Mendeley Data Showcase

This repository contains the firmware, analysis scripts, and raw data supporting the evaluation of a wireless 12-lead ECG telemonitoring architecture. The project utilizes a dual-core Real-Time Operating System (RTOS) on an ESP32-S3 microcontroller to simulate the clinical-grade ADS1298 analog front-end. To mitigate Operating System-level Bluetooth Low Energy (BLE) bottlenecks, the firmware employs a batching protocol that groups 15 samples into 405-byte payloads transmitted at 15 ms intervals. Repository Contents: Firmware: This folder contains only the src directory with the C++ source code and the platformio.ini configuration file for the ESP32-S3 hardware simulator. The project is specifically designed for a CYD (Cheap Yellow Display) ESP32 board, and the firmware manages the 1000 Hz acquisition loop and the optimized BLE transmission stack. Raw Data: Captured CSV logs of continuous streaming trials. Note: These logs were captured using a custom cross-platform frontend interface that is not included in this repository. Data Scripts: Python scripts utilized to calculate network metrics (packet loss, jitter) and mathematically evaluate signal fidelity (cross-correlation and Root Mean Square Error) between the original digital source and the transmitted BLE payloads.

UPLC-QTOF-MS/MS analysis of the Gossypium hirsutum root extracts. Results of these extracts combined and intrinsic anti-fungal activity, anti-biofilm and anti-hyphal formation against C. albicans and C. tropicalis.

Second-hand fashion is often presented as a circular solution, yet resale growth does not necessarily reduce new-product purchases. This systematic review examines how governance arrangements, exchange-supporting devices, and market context shape trust, scale, and circularity outcomes in second-hand fashion markets. We searched Scopus, Web of Science Core Collection, Business Source Complete, and ABI/INFORM for peer-reviewed English- or Spanish-language articles and reviews published from January 2010 through 21 January 2026. Two reviewers independently screened records (Cohen’s κ = .86). Thirty-two studies met the eligibility criteria. Because the corpus combined empirical, conceptual, and review studies and did not support causal effect estimation, methodological transparency was appraised qualitatively and findings were synthesized through a configurational Requirements–Fit–Outcomes (R→F→O) framework that distinguishes Governance Intensity from Device Completeness. The review identifies five recurring market configurations. Sixteen studies were coded as high governance, 11 as moderate, and five as low, while the mean Device Completeness score was 2.28 of seven. Most studies emphasized trust, participation, adoption, or transaction growth; direct evidence on displacement of new purchases, rebound, product-life extension, and net environmental effects was limited and inconsistent. The heterogeneity of study designs and the scarcity of direct circularity measures preclude causal or comparative effect estimates. The findings suggest that responsible scaling is best understood through the fit among governance, exchange-supporting devices, and context, while circularity must be evaluated independently from market performance. The review received no specific funding, was not registered, and had no formal protocol.

This dataset and code form the replication package for “The Impact of AI and Cross-Border Data Regulation on International Trade in Digital Services” by Ruiqi Sun and Daniel Trefler. The research tests two related hypotheses: deploying artificial intelligence increases international demand for digital services, and discriminatory restrictions on cross-border data flows reduce the effectiveness of AI. The package contains Stata programs, a focused Python program, and a table-and-figure crosswalk. Original Sensor Tower identifiers, Orbis patent records, patent identifiers, and the app-firm-patent concordance are excluded because of commercial licensing restrictions. Researchers with authorized access to both Sensor Tower and Orbis IP may contact the authors to request the data and related replication materials, subject to the providers’ license terms.

Mendeley Supplementary Material for the IJD manuscript titled "Procurement Patterns in Two Dermatologic Practices: Opportunities for Environmental Stewardship"

Supplemental material includes injection-site pain scores and exploratory pairwise comparisons by biologic, patient demographic characteristics, biologic formulation and delivery-device details, and the injection-site pain survey instrument.

This dataset comprises the quantification of parasite load across three distinct sites (outer edge, inner edge and center) of cutaneous leishmaniasis lesions (CL) from human patients. Quantification was performed using imprint, immunohistochemistry (IHC), and quantitative PCR (qPCR). The dataset provides a basis for evaluating which site of CL lesions yields the most reliable samples for diagnostic purposes.

This repository contains a Bangla Named Entity Recognition (NER) dataset along with the source code used for data preprocessing. The dataset is a hybrid Bangla Named Entity Recognition (NER) corpus designed to support research on low-resource natural language processing. It combines translated and native Bangla text to provide a diverse and high-quality benchmark for training and evaluating NER models. Approximately 70% of the corpus was created by translating the English CoNLL-2003 dataset into Bangla using neural machine translation models (BanglaT5, NLLB, and Google Translate), followed by manual verification and correction to preserve semantic meaning and linguistic fluency. The original BIO entity annotations for Person (PER), Organization (ORG), Location (LOC), and Miscellaneous (MISC) were retained and carefully realigned with the translated text. The remaining 30% consists of manually collected and annotated Bangla news sentences spanning multiple domains, including politics, sports, international affairs, culture, weather, mythology, religion, science, and geography. The complete corpus contains 6,162 annotated sentences and 69,193 tokens, with a vocabulary of 14,349 unique tokens. Entity annotations include 4,609 PER, 2,904 ORG, 4,678 LOC, and 3,172 MISC tokens, alongside 53,830 non-entity (O) tokens. All annotations follow the BIO tagging scheme and are provided in CoNLL format, making the dataset directly compatible with modern NLP frameworks such as Hugging Face Transformers, PyTorch, TensorFlow, and spaCy. In addition to the dataset, this repository includes the complete source code for data preprocessing. This dataset is intended for training, evaluation, benchmarking, and comparative research on Bangla NER systems, particularly transformer-based and hybrid sequence-labeling models. It also serves as a valuable resource for downstream Bangla NLP applications, including information extraction, question answering, and knowledge graph construction.

Matlab and stata code and data files