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Respondent-level data from on-site soundscape surveys (ISO/TS 12913-2 Method A) at 13 urban public spaces in Shenyang and Changshu, China, collected in spring-autumn field campaigns from 2018 to 2025 (2,821 records). Each record combines the questionnaire responses with matched physical measurements: perceived sound sources, the eight ISO 12913 perceptual attributes, ISO pleasantness and eventfulness, overall soundscape evaluation, appropriateness, perceived loudness, current and desired visit frequency, WHO-5 well-being, perceived-restorativeness ratings and participant demographics; per-channel acoustic and psychoacoustic indicators from one-minute binaural recordings made beside each respondent (A-weighted equivalent and percentile levels, level ranges, Zwicker loudness with percentiles and ratios, sharpness, roughness, fluctuation strength and tonality, each with the larger-channel, channel-mean and left-right-difference summaries); and photo-derived semantic-segmentation area shares describing the visual context of each survey point. Data-quality flags mark records whose questionnaire, audio or photograph was excluded during checking. An English variable dictionary and per-site questionnaire, audio and photo coverage notes are included. The data support studies of well-being equity, age differences, soundscape appropriateness, perceived restorativeness and audio-visual interaction in urban public space.

This dataset supports the research article, Assessment of Atmospheric Heavy Metal Pollution Using Spanish Moss (Tillandsia usneoides) as a Biomonitor in Kandy, Sri Lanka. It contains the original data generated during the study, including raw ICP-MS measurements of atmospheric heavy metals (As, Cd, Cr, and Pb) in Tillandsia usneoides samples, raw mercury (Hg) analysis data, and supporting weather data used to evaluate the influence of seasonal precipitation on heavy metal accumulation. The dataset was collected from sampling sites in Kandy District, Sri Lanka, during the period from November 2024 to January 2025. It is provided to support the reproducibility of the analyses and the findings presented in the associated manuscript.

This dataset is a balanced four-dialect parallel corpus for Bangla dialect normalization and dialect-bias evaluation. It contains 15,376 dialect-to-standard Bangla sentence pairs representing 3,844 shared meanings across four regional varieties: Barishali, Chittagonian, Sylheti, and Noakhali. Each dialect sentence is aligned with the same Standard Bangla reference, enabling controlled comparison of translation and normalization performance across dialects. The corpus was compiled by merging four publicly available resources: Vashantor (approximately 48%), ONUBAD (23%), BanglaDial (17%), and the Bangla Dialect Dataset (11%). To address gaps in aligned Noakhali data, 1,807 sentences were manually translated by native Noakhali speakers and independently checked by a second speaker from the same region. This annotation effort enabled complete four-way parallel coverage. The dataset is provided with training, validation, and test splits containing 12,304, 1,540, and 1,532 pairs, respectively. Because multiple dialect versions correspond to the same underlying meaning, the dataset follows a meaning-level splitting protocol. Meaning groups are assigned to splits first and expanded into dialect-to-standard pairs afterward, preventing direct meaning overlap between splits. As an additional diagnostic, pair-level analysis shows that 99.8% of test targets are represented in the training data through a sibling dialect. This property supports research on cross-dialect generalization, transfer learning, dialect bias, and low-resource Bangla neural machine translation.

Members of the HSP70 family are indispensable components of host innate immunity, and their functions are finely tuned by post-translational modifications. However, the role of HSP70 post-translational modifications in modulating immune functions during pathogenic infection remains poorly understood. This study investigated the mechanisms that underlie acetylation modification of heat shock cognate 71 kDa protein (HSC71)-mediated resistance to intracellular pathogen Spiroplasma eriocheiris infection in crabs. Mechanistically, carnitine O-acetyltransferase (Crat) acetylated HSC71 at lysine 579 (K579), which prevented its ubiquitination by promoting the disassociation of E3 ubiquitin ligase CHIP, thereby improving HSC71 stability. In HSC71-deficient or Crat-deficient crabs, hemocyte apoptosis was markedly enhanced, leading to higher host mortality upon S. eriocheiris challenge. Meanwhile, K579 acetylation on HSC71 weakened the interaction between HSC71 and superoxide dismutase (SOD), resulting in the accumulation of intracellular ROS and thereby restricting S. eriocheiris propagation. Pharmacological inhibition of the deacetylase SIRT1 with EX-527 enhanced HSC71 acetylation, elevated ROS production and reduced host susceptibility to S. eriocheiris infection in crabs. Notably, EX-527 similarly enhanced the acetylation of Drosophila melanogaster HSC71 homolog, HSPA8, which in turn impaired its interaction with SOD. This led to elevated ROS levels and restricted intracellular proliferation of S. eriocheiris in Drosophila S2 cells, demonstrating evolutionary conservation of this mechanism among arthropods. Therefore, this study established the modulation of HSC71 acetylation as a promising avenue to combat S. eriocheiris infection.

Initial survey raw data and exit survey raw data.

This dataset contains the anonymised responses of 100 middle school students (Grades 6 to 8) to the Science Self-Efficacy Inventory, a 36-item instrument measuring science self-efficacy among middle school learners in Tripura, India. Data were collected at two schools using a paper-based, self-administered questionnaire. The inventory measures six dimensions, each represented by six items: D1 Academic Task Self-Efficacy (items 1 to 6), D2 Motivation and Persistence (7 to 12), D3 Emotional Regulation and Science Anxiety (13 to 18), D4 Social Support (19 to 24), D5 Science Identity and Aspiration (25 to 30), and D6 Contextual and Socio-cultural Relevance (31 to 36). Every item uses a five-point Likert format (1 = Strongly Disagree to 5 = Strongly Agree) and no item is reverse-scored. A dimension score is the sum or mean of its six items; the total score is the sum of all 36 items, ranging from 36 to 180, higher scores indicating higher science self-efficacy. The data file holds 100 rows and 46 columns: the 36 item responses plus participant code, anonymised school code, gender, grade, medium of instruction, school board, favourite subject, preferred language and career aspiration (as reported and recoded). The sample comprises 71 boys and 29 girls; 40 in Grade 6, 28 in Grade 7 and 32 in Grade 8; 93 studying through English medium and 7 through Bengali; 85 affiliated to the Tripura Board and 15 to CBSE. The dataset is complete apart from one missing value (item I18); blank cells denote non-response and should be declared missing, not zero. Item variables are documented as Likert rather than ordinal, following established practice in educational and psychological measurement, which treats summated Likert data as approximately continuous; means and other parametric statistics may therefore be computed on item and scale scores. Psychometric evidence is given in the accompanying report. Internal consistency is good (Cronbach's alpha = .891; McDonald's omega = .895), and no item deletion would raise alpha above .893. Item-total correlations range from .186 to .629, with 33 of 36 items meeting the .30 criterion. Each dimension correlates strongly with the total (.737 to .884), while inter-dimension correlations (.441 to .699) show the dimensions to be related but distinct. Comparing the upper and lower 27 per cent groups, 35 of the 36 items discriminate significantly. Participation was voluntary. Consent and assent were obtained, participants could skip any item or withdraw at any time without penalty, and were told that their anonymous responses would be deposited in a public repository for reuse. No direct or indirect identifiers are present: student and school names were permanently removed before deposit, participants identified only by a sequential code and schools by a non-reversible code. Four files are provided: the data sheet, the variable codebook, the blank response sheet, and the descriptive and psychometric analysis report.

This dataset presents a balanced, manually annotated collection of 4,000 Bangla news articles for fake news detection research, comprising 2,000 Real and 2,000 Fake news samples published between 2021 and 2025. Fake news articles were collected from established fact-checking organizations operating in Bangladesh, including Rumor Scanner, DhakaFactCheck, FactWatch, Jachai, Boom BD, Aaj Tak Bangla Fact Check, and eArki, also from the social media posts whose published verification reports served as reliable sources of labeled fake content. Real articles were collected from credible mainstream Bangla news outlets, including Prothom Alo, BBC News Bangla, Jamuna TV, Samakal, News24, Dhaka Tribune, Somoy TV, Independent TV, Ittefaq, and Bangladesh Pratidin. All data were obtained from publicly available sources for academic research purposes. Each article is stored with ten structured fields: Article ID (unique identifier), Domain (publisher website), Date (publication date), Category (one of twelve topical categories: Politics, National, International, Sports, Entertainment, Crime, Education, Technology, Finance, Lifestyle, Editorial and Miscellaneous), Headline, Content (full article body), Label (1 = Real, 0 = Fake), Source (verifying source for the claim), Relation (Related if the headline accurately reflects the content's claim, otherwise Unrelated), and F-type (fine-grained fake news type: Fabricated, Clickbait, Satire, or Altered; Not Applicable for real articles). Every article was manually reviewed and labeled, and the Relation field additionally captures a common form of soft misinformation in which a factual article body is paired with a misleading headline. The dataset is provided in four files: two Bangla files containing the original real and fake news articles, and two English files in which the Headline and Content fields were translated from Bangla to English using Google Translate, enabling multilingual misinformation research. Unlike earlier Bangla fake news resources, this dataset is perfectly class-balanced, covers contemporary news from 2021–2025 and includes rich metadata supporting binary fake news detection, fake news type classification, headline–content consistency analysis, topical category prediction, and temporal generalization studies. It is intended as a benchmark resource for researchers working on misinformation detection in Bangla.

This repository contains the replications files for the manuscript "Preferential Trade Agreements and Non-tariff Measures" by David Kuenzel and Rishi Sharma.

This dataset supports the manuscript “A geomorphology-informed terrain-matching framework for regional geohazard-chain runout endpoint prediction”. It contains processed tabular data used for event-level model development, path-conditioned endpoint prediction, uncertainty analysis, residual diagnostics, and regional application in southeastern Tibet. The release includes 591 interpreted geohazard-chain events, 267,045 path-step records under the path-extended Protocol2 representation, predictions from five Stage-1 candidate models, Stage-2 residual-analysis outputs, attributes for 8,369 regional candidate sources, 8,355 successfully generated regional prediction records, and Monte Carlo dropout and random-seed uncertainty outputs. Raw optical satellite imagery is not redistributed. The repository contains derived interpretation products, model inputs, and model outputs used in the associated study.

This dataset comprises high-resolution environmental monitoring and window-opening behavior data collected from a middle school classroom in Qingdao, China (cold-climate region), over four seasons in 2025. The monitoring campaign covered 84 school days across spring (April, 22 days), summer (June, 21 days), autumn (October, 18 days), and winter (December, 23 days), yielding a total of 120,960 raw data records. The dataset includes continuous measurements of indoor and outdoor air temperature (°C), relative humidity (%), indoor CO₂ concentration (ppm), indoor and outdoor PM₂.₅ (μg/m³), indoor and outdoor PM₁₀ (μg/m³), outdoor CO₂ concentration (ppm), and window-opening status (binary: open/closed). Environmental parameters were recorded at 1-minute intervals using Xingzong Intelligent AM308-470M industrial-grade sensors for indoor monitoring and HOBO U12-012 data loggers for outdoor measurements. Window status was tracked via magnetic contact switches mounted on the classroom's exterior windows. The merged Excel file contains 10 worksheets: (1) All_Raw_Data: 56,760 rows of merged raw monitoring data from all four seasons with a 'Season' identifier column (sampling interval: 1 minute during school hours, 6:30–18:30). (2) All_Calculated_Data: 2,640 rows of aggregated statistical summaries (hourly and daily averages) with a 'Season' identifier column. (3–10) Season-specific sheets (Spring_Raw, Spring_Calculated, Summer_Raw, Summer_Calculated, Autumn_Raw, Autumn_Calculated, Winter_Raw, Winter_Calculated) preserving original file structure for reproducibility. This dataset supports machine learning-based analysis of window-opening behavior as a climate-resilient adaptive mechanism under institutionalized teaching schedules. It has been used to train a Random Forest model (AUC-ROC = 0.897, F1-score = 0.749) with SHAP (SHapley Additive exPlanations) analysis to identify threshold-driven behavioral patterns across four daily activity phases: arrival, break, class, and leaving. Variable list (All_Raw_Data sheet): - Season: Spring / Summer / Autumn / Winter - Timestamp: Date and time (YYYY-MM-DD HH:MM:SS) - Indoor_Temperature_C: Indoor air temperature (°C) - Outdoor_Temperature_C: Outdoor air temperature (°C) - Indoor_CO2_ppm: Indoor CO₂ concentration (ppm) - Outdoor_CO2_ppm: Outdoor CO₂ concentration (ppm) - Indoor_PM2_5_ugm3: Indoor PM₂.₅ concentration (μg/m³) - Outdoor_PM2_5_ugm3: Outdoor PM₂.₅ concentration (μg/m³) - Indoor_PM10_ugm3: Indoor PM₁₀ concentration (μg/m³) - Outdoor_PM10_ugm3: Outdoor PM₁₀ concentration (μg/m³) - Indoor_RH_percent: Indoor relative humidity (%) - Outdoor_RH_percent: Outdoor relative humidity (%) - Window_Status: Window opening state (1 = open, 0 = closed)