Share your research data

This dataset presents survey responses from young Indian investors regarding their perceptions of financial influencers and investment decision-making. Data were collected through a structured questionnaire capturing respondents' demographic characteristics, investment experience, social media preferences and exposure to financial content creators. The dataset includes 32 measurement items representing four dimensions: Financial Influencer Credibility (FIC), Investor Trust (IT), Perceived Investment Risk (PIR) and Investment Intention (II), with eight items per construct. Responses were recorded using a seven-point Likert scale. The dataset offers opportunities to examine how influencer credibility, investor confidence and perceived financial risks relate to investment intentions, supporting research in behavioural finance, digital financial communication, social media marketing and investment behaviour.

This dataset provides supplementary materials for the manuscript entitled “Lymphovascular invasion on haematoxylin–eosin predicts nodal metastasis but not short-term survival in invasive breast cancer.” The study retrospectively analysed 120 patients with invasive breast cancer diagnosed between January 2022 and July 2025 in Uzbekistan. The supplementary materials include Tables S1–S8 and Figures S1–S2, providing additional information on data quality control, cohort characteristics, clinicopathological comparisons, diagnostic performance of lymphovascular invasion (LVI) for predicting lymph node metastasis, survival analyses, sensitivity analyses, and supplementary figures. LVI was assessed using haematoxylin–eosin-stained sections and classified as focal or extensive. The dataset supports the results reported in the manuscript, including the association between LVI and lymph node metastasis, as well as the short-term disease-free and overall survival analyses. These materials are intended to improve transparency, reproducibility, and accessibility of the study findings. All shared data should be de-identified to protect patient confidentiality.

This dataset accompanies the systematic review "Acute, subacute, and subchronic toxicological profile of Vitex doniana (black plum) in preclinical studies: a systematic review" (PROSPERO CRD420261427315). It contains the complete, structured data extracted from 10 controlled in vivo rodent studies that evaluated acute, subacute, and subchronic toxicity of Vitex doniana Sweet (black plum) extracts (leaf, root bark, stem bark, or fruit; aqueous, methanolic, ethanolic, or syrup). Studies were identified through PubMed/MEDLINE, Google Scholar, African Journals Online (AJOL), and Scopus Preview from inception to June 2026, following PRISMA 2020. Risk of bias was assessed with SYRCLE. The dataset contains 17 linked files: - DS-0_Abbreviations.csv — data dictionary of all abbreviations used - DS-1_Study_identification.csv — study IDs, countries, designs, randomisation, OECD compliance - DS-2_Plant_material_authentication.csv — plant parts, solvents, voucher specimens, chemical profiling - DS-3_Animal_model_dosing.csv — species, sex, sample sizes, routes, doses, durations - DS-4_LD50_mortality.csv — LD50 values, methods, mortality, clinical signs - DS-5a_Liver_enzymes.csv — ALT, AST, ALP by study and dose - DS-5b_Kidney_markers.csv — creatinine, urea, uric acid, electrolytes - DS-5c_Other_biochemical.csv — additional biochemical markers (Akharaiyi et al.) - DS-6_Haematology.csv — RBC, WBC, Hb, PCV, differential counts, platelets - DS-7a_Organ_weights.csv — absolute and relative organ weights - DS-7b_Histopathology.csv — histopathological findings - DS-7c_Clinical_signs.csv — observed clinical signs and mortality - DS-7d_Body_weight.csv — body weight changes - DS-8_Risk_of_bias_SYRCLE.csv — SYRCLE 10-domain risk of bias - DS-9_Notes_duplicates_deviations.csv — duplicates, inconsistencies, protocol deviations - README.txt — dataset overview, file list, data notes, citation - Codebook.txt — column definitions for every file Key notes: 1. Abdulrahman et al. (2007a) and (2010) report the same acute i.p. experiment; acute data are counted once, and the 2010 study additionally contributes a 21-day oral haematology experiment. 2. No subchronic (≥90-day) study was identified. 3. All values are as reported in the source publications; no data were imputed. 4. Statistical significance markers are indicated as "(sig)" per the source paper. 5. Voucher specimens were reported in 7 of 10 studies. The dataset supports reproducibility of the review and future updates. Shared under CC BY 4.0. Contact: amirmuhammadabdullahi@gmail.com.

The data for "Effects of root system variation on soil saturated hydraulic conductivity across pine, oak, and bamboo forest stands in eastern China"

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.