Mendeley Data Showcase

In pediatric allergic rhinitis, achieving strong symptomatic control and preventing secondary complications is crucial. Intranasal treatments are important for this treatment strategy. This study aimed to investigate the therapeutic efficacy of intranasal corticosteroids, saline solutions, and combination therapy in patients with moderate-to-severe pediatric allergic rhinitis.

Research data for the manuscript of “Early adaptive response to doxorubicin in B16.F10 melanoma spheroids involves stress-associated transcriptional reprogramming and pro-survival microenvironmental remodelling" These data represent: 1) Other materials (methods description and supplementary figures) 2) Supplementary Table 1. RT-qPCR Primers sequences. 3) Supplementary Table 2. All DEGs. 4) Supplementary Table 3. KEGG analysis results 5) Supplementary Table 4. DEG in selected processes. 6) Supplementary Table 5. Protein arrays results   7) GSEA analysis

This dataset accompanies the study Use of the terms sex and gender in Brazilian nursing dissertations and theses. It contains the analytical matrix used for conceptual evaluation, the data extraction instrument, the public analytical dataset of 58 studies, the R script used to reproduce the statistical analyses, and supporting documentation. The materials are provided to promote methodological transparency and reproducibility of the analyses.

This Version 2 release is synchronized with manuscript version V12.7 of “Congestion-Responsive Management of Reserve Treatment Capacity in Emergency Care with Evolving Patient Acuity.” It contains the sparse continuous-time Markov chain implementation, an independently implemented discrete-event simulation, synthetic configurations, threshold-policy grids, source data for Tables 6–12, arrival-load and switching-action-cost reoptimization results, baseline and non-diagonal MMAP validation, simulation seeds, environment specifications, automated tests, verification records, and the seven high-resolution manuscript figures. Version 2 corrects the narrative cost-component decomposition and the reference mapping in Supplementary Table A.1. The mathematical model, computational inputs, numerical output files, policy selections, and figures remain unchanged. The legacy K = 2 policy is represented as the mathematically equivalent adjacent threshold pair (1, 2) under a common action-counting convention. All inputs are synthetic. No patient-level, hospital-identifying, or personally identifiable data are included. Code is provided under the MIT License; data, documentation, configurations, and generated outputs are provided under the CC BY 4.0 License.

This dataset contains 10,000 manually curated Bangla grammar question-answering instances. It was developed for hint-based learning, intelligent tutoring systems, adaptive and curriculum-aware learning, knowledge tracing, educational NLP, and LLM evaluation. All questions, grammar concepts, reference answers, explanations, and instructional content were collected from the official SSC textbook "বাংলা ভাষার ব্যাকরণ ও নির্মিতি" ("Bangla Bhashar Byakaran O Nirmity"), published by the National Curriculum and Textbook Board (NCTB), Bangladesh. The source material was manually curated into structured QA instances. Each record contains a Bangla grammar question, reference answer, explanation, topic, subtopic, difficulty, prerequisite concepts, semantic tags, five progressive hints, and stage-wise hint convergence scores. Hints progress from general contextual guidance to near-explicit support. Each 0–1 convergence score represents intended guidance toward the correct answer: lower scores are broader and higher scores are more specific. Scores reflect pedagogical progression, not empirical probabilities or model-confidence values. The dataset is distributed in UTF-8 JSON Lines (.jsonl): full.jsonl (10,000 instances), train.jsonl (70%), valid.jsonl (15%), test.jsonl (15%), and sample.jsonl (20 instances). Each record includes: id, topic, subtopic, difficulty, question, prerequisites, hints, convergence, candidate_answers, exact_answer, explanation, and topic_tags. Example Record: { "id": "bn_grammar_05900", "topic": "কারক ও বিভক্তি", "subtopic": "বিভক্তির প্রকারভেদ ও প্রয়োগ", "difficulty": "medium", "question": "বাক্যে ব্যবহৃত একটি পদের সাথে অন্য পদের অর্থগত সম্পর্ককে অর্থবহ করতে কোন ব্যাকরণিক উপাদানের ভূমিকা প্রধান?", "prerequisites": ["বিভক্তি", "বাক্যতত্ত্ব"], "hints": [ "শব্দের শেষে যুক্ত হয়ে পদের অন্বয় রক্ষা করে।", "বাক্যের কারক সম্পর্কের মূল ভিত্তি এটি।", "বিভক্তি নির্দেশক ব্যাকরণিক উপাদান।", "বিভক্তি রূপ নির্দেশক বিকল্প।", "বিভক্তি নির্দেশক সঠিক উত্তর।" ], "convergence": { "1": 0.35, "2": 0.55, "3": 0.74, "4": 0.88, "5": 0.97 }, "candidate_answers": ["বিভক্তি", "উপসর্গ", "সন্ধি", "সমাস"], "exact_answer": "বিভক্তি", "explanation": "বাক্যস্থিত পদসমূহের পারস্পরিক অন্বয় বা অর্থগত সম্বন্ধ স্পষ্ট ও অর্থপূর্ণ করতে শব্দবিভক্তি ও ক্রিয়াবিভক্তির ভূমিকাই প্রধান।", "topic_tags": ["Bangla Grammar", "কারক ও বিভক্তি", "বিভক্তি"] } Records were reviewed for completeness, annotation consistency, factual accuracy, and valid JSON formatting before release. The dataset supports controlled studies of how progressively revealed hints influence learning and answer selection. It is suitable for evaluating AI models that generate, rank, or select pedagogically useful hints at different levels of instructional support. This dataset is intended for research and educational purposes, particularly for building progressive, adaptive, and concept-aware hint-based systems for Bangla grammar learning.

Raw research data used to plot Figs. 1, 3, and 4, and Supplementary Movie 1.

This replication package accompanies the working paper “Macroeconomic Adjustment under Sanctions, Monetary Expansion, and Exchange-Rate Pressure: A Descriptive and Econometric Analysis of Iran.” It contains the quarterly free-market USD/IRR exchange-rate and goods-exports dataset for 2012Q1–2024Q2, together with a Python replication script and documentation. The files reproduce the preferred quarterly mean-depreciation and absolute exchange-rate-change regressions using Newey-West HAC standard errors

This dataset contains a summary of pawpaw (Asimina triloba) pollinator monitoring data obtained in the framework of the present study titled: "Yield performance and pollination ecology of pawpaw (Asimina triloba) as a potential low-input fruit crop in Europe". Available data relate to the flower visiting insects and pollen loads per species: - Floral visitor richness and floral visits (section 3.2.1.) - Pollen load of flower visitors (section 3.2.2)

This dataset contains anonymized data from 169 nursing students enrolled at a public university in northern Chile. It includes sociodemographic variables and item-level responses and derived scores from the Beck Hopelessness Scale (BHS), Depression Anxiety Stress Scales-21 (DASS-21), Maslach Burnout Inventory–Student Survey (MBI-SS), and Utrecht Work Engagement Scale–Student (UWES-S). The psychometric refinement of the BHS analytical score and the exclusion of two influential observations are described in the associated manuscript. The dataset contains no direct participant identifiers and corresponds to the data underlying the reported analyses.

This dataset accompanies a laboratory-based repeated-interaction study of disclosure governance during communication with Xuefei 2.0, a Mandarin-speaking, photorealistic embodied conversational agent. It includes 252 participants, each of whom completed two interaction rounds, yielding 504 participant-round observations. In each round, participants completed a brief attention-balancing exercise and pre-session questionnaire, followed by an approximately 13-minute structured interaction consisting of a greeting, icebreaker, personally consequential topic discussion, and closing exchange. A post-session questionnaire followed each interaction, and a brief semi-structured interview was conducted after both rounds. The Excel workbook is organized into three participant-linked tables. Table 1 contains demographic information, self-reported AI-literacy scores, response configurations, topic assignments, and questionnaire measures. The measures cover disclosure readiness, disclosure breadth and depth, social-mask lowering, felt epistemic recognition, reflective reappraisal, privacy–interpretive authority delegation, boundary control, evaluation concern, affective states, topic activation, unresolved meaning, and willingness to continue interacting. Table 2 contains derived participant-language and acoustic indicators for both rounds. Language variables describe participant-only production during the icebreaker and topic-discussion stages, including utterance volume, token and type counts, lexical diversity, and transparent dictionary-based indicators. Acoustic variables cover speech share, articulation, pauses, fillers, pitch, loudness, and spectral change when usable audio was available. Table 3 reports interview availability and participant-level consensus codes concerning disclosure experiences, recognition, reflection, interpretive boundaries, authorization, correction, refusal, and interaction conditions. Rows are aligned by participant identifier, with round-specific variables presented in wide format. Supplementary language, acoustic, and interview fields are populated only when the corresponding source material was available and analysable. The workbook contains de-identified analytic variables supporting the reported questionnaire, corpus, acoustic, and interview analyses; raw audio and video are not included.