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This dataset contains high-frequency, continuous urban air quality and meteorological observations monitoring the environmental conditions in Tashkent, the capital city of Uzbekistan. The data spans from February 26, 2024, to September 18, 2024, capturing a total of 9,546 sequential temporal observations recorded at approximately 30-minute intervals. The dataset is structured as a single comprehensive comma-separated values (.csv) file named "all_sensor.csv". It tracks 8 core air pollutant metrics alongside 4 vital meteorological parameters, creating a robust baseline for multi-sensor environmental analysis, temporal air pollution modeling, and weather-driven dispersion tracking in Central Asian urban microclimates. Data Attributes and Column Definitions: Date: The calendar date of data logging (MM/DD/YYYY format). Time: The local continuous timestamp of data capture (HH:MM:SS format). CO/ppm: Carbon Monoxide concentration measured in parts per million. NO/ppb: Nitric Oxide concentration measured in parts per billion. O3/ppb: Ground-level Ozone concentration measured in parts per billion. PM10/ug/m3: Mass concentration of coarse particulate matter (≤10μm) in micrograms per cubic meter. PM25/ug/m3: Mass concentration of fine particulate matter (≤2.5μm) in micrograms per cubic meter. SO2/ppb: Sulfur Dioxide concentration measured in parts per billion. Temperature: Ambient outdoor air temperature recorded in Celsius (°C). tsp/ug/m3: Total Suspended Particulates mass concentration in micrograms per cubic meter. tvoc/ppm: Total Volatile Organic Compounds measured in parts per million. wind_speed/m/s: Local near-surface wind speed measured in meters per second. Humidity/%RH: Relative Atmospheric Humidity expressed as a percentage. wind_degree/deg: Continuous wind direction vector expressed in degrees (0∘ to 360∘). Potential Reuse and Value: This raw, un-gap-filled time-series dataset is immediately ready for scientific reuse, specifically for training machine learning forecasting architectures (such as LSTM or GRU networks), validation of air quality indexes (AQI), urban health risk assessments, and meteorological correlation analyses.

3CIsoInvert is a Python package that implements a probabilistic Bayesian framework for testing whether measured isotope compositions of lamproite are consistent with mixing between mantle (plume) and metasome endmembers (MARID; PIC; mica-peridotites, etc.). The code evaluates both two-component (plume + one metasome) and three-component (plume + hybrid mixture of two metasomes) mixing models against multi-dimensional Sr–Nd–Hf–Pb isotope data. The three-component mixing is critical to this approach because it circumvents the limited availability of isotope data of metasome end-members by generating a spectrum through hybrid mixtures. The inversion is built on concentration-weighted mixing equations that predict isotope ratios as functions of mixing fractions. Model misfit is quantified via χ², computed across all usable isotope ratios. The code propagates analytical uncertainties through Monte Carlo sampling instead of treating endmember ratios and concentrations as fixed. Isotope ratios are drawn from Gaussian distributions, while element concentrations are drawn from log-normal distributions to ensure strictly positive values. At each grid point, thousands of sampled candidates are evaluated, and the resulting likelihoods are averaged to produce a likelihood surface that reflects both model fit quality and the impact of end-member uncertainty. We used a two-tier screening process to keep the analysis computationally efficient. Tier 1 evaluates each candidate per isotope space individually using relatively few Monte Carlo draws; two-component candidates must pass a genuine goodness-of-fit test (degrees of freedom = 1), while three-component candidates automatically survive (degrees of freedom = 0) and defer discrimination to Tier 2. Surviving candidates are then re-evaluated jointly across all usable ratios with higher Monte Carlo sample counts, yielding final χ², p-values, and log-evidence rankings. Candidates are compared only within groups sharing the same usable-ratio set, since different ratio combinations carry different likelihood normalisations. For multi-grain analyses of the same mineral phase, inverse-variance pooling is applied with MSWD-based uncertainty inflation to account for disequilibrium scatter. Results are written to Excel workbooks containing ranked candidate solutions, per-space deterministic fits, multiple-comparison diagnostics, and cluster statistics. The complete package, including source code, configuration parameters, and convergence diagnostics, is available for peer review and independent replication.

This dataset is a publicly available, multi-clinic dental imaging database that aims at furthering studies on artificial intelligence (AI), computer vision, and computer-aided diagnosis (CAD) based on panoramic dental radiographs (orthopantomogram or OPG). This database includes 2,095 completely anonymized panoramic dental radiographs gathered retrospectively from four separate dental centers in Bangladesh, namely Sonia Nursing Home, Tangail (1,433 radiographs); Ibn Sina D. Lab & Consultation Center, Dayaganj (533 radiographs); Niramoy Diagnostic Center, Tangail (114 radiographs); and Health City Diagnostic Center, Gaibandha (15 radiographs). Every image comes with bounding box annotations in both YOLO (.txt) and standard COCO JSON format, validated by experts, making the dataset easy to use with frameworks such as Ultralytics YOLO as well as any COCO-compatible model or data loader. Initial bounding boxes were drawn by a licensed dentist and independently reviewed by a second dental professional. A rigorous quality-control process followed, removing 127 substandard bounding boxes, resulting in 9,834 validated bounding boxes. Dataset Classes The dataset includes annotations for seven clinically relevant dental categories: - Missing Teeth: 2,609 annotations - Dental Crown: 1,984 annotations - Root Canal: 1,956 annotations - Caries: 1,410 annotations - Wisdom Teeth: 869 annotations - Broken Down Teeth: 795 annotations - Healthy OPG: 211 annotations Dataset Contents The released dataset includes: - Panoramic dental radiographs (.jpg) - COCO-format JSON annotation files (instances_train.json, instances_val.json, instances_test.json) - YOLO bounding-box annotation files (.txt) - Metadata files (class_definitions.csv, image_metadata.csv, split_assignments.csv) - Documentation (README.md) describing the dataset structure, annotation format, and usage instructions Potential Research Applications This dataset supports multi-class dental object detection, localization of dental abnormalities, CAD, deep learning for medical imaging, computer vision research, transfer learning and foundation models, explainable AI (XAI), medical image analysis, object detection benchmarking, AI-enabled dental diagnosis, dental AI learning, and reproducibility research. Benchmark Performance Three YOLO variants were evaluated to establish a baseline. YOLOv8m achieved the highest precision (71.9%) and mAP@0.5 (72.9%), with the fastest inference latency (1.9 ms per radiograph). YOLOv10m achieved the highest recall (74.2%) and mAP@0.5:0.95 (34.4%), with a latency of 3.4 ms. YOLOv11m reached a precision of 70.9%, recall of 71.6%, mAP@0.5 of 71.7%, and mAP@0.5:0.95 of 33.5%, with a latency of 4.2 ms. These results show the comparative detection performance and computational efficiency of the evaluated YOLO variants on this dataset.

The provided data consists of 5,035 reviews that can be found at Amazon India store. Every record includes , the complete text of the review, the star rating (only three, four or five stars – there are no reviews with one or two stars in this dataset), the number of “helpful” votes, an optional product image link associated with this review, and the date and country of the review. This dataset was de-duplicated and slightly preprocessed (character encoding artifacts were corrected, whitespaces were normalized, dates were transformed into ISO 8601 format), but not cleaned.

This supporting-data release provides Supplementary Tables S1–S2, station-level four-model GNSS results, prepared horizontal and tide-removed Up-component coordinate series, representative HectorP controls and tidal-response fit diagnostics for the 2025–2026 Drygalski Ice Tongue GNSS campaign. It documents the tide-reference context of the prepared coordinate-series products.

- An Integrated Large-Scale Multi-Objective Optimization Framework for Subcatchment-Scale Allocation of Low Impact Development (LID) Practices: Hydrological Control and Total Suspended Solids (TSS) Reduction - Model input files, parameter files, coupling scripts, optimization settings

Getah virus (GETV) is an important mosquito-borne zoonotic pathogen, like Chikungunya virus (CHIKV), belonging to the genus Alphavirus of the family Togaviridae. Since 2024, the highly virulent GETV GIII variant strain has emerged in China, causing severe abortions in sows and piglet mortality rates as high as 80%, resulting in substantial economic losses to the swine industry. However, no approved vaccines or effective antiviral drugs are currently available for the prevention and control of GETV. In this study, we screened a library of 113 natural compounds using a GETV-GFP reporter strain and identified 14 candidate molecules with anti-GETV activity. Among them, punicalagin (PUN) showed the most potent antiviral activity. In vitro experiments demonstrated that PUN exhibited significant dose-dependent inhibitory effects against multiple GETV strains, including the GETV-GFP strain, the highly virulent GIII variant strain HeN01 and the non-variant GIII strain HNny25. Mechanistic studies revealed that PUN blocks the viral attachment and internalization stages to exert its antiviral action. In addition, we found that PUN significantly inhibited the replication of other enveloped viruses, including porcine epidemic diarrhea virus (PEDV), pseudorabies virus (PRV), and vesicular stomatitis virus (VSV), but showed no activity against the non-enveloped virus Senecavirus A (SVA). Furthermore, in suckling mice challenged with the highly virulent HeN01 strain, PUN treatment significantly increased the survival rate to 66.7% (4/6), ameliorated growth and development indicators, and effectively reduced the viral load in major target organs. These findings not only provide a solid theoretical basis for the future clinical translation of PUN but also hold great promise in bridging the gap in specific GETV therapeutics.

This study evaluates whether 3D human gait kinetics can be accurately predicted outside a laboratory setting. The core hypothesis is that wearable linear acceleration data (3 DoF), combined with artificial neural networks (LSTM and MLP), can successfully estimate clinically relevant parameters without the need for resource-intensive camera systems and force plates. The repository contains anonymized time-series datasets from 32 healthy subjects alongside the Python pre-processing pipelines. The 'ReadMe.txt' file explains the scripts and the overall data structure.

This dataset contains the experimental data supporting the article "Use of Al2O3-supported manganese oxide-based catalysts in fixed-bed reactors for phenol decomposition and intermediate tracking" (Surfaces and Interfaces). A series of MnO2/γ-Al2O3 catalysts with nominal MnO2 loadings of 0.1, 0.5, 1.0, 2.0, 5.0 and 10.0 wt.% was prepared by wet impregnation and evaluated for H2O2-mediated phenol oxidation in a fixed-bed continuous-flow reactor, using an initial phenol concentration of 50 mg/L and a residence time of 35 min. The dataset includes the manganese content determined by flame atomic absorption spectrometry, together with BET surface areas and average pore diameters; X-ray diffraction patterns of the γ-Al2O3 support and of three of the catalysts, provided both as measured and after background subtraction and accompanied by the unmodified diffractometer files; XPS binding energies and surface Mn/Al atomic ratios; integrated hydrogen consumption from H2-TPR; integrated ammonia desorption and isoelectric points, together with the complete NH3-TPD profile of one catalyst; and the complete set of catalytic results, namely phenol conversion as a function of H2O2 concentration (30-480 mM), reaction temperature (5-45 C) and initial pH (3-9), CO2 production, and the concentrations of hydroquinone, catechol, resorcinol, malic acid and maleic acid. Each catalytic value is the mean of three independent runs with its standard deviation. Data are provided as UTF-8 CSV files organised by characterisation technique. A README file describes the folder structure, the experimental conditions, the instruments used and the scope of the dataset.

Panel data using Periodic Labour Force Survey (PLFS) from six annual rounds from 2017–18 to 2023–24. Use Do file for replication.