Mendeley Data Showcase

The dataset includes the results of synthetic aperture radar (SAR) processing and identification of internal solitary waves (ISWs) in the Western Arctic Ocean in summer periods of 2007, 2011, 2016, 2018 and 2020. The processed data include those acquired by the European Space Agency's Envisat Advanced SAR (ASAR) instrument in June-October 2007 and 2011, and by the contemporary Sentinel-1 A/B SAR-C missions in August-September 2018 and June-October 2020. In addition, L-band SAR images from the Japanese ALOS-2 PALSAR-2 instrument operated by the Japan Aerospace Exploration Agency (JAXA) were used for August-October 2016. Envisat ASAR data were obtained from ESA's rolling archive. These ASAR images were in Wide Swath mode with a spatial resolution of 150 m. In total, 369 ASAR images were analyzed: 159 from 2007 and 210 from 2011. Sentinel-1 A/B data were obtained from the Alaska Satellite Facility portal (https://search.asf.alaska.edu/). These consisted of high- and medium-resolution Ground Range Detected images captured in Interferometric Wide (IW) and Extra-Wide (EW) swath modes, with approximate spatial resolutions of 20 m and 90 m, respectively. A total of 2322 (694) S1A/B images acquired in June-October 2020 (August-September 2018) were analyzed. In addition, 36 ALOS-2 PALSAR-2 images acquired in August-October 2016 were also utilized. These L-band SAR images were acquired in ScanSAR nominal, Fine, and Ultra-fine modes, with spatial resolutions of 50 m, 12.5 m, and 5 m, respectively. Overall, 3421 SAR images were used in the analysis. The dataset includes 2 mat files. The first, SAR-coverage.mat is the total SAR coverage of the study region by SAR data at 900x1200 grid. The second, isw_properties_gridded.mat includes parameters num_waves (total number of ISW detections), isw_probability (probability of ISW observations) and overlap (SAR coverage) interpolated to 29х49 horizontal grid (grid cell size is 0.5° in latitude and 1.5° in longitude) covering the region spanning 64.5-79.5° N, 110-185° W.

This dataset provides the extracted data used in our systematic review and meta-analysis on the effects of microplastics on zooplankton. It accompanies the manuscript “Meta-analysis Reveals Microplastics Pose High Risks to Zooplankton in a Warming World” (submitted to the Journal of Hazardous Materials). The dataset is organized as three Excel files: 1) EffectSize_Dataset.xlsx Extracted observation-/effect-size–level dataset used for all meta-analyses (n = 1,468 observations from 48 studies). It contains study metadata and experimental descriptors (e.g., year, location, taxon/species, polymer, particle shape/size/color, exposure duration, temperature, feeding, etc.), concentration information (original and standardized units where applicable), endpoint category (growth/survival/reproduction/physiology), and effect-size statistics (effect size and corresponding variance/SE as used in the meta-analysis models). 2) Included_Studies.xlsx Full list of studies included after screening, with bibliographic information (author, year, title, etc.) and identifiers used to link to EffectSize_Dataset.xlsx. 3) Endpoint_Dictionary.xlsx A dictionary mapping the detailed response indicators reported in primary studies to the four endpoint categories used in the meta-analysis (growth, survival, reproduction, physiology). Notes: Missing values are coded as blank/NA. Variable names and endpoint definitions follow the manuscript and supplementary tables/figures. When citing this dataset, please use the DOI assigned upon publication: DOI: [to be added].

The Chattogram dialect (Chittangga), widely spoken in southeastern Bangladesh, is primarily an oral language with no standardized writing system. Despite its large speaker population, the dialect remains underrepresented in computational linguistics due to the scarcity of high-quality, manually curated digital resources. This dataset introduces a fully manual, native-curated multilingual sentiment corpus developed entirely by researchers who are native speakers of the Chattogram dialect. ​It consists of 4,451 parallel sentences aligned across five distinct columns: Standard Bangla, Chattogram dialect, English, Sentiment labels, and the Source of Data. The inclusion of the 'Source of Data' column provides essential context by categorizing each entry based on its origin, such as social media posts, regional drama scripts, and everyday conversations. ​The Chattogram dialect is predominantly spoken in Chattogram city, Cox’s Bazar, and the coastal regions of the Chittagong Hill Tracts, as well as nearby districts of southeastern Bangladesh. Given the oral nature of the dialect, all Chattogram sentences were phonetically transcribed into Bengali script. The dataset follows a translation-first pipeline: each Chattogram sentence was translated into Standard Bangla and then English by the same native speakers to maintain semantic fidelity and cross-lingual alignment. ​Sentiment annotation was performed after multilingual alignment, with each sentence categorized as Neutral, Negative, or Positive (Neutral: 1,969; Negative: 1,467; Positive: 1,015). The dataset represents the first high-quality benchmark for sentiment analysis in the Chattogram dialect, enabling researchers to develop low-resource NLP models, dialectal sentiment classifiers, and cross-lingual transformer-based systems. Its native-driven design ensures linguistic authenticity, cultural accuracy, and contextual relevance, providing a valuable resource for the computational study of underrepresented languages. ​By combining manual transcription, expert multilingual translation, source-based categorization, and careful sentiment annotation, this corpus supports both academic research and practical applications in natural language processing, multilingual AI systems, and digital preservation of oral language traditions.

This package contains the data and Stata replication codes for Cao, Siwei, Lei Zhen, and Brian Wright, 2026 "Distinguishing Hares and Tortoises in the Field: Applicants' anticipation of prediction of patent value flows" (Research Policy)

ANUBHUTI, a comprehensive dataset consisting of 2,500 sentences manually translated from standard Bangla into four major regional dialects—Mymensingh, Noakhali, Sylhet, and Chittagong. The dataset predominantly features political and religious content, reflecting the contemporary socio-political landscape of Bangladesh, alongside neutral texts to maintain balance. Each sentence is annotated using a dual annotation scheme: (i) multiclass thematic labeling categorizes sentences as Political, Religious, or Neutral, and (ii) multilabel emotion annotation assigns one or more emotions from Anger, Contempt, Disgust, Enjoyment, Fear, Sadness, and Surprise.

This repository contains the replication materials for the paper: “Upstream Intergenerational Transfers, Agricultural Technology Choice, and Conspicuous Consumption: Evidence from Rural Togo”. Contents: - Stata do-files to clean the row data and reproduce tables and figures in the manuscript. - Derived datasets. -A read_me file Data access: The underlying microdata come from the LSMS Togo 2021–2022 survey and are available through the World Bank Microdata Library, subject to the provider’s terms of use. Embargo: This dataset is under embargo until acceptance-publication of the associated article.

The data is filled by the evidence of the research

This is an original document titled "Lncrna6470 Sponges ace-miR-750-Y to Modulate the Immune Response of Honeybee Larvae to Fungal Infection"

This dateset contains three stage of liver condition along with clinical data and demographic data on the classification of Non-Alcoholic Fatty Liver Disease (NAFLD). This dataset meant to help in medical image analysis, machine learning, and deep learning for early liver disease detection and severity assessment. The ultrasound images are acquired with standard diagnostic ultrasound equipment in B-mode. Ultrasound imaging is a widely used, and cost-effective modality for liver assessment, making it particularly suitable for large-scale screening and early detection of NAFLD. Each sample is labelled according to NAFLD status, and disease severity grades (Normal, Benign, Malignant). These labels enable both binary and multi-classification tasks. Along with images of liver, we include clinical and demographic data relevant to NAFLD diagnosis. The clinical data included with patient age, gender, body mass index (BMI), liver enzyme measurements such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST), waist size, glucose, cholesterol such as (LDL,HDL, Triglycerides). This combination of data (image and Tabular) makes possible research on multi-modal learning, which improve diagnostic accuracy and model robustness. All data on our dataset are anonymized to de-identified patient's personal information and to ensure compliance with ethical research standards and data protection guidelines. The dataset is strictly provided for research and educational purposes and does not contain any information that can be used to identify individual patients. This dataset can be used for image preprocessing, classical machine learning classification, convolutional neural network (CNN)-based deep learning, disease severity grading, and comparative studies between image-only and multi-modal diagnostic approaches. the dataset Publicly accessible under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

This dataset was generated from a quantitative study examining the gender equality paradox within the Vietnamese teaching profession. Although women constitute the majority of the teaching workforce in Vietnam, they remain underrepresented in leadership, decision-making, and career advancement. The dataset was developed to investigate how gender influences teachers’ perceptions of gender inequality and to assess the mediating role of employee experience within an organizational and cultural context. The data were collected through an online survey administered to 283 in-service teachers across multiple educational levels in Vietnam between March and August 2025. The questionnaire measured two main constructs: perceived gender inequality and teacher employee experience. All items were assessed using a five-point Likert scale and were adapted from established theoretical and empirical studies, contextualized to the Vietnamese education system. The dataset was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Measurement reliability and validity were confirmed through outer loadings, Cronbach’s alpha, composite reliability, average variance extracted, and discriminant validity criteria. Structural model analysis examined direct and indirect relationships between gender, employee experience, and perceptions of gender inequality. The findings derived from this dataset provide empirical evidence that gender differences in perceived inequality are shaped primarily through organizational and professional experiences rather than individual prejudice alone. The dataset supports research on substantive equality and highlights the relevance of human rights education as an institutional mechanism for addressing structural gender inequality in education, contributing to policy discussions related to Sustainable Development Goal 4.7. Limitations of the dataset include its cross-sectional design and convenience sampling approach, which restrict causal inference. Nevertheless, the dataset offers valuable opportunities for secondary analysis, comparative studies, and longitudinal extensions focusing on gender, professional experience, and equity in education systems.