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All parameters required for the calculations presented in this article are provided to facilitate readers’ understanding of the equations and the underlying research logic.

Supplementary File 1: Materials, Methods, an example narrative, and complete transcripts, including Supplementary Text (Materials, Methods and Complete Transcripts), Supplementary Figures 1-2, and Supplementary Tables 2-5. Supplementary Table 1: Case IDs, case sources, original diagnoses, psychiatrists' ratings on narratives, answers to the three questions (Q1-Q3) by reviewing ChatGPT-5's responses, across all the 50 narratives.

Figure 8, titled Stablecoin Issuer Dependencies and Correlating Effects on Stability, is derived from Decker, Nicolin (2026), The Legal Tender Closure Gap Doctrine: Private Crypto Settlement, Transactional Use, and the Limits of Securities and Commodities Classification. The figure translates Table 11 into a full-page matrix for legal, regulatory, policy, payment-system, financial-stability, and public interpretability. The figure identifies ten issuer-dependent conditions that support stablecoin payment functionality: issuer solvency, reserve quality, redemption rights, custodial arrangements, banking relationships, regulatory compliance, freeze or blacklist authority, cross-border recognition, operational resilience, and contractual terms. For each dependency, the matrix explains what the stablecoin depends upon, how that dependency affects stability, and why the dependency matters doctrinally. The purpose of the figure is to show that stablecoin payment functionality is conditional rather than sovereign. A stablecoin may remain near parity when issuer solvency, reserve access, redemption mechanisms, custody systems, banking relationships, compliance controls, operational infrastructure, and contractual terms hold. But those conditions are not equivalent to legal-tender status, central bank finality, or sovereign correction capacity. The figure does not argue that stablecoins are invalid, unlawful, or commercially ineffective. Rather, it demonstrates that stablecoin stability depends on private or intermediary-controlled systems external to the token itself. These systems may reduce volatility and support payment efficiency, but they do not confer sovereign monetary authority. The central distinction is simple: stablecoin payment is issuer-dependent; legal-tender closure is sovereign-authority-dependent. Stablecoins may stabilize toward the dollar, but they are not stabilized as the dollar.

This data was generated by a neural network. It includes mean irradiance, weighed irradiance, cloud coverage, atmospheric indicator, irradiance obtained by equation, sun's position (real and obtained via picture), and their predicted values. They were obtained by using a convolutional neural network and a recurrent neural network hybrid. LOSS: 0.0131 // MAE: 0.0607 This dataset is meant to be used as a foundation of what neural networks can do and the accuracy of the data they can predict. Also, this dataset may be used to describe the movement of the sun and the climate conditions of the location in the photographs.

The results of the multiple sentence research from thesis research of students in the Indonesian Language and Literature Education study program, Faculty of Cultural Studies, Brawijaya University, and students in the Indonesian Language and Literature Education study program, Faculty of Arts and Sciences, IKIP PGRI Bojonegoro, in Faculty of Languages and Arts thesis years 2023, 2024, and 2025.

Research Hypothesis: this study assumes that the random error terms of the univariate linear correction model based on combined information follow a normal distribution, and the variances of random error terms are different across data sources of distinct dependent variables. Data Collection: since this study focuses on model performance validation, all analytical data were randomly simulated in R with preset parameters of the Beta distribution. The data generation procedure strictly complied with the research hypotheses to construct the final analytical dataset. Both data generation and statistical computation were implemented via R programming. Main Findings: numerical simulations on the generated datasets indicate that the proposed confidence intervals satisfy the coverage probability requirements. Moreover, when the intervals of covariates are relatively narrow, the proposed method presents a prominent advantage in average interval length under both skewed and symmetric distributions. Data Interpretation and Usage: the research results can be interpreted in combination with figures, tables and textual explanations in this paper. The rules for data collection and data processing procedures are fully documented. The dataset can be used for comparative analysis, model construction and empirical research by other researchers, and all analytical procedures are fully reproducible under identical conditions.

The material properties data uploaded here is used to assess the influence of soil heterogeneity on buried pipeline performance under strike-slip fault displacement. However, the dataset of final outcome of this study is also uploaded, in terms of von-Mises stresses, which was derived from Finite Element Analysis at a given boundary condition (mentioned in the manuscript). This outcome indicates the distortion resistant or shape alteration of pipelines under combined loading.

Este dataset contiene imágenes etiquetadas en formato YOLO para el entrenamiento de un modelo de visión artificial capaz de reconocer mascotas autorizadas (perros y gatos) para un sistema de control de acceso automatizado.

Figure 6, titled Legal Residue Categories: Downstream Evidence of Non-Closure, is derived from Decker, Nicolin (2026), The Legal Tender Closure Gap Doctrine: Private Crypto Settlement, Transactional Use, and the Limits of Securities and Commodities Classification. The figure translates Table 9 into a full-page matrix for legal, regulatory, policy, tax, and public interpretability. The figure organizes the principal forms of legal residue that may remain after a private cryptocurrency payment appears complete. Its purpose is to show that crypto payment may move value and settle a private bargain while still leaving downstream legal, fiscal, evidentiary, accounting, reporting, compliance, enforcement, and remittance consequences unresolved across multiple legal regimes. The matrix identifies fourteen categories of legal residue: capital gain or loss, basis calculation, tax reporting, payroll withholding, accounting treatment, contract valuation, timing disputes, wallet attribution, recordkeeping burdens, compliance screening, sanctions exposure, money-transmission questions, enforcement exposure, and remittance obligations. Each category distinguishes what remains after private crypto payment, the primary legal or institutional lens implicated, and why the residue is evidence of non-closure. The figure does not suggest that every crypto payment triggers every category, nor does it imply that crypto payment is invalid, unlawful, or commercially ineffective. Rather, it shows that private acceptance does not necessarily eliminate tax, property, payroll, accounting, contract, attribution, sanctions, BSA/AML, money-transmission, enforcement, or public remittance questions. The central distinction is that blockchain confirmation or private acceptance may show that value moved, but it does not necessarily resolve legal attribution, valuation, reporting, sanctions exposure, tax treatment, payroll withholding, or fiscal closure. Where the law must continue reconstructing value, identity, timing, tax, compliance, and remittance after transfer, the transaction has not disappeared into sovereign monetary closure. The public-facing bridge is simple: accepted is not always closed. Legal residue is the operational evidence that private crypto payment and legal-tender closure are distinct legal events.

This dataset contains supplementary tables from a study describing qualitative trichoscopic features and quantitative hair shaft parameters in healthy women of African descent. The tables include demographic data, qualitative trichoscopic findings by scalp region, quantitative hair shaft measurements obtained using digital trichoscopy and TrichoLAB®, and a comparison between healthy and alopecic trichoscopic findings. These data support the findings reported in the associated Brief Report submitted to the Journal of the American Academy of Dermatology.