Speech emotion recognition (SER) has become increasingly important in areas such as healthcare, customer service, robotics, and human–computer interaction. The progress of this field depends not only on advances in algorithms but also on the databases that provide the training material for SER systems. These resources set the boundaries for how well models can generalize across speakers, contexts, and cultures. In this paper, we present a narrative review and comparative analysis of emotional speech corpora released up to mid-2025, bringing together both psychological and technical perspectives. Rather than following a systematic review protocol, our approach focuses on providing a critical synthesis of more than fifty corpora covering acted, elicited, and natural speech. We examine how these databases were collected, how emotions were annotated, their demographic diversity, and their ecological validity, while also acknowledging the limits of available documentation. Beyond description, we identify recurring strengths and weaknesses, highlight emerging gaps, and discuss recent usage patterns to offer researchers both a practical guide for dataset selection and a critical perspective on how corpus design continues to shape the development of robust and generalizable SER systems.

This study explores the potential of generative AI, specifically Large Language Models (LLMs), in automating unit test generation in Python 3.13. We analyze tests, both those created by programmers and those generated by LLM models, for fifty source code cases. Our main focus is on how the choice of model, the difficulty of the source code, and the prompting strategy influence the quality of the generated tests. The results show that AI models can help automate test creation for simple code, but their effectiveness decreases for more complex tasks. We introduce an embedding-based similarity analysis to assess how closely AI-generated tests resemble human-written ones, revealing that AI outputs often lack semantic diversity. The study also highlights the potential of AI models for rapid test prototyping, which can significantly speed up the software development cycle. However, further customization and training of the models on specific use cases is needed to achieve greater precision. Our findings provide practical insights into integrating LLMs into software testing workflows and emphasize the importance of prompt design and model selection.

This study presents a comprehensive dataset developed to monitor coastal water quality in the south-central region of Vietnam, focusing on Nha Trang Bay. Environmental data were collected from four research cruises conducted between 2013 and 2024. Water samples were taken at two depths: surface samples at approximately 0.5–1.0 m below the water surface, and bottom samples 1.0 to 2.0 m above the seabed, depending on site-specific bathymetry. These samples were analyzed for key water quality parameters, including biological oxygen demand (BOD5), dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and Chlorophyll-a (Chl-a). The data establish a valuable baseline for assessing both spatial and temporal patterns of water quality, and for calculating eutrophication index to evaluate potential environmental degradation. Importantly, it also demonstrates practical applications for environmental management. The dataset can support assessments of how seasonal tourism peaks contribute to nutrient enrichment, how aquaculture expansion affects dissolved oxygen dynamics, and how water quality trends evolve under increasing anthropogenic pressure. These applications make it a useful resource for evaluating pollution control efforts and for guiding sustainable development in coastal areas. By promoting open access, the dataset not only supports scientific research but also strengthens evidence-based management strategies to protect ecosystem health and socio-economic resilience in Nha Trang Bay.

This work presents an EEG signal database derived from the induction of three emotional states using auditory stimuli. To this end, an experiment was designed in which 30 selected affective sounds from the IADS database were presented to 36 volunteers, from whom EEG signals were acquired. Stimuli were randomly configured in the Psychopy platform and synchronized via the LSL library with the OpenVibe signal acquisition platform. The 16-channel NautilusG brain computer interface was used for signal acquisition. As part of the database validation, a recognition system for the three emotional states was developed. This system utilized machine-learning-based parameterization and classification techniques, achieving detection percentages of 88.57%.

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

In the era of digital healthcare, electronic health records generate vast amounts of data, much of which is unstructured, and therefore, not in a usable format for conventional machine learning and artificial intelligence applications. This study investigates how to extract meaningful insights from unstructured radiology reports written in Finnish, a minority language, using machine learning techniques for text analysis. With this approach, unstructured information could be transformed into a structured format. The results of this research show that relevant information can be effectively extracted from Finnish medical reports using classification algorithms with default parameter values. For the detection of breast tumour mentions from medical texts, classifiers achieved high accuracy, almost 90%. Detection of metastasis mentions, however, proved more challenging, with the best-performing models Support Vector Machine (SVM) and logistic regression achieving an F1-score of 81%. The lower performance in metastasis detection is likely due to the more complex problem, ambiguous labeling, and the smaller dataset size. The results of classical classifiers were also compared with FinBERT, a domain-adapted Finnish BERT model. However, classical classifiers outperformed FinBERT. This highlights the challenge of medical language processing when working with minority languages. Moreover, it was noted that parameter tuning based on translated English reports did not significantly improve the detection rates, likely due to linguistic differences between the datasets. This larger translated dataset used for tuning comes from a different clinical domain and employs noticeably simpler, less nuanced language than the Finnish breast cancer reports, which are written by native Finnish-speaking medical experts. This underscores the need for localised datasets and models, particularly for minority languages with unique grammatical structures.

The adoption of Electronic Lab Notebooks (ELNs) significantly enhances research operations by enabling the streamlined capture, storage, and dissemination of data. This promotes collaboration and ensures organised and efficient access to critical research information. Microsoft SharePoint® (SP) is an established, widely used, web-based platform with advanced collaboration capabilities. This study investigates whether SP can meet the needs of engineering research projects, particularly in a collaborative environment. The paper outlines the process of adapting SP into an ELN tool and evaluates its effectiveness compared to established ELN systems. The evaluation considers several categories related to data management, ranging from data collection to publication. Six distinct application scenarios are analysed, representing a spectrum of collaborative research projects, ranging from small-scale initiatives with minimal processes and data to large-scale, complex projects with extensive data requirements. The results indicate that SP is competitive in relation with established ELN tools, ranking second among the six alternatives evaluated. The adapted version of SP proves particularly effective for managing data in engineering research projects involving both academic and industrial partners, accommodating datasets for around 1000 samples. The practical implementation of SP is demonstrated through a collaborative engineering research project, showing its use in everyday research tasks such as data documentation, workflow automation, and data export. The study highlights the benefits and usability of the adapted SP version, including its support for regulatory compliance and reproducibility in research workflows. In addition, limitations and lessons learned are discussed, providing insights into the potential and challenges of using SP as an ELN tool in collaborative research projects.

Super-enhancers (SEs) are large clusters of highly active enhancers that play key regulatory roles in cell identity, development, and disease. While conventional methods classify SEs in a binary fashion—super-enhancer or not—this threshold-based approach can overlook significant intermediate states of enhancer activity. Here, we present a dataset and accompanying framework that facilitate a more nuanced, non-binary examination of SE activation across mouse tissue types (mammary gland, lung tissue, and NMuMG cells) and various experimental conditions (normal, tumor, and drug-treated samples). By consolidating overlapping SE intervals and capturing continuous enhancer activity metrics (e.g., ChIP-seq signal intensities), our dataset reveals gradual transitions between moderate and high enhancer activity levels that are not captured by strictly binary classification. Additionally, the data include extensive functional annotations, linking SE loci to nearby genes and enabling immediate downstream analyses such as clustering and gene ontology enrichment. The flexible approach supports broader investigations of enhancer landscapes, offering a comprehensive platform for understanding how SE activation underpins disease mechanisms, therapeutic response, and developmental processes.

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

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