Mendeley Data Showcase

RiceLeafDisease-BD5: A Field-Collected Five-Class Image Dataset from Bangladesh developed to support research in plant disease detection, deep learning, computer vision, explainable artificial intelligence, and precision agriculture. The dataset was collected from paddy fields located in Savar Upazila, Akran, Birulia, and Savar Municipal Area of Dhaka District, Bangladesh, during October–November 2024. The dataset contains 2,545 annotated images distributed across five classes: Blast (339 images), Narrow Brown Spot (329 images), Sheath Blight (454 images), Tungro (500 images), and Normal Leaf (923 images). Images were captured under real agricultural field conditions using consumer-grade smartphone cameras with varying lighting conditions, backgrounds, and disease severity levels. All images were manually verified by agricultural experts and extension officers. Ambiguous samples, poor-quality images, and leaves showing multiple concurrent infections were excluded to ensure annotation reliability. The dataset is intended for research in image classification, transfer learning, convolutional neural networks (CNNs), vision transformers (ViTs), explainable AI, and smartphone-based disease diagnosis systems. It was used in the study titled “A Comparative Study of CNN and Vision Transformer Models for Rice Leaf Disease Detection Using a Field-Collected Bangladeshi Dataset.” This dataset is publicly released to support reproducibility, benchmarking, and future research in agricultural artificial intelligence.

This dataset contains 33 fermentation performance observations for bioethanol production from marine macroalgae, compiled from 30 peer-reviewed studies (2012-2025). Of these, 24 observations report a directly usable numeric ethanol yield and are used in the taxonomic-division meta-analysis (Section 5.4) of the associated manuscript; the remaining 9 report only a sugar yield, substrate concentration, or qualitative outcome (or a yield that could not be confirmed against the primary source) and are retained for narrative completeness. The dataset accompanies the manuscript "Third-Generation Bioethanol from Marine Macroalgae: A Comparative Review of Pretreatment Techniques, Process Conditions, Fermentation Yields, and Integrated Valorization of Process Residues" (Luna Babilonia, E., prepared for submission to Biomass and Bioenergy, 2026). The file contains one sheet with the full dataset (species, pretreatment method, fermenting organism, reported ethanol yield or concentration, experimental conditions, inclusion status in the quantitative synthesis, and source reference for each observation) and one README sheet describing the normalization methodology used. Ethanol yields are expressed as percent of a theoretical maximum, using one of two constants depending on the basis of the originally reported value: 0.511 g ethanol per g free glucose or reducing sugar, or 0.568 g ethanol per g glucan or cellulose for values reported on a polysaccharide basis (this higher constant accounts for the water added during acid or enzymatic hydrolysis of the polymer to glucose). Where a source reported yield per gram of dry algal biomass rather than per gram of sugar, glucan, or cellulose, the value was first normalized using that source's own reported carbohydrate content before applying the appropriate constant. Values computed this way are marked with "~" in the yield column; values without "~" were reported directly as a percentage or yield coefficient by the original source.

This dataset is a collection used during my final year undergraduate project

Bibliometric dataset on instructional coaching and curriculum leadership (215 articles). Based on the Scopus Databased as of 27 July 2026

This dataset contains the raw liquid chromatography–high-resolution mass spectrometry (LC-HRMS) data used for non-target screening of chemical compounds associated with polyvinyl chloride (PVC) leaching. Data were acquired in both positive and negative ionization modes for samples representing the experimental conditions evaluated in the associated study. The deposited files provide the underlying raw instrumental data used for subsequent feature processing, compound identification, and comparative analysis reported in the manuscript.

This dataset relates to research on the development of a novel method for assessing the tensile bond strength of rendering mortar applied to structural masonry at elevated temperatures. It contains the raw data collected during the experimental program, as well as the corresponding data analyses. The raw data include temperature measurements recorded by thermocouples and load measurements obtained from the load cell. These data were used to generate the graphs and perform the statistical analyses presented in the study.

Replication package Host-Country Digitalization and Firm Productivity Gains from Outward Foreign Direct Investment (Original Data) README: Replication Package Paper Title Host-Country Digitalization and Firm Productivity Gains from Outward Foreign Direct Investment Authors: Yaohan Duan (College of International Economics and Trade, Shanghai Lixin University of Accounting and Finance) Haoyu Sun (College of International Economics and Trade, Shanghai Lixin University of Accounting and Finance) Kexin Cheng (Institute of Mathematical Sciences, Faculty of Science, Universiti of Malaya)

Replication package Final Version: Host-Country Digitalization and Firm Productivity Gains from Outward Foreign Direct Investment (Original Data) README: Replication Package Paper Title Host-Country Digitalization and Firm Productivity Gains from Outward Foreign Direct Investment Authors: Yaohan Duan (College of International Economics and Trade, Shanghai Lixin University of Accounting and Finance) Haoyu Sun (College of International Economics and Trade, Shanghai Lixin University of Accounting and Finance) Kexin Cheng (Institute of Mathematical Sciences, Faculty of Science, Universiti of Malaya)

Tiny Tapeout Projects for TT06 and TT07. They are also available at: https://github.com/devinatkin/tt06-fastreadout and https://github.com/devinatkin/tt07-dual-oscillator