Physiological artifacts pose persistent challenges in electroencephalogram (EEG) data acquisition, often compromising interpretation and post-analysis of EEG signals across research and clinical applications. To address such limitations, including various artifact types, insufficient annotations, and low spatial resolutions, we present PhysioMotion Artifact, a large-scale, task-driven EEG dataset with point-wise artifact annotations. EEG data was acquired from 30 healthy participants performing 16 systematically designed single-type and multi-type movement tasks, inducing 14 distinct types of physiological artifacts. To demonstrate the utility of the dataset, we implemented a Convolutional Neural Networks-Transformer hybrid model for artifact detection and classification, achieving 95.4% accuracy in binary classification and 79.7% in 14-class classification tasks.

The International Cooperative Programme on Integrated Monitoring of Air Pollution Effects on Ecosystems (ICP IM) presents a comprehensive long-term dataset of ongoing integrated ecosystem monitoring from European forested catchments. The dataset encompasses measurements from 46 monitoring stations across 14 European countries, with temporal coverage mostly extending from the early 1990s to 2020 (48 sites are currently active). The integrated monitoring approach applies over 20 monitoring subprogrammes to simultaneously measure physical, chemical, and biological properties across multiple ecosystem compartments including atmosphere, precipitation, throughfall, soil water, groundwater, runoff water, soil, vegetation, and biota. All measurements follow standardised protocols detailed in the ICP IM Manual, ensuring data quality and comparability across sites and time periods. The dataset supports research on ecosystem responses to air pollution, climate change impacts, and biogeochemical cycling. Data are available under a Creative Commons By Attribution (CC BY) licence, providing valuable long-term environmental monitoring data for the scientific community.

Historically, antibiotic development has been driven by research into secondary metabolism which is responsible for the biosynthesis of so-called natural products. This research has been revolutionized by our growing capacity to rapidly generate and analyze prokaryotic genome sequences, facilitating the identification of biosynthetic gene clusters (BGCs) involved in secondary metabolism. We present the genomic analysis of 82 bacterial isolates from our collection, covering a broad taxonomic spectrum. The bulk of analyzed isolates belong to the actinobacterial class of Actinomycetes, known for their remarkable ability to generate antimicrobial compounds with diverse chemistries. Whole genome sequencing was performed using the Oxford Nanopore technology and the majority of assembled sequences exhibit over 90% gene completeness, enabling reliable functional annotation. Genome mining analyses revealed variability in BGC content across different phyla, confirming isolates in the Streptomyces genus as preeminent producers of secondary metabolites. Utilization of the current dataset holds potential not only for the elucidation of the bacterial biosynthetic machinery, but also for phylogenetic comparison and biotechnological exploitation of the isolates under study.

The blackchin tilapia (Sarotherodon melanotheron) is an aquaculture species in Southeast Asia, recognized for its adaptability to diverse environmental conditions. Genomic resources for this species remain limited, hindering progress in genetic research and breeding programs. In this study, we generated a high-quality, chromosome-level genome assembly of blackchin tilapia by utilizing Illumina short reads, ONT long reads, Hi-C, and RNA sequencing data. The final assembly spans 1,027.41 Mb (scaffold N50 = 38.87 Mb), with 93.42% of the assembly anchored to 22 chromosomes. Genome completeness, assessed using BUSCO, reached 99.50%. We predicted 27,896 protein-coding genes, of which 90.87% were functionally annotated. Repetitive elements accounted for approximately 42.62% of the genome. This chromosome-scale genome assembly provides a reference for comparative genomic and genetic analyses of blackchin tilapia, supporting investigations into the genetic basis of salinity tolerance and traits relevant to aquaculture breeding.

This paper presents a publicly available dataset from wind tunnel experiments carried out under the European Project ERIES (Engineering Research Infrastructures for European Synergies) at the Giovanni Solari Wind Tunnel of the University of Genoa. The dataset contains synchronized measurements acquired on a small-diameter, high-rotational-speed Savonius wind turbine under controlled laboratory conditions. Experiments were performed for wind tunnel velocities up to 21 m/s and rotor speeds up to 2500 rpm, under both smooth and turbulent flow conditions, with turbulence intensities of approximately 4% and 8%. The dataset includes: the inlet wind speed measured by a Pitot-static tube; the undisturbed wind speed, measured by a Cobra probe; the rotor's rotational velocity, measured by Hall sensors; generated power, estimated from the generated current and rotational velocity; the upstream and downstream flow velocities, recorded by hot wires. Additionally, noise emissions were recorded by four microphones downstream, providing insight into a relatively unexplored aspect of wind turbine performance in the literature.

We propose an emotion recognition dataset based on millimeter-wave (mmWave) radar and physiological reference signals. Compared to conventional methods, mmWave radar could obtain vital signs in a non-contact method without privacy concerns. We used validated stimuli to induce participants' emotions and simultaneously recorded three types of signals: mmWave signals, photoplethysmography (PPG) pulse signals, and galvanic skin response (GSR) signals. Participants used the Self-Assessment Manikin (SAM) to provide subjective emotion ratings. We collected signals and emotion rating data from 15 participants and validated the effectiveness of emotion induction and the data quality. The dataset can be used for research such as: (1) mmWave radar-based vital sign extraction; (2) comparison of emotion recognition performance across different signals; (3) multi-modal fusion for emotion recognition; (4) individual differences in emotional responses; (5) cross-subject emotion recognition, among others.

MACH is a comprehensive hydrometeorological dataset spanning 1,014 watersheds across the United States. It unifies, extends, and enhances existing large-sample hydrology resources by integrating daily meteorological forcings and streamflow observations at the basin scale over a 44-year period (1980-2023). For 395 catchments, MACH also includes records dating back to 1948, extending the hydrologic analyses period to 75-years. MACH provides a rich set of watershed attributes, encompassing topography, soils, geology, land cover, anthropogenic influences, and a wide range of hydroclimatic indices. This comprehensive structure supports investigations of climate-runoff interactions, hydrologic sensitivity, model calibration, and long-term watershed change across diverse spatial and temporal gradients. MACH serves as a flexible, high-resolution dataset for advancing large-sample hydrology, hydroclimatic assessment, and data-driven water science.

Identification of malignant and non-malignant regions in breast cancer whole slide images (WSIs) is essential for understanding tumor heterogeneity and histopathological evaluation. Annotated data allows deep learning models to learn features and analyze histological structures. In this study, we obtained 50 breast cancer WSIs from The Cancer Genome Atlas (TCGA) and had an expert pathologist manually annotate malignant (n = 1,882) and non-malignant (n = 374) regions using QuPath. The annotated regions were independently reviewed by a second pathologist, achieving an inter-observer agreement of 99.95%. In addition, to assess annotations' quality, we trained a hybrid contrastive-supervised machine learning pipeline for patch-level malignant vs. non-malignant classification. The model achieved a high F1-score of 0.90 indicating that our annotations are comparable in quality to those presented in public datasets. The proposed dataset provides expert-quality annotations and a unique resource for benchmarking and developing AI models for breast cancer histopathology.

Freshwater Gammarus species represent important keystone organisms in aquatic ecosystems, and are sensitive to environmental changes. Here, we present the first pseudo-chromosome-level genome of Gammarus nekkensis, endemic to north China. We integrated PacBio HiFi long-read sequencing, Illumina short-read sequencing, and Hi-C scaffolding to generate a high-quality, pseudo-chromosome-scale genome assembly. The assembled genome is approximately 6.24 Gb in size, with a scaffold N50 of 233.63 Mb, and 96.76% of the sequences were successfully anchored to 26 pseudo-chromosomes. A total of 39,474 protein-coding genes were predicted, and approximately 70% of these genes obtained functional annotations. Repetitive elements constituted about 63.93% of the genome, with long interspersed nuclear elements (LINE) being the most abundant (23.98%). BUSCO analysis indicated that both the assembly and annotation are highly complete compared with published amphipod genomes. This high-quality genome enables studies of sex determination, adaptive evolution, and genomic diversity in G. nekkensis, with applications for its conservation and breeding.

A precise evaluation of carbon (C) concentration, storage, and flux dynamics in China's inland waters is essential for assessing the global C sink capacity of aquatic ecosystems and for understanding regional biogeochemical cycles. However, there is a lack of a comprehensive, spatially explicit, and long-term dataset that quantifies applicable parameters. To address this significant data gap, here we provide a comprehensive dataset that systematically records the concentration, storage, and flux dynamics of dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) across China's rivers, lakes, and reservoirs over a long timescale. This dataset is meant to consolidate and standardize historical measurements from published literature, field surveys, remote sensing inversion processes, and model simulations since the 1960s. The dataset includes dissolved carbon (DC) records from China's lakes (3,515), reservoirs (841), and rivers (32,735) that span decades, namely, from 1960 to 2022, as well as basin-scale C storage and flux data from the 1990s to the 2010s. The dataset is intended to serve as a fundamental resource for quantifying the role of China's inland waters within the global C cycle, for validating C assessment models, and for informing water quality and climate change policies.