Share your research data

This dataset contains derived publication source data supporting a study of multi-frequency multibeam echosounder (MBES)-based prediction of marine vegetated habitats at Hujeong and Bongpyeong, Republic of Korea. The package includes source data underlying the main and supplementary figures and tables, validation summaries, candidate-selection results, mapping summaries, publication metadata, a data dictionary, a file manifest, and SHA-256 checksums. Raw MBES survey data, model binaries, proprietary imagery, manuscript artwork, and large derived raster products are not included. The associated analysis code is available at https://github.com/neobassist/MBES-Habitat-Framework.

OMANISHA (Online Misogynistic Annotated Natural-language Instances for Sentiment and Hate Analysis) is a Bengali dataset developed to support the automatic detection of misogynistic discourse in online spaces. Misogynistic content on online platforms has serious psychological, social, and institutional consequences for women, as it contributes to gender inequality, normalizes gender-based violence, and discourages women from participating freely in digital communities. Despite the global significance of Bengali, computational resources for detecting gender-based online abuse in Bengali remain limited. OMANISHA addresses this gap by providing a reliable, publicly accessible dataset for online misogyny detection. The dataset consists of 7,017 instances, with each instance assigned to one of four predefined categories. • Non-misogynistic: 2,420 samples • Stereotype: 1,744 samples • Derogation: 1,527 samples • Sexual harassment: 1,326 samples To develop the dataset, we reviewed public online discourse to understand common linguistic patterns, abusive expressions, and contextual features of misogynistic text. Based on these observations, all dataset samples were independently constructed by the authors, ensuring originality, ethical compliance, and suitability for responsible academic research. To avoid copyright, privacy, and platform-related concerns, we ensured that the dataset does not contain any verbatim or directly paraphrased social media comments. The dataset includes both formal and informal Bengali texts, reflecting real-world online communication patterns. English translations are also provided to enhance cross-lingual accessibility and support comparative NLP research. To ensure annotation reliability, each sample was independently annotated by two native Bengali annotators selected from a pool of four annotators with diverse gender, religious, ethnic and geographical backgrounds. Annotation disagreements were resolved through structured consultation with a third annotator. Annotation quality was evaluated using Cohen’s Kappa (κ = 0.76) and Krippendorff’s Alpha (α = 0.75), indicating substantial inter-annotator agreement. Additionally, pairwise Jaccard Similarity scores among the classes range from 0.12 to 0.21, suggesting clear taxonomic distinction across the defined categories. Overall, OMANISHA offers fine-grained category-level annotations, enabling more precise analysis and content moderation. By making this dataset publicly available for research purposes, OMANISHA aims to advance low-resource Bengali NLP, support explainable AI-driven content moderation and encourage further innovation and collaboration within the Bengali NLP community. The following GitHub repository contains the dataset in CSV format, the source code used for dataset analysis, and a detailed data dictionary: https://github.com/Shazzad-Hossen/OMANISHA-Bengali-Misogynistic-Text

This dataset contains detailed synthetic procedures, yields, compound characterization data, and corresponding ¹H NMR, ¹³C NMR, and HRMS spectra for the synthesized compounds reported in the associated research article.

Source Immunoblot data for "A TORC1-regulated H3K4me reader preserves promoter chromatin stability, and its degradation promotes meiotic remodeling" Yun et al 2026 Molecular Cell

This dataset contains the data extraction, coding sheet, and statistical synthesis records for the systematic review and meta-analysis investigating music interventions in second language (L2) learning (2021–2025), following PRISMA 2020 guidelines.

This dataset supports a longitudinal computational study of organizational R&D capability and external knowledge integration across 40 aviation-focused higher education institutions from 2015 to 2025. Derived from OpenAlex metadata using institutional-lineage matching and AVI-TAX-v1.0, it contains 49,274 strict and 62,624 broad institution–work records, collaboration edges, and a 440-row institution-year panel. It includes RORC, EKII, CSLI, component indicators, controls, provenance records, analytical outputs, quality audits, scripts, and checksums. A 600-record AI-assisted audit is included as computational sensitivity evidence, not independent human validation. Patent, institutional-report, Research Translation Index, and causal-translation claims are excluded.

The research data of the article of 'An Experimental Investigation on the Two-dimensional Distribution of Soot Morphology and Nanostructure in Axisymmetric co-flow Flames'

This dataset contains the supplementary materials associated with the manuscript “Treatment response and associated factors of hemoporfin-mediated photodynamic therapy for port-wine stains: a systematic review and meta-analysis,” accepted for publication in the Journal of the American Academy of Dermatology. The supplementary materials include detailed search strategies, study selection, risk-of-bias assessments, study characteristics, subgroup and meta-regression analyses, sensitivity analyses, publication bias assessment, the PRISMA 2020 checklist, and supplementary references.

This package provides the MATLAB code and dataset required to reproduce the component-ablation experiments of the CWF (Convolutional Neural Network Integrating Attention and Wavelet-Fourier Layers for Agricultural Image Segmentation) semantic-segmentation framework, perform repeated-training and statistical analyses under a common protocol, and characterize both the final CWF implementation and the comparative computational complexity of the models reported in the manuscript.

This deposit contains the complete supporting data for the systematic review "Microplastics Contamination in Canal Network: A Systematic and Bibliometric Analysis of Occurrence and Transport Dynamics Across Global Waterways" (Journal of Contaminant Hydrology, manuscript CONHYD-D-26-00786). The review synthesises 37 peer-reviewed studies of microplastic occurrence in canals and other hydraulically managed conveyance channels, published between 2017 and 2025 and identified through a Scopus search executed on 1 January 2026. The deposit allows every count, proportion, and figure reported in the manuscript to be independently verified and reproduced. Contents Scopus search export (563 records, unfiltered, as retrieved) Study selection record: search strategy, eligibility criteria, canal definition, stage-by-stage PRISMA counts, and reasons for exclusion at full text Identifiers of the 37 included studies (author, year, title, source, DOI, Scopus EID, category) Extraction table: dominant morphology, size class, colour, and polymer for each study-matrix record, with abundance and units as originally reported Quality assessment: scoring rubric and item-level scores across five domains for all 37 studies Bibliometric input file (37 records, full Scopus metadata) Analysis notes: software versions, import settings, and the Biblioshiny panels used Figure source data: numerical values underlying each figure Notes on the data. The unit of analysis for composition results is the study-matrix record, defined as one combination of a single study and a single environmental matrix; the 37 studies yield 48 records. Records are counted as reporting a characteristic where the study identified either a single dominant category or explicitly reported no single dominant category. Denominators therefore differ between attributes: 46 for morphology, 39 for polymer, 34 for size class, and 30 for colour.