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This dataset supports a 2 × 2 experiment on visual complexity and concreteness in industrial safety icons. It contains 5,031 valid trials from 387 manufacturing workers, pretest summaries for 32 candidate icons, objective structural measurements for 87 reference icons and 52 visual variants, OCI reference parameters, and expert ratings. The Excel workbook contains five worksheets. Each worksheet provides a data table on the left and variable definitions and analysis notes on the right. Higher semantic distance ratings indicate a closer icon–meaning association. Participant identifiers have been replaced and identifying metadata removed.

Enhancing Wave Energy Dissipation in Permeable Coastal Defenses through Composite Curtain Integration

**Dataset Description** This dataset supports the systematic literature review entitled *“Tourism Dependence, External Shocks and Macroeconomic Vulnerability in Emerging Markets: A Systematic Review.”* The dataset contains 351 bibliographic records of scientific publications examining the relationships between tourism, economic growth, tourism dependence, external shocks, macroeconomic vulnerability, and economic resilience. Of these records, 186 are classified as included and 165 as excluded according to the screening decisions documented in the database. The dataset provides bibliographic information, including publication titles, authors, years, and Digital Object Identifiers (DOIs), together with screening decisions, exclusion codes, thematic classifications, evidence categories, units of analysis, and descriptive notes. The thematic classification covers seven analytical dimensions: economic growth, tourism dependence, external shocks, macroeconomic volatility, nonlinear relationships, structural factors, and moderating variables. The dataset was developed to support a transparent and reproducible literature screening process, thematic mapping, descriptive analysis, and narrative synthesis of empirical and theoretical evidence. The accompanying PRISMA 2020 checklist documents the reporting framework adopted for the systematic review. The dataset is intended to facilitate methodological transparency, verification of study selection decisions, and further academic research on tourism-dependent economies and their vulnerability to external disturbances.

This dataset contains the inputs and results of the Monte Carlo ensemble of models. Files: inputs.dat contains the sampled inputs for all models. ensemble.dat contains results for the successful models. Model IDs are shared between the two files, so each successful model can be matched to its inputs. inputs.dat columns: Column 1: Model ID Column 2: Present-day net regassing flux, F_R(0) (10^11 kg H2O/yr) Column 3: Initial (4.5 Ga) seawater δD (‰) Column 4: Initial (4.5 Ga) mantle water inventory (ocean masses) Column 5: Fractionation factor for mantle degassing, 10^3 ln f_D (‰) Column 6: Fractionation factor for slab dehydration, 10^3 ln f_DH (‰) Column 7: Fractionation factor for seafloor alteration relative to seawater, 10^3 ln f_AO^SW (‰) Column 8: Hadean tectonics option (0 = sluggish, 1 = rapid) Column 9: Hadean ridge length option (0 = constant, 1 = 10 times the present-day value during the Hadean); applies only when column 8 = 1 ensemble.dat columns: Column 1: Model ID Column 2: Present-day surface water inventory (ocean masses) Column 3: Present-day mantle water inventory (ocean masses) Column 4: Present-day seawater δD (‰) Column 5: Seawater δD at 3.8 Ga (‰) Column 6: Present-day mantle δD (‰) Column 7: Present-day continental crust δD (‰) Column 8: Present-day oceanic crust δD (‰) Column 9: Maximum deviation of sea level from its present-day level during 0–2 Ga (km) Column 10: Initial (4.5 Ga) mantle δD (‰) Column 11: Initial (4.5 Ga) surface water inventory (ocean masses) Column 12: Bulk silicate Earth δD (‰)

Under eriocitrin treatment, alterations were observed in the osteoclast differentiation status and related pathways in BMMs, the transcriptome of BMMs, and the progression of periodontitis in animals.

LA-ICP-MS U-Pb data and calculation sheets for 23 samples from the Kathrina Ring Complex, Sinai, Egypt.

sourcedata

The dataset contains Internet of Things (IoT) data collected by different commercial Long Range Wide Area Network (LoRaWAN) devices in a Solanum lycopersicum L. cv. HEINZ 1301 tomato testbed. The experimentation involved the deployment of IoT devices into 3 experimental crops, located at the “Azienda Agricola Sperimentale Stuard,” Parma, Italy (44° 48' 29.888'' N, 10° 16' 29.074'' E), over 3 consecutive growing seasons (namely, 2023, 2024, 2025). The testbed setup evolved over time, in terms of amount and variety of involved devices, and policies for the irrigation management. Some of the applied irrigation regimes are based on the Italian Irriframe platform (https://www.irriframe.it/) recommendation. In detail, the experimental irrigation lines are the following. • 2023: Line #1 received 100% of Irriframe recommendation (named as R); Line #2 received 60%R; and Line #3 received 30%R. Irrigation was manually managed by the farmer, while data were collected from environmental, soil, and water meter sensors. • 2024: Line #1 received 80%R; Line #2 received 60%R; Line #3 followed a threshold-based soil humidity control; Line #4 received 100%R; and Line #5 received 30%R. Irrigation was managed using a fully-automated irrigation system, while data were collected from environmental, soil, water meter, valve controllers, pressure, and water potential sensors. • 2025: Line #1 received 100%R; Line #2 received 30%R; Line #3 followed a soil humidity threshold-based control; Line #4 followed a leaf temperature threshold-based control; and Line #5 followed an AI-based irrigation strategy. Irrigation was managed through a fully-automated irrigation system, while data were collected from environmental, soil, water meters, valve controllers, pressure, fruit dendrometers, stem dendrometers, and leaf temperature sensors. The dataset is organized into 3 directories (2023, 2024, 2025), each containing CSV files with data from a single crop, recorded every 10 minutes by IoT sensors and including: timestamp, device identifier, tomato line identifier, air temperature and humidity, CO2, barometric pressure, soil moisture, soil temperature, electrical conductivity, water volume and temperature, valve state, water pressure, soil water potential, fruit size, stem size, and leaf temperature. Each directory also includes a CSV file with the Growing Degree Days and 6-Heat Units daily agronomic indicators. A complete description is provided in the README.md file within each directory. The dataset has been generated in the context of the Agritech project – “National Research Centre for Agricultural Technologies,” project code CN00000022, funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender no. 3138 of 16/12/2021 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU, Concession Decree no. 1032 of 17/06/2022 adopted by the Italian Ministry of University and Research.

This dataset accompanies the systematic review “Explainable and Energy-Efficient Spiking Neural Networks for EEG-BCI: A Systematic Review of Generalisation and Resource-Constrained Deployment.” It contains the definitive source-level evidence ledger used for the review synthesis, with a formal evidence cutoff of 10 August 2026. The master ledger contains 229 source records. Of these, 148 are evidence records comprising 49 primary cognitive/cognitive-affective EEG-SNN records (A1), 58 supporting direct EEG-SNN records (A2), and 41 supporting SNN/neuromorphic/hardware records (B). The direct EEG-SNN evidence subset therefore contains 107 records. The workbook contains source metadata, evidence-boundary classifications, verification notes, generalisation coding, methodological-quality and reporting appraisal (MQRA-v2), temporal summaries, search/corpus provenance, and companion-record decisions. The A1/A2/B evidence categories, G0–G4 generalisation levels, E0–E4 energy-evidence levels, and MQRA-v2 appraisal framework are review-derived analytical structures developed for this review and should not be interpreted as established or externally validated standards. Counts represent publication/evidence records rather than necessarily independent participant cohorts. Historical search provenance is reported separately from later source-level reconstruction and completeness auditing because the original row-level screening/exclusion trail and exact archived literal database search histories are not fully recoverable. Missing historical information has not been reconstructed retrospectively.

BanglaCAT is a context-aware toxicity dataset for Bangla (Bengali) social-media text. Each instance is a conversational pair consisting of a preceding comment (context) and a target comment whose toxicity is judged in light of that context. Unlike most existing Bangla resources that label isolated comments, this dataset is designed so that conversational context can both amplify and mitigate perceived toxicity (e.g., sarcasm, veiled threats, toxic endorsement, consensual banter, quoted or reported speech). Partitions: Train (19,453 pairs): LLM-generated candidates, cross-validated by independent LLMs, and human spot-checked. Released in original Bangla and English. Test (2,329 pairs): Real comments collected from public Facebook, YouTube, and Bangladeshi news-portal threads. Triple-annotated by human annotators; gold labels obtained by exact 2-of-3 joint hierarchical majority vote. Released in Bangla and English (row-aligned). Label schema (hierarchical): Level-1: Toxic / Non-toxic Level-2 (toxicity_type): None (when Non-toxic), or one of 11 fine-grained types when Toxic — Insult, Threat, Hate Speech, Profanity, Harassment, Identity Attack, Toxic Endorsement / Agreement, Incitement / Encouragement of Harm, Mockery / Ridicule, Sexual Harassment / Objectification, Dehumanization. The gold label is always the complete pair (level_1_class, toxicity_type). Level-1 and Level-2 are never voted independently, preventing invalid combinations. ## Dataset structure ``` BanglaCAT_Mendeley/ ├── train/ │ ├── bangla_context_toxicity_train.csv # Bangla (19,453 rows) │ └── bangla_context_toxicity_train_ENGLISH.csv # English translation, row-aligned ├── test/ │ ├── bangla_toxicity_test_set_HIERARCHICAL_FINAL.csv # Bangla (2,329 rows) │ └── bangla_toxicity_test_set_HIERARCHICAL_FINAL_ENGLISH.csv # Bangla + English columns ├── docs/ │ └── DATA_DICTIONARY.md ├── CITATION.cff ├── LICENSE.md ├── CHECKSUMS.txt └── README.md Train and test are strictly disjoint. Intended use is research on content moderation, hate-speech detection, and conversational AI safety.