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Observations (ROV video images collected using a Seaeye Lynx 1500 during the months of November 2009, and May and June 2010) and occurrence probability (maximum entropy prediction) in La Fonera canyon head of Madrepora oculata, Dendrophyllia cornigera and Corallium rubrum. Maximum entropy prediction of habitat suitability was performed with Maxent software, version 3.3.3k available online, using the bathymetric parameters described in Lastras et al. 2015 (10.1371/journal.pone.0155729) in combination with ROV positive observations of the three coral species. The parameters used were depth, gradient, rugosity, curvature, and fine scale and broad scale bathymetric position indexes. Data included is represented in Fig. 9A, B and C of Lastras et al. 2015. See publication for reference.

This dataset contains survey responses collected to investigate the influence of artificial intelligence (AI) advice framing, accountability, and normative alignment on human decision-making, ethical judgments, and behavioral responses in higher education contexts. The study employs a vignette-based experimental approach to examine how individuals evaluate and respond to AI-generated recommendations across academic and ethical decision-making scenarios. The dataset incorporates experimental conditions involving AI advice framing, accountability levels, and the alignment of AI recommendations with established norms. Participants evaluated hypothetical scenarios involving academic activities, including assignment writing and the use of citations and sources. Their responses capture varying levels of compliance with AI recommendations, perceived moral acceptability, ethical justification, and responsibility attribution. The dataset includes measures of behavioral intention and ethical evaluation, such as compliance with AI recommendations, moral acceptability, ethical justification, perceived responsibility, and decision confidence. Emotional responses, including guilt, shame, outrage, and anxiety, are also recorded to examine the affective dimensions of AI-assisted decision-making. Additional measures assess participants' willingness to recommend decisions to peers and their perceptions of AI agency and accountability. Information on participants' AI usage frequency, comfort with AI technologies, AI-related knowledge, and previous AI training is included to support investigations into the relationship between AI familiarity and ethical decision-making. Demographic and academic characteristics, including age, gender, academic discipline, educational level, professional experience, and institutional role, are also provided. This dataset may support research in human–AI interaction, responsible artificial intelligence, educational technology, AI ethics, behavioral compliance, moral psychology, and accountability in AI-assisted decision-making. It can also facilitate comparative investigations of how AI advice presentation and accountability conditions influence ethical judgments, responsibility attribution, and behavioral responses across different academic scenarios. The dataset provides a resource for researchers examining the behavioral, cognitive, and affective implications of AI-supported decision-making in higher education.

This dataset supports a systematic thematic synthesis of community participation and spatial justice in post-conflict urban recovery, conducted following the PRISMA 2020 guidelines. The search covered Scopus, Web of Science Core Collection, Dimensions, JSTOR and ProQuest Dissertations & Theses Global, with Google Scholar as a supplementary source (search completed 18 May 2026). The review identified 472 records, of which 144 studies were included. The dataset contains the full search strategies for each source, the data extraction matrix for the 144 included studies, the CASP quality appraisal results, the codebook used for thematic coding in NVivo 14, the PRISMA 2020 flow diagram, the PRISMA 2020 checklist, and the graphical abstract.

This study aimed to investigate the safety attitudes of general practice (GP) trainees. Quantitative data were analysed using SPSS, whilst qualitative data were analysed using NVivo. Qunatitative Analysis: Data analysis was conducted to examine the effects of demographic factors on safety attitudes. These relationships were measured using ANOVA and the Independent Sample T-test. Question 11 was administered only to participants who responded with 'Strongly Agree' or 'Agree a little' to Question 10. Responses to Question 10 were divided into 'Positive' and 'Negative' groups, and the impact of regular operational meetings on safety attitudes was analysed using a T-test. Qualitative Analysis: Additionally, open-ended questions were employed to identify the safety-related concerns faced by GP Trainees and their needs regarding current practices. Data collection was conducted via a questionnaire comprising 34 items on a 5-point Likert scale and 8 open-ended questions.

This dataset contains simulated spectral data supporting the manuscript. All spectra were obtained by full-wave electromagnetic simulations using COMSOL Multiphysics under periodic boundary conditions, with plane-wave illumination normal to the structure unless otherwise stated. The dataset comprises three groups: (1) Absorption spectra of the proposed DMRA and control configurations (Case 1: dielectric auxiliary ring removed; Case 2: central Al₂O₃ cylinder removed; Case 3: both removed) over 300–4000 nm, quantifying the individual and combined contributions of the two dielectric cavities to the broadband response. (2) Parameter-sweep spectra, in which one geometric parameter (e.g., ring dimensions, cylinder radius/height, layer thicknesses, lattice constant) is varied around its optimized value while all others are fixed, supporting the fabrication-tolerance analysis. (3) Material-substitution spectra, in which the constituent metals or dielectrics (Ti, Al₂O₃) are replaced while the geometry is unchanged, supporting the material-selection discussion.

This dataset contains electroencephalographic (EEG) recordings collected from 93 participants using the BrainLink Pro portable EEG device. The dataset was designed to investigate EEG activity under two experimental conditions: (1) Calm Rest, in which participants were seated quietly with their eyes open and without performing any specific task, and (2) Mental Concentration, in which participants performed a mental arithmetic task. The dataset includes EEG recordings from both experimental conditions, organized using anonymized participant identifiers. It also provides processed EEG features, including the power values of the Delta, Theta, Low Alpha, High Alpha, Low Beta, High Beta, Low Gamma, and Mid Gamma frequency bands. The dataset is intended to support research on EEG signal analysis, frequency-band characterization, and the investigation of differences in brain electrical activity between resting and mental concentration conditions. The shared data are provided in Excel format to facilitate inspection, processing, statistical analysis, and reuse by the scientific community. This dataset may be useful for researchers working in biomedical signal processing, EEG analysis, cognitive activity assessment, and machine learning applications involving EEG signals.

This dataset contains spectral data and hydrological and water quality simulation results for the Nielba study area. It combines multispectral spectral information from WorldView-3 satellite imagery with daily outputs generated using the Soil and Water Assessment Tool (SWAT) model. The dataset is provided as an Excel workbook (XLSX format) containing two worksheets: 1. wv3_all_point_spectra This worksheet contains 40 spectral records grouped into five sub catchments (SUB), with individual sampling points identified by the point_ID attribute. Spectral values are provided for eight WorldView-3 multispectral bands: Coastal, Blue, Green, Yellow, Red, Red Edge, NIR1, and NIR2, covering the visible and near-infrared regions of the spectrum. The data represent spectral characteristics associated with individual sampling points. Remote Sensing Spectral Surface Reflectance Data: • SUB: Sub-catchment; • point_ID: Unique spatial point identifier within the sub-catchment; • Coastal / Green / Yellow / Red / Red edge / NIR1 / NIR2: Surface reflectance values for spectral bands. 2. SWAT_model_sim This worksheet contains daily SWAT model simulation outputs covering the period from 1 January 2004 to 31 December 2018. It includes five consecutive time-series datasets for five sub-catchments, each covering the entire simulation period. The following variables are provided: • SUB – sub catchment; • DATE – Simulation date; • FLOW_OUTcms – Simulated water discharge leaving the river reach, measured in cubic meters per second (m3/s); • CHLA_OUTkg – Chlorophyll-a discharge transported out of the river reach, measured in kilograms per day (kg/day); • CHLA_CON – Chlorophyll-a concentration (kg/m3). Potential Applications: The dataset provides integrated information on spectral characteristics, simulated water discharge, and chlorophyll-a dynamics. It is dedicated for research on water quality assessment, investigating the relationship between remotely sensed spectral information and water quality parameters, and the application of hydrological modeling in river monitoring.

This dataset supports research on the prediction of physicochemical properties of sustainable aviation fuel (SAF) blends using attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy and machine learning. The dataset contains raw ATR-FTIR absorbance spectra of 50 fuel samples, with 2,385 spectral variables covering approximately 3997–599 cm⁻¹. Reference measurements include density, 10% and 90% distillation temperatures (T10 and T90), and kinematic viscosities at −20 and −40 °C for all 50 samples. Freezing-point measurements are available for 33 samples. Fuel-blend composition information and sample identifiers are provided to support data interpretation and sample matching. Where included, Python scripts and outer-fold out-of-fold prediction results document the machine-learning evaluation workflow. Missing freezing-point measurements are retained as missing values rather than imputed.

This dataset contains the computational and experimental data underlying the study “Umbelliferone Enhances TRAIL-DR5 Binding in vitro at Micromolar Concentrations: a Mechanism Resolved by Staged Oligomer-State Interaction Profiling,” submitted to the Indonesian Journal of Chemistry. It lets readers reproduce the docking rankings, the interaction analysis, and the binding-assay results reported in the article and its supplementary material. The deposit is organised in four folders, accompanied by the supplementary material document (Methods S1, Text S1, Tables S1 to S8, and Figures S1 and S2). Docked structures: the four rigid-body poses of coumarin, ellagic acid, gallic acid, and umbelliferone at the dimeric DR5-TRAIL-DR5 interface, generated with Hex 8.0.0 from PDB entry 1D4V (COU_dimer_clean.pdb, EA_dimer_clean.pdb, GA_dimer_clean.pdb, UMB_dimer_clean.pdb). In every file, chains A and C are DR5 (residues 69 to 185), chain B is TRAIL (residues 119 to 281), and chain X is the docked ligand, written as UNK 1. Because residues 119 to 185 occur in both proteins under this numbering, contacts in that range must be assigned from the chain identifier, not the residue number. Interaction analysis: a workbook with Hex E-values for all fifteen compounds at the monomeric and dimeric states, the ranking sheet, and the per-compound BIOVIA Discovery Studio contact listings. These listings are unfiltered and include the chain-terminus contacts excluded from the article. Binding assay: the unmodified GloMax reader export (3 June 2026) behind the dose-response data. Vina cross-check: the AutoDock Vina versus Hex comparison table. Vina was run as a single job per ligand with no fixed random seed, so the scores are not bit-reproducible. A README in the archive root repeats this layout and the reuse caveats. The data are shared under a CC BY 4.0 licence.

This dataset contains the gamma ray and meteorological observations used in the study of the 2024 Hualien earthquake sequence in eastern Taiwan. The data support the analysis of gamma ray variations associated with meteorological disturbances and seismic activity.