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These data constitute the raw data for the figures and the original histopathological images presented in the manuscript.

This dataset tests the hypothesis that Nigerian public health institutions manage anticipated interpretive trouble prospectively rather than only reactively — extending Elder and Haugh's (2023) interactional concept of unwanted inference to written, non-sequential institutional communication, where audiences give no observable uptake for institutions to repair. The data was gathered by close, iterative coding of five public-facing texts from three Nigerian federal health agencies (a NACA HIV/AIDS FAQ, an NPHCDA COVID-19 vaccine FAQ, and NCDC advisories on Ebola virus disease, Human Metapneumovirus and Lassa fever), following a six-stage sequence: identifying each available inferable, its institutional status, the inferential risk attached to it, the management strategy used, the preferred inference established, and the behavioural action that preferred inference supports. The data shows that of 68 inferables coded across 28 inferential episodes, 28 were institutionally preferred, 12 institutionally neutral, and 28 institutionally unwanted — meaning institutions do not treat all available meanings as problematic, but selectively intervene on a specific subset. The 28 unwanted inferables cluster into six recurring risk types (delayed or unsafe action, 8 episodes; stigma/exclusion and distrust/refusal, 6 each; panic/alarm, 5; complacency, 4; distorted responsibility, 1), each with the specific episode IDs that instantiate it. The notable finding is that institutions typically manage two opposed risks at once within a single episode (for example, panic against complacency in outbreak advisories) using recurrent strategies — explicit denial, inferential narrowing, causal reconstruction, authority invocation and calibration — and that recommended actions (testing, vaccination, reporting) are consistently anchored to a preferred inference established immediately beforehand, supporting the paper's claim that behavioural alignment depends on prior interpretive alignment. The "Codebook" sheet gives the operational definition, inclusion/exclusion criteria and decision question for every category, so the status and risk labels in "Inferential Field" can be checked against the textual evidence they claim to be grounded in — status was assigned only where the source text itself displayed denial, correction, qualification or a comparable form of distancing, not on the coder's own judgement of accuracy or harm. Coding was conducted by the first author and reviewed by the second, with disagreements resolved through discussion. Researchers extending this framework to other institutional genres should treat the codebook, not the raw counts, as the reusable component.

This paper-specific archive contains 47 research case records spanning calculations, estimator and null-model audits, mathematical derivations, candidate screening, and literature/source assessment. The 47 records are not 47 experiments, independent validations, or successful applications. The archive also contains file-level evidence maps for 19 cases, provenance-labelled transcribed source-table inputs, dated reconstruction code and outputs, a preserved historical implementation and test suite, and a 60-row DPPC source audit. No new experimental measurements are reported. Copyrighted source publications and PDFs are not redistributed. The available record does not identify a termination coordinate for the hydrated-DPPC Lβ′/LβI boundary; the associated article concludes that the proposed cross-arm correspondence is presently non-evaluable rather than positive or negative.

This dataset provides the underlying numerical data supporting the findings of a study investigating a Crh-defined insular cortex–parabrachial nucleus–posterior paraventricular thalamus (IC–PBN–pPVT) circuit involved in the regulation of anxiety-related behavioral states. The dataset contains the raw numerical values used to generate the quantitative graphs presented in the manuscript, including anatomical tracing and quantification, molecular characterization, electrophysiological analysis, calcium imaging, functional circuit manipulation, and behavioral assessments in mice. These data support the statistical analyses and visualization of the reported findings and are shared to promote transparency, reproducibility, and further investigation of the neural circuit mechanisms underlying anxiety-related behavioral states.

This dataset contains publicly available images of 20 Himalayan-native medicinal plant species collected from various online sources. It includes two main folders: one containing the raw images as collected, and another containing the images organized into training, validation, and test sets. The dataset is intended for research and applications in medicinal plant identification, image classification, and computer vision.

Operational efficiency remains a major governance concern for listed deposit money banks in Nigeria because banks are exposed to rising operating costs, digital transformation pressure, cyber-risk exposure, risk governance demands and shareholders’ expectation of sustainable value creation. This study examined the relationship between value for money audit and operational efficiency of listed deposit money banks in Nigeria. Value for money audit was decomposed into economy audit, efficiency audit, effectiveness audit, ethics audit and equity audit, while operational efficiency was proxied by the cost-to-income ratio. The study adopted an ex-post facto and content analysis research design. A balanced panel of one hundred and thirty firm-year observations was obtained from thirteen listed deposit money banks and banking holding companies for the ten-year period 2015-2024. Data were analysed with descriptive statistics, correlation analysis, variance inflation factors, residual diagnostics, fixed effects regression with robust standard errors, panel-corrected standard errors, feasible generalized least squares and dynamic panel robustness checks. The empirical outputs were presented in the EViews 10.0 result format. The findings revealed that economy audit has a significant negative relationship with cost-to-income ratio (coefficient = 0.0198; p = 0.0000), efficiency audit has a significant negative relationship with cost-to-income ratio (coefficient = 0.0259; p = 0.0000), and effectiveness audit has a significant negative relationship with cost-to-income ratio (coefficient = -0.0143; p = 0.0000). Ethics audit and equity audit also showed negative coefficients, but their effects were not significant at the 5% level in the main fixed-effects model. The study concluded that value for money audit improves operational efficiency when audit work is strongly linked to cost discipline, process productivity and strategic outcome assessment. It recommended that boards, audit committees and internal audit departments of listed deposit money banks should institutionalize value for money audit scorecards across procurement, branch operations, digital platforms, outsourcing arrangements, customer-service processes and strategic projects.

DSGE Model Estimation Definition of data variables Real output = LN(Gross Domestic Product/ PCE Deflator/ Population) * 100 Real consumption = LN((Personal Consumption Expenditures/ PCE Deflator) / Population) * 100 Real investment = LN((Private Non-Residential Investment/ PCE Deflator) / Population) * 100 Hours worked = LN((Average Weekly Hours * Employment/ 100)/ Population) * 100 Inflation = LN(PCE Deflator / PCE Deflator (-1) ) * 100 Real wage = LN(Hourly Compensation / PCE Deflator) * 100 Policy interest rate = Federal Funds Rate / 4 Relative price of investment = -1 * LN(Price Index of Private Non-Residential Investment/ PCE Deflator) *100 Source of the original data Gross Domestic Product: Gross Domestic Product, Table 1.1.5. Gross Domestic Product, NIPA Personal Consumption Expenditures: Personal Consumption Expenditures, Table 1.1.5. Gross Domestic Product, NIPA Private Non-Residential Investment: Private Non-Residential Investment, Table 1.1.5 Gross Domestic Product, NIPA PCE Deflator: Personal Consumption Expenditures, Table 1.1.9. Implicit Price Deflator for Gross Domestic Product Source: U.S. Bureau of Economic Analysis Price Index of Private Non-Residential Investment: Private Non-Residential Capital Formation, Deflator (PIB), OECD Economic Outlook Database Source: Organisation for Economic Co-operation and Development Population: Population level, Civilian Noninstitutional Population, 16 Years and Over, Labor Force Statistics from the Current Population Survey, Series ID = LNS10000000 (Period: 1947 – 1975) Population: Population level, Civilian Noninstitutional Population, 16 Years and Over, Labor Force Statistics from the Current Population Survey, Series ID = LNU00000000 Employment: Employment level, Employed, 16 Years and Over, All Industries, All Occupations, Labor Force Statistics from the Current Population Survey, Series ID = LNS12000000 Average Weekly Hours: Average Weekly Hours, Major Sector Productivity and Costs, Nonfarm Business, Series ID = PRS85006023 Hourly Compensation: Hourly Compensation, Major Sector Productivity and Costs, Nonfarm Business, Series ID = PRS85006103 Source: U.S. Bureau of Labor Statistics Federal Funds Rate: Averages of Monthly Figures - Percent Source: Board of Governors of the Federal Reserve System Market Capitalization and Capital Stock Source of the original data Total Liabilities and Equity, Nonfinancial Corporate Business, Financial Accounts, Series ID: FL104194005.Q Total Financial Assets, Nonfinancial Corporate Business, Financial Accounts, Series ID: FL104090005.Q Nonresidential Structures, Equipment, and Intellectual Property Products, Current Cost Basis, Nonfinancial Corporate Business, Financial Accounts, Series ID: FL105013865.Q Source: Board of Governors of the Federal Reserve System

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This dataset comprises 784 topsoil samples selected from the Open Soil Spectral Library (OSSL), including soil organic carbon content, geographic coordinates, and visible–near-infrared spectral data covering 400–2400 nm at a standardized 10 nm interval. The dataset also integrates ten environmental covariates: clay content, silt content, cation exchange capacity, soil pH, elevation, slope, annual mean temperature, annual precipitation, NDVI, and land-cover type. Based on these covariates, the samples were classified into five geographical environmental zones using a Gaussian mixture model. The dataset supports comparisons between global and geographically stratified models for soil organic carbon estimation and facilitates the regional application of global soil spectral libraries.

Successful image ads are liked, shared, and remembered. Although the success of an image ad can often be understood with the benefit of hindsight, predicting image ad success ex ante remains challenging. We integrate established ideas from advertising and visual-evaluation research into an ad-level framework that relates five constructs—typicality, complexity, ease of understanding, enjoyment, and interest—to liking, sharing, and memorability. The framework provides an interpretable representation of an image’s aggregate evaluative profile. We assess the proposed structure using human judgment data for 300 Facebook image ads posted by three fast-food chains and estimate a measurement-error corrected path model at the image level. To enable scalable implementation, we operationalize the five constructs using computational measures derived from pixel-level, object-level, and design-level image features. These measures approximate human image-level evaluations and provide automated image-level scores associated with ad success. As a supplementary robustness check, we examine whether a persona-conditioned synthetic consumer panel recovers similar image-level ratings. Agreement is strongest for typicality and complexity and weaker for enjoyment and interest, indicating that synthetic panels can complement, but not replace, direct computational measures and observed human judgments. By combining conceptual integration with interpretable computer-vision operationalization, the proposed framework provides a scalable and practically implementable approach to image ad diagnosis, pretesting, and design iteration.