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This dataset addresses a gap in vehicle detection research by providing a street-level image corpus of heterogeneous urban traffic with regional vehicle types absent from established driving benchmarks. It contains 6,989 manually annotated images captured by the authors at various points in Sylhet, Bangladesh, between August and December 2025, enabling researchers to develop and benchmark multi-class vehicle detection models using deep learning and computer vision under real-world traffic and imaging variability. Data Collection Sources and Scope Data was collected at street level under real road conditions: Sources: Smartphone photography by the authors using high-resolution mobile devices in Sylhet, Bangladesh. Collection period: August 2025 to December 2025. Final released volume: 6,989 images. Frames without a target vehicle class were removed during collection, so every image contains at least one annotated instance. Illumination coverage: Each image carries one illumination label assigned by visual inspection: 3,350 day and 3,639 night, a near-balanced split across lighting conditions. Processing: No augmentation, resizing, or colour correction was applied, so the images retain natural variability in brightness, contrast, sharpness, and overall quality from real-world imaging environments. Ethics: No personal information was accessed, recorded, or disclosed at any stage of the study; all images were fully anonymised prior to dataset creation to ensure confidentiality and ethical compliance. Class Definitions: The dataset includes eleven vehicle classes: i. Bicycle ii. Bike iii. Bus iv. CNGRickshaw v. Car vi. CycleRickshaw vii. Leguna viii. Microbus ix. Pickup x. Truck xi. VanRickshaw The taxonomy includes regional types — motorised three-wheeler CNG rickshaws, cycle rickshaws, van rickshaws, and Legunas — absent from benchmarks such as KITTI, Cityscapes, BDD100K, and COCO. Key Features: Regional vehicle types absent from established driving benchmarks Near-balanced split across day and night lighting conditions Exhaustive, non-overlapping manual annotation across all eleven classes Unedited images retaining natural real-world imaging variability Visually similar three-wheeler body types within a single taxonomy Use Cases: i. Multi-class vehicle detection with deep learning and computer vision ii. Traffic image analysis iii. Detection under low illumination iv. Long-tailed and few-shot detection v. Fine-grained discrimination between visually similar three-wheeler body types vi. Evaluating detector robustness and generalisation under real-world conditions File Format JPG images at 1280 × 720, with one plain-text annotation file per image in the Ultralytics YOLO convention. Annotations are axis-aligned bounding boxes produced manually in Roboflow, with coordinates normalised to the image dimensions so they remain valid after rescaling. Each image also carries one illumination label (day or night).

The present dataset encompasses both derived data and reproducibility materials, providing a comprehensive foundation for a large-scale analysis of the length of English-language European patent titles and abstracts. The patent-title data were obtained from the European Patent Office (EPO) PATSTAT database through the Technology Intelligence Platform (TIP), while the abstract data were obtained from the EPO EPAB database through TIP. The two corpora were analyzed independently. The primary title dataset contains 4,686,155 valid English-language EP titles after deduplication at application level. The mean title length is 8.06 words, the median is 7 words, and approximately 75.0% of titles contain no more than 10 words. The primary abstract dataset contains 2,155,267 valid English-language abstracts, which were selected deterministically to ensure a single abstract per EP application. The mean abstract length is 125.20 words, and the median is 122 words. A survey of abstracts reveals that approximately 72.36% of them contain no more than 150 words. The modal abstract length is precisely 150 words (24,232 observations), succeeded by 14,768 abstracts at 151 words, corresponding to a 39.06% relative decrease immediately above the 150-word threshold. The repository contains comprehensive observed word-count frequency distributions, cumulative threshold statistics, annual publication-level descriptive series, application-versus-publication scope checks, language counts, outlier sensitivity analyses, tokenizer validation examples, derived figures, Python code, and supplementary methodological documentation. The raw EPO patent-title and abstract corpus is not redistributed; the deposited files contain aggregate or derived research data only. The extraction encompasses the period from 1978 to 2026. It should be noted that the data for 2026 represent a partial publication year at the time of extraction. The package facilitates verification of the primary findings in the absence of access to the TIP database. Additionally, it provides scripts for users with access to the EPO TIP environment. The OpenAI Codex was utilized as a programming assistant to develop, test, and debug Python workflows for EPO TIP. The research data that has been deposited is derived from EPO data and is not AI-generated.

Three data files in csv format: S1: The analyzed dataset - flower-bee interactions, flower traits, bee traits. S2: Flower traits with references. S3: Bee traits with references. One code file in R: The script used for data analyses and visualization.

This dataset presents survey-based evidence on how Indian Generation Z consumers perceive sports influencer promotions of energy drink brands and how these perceptions relate to their attitudes and purchase intentions. The study captures consumer responses to promotional content shared by athletes, fitness creators and sports personalities across social media platforms, providing insights into the intersection of digital advertising, brand perception and youth consumption behaviour. The data were obtained through a structured questionnaire administered to eligible Indian Gen Z respondents aged 18 years and above who had been exposed to sports influencer promotions of energy drinks. The survey recorded participants' demographic characteristics, social media preferences, energy drink consumption frequency and familiarity with energy drink brands. The dataset contains 40 seven-point Likert-scale items representing five dimensions: Sports Influencer Marketing (SIM), Content Value (CV), Brand Value (BV), Consumer Attitude (CA) and Purchase Intention (PI), with eight items measuring each dimension. These variables capture perceived influencer credibility, promotional content relevance, brand attractiveness, consumer evaluations and willingness to purchase promoted products. The dataset offers opportunities to explore consumer differences based on age, gender, occupation, consumption frequency and preferred social media platforms. It can also support measurement validation, multivariate analysis, predictive modelling and investigations of relationships between influencer-related perceptions and purchasing intentions. Its potential applications extend to sports marketing, digital branding, advertising effectiveness and consumer behaviour research within India's Generation Z population.

This collection supports a comparative study of democratic accountability, political integrity and equal rights among ten French political figures and their parties ahead of the 2027 presidential election. It examines public commitments, institutional practices, historical records and documented controversies using sources available up to 24 October 2026. The analysis distinguishes established facts, unresolved allegations and conditional risks, while separating individual responsibility from party history. Methodological assumptions and evidence limitations are explicitly identified.

Supplementary Material: description This file supports the manuscript on spatial configuration and pedestrian walkability in the Farmgate MRT station catchment, Dhaka. It documents the study area and its street condition and provides the full space syntax output and the pedestrian count data behind the results. Study area and field context • Figure S1: the study area marked on the Detailed Area Plan (RAJUK, 2023). • Figures S2 and S3: land use maps of the study area, from the 2025 field survey and from 2016 GIS datasets (RAJUK). • Figure S4: the primary, secondary and tertiary streets of the study area (field survey, 2025). • Figures S5 ,S6, and S7: photographs of street conditions in East Rajabazar, West Rajabazar and Monipuripara. • Figure S8 Syntactic Analysis of Farmgate (Embedded in Dhaka) • Figure S9 Map showing the selection of the gates • Figure S10 Pedestrian intensity at the selected gates Space syntax output (generated in DepthmapX v0.8.0) • Tables S1 and S2: global integration (Rn), local integration (R4), connectivity and control values for every axial line, with Farmgate analyzed as an independent system. S1 covers East and West Rajabazar and S2 covers Monipuripara (157 lines in total). • Tables S3 and S4: the same measures for East and West Rajabazar (S3) and Monipuripara (S4) with Farmgate embedded in Dhaka’s wider street network, accompanied by a map of the embedded analysis. Pedestrian movement data (field survey, 2025) • Gate selection map: the locations of the 31 observation gates. • Tables S5 and S6: counts for the 16 gates in East and West Rajabazar (S5) and the 15 gates in Monipuripara (S6). Each gate has counts for four daily time slots on four days. The tables give the average per five-minute count and per hour, and the average hourly flow used in the correlations. • Pedestrian intensity map: pedestrian intensity at the selected gates.

This package provides processed time-series data, a lumped thermal model, Python analysis code, results tables and figures for a laboratory-scale far-infrared (FIR) emitter system operated under proportional control with pulse-width modulation (PWM). The analysis covers 14 unloaded experimental trials at temperature setpoints of 100–300 °C, comprising 21,521 observations. The package includes data preparation and quality flagging; audits of measurement resolution, conversion and timing; controller-performance assessment; leave-one-trial-out thermal prediction at 10, 30 and 60 s horizons; closed-loop simulation using the experimental control law; comparisons of one-step temperature predictors at approximately 0.402 s; and a measurement uncertainty budget. Peak emission wavelengths are estimated from temperature using Wien’s displacement law. The accompanying Jupyter notebooks reproduce the analyses using the separately published raw experimental dataset (DOI: 10.17632/7pp4rx65mf.1). The raw dataset is not included in this package and must be downloaded separately.

This dataset contains experimental time-series data from a laboratory-scale far-infrared (FIR) emitter system operated under proportional (P) control with pulse-width modulation (PWM). Emitter temperature was derived from the sensor output using an empirical calibration equation and converted to an estimated peak emission wavelength using Wien’s displacement law. The controller acted on the difference between this estimated wavelength and the wavelength corresponding to the temperature setpoint. This represents temperature feedback expressed on a wavelength scale; no spectroscopic measurements were performed. The dataset comprises 15 trials and 22,527 observations at temperature setpoints of 50, 100, 150, 200, 250 and 300 °C. All trials were conducted in a closed chamber without a product load. Recorded variables include apparent resistance, emitter temperature, estimated peak wavelength, bottom-plate temperature and PWM duty cycle. Trial metadata, experimental history and variable definitions accompany the data. The measurement arrangement, calibration limitations, timing assumptions, interpolated bottom-plate values and other known data-quality issues are documented in README.txt.

This repository contains source code relevant of the manuscript: A general model based on Riemannian manifold for stable decoding continuous hand movement trajectory from ECoG signals in non-human primate. Reza Eyvazpour, Behraz Farrokhi, and Abbas Erfanian Summary: This study proposes a novel method for decoding continuous 3D hand trajectories from electrocorticographic (ECoG) signals for brain-computer interface (BCI) applications, addressing the challenge of inter-session variability. The approach combines Riemannian geometry-based feature extraction with LSTM-BiLSTM networks to enable transfer learning across sessions. Using ECoG data from five monkeys performing reaching tasks, spatial covariance matrices were computed from ten frequency band powers and mapped onto a Riemannian manifold to extract session-invariant features. These features, along with spectral data, were used to train deep learning models (LSTM-BiLSTM). The proposed method outperformed baseline models using only spectral features, showing enhanced generalization across sessions and supporting the utility of geometric-temporal modeling in BCI development. For more details and code usage, please refer to the description file.

Urban hot semi-arid regions are particularly vulnerable to heat extremes, given that almost all are located in low- and middle-income countries (LMICs). Cities are therefore expanding rapidly. Most urban residents in LMICs have low per capita income, live in densely populated neighbourhoods with little tree canopy, and have limited resources to act in contexts that leave little physical space. The drought characteristic of the hot semi-arid climate zone (BSh according to the Köppen-Geiger classification) hinders the increase in tree canopy recommended in other climate zones to mitigate the impact of heatwaves. Since 2017, researchers have increasingly studied urban warming in BSh zone and proposed measures to reduce them. Nowadays, satellite-detected land surface temperature (LST) is the preferred source of information for investigating the thermal issue. This dataset covers 233 LST assessments published between 2007 and 2026 on 43 cities in BSh zone. The dataset summarize the LST reduction measures in 8 tables: 1. List of the 225 cities in the BSh climate zone; 2. Articles published on 43 cities in the BSh zone, with download links; 3. List of cities studied, broken down by macro-region and number of studies; 4. Cities exhibiting an urban cool island; 5. Sensors used to measure LST by the 233 assessments; 6. Information source on land cover used by the 233 assessments; 7. Measures to reduce LST according the 233 studies; 8. Cities in the BSh zone exhibiting a reduction in vegetated area. The dataset shows a rapid increase in the number of studies after 2017. However, 43 per cent of the studies do not propose measures to reduce LST. The remaining studies propose mostly generic measures followed, in order of frequency, by greening, cool materials, sustainable urban planning, water bodies and improved urban morphology.