavior, and are provided in both hierarchical and CSV formats, including prompts, generated code, and error annotations. Preliminary analysis reveals that prompt tone influences output characteristics such as verbosity, with model-specific patterns: for instance, some models exhibit increased output length with more polite prompts, while others remain stable. Differences also emerge across programming paradigms, suggesting an interaction between tone and code structure. These findings highlight that LLMs are sensitive not only to semantic content but also to pragmatic aspects of input. Overall, the dataset provides a novel benchmark for studying human–LLM interaction in code generation, supporting future research on prompt engineering, model evaluation, and socially-aware Artificial Intelligence (AI)-assisted development tools. Full article

The shift toward Smart Cities heavily relies on adopting energy-efficiency strategies to meet ambitious decarbonization targets. However, the rebound effect, where improvements in technical efficiency are partly offset by increased energy consumption, often reduces the expected environmental and economic benefits. Traditional Marginal Abatement Cost Curves (MACC) often ignore this behavioral feedback, which can lead to an overestimation of mitigation potential. This paper introduces a data-driven probabilistic framework for assessing the influence of the rebound effect on a portfolio of urban mitigation strategies by integrating behavioral feedback into a bottom-up MACC. By combining Monte Carlo (MC) simulations to address parametric uncertainty with Bayesian Networks (BN) for conditional inference, the robustness of nine strategies is examined across residential, commercial, and transportation sectors. The results demonstrate that even a moderate rebound effect ($\eta =0.5$) causes a $10.09\%$ decrease in total net abatement, dropping from 24.86 to 22.35 tCO₂e, and significantly raises costs. Notably, the number of strictly cost-effective strategies ($MAC<0$) decreases from six to three, highlighting the fragility of certain “win–win” measures. This framework introduces the concepts of Financial Backfire Probability (FBP) and Environmental Backfire Probability (EBP) as new metrics for urban planning. These findings emphasize that rebound tolerance is a critical factor in climate policy, indicating that additional measures, such as Internet of Things (IoT)-based monitoring and demand-side management, may be necessary to prevent performance erosion amid behavioral uncertainty. Full article

The life insurance sector plays a critical role in financial system stability but is inherently exposed to extreme market fluctuations due to long-term liabilities and asset–liability mismatches. This study investigates extreme losses in the growth rates of the JSE Life Insurance Price Index (LIPI) using the Generalised Extreme Value Distribution (GEVD) and the Generalised Pareto Distribution (GPD) under the Extreme Value Theory (EVT) framework. Monthly data from January 2000 to October 2023 were transformed into a loss series, and extreme events were captured using quarterly block maxima and a POT threshold at the 95th percentile. Model parameters were estimated through Maximum Likelihood Estimation, and downside risk was assessed using return levels, Value-at-Risk (VaR), and Tail Value-at-Risk (tVaR). The GEVD model produced a negative shape parameter, consistent with a bounded Weibull-type tail, while the GPD indicated a heavy-tailed distribution. Return level estimates show escalating loss magnitudes and widening uncertainty over longer horizons, reflecting the challenges of projecting rare events. Kupiec backtesting confirms the adequacy and reliability of the GEVD-based VaR across all confidence levels, whereas the GPD underestimates risk at lower thresholds. These findings indicate significant tail risk within the South African life insurance equity segment and underscore the importance of EVT-based risk measures for capital planning and regulatory oversight. The study contributes to financial risk modelling in the life insurance sector and offers practical insights for strengthening solvency assessment and enterprise risk management frameworks. Full article

Empathy—the ability to understand and respond to others’ emotions and perspectives—is a key communication skill for humans; however, it is under-explored within current conversational systems. While large language models (LLMs) have demonstrated a remarkable capability to generate coherent and contextually relevant output, they often struggle to exhibit genuine empathy, resulting in artificial and dull responses, particularly in low-resource languages such as Arabic. Notably, the research on empathetic conversational systems in Arabic is still in its early stages, mainly due to the scarcity of open-domain conversational data. To address this gap, we introduce Arabic Empathetic Conversations (AEConvs), a genuine Arabic conversational dataset featuring more than 4K open-domain dyadic empathetic conversations. This dataset provides a valuable resource that captures nuanced emotional and empathetic cues in the Arabic language. Using AEConvs, we evaluate and compare the empathetic capabilities of two state-of-the-art generative Arabic LLMs—AceGPT-chat and Jais-chat—under zero-shot and fine-tuning training settings. Human evaluation results demonstrate that while both models exhibit some form of empathy in zero-shot settings, fine-tuning on AEConvs improved their ability to generate more fine-grained empathetic responses while also yielding enhancements in fluency and context adherence. Additionally, automatic evaluation indicated improved language modeling and better lexical and semantic similarity with human reference responses. This study highlights the importance of culturally and linguistically tailored datasets in advancing empathetic conversational AI. We publicly release the AEConvs dataset, providing a valuable resource for future advancements in the field. Full article

Introduction: Monitoring training load is essential in elite soccer to optimize performance and reduce injury risk. The training-to-match load ratio (TMr) has emerged as a useful metric to contextualize training demands relative to competitive match exposure. The objective of this study was to compare daily and accumulated TMr between starters and non-starters over a professional season, considering microcycle day and playing position. Methods: Twenty players (Tier 3) from a professional team were monitored during a full competitive season (30 microcycles; 144 training sessions; 30 matches). External load variables, namely total distance (TD), high-speed distance (HSD), sprint distance (SPD), high metabolic load distance (HMLD), acceleration (ACC) and deceleration (DCC), were collected using 10 Hz GPS devices (STATSports). Daily and microcycle TMr were calculated relative to each player’s maximal match value registered during a full competitive period. Linear mixed-effects models examined the effects of starting status, microcycle day, and playing position. Results: Linear mixed models revealed significant three-way interactions (status × day × position) for locomotor variables: TD (F = 3.36, p < 0.001), HSD (F = 2.49, p < 0.001), and SPD (F = 3.37, p < 0.001). Starters accumulated higher loads on match day, whereas non-starters showed higher TMr on MD + 1 and MD + 2. Position-specific differences emerged during acquisition sessions (i.e., MD − 5 to MD − 3), particularly for wide midfielders (WMs) and central defenders (CDs). No significant three-way interactions were observed for ACC, DCC, or HMLD absolute loads (p > 0.05), nor for any accumulated microcycle TMr metrics (p > 0.05). Conclusions: TMr effectively differentiates preparation strategies between starters and non-starters. Although “top-up conditioning” sessions increase early-week relative loads for non-starters, position-specific variations–particularly in mechanical variables during acquisition sessions–highlight the need for individualized load prescription. Full article

This paper describes the benchmark dataset for the electric vehicle routing problem with time windows. It is designed to facilitate the large-scale and reproducible evaluation of routing approaches under diverse charging scenarios. It is an extension of the Homberger 1000-customer vehicle-routing benchmark dataset through the incorporation of computationally derived charging-station data. For the 60 base instances included in the dataset, charging-station locations are randomly generated within the customer-coordinate bounds, and two variants are provided, resulting in 120 benchmark problems used in the validation and baseline analyses. A normalized local customer-density score is derived for each station. It is used to determine charging rates and log-normal parameters for prices and waiting times. Two variants are included in the dataset. Variant A maintains the original customer time-window constraints, while Variant B relaxes customer due dates based on the distance from the depot, subject to the depot closing time. The dataset is complemented by instance files, station attributes, parameters, and scripts. It also includes the results of feasibility tests, baseline solver tests, difficulty analyses, and sensitivity tests. These results show that the benchmark includes both easier and harder instance classes under different charging settings. Overall, the dataset is intended to support its use as a reproducible benchmark. Full article

This Data Descriptor presents an anonymized, shuffled dataset of creatinine-normalized urinary metabolite measurements from 73 Bulgarian children with autism spectrum disorder (ASD), released to support reuse in secondary analyses and cross-cohort comparisons. The public release represents a pathway-oriented 24-marker subset from a broader urinary diagnostic panel, assembled as a self-contained resource for investigators working in these metabolic domains. Spot urine results are provided as individual-level values after creatinine normalization; for trimethylamine, values below the limit of quantification (LOQ) were replaced with LOQ/2. The deposit contains measurements for 24 urinary markers grouped into three functional classes (neurotransmitters and aromatic amino acid precursors; one-carbon/methylation and vitamin-related metabolites; and energy metabolism/organic acids with microbiome-related amines). The underlying cohort comprised children aged 3–13 years, and no contemporaneous neurotypical control group was enrolled. Second-morning, midstream, acid-stabilized spot urine samples were collected within the provider’s workflow; metabolites were measured by LC–MS/MS, and spot urinary creatinine was measured enzymatically for normalization. The release includes the results table in both XLSX and CSV formats, a reference limits and units file for contextual interpretation, a data dictionary, a README, a changelog, and SHA-256 checksums for integrity verification. The public files contain de-identified analytical variables only and omit individual-level demographics, dates, standalone urinary creatinine, and richer clinical metadata to preserve anonymity. Full article

As part of a NASA University Leadership Initiative (ULI) program, this work supports the continued development and evaluation of a fatigue-based process window for stress-relieved Ti-6Al-4V specimens produced via laser powder bed fusion (PBF-LB/M). Four-point bend and axial fatigue specimens were fabricated by NASA ULI collaborators across a range of scan velocities (800–2000 mm/s) at a constant power of 370 W using an EOS M290 system. All fatigue specimens were low-stress-ground by a commercial vendor and tested at Case Western Reserve University (CWRU) under load-controlled cyclic loading at a stress ratio of R = 0.1. This paper presents a curated dataset linking PBF-LB/M process parameters to fatigue outcomes across 175 specimens. Of these, 136 fractured and this study includes fatigue crack initiation site identification and defect morphology metrics derived from post mortem SEM analysis. Specimens that reached runout (10⁷ cycles) and did not fracture under subsequent fatigue testing are retained in the dataset, with fractographic fields marked as ‘NA’ to indicate non-applicability. The dataset includes specimen metadata, processing parameters, fatigue life data, fatigue initiation site classification (e.g., keyhole, gas-entrapped pore (GeP), lack-of-fusion (LoF), contamination), defect size and shape descriptors, and spatial location relative to the free surface. These data are intended to support defect-based fatigue life prediction, probabilistic modeling, process–structure–property studies, and machine learning frameworks linking process parameters to fatigue performance in PBF-LB/M Ti-6Al-4V. Full article

Disaster records retrieved from historical diaries are valuable for examining past seasonal variations in disaster occurrence. We extracted 154 fire and 103 flood records between 1807 and 1838 from the Zayu-Nichiroku (the Kakuson Diaries), written by KANEKO Kakuson in Kanazawa, Japan. We analyzed the seasonal probability of these events using the Poisson distribution. The probability of fire peaked between March and June, while that of floods was highest in June, July, and September. These trends align well with the current climate in Kanazawa, where low humidity and strong winds elevate fire risk, while prolonged rainfall and localized heavy precipitation during the rainy and typhoon seasons increase flood risk. However, extracting disaster records from historical diaries involves uncertainties stemming from omitted entries, the loss of archival material, ambiguous descriptions, unique local recording bias, and short-term missing records. To reduce these uncertainties, we should employ an interdisciplinary approach utilizing multiple historical sources and probabilistic analyses of disaster occurrence. Full article

This research-related study examines the relevance of mud pulse telemetry (MPT) systems and their intersection with drilling performance, focusing on data transmission signal propagation performance and overall operation under different drilling parameters conditions, with an additional focus on drilling fluid flow rate and downhole pressure conditions. The novelty of this study lies in the investigation of adjustments to drilling operating parameters that could potentially improve the transmission of telemetry signals during drilling, in real time, without requiring mechanical or functional modifications to the MPT system itself. Improvements on transmission performance in situations where the data rate may be limited are also addressed, presenting an alternative through possible propagation velocity improvements to counterbalance it. A detailed chronological technical scientific literature review details important parts on analyses of pressure pulse propagation velocities focused on data transmission. A systematic experimental approach was developed and put into practice to evaluate the MPT systems in regard to tendencies on transmission performances, emphasizing pressure pulse propagation velocity. The laboratory-scale experiments were conducted at the Institute of Drilling Engineering and Fluid Mining (IBF) from the Technical University Bergakademie Freiberg (TUBAF), namely the Flow-loop Research Facility, to assess the impact of fluid flow rate (and subsequent pressure) on data transmission efficiency. Experimental results demonstrate that increasing the flow rate significantly speeds up signal propagation. In the performed experiments, for the mud siren configuration, increasing the flow rate from 15 to 25 m³/h improved the data transmission performance by approximately, at minimum, 18%, while for the positive mud pulse system, an increase in flow rate from 11.5 to 14 m³/h resulted in a propagation velocity rise of about 19%. The results also showed that higher concentrations of glycerin in the working fluid reduced the propagation velocity, confirming the influence of the fluid’s rheological properties on telemetry performance. At the end, in the presented case study, for 6 bps data rate configurations and for a transmission of a 40-bit string, it was demonstrated that the propagation time from downhole to the surface could potentially represent approximately 40% of the total time demanded for transmitting the desired information (generation plus propagation time). It was verified that an increment of 0.02208 m³/s (350 gpm) could lead to shortening eventual surveying procedures by 1–2 s, and that it could equally represent 1.137 bps. This is a relevant outcome, since, without any physical or functional alteration to the MPT system, one could have the data transmission performance improved, an approach not yet analyzed in the literature nor at the industrial park. These results, added to the detailed literature investigation and interaction with field personnel, indicate that the drilling fluid flow rate is a critical operational parameter affecting both the telemetry signal transmission speed and the overall drilling efficiency. Increasing the flow rate can reduce survey transmission time and decrease operational exposure to drilling hazards, such as drill string sticking. The results provide quantitative information applicable in optimizing measurement-drilling telemetry and help support the development of integrated drilling optimization strategies that balance drilling performance with real-time data transmission assurance in deep drilling operations. Full article

Spam consists of unsolicited messages, and the posting of such irrelevant messages often presents significant challenges in technical forums. Two particular challenges are the dynamic nature of spamming tactics and the inadequacy of adaptable spam databases for automated classifiers. Our work addresses the need for a robust spam classification solution that can be seamlessly integrated with database, SQL, and APEX applications. We developed a labeled spam database by asking experts to categorize 1916 posts as spam or regular posts to ensure accurate classification and then created an SVM-based spam classification model that achieves an average validation accuracy of 90%. Our research enhances the current understanding of spam in technical forums and represents a solution for embedding spam classifiers into widely used platforms with an accuracy of 98.1%. Furthermore, we explore the incorporation of generative topics into our approach by integrating generative topic modeling techniques, such as latent Dirichlet allocation. In our work, the spam classifier is dynamically updated to account for emerging spam patterns and topics based on a generative approach that improves the robustness of the classifier against new spamming tactics and enables nuanced, context-aware filtering of messages. In addition, our experiments highlight the potential of text SVM classifiers for real-time applications through the fine-tuning of text features. Full article

This paper presents ANAID (Autonomous Naturalistic obstacle-Avoidance Interaction Dataset), a new multimodal dataset designed to support research on autonomous driving, particularly with regard to obstacle avoidance and naturalistic driver–vehicle interaction. Data were collected using a Hyundai Tucson Hybrid equipped with a Comma-3X autonomous-driving development kit, combining high-resolution front-facing video with detailed CAN-bus telemetry. The dataset comprises four data collection campaigns, each corresponding to a single continuous driving session, yielding a total of 208 videos and 240,014 synchronized frames. In addition to the video data, the dataset provides vehicle state measurements (speed, acceleration, steering, pedal positions, turn signals, etc.) and an additional annotation layer identifying evasive maneuvers derived from steering-related signals. Data were recorded across four driving campaigns on an urban circuit at Universidad Europea de Madrid, capturing diverse real-world scenarios such as roundabouts, intersections, pedestrian areas, and segments requiring obstacle avoidance. A multi-stage processing pipeline aligns telemetry and visual data, extracts frames at 20 FPS, and detects evasive maneuvers using threshold-based time-series analysis. ANAID provides a fully aligned and non-destructive representation of naturalistic driving behavior, enabling research on control prediction, driver modeling, anomaly detection, and human–autonomy interaction in realistic traffic conditions. Full article

Auditory pseudowords are widely used in psycholinguistics and cognitive neuroscience, but their construction requires control of sublexical familiarity and careful characterization of how acoustic cue manipulations may shift perceived lexical plausibility. Here we introduce the Minho Pseudoword Wordlikeness Ratings (MPWR), the first normative dataset of wordlikeness judgments for European Portuguese (EP) auditory trisyllabic CV pseudowords, and evaluate whether adding a localized F0-based prominence cue modulates wordlikeness beyond distributional familiarity. One hundred and twenty pseudowords were assembled from naturally produced syllables drawn from the Minho Spoken Syllable Pool (MSSP) and recorded under uniform conditions. Each item was implemented in three token types with constant segmental content: a flat baseline and two F0-enhanced versions (+15%) targeting either the penultimate or final syllable. Native EP listeners (N = 101) provided wordlikeness ratings on a 7-point scale. MSSP-derived indices quantified pseudoword syllable familiarity (SWI_All, SWI_N3) and stress-position propensity for the targeted syllable (SPP_marked). Ratings were intentionally low overall yet showed substantial item-to-item variability. F0 enhancement produced a small but reliable decrease in wordlikeness relative to flat tokens, with no reliable difference between penultimate and final targeting positions. SWI_All robustly predicted ratings, whereas SPP_marked added little explanatory value. MPWR provides a practical EP resource for selecting and matching auditory pseudowords using normative wordlikeness ratings and transparent corpus-based descriptors. Full article

Student attrition remains a persistent challenge in higher education and is shaped by interacting socioeconomic, academic, institutional, and wellbeing-related mechanisms. Although learning analytics and educational data mining increasingly support early-warning and intervention workflows, dataset reuse is often limited by incomplete documentation and inconsistent variable definitions. This Data Descriptor presents a structured cross-sectional survey dataset on factors influencing student persistence at a Colombian public university campus (La Paz). Data were collected between August and December 2025 through an online questionnaire and subsequently cleaned to remove duplicate entries and personally identifiable information. The released dataset contains 333 student records and 33 variables covering demographics (e.g., age, gender, first-generation status), socioeconomic conditions (e.g., residential stratum, housing, financial aid), academic experience and satisfaction (multiple 1–5 Likert items), perceived dropout intention across personal/socioeconomic/academic domains, thematically coded open-ended items describing challenges and motives, and a self-allocation of 0–100 weights across three dropout-factor domains. We provide a machine-readable codebook, a transparent preprocessing description, and technical validation checks (value ranges, category consistency, and composite-score integrity). The dataset is intended to support reproducible retention research, equity-oriented analyses, and benchmarking of predictive models, while encouraging responsible reuse through privacy-preserving release practices and FAIR-aligned metadata, repository deposition, and versioning. Full article

Rupture of Intracranial Aneurysms (IAs) is the leading cause of non-traumatic intracranial hemorrhage. Early detection of aneurysms prior to rupture or their prompt identification in cases of intracranial hemorrhage is critical and guides treatment strategies. The development of artificial intelligence tools to automate the labor-intensive detection and analysis of IAs is an active research field, but it depends on the availability of large, well-curated datasets for robust model training, validation, and testing. Collaborative data sharing is essential for advancing this field, yet remains relatively uncommon. Here, we present a collection of 172 Computed Tomography Angiography (CTA) scan series—a widely available and commonly used modality for the diagnosis of IAs—supplemented with structured metadata. The dataset comprises 90 scans from healthy patients and 82 scans from patients with IAs of diverse shapes, sizes, and anatomical locations, annotated and validated by two experts. The annotations include 122 surface mesh models in STL format. This openly accessible dataset is intended to support the development of automated segmentation or classification tools, medical image analysis, and assessment of disease progression risks through morphometric and hemodynamic evaluations. Full article

Physical inactivity remains a major modifiable risk factor for non-communicable diseases and continues to exhibit marked socioeconomic and gender disparities in Latin America. Identifying robust and interpretable predictors of inactivity in nationally representative datasets is essential for informing public health strategies. This study compared a survey-weighted logistic regression model and an explainable machine learning approach (XGBoost) to predict physical inactivity among Chilean adults using data from the 2024 National Physical Activity and Sports Survey (ENAFyD; n = 5248). Models were evaluated on a stratified held-out test set (n = 1050) using weighted and unweighted area under the ROC curve (AUC), Brier scores, and calibration curves. Survey-weighted logistic regression achieved a weighted AUC of 0.801, while XGBoost achieved 0.797, demonstrating comparable discrimination. XGBoost showed marginally lower Brier scores, indicating slightly improved probabilistic calibration. Low socioeconomic status, female sex, lower monthly physical activity expenditure, limited facility access, and lower engagement with digital resources were consistently associated with higher inactivity risk. SHAP-style contribution analysis provided additional insight into feature-level influence within the machine learning framework. Overall, both approaches demonstrated similar predictive capacity, supporting the complementary use of classical regression and explainable machine learning for population-level physical inactivity research. Full article

This study presents a novel neural parameter diffusion approach (FWA-PDiff) designed to enhance data quality. To address the limitations of conventional diffusion models—such as inefficient sampling and insufficient feature sensitivity, which may compromise output fidelity—this study introduces four key innovations. First, the proposed model introduces an adaptive recalibration of the sampling frequency in the Fourier domain to optimize feature extraction for image data. Second, a dual-channel autoencoder architecture is employed, featuring a multi-scale, fine-grained encoder (MFE) that enables the simultaneous capture of features at multiple resolutions. Third, a wavelet-attention mechanism (WA) is incorporated into the decoder to highlight subtle high-frequency details. Fourth, the proposed model introduces a hybrid loss function that combines Mean Squared Error (MSE) and Kullback–Leibler (KL) divergence to improve data reconstruction. Collectively, these improvements enable the generation of high-fidelity parameters, thereby contributing to enhanced data quality. Extensive experiments conducted on benchmark datasets—including MNIST, CIFAR-10, CIFAR-100, and STL-10—demonstrate the effectiveness of the proposed approach, which consistently achieves superior performance in improving data quality. Full article

Background: Research on green-synthesized nanomaterials (GSNs) for environmental remediation is growing rapidly, yet data remains fragmented in non-interoperable formats. Methods: We present OntoNanoMat, a comprehensive semantic resource consisting of a modular OWL 2 DL ontology and a curated dataset of two illustrative case studies serving as proof-of-concept demonstrations. The data was structured into five thematic modules: Identification, Synthesis, Mechanism, Performance, and Provenance. Results: The dataset is provided in three interoperable formats: CSV, JSON, and Turtle (RDF) and is validated through SHACL shapes to ensure structural integrity and FAIR compliance. Conclusions: OntoNanoMat provides a FAIR-compliant (Findable, Accessible, Interoperable, and Reusable) foundation for future machine learning applications and knowledge graph integration in sustainable nanotechnology. Full article

This data descriptor provides a standardized and reproducible subsystem-level representation of the NREL wind turbine gearbox condition monitoring benchmarking dataset. The released records are derived from Healthy (H1–H10) and Damaged (D1–D10) measurement files and include subsystem-level standardized indices (KHI_HS, KHI_IMS, KHI_PL) together with a calibrated 0–1 Gearbox Health Index (GHI). The indices are generated using a fully specified and deterministic feature extraction and aggregation workflow based on established vibration indicators and healthy-referenced normalization. The Zenodo deposit contains machine-readable CSV tables intended to support transparent benchmarking across supervised classification and anomaly detection studies. The proposed GHI is introduced as an interpretable and reproducible reference baseline rather than an optimized diagnostic model. Technical validation demonstrates condition-level separability within the analyzed dataset while emphasizing the descriptive nature of the index. By releasing structured derived records and a documented regeneration procedure, this work enables an implementation-independent comparison of gearbox condition monitoring approaches and supports reproducible evaluation of alternative health index formulations. Full article

In this paper, we present a curated dataset derived from continuous multi-object tracking observations over a two-year period from a signalized urban intersection in Salzburg, Austria. The dataset includes time-resolved trajectories of multimodal road users, post-processed object attributes, movement relations, and Post-Encroachment Time values computed for a fixed set of eight predefined multimodal traffic conflict scenarios. Moreover, traffic signal data are included and can be used as contextual information. A temporal six-month subset is published via Zenodo including usage examples written in python. The full dataset can be provided on request. Potential applications include traffic safety analysis, behavioral modeling, method development for interaction detection, and educational use in data-driven traffic research. Full article

Open and reusable road-safety microdata remain scarce in Latin America, particularly when incident records combine detailed temporal information, geocoded event locations, and a clear pathway for extracting fatal outcomes. This article documents a curated administrative dataset for Medellín, Colombia, containing 702,540 reported road-traffic incidents recorded between 1 January 2008 and 31 August 2025. The dataset includes 13 variables describing incident identifier, date, time, incident class, severity, interpolated address, geographic coordinates (latitude and longitude), and planning-unit identifiers. Although the complete dataset contains three severity levels—property damage only, injured, and fatal—it also enables the construction of a fully reproducible fatality subset by filtering incidents classified as fatal, yielding 2762 records. The database covers 21 planning units (communes) in Medellín and includes named neighborhood information for 394 neighborhoods in the complete dataset and 274 neighborhoods in the fatal subset. Spatial completeness is high for administrative data: geographic coordinates are available for 93.63% of all records and 90.77% of fatal incidents. To keep the emphasis on dataset documentation, this data descriptor focuses on compact statistical tables and an illustrative grouped logistic regression model of fatal outcomes. The dataset, accompanied by a complete data dictionary and reproducible R script, is intended to support secondary research in road-traffic safety, spatial epidemiology, transportation planning, urban mobility, and public health. Full article

Agent-based systems built on large language models (LLMs) increasingly rely on complex internal reasoning processes, tool interactions, and memory mechanisms. However, the internal decision-making dynamics of such agents remain difficult to observe, analyze, and compare in a systematic manner. To address this limitation, we present AgentSec, a curated dataset of structured agent interaction traces designed to support the analysis of agent-level reasoning and action behaviors. The dataset consists of 30 deterministic and non-redundant scenario instances, each capturing a complete agent interaction session under a fixed and validated schema. Quantitatively, the 30 released sessions comprise 67 decision nodes and 45 tool calls (73.3% successful), with provenance graphs exhibiting an average depth of 4.53 (max 7) and a maximum branching factor of 3. Scenarios are organized according to a predefined taxonomy of agent behavioral patterns, including tool success and failure modes, fallback strategies, memory conflicts and overwrites, decision rollbacks, and provenance branching structures. Each scenario encodes a distinct analytical case rather than a parametric variation, enabling focused and interpretable study of agent decision-making processes. AgentSec provides detailed records of decision traces, tool calls, memory updates, and provenance relations, and is intended to facilitate reproducible research on agent behavior analysis, auditing, and evaluation. The dataset is released alongside its schema, scenario manifest, and validation tooling to support reuse and extension by the research community. Rather than serving as a large-scale performance benchmark, AgentSec is explicitly designed as a diagnostic and unit-test suite for auditing agent-level reasoning logic and provenance consistency under controlled structural conditions. Full article