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This dataset provides supplementary appendix materials associated with an accepted journal article on presenter-level affinity, ideology, and audience evaluations in Spanish late-night television. The files include fieldwork and case-flow documentation, quality-control summaries, scale/item diagnostics, descriptive statistics, ideology-stratified summaries, OLS model summaries, and coded open-ended response frequencies. The materials are harmonized to the primary adult analytic frame of respondents aged 18–100 years (N = 806). Appendix A should be treated as the authoritative source for fieldwork, case-flow, and quality-control counts. Appendix B provides revised supplementary analyses and flags statistics that require recalculation from respondent-level data. The deposit does not include respondent-level raw data. It is intended to support transparency regarding the supplementary materials and analytical documentation underlying the accepted article. (2026-07-08)

This dataset contains the raw and uncropped data underlying key figures from the manuscript titled “TNFα-dependent modulation of WT1-MMP9 regulatory axis links developmental and inflammatory pathways in glaucoma” The data include original, unprocessed image files corresponding to representative figures presented in the manuscript.

In this compilation of data, we report the dilatation behavior and the temperature dependence of elastic moduli of the polycrystalline, equiatomic, and single-phase face-centered cubic VMnFeCoNi high-entropy alloy. After processing, the alloy can be considered as a chemically homogeneous and isotropic polycrystal with a grain size of 60µm. All the data are origin files (.opju). Here is a short description of the files, which are numbered according to their order of appearance in the corresponding research article: - Fig.1: Dilatation behavior between 100 and 673 K - Fig.2: Temperature dependence of the elastic moduli, including the Young's modulus (E), bulk modulus (B), shear modulus (G) and Poisson's ratio (ν).

Spike data (in vivo electrophysiology)

This dataset provides a firm-year panel of S&P 500 companies for the period 2020–2024, designed to study the relation between artificial intelligence (AI) exposure, valuation diagnostics, and the cost of equity in U.S. capital markets. The dataset combines AI-related disclosure measures, governance/risk disclosure counts, standard firm controls, market-level equity risk premium inputs, and firm-level valuation outcomes. AI exposure is measured through keyword-based counts of AI-related terms in annual disclosure text, with log-transformed versions using log(1 + count). Governance/risk variables are based on counts of disclosure terms related to oversight, accountability, control, risk, and governance. Firm controls include standard accounting variables such as size, profitability, leverage, and sales growth. The valuation block includes firm-level implied cost of equity estimates based on the modified PEG (MPEG) model, firm implied ERP, CAPM-based cost of equity, and the ICC–CAPM gap. The main formulas are: ICC_MPEG = [DPS/P + sqrt((DPS/P)^2 + 4((EPS2 − EPS1)/P))]/2; firm_implied_erp = ICC_MPEG − risk_free_rate; capm_coe = risk_free_rate + beta × market_erp; and icc_capm_gap = ICC_MPEG − capm_coe. These variables allow comparison between analyst-growth-implied required returns and beta-based CAPM required returns. Most valuation inputs are based on Bloomberg point-in-time data as of each year-end valuation date. When Bloomberg data were unavailable, reliable archival financial sources were used and documented through source flags. The dataset is intended for research on AI exposure, cost of equity, equity risk premia, and the expectation–risk wedge in large U.S. listed firms during the post-2020 AI diffusion period.

The dataset comprises raw data of the position shift (from center point) and morphological characteristics of 100 drip stains under the influences of different combinations of wind velocity (WV1-5) and height of fall (HOF1-5). It also comprises 100 photographs from 20 drip stains showing the physical appearance changes under prolonged exposure to different wind velocities (WV1-5) after falling from 60 cm height (HOF2). Wind velocity: i) 0 m/s (WV1) ii) 1.15 - 1.75 m/s (WV2) iii) 1.72 - 2.35 m/s (WV3) iv) 2.40 - 2.92 m/s (WV4) v) 2.90 - 3.79 m/s (WV5) Height of fall: i) 30 cm (HOF1) ii) 60 cm (HOF2) iii) 90 cm (HOF3) iv) 120 cm (HOF4) v) 150 cm (HOF5) This serves as the dataset for the article titled "Examining the effect of wind exposure on drip stains fallen from different heights".

This dataset was generated in the following publication: Stengl C, Mooshammer C, Mahnke J, Runz A, Vedelago J, 2026. A review on radiological properties of fused deposition modelling material for three-dimensional printing in proton and light ion beam therapy. Submitted to Physics and Imaging in Radiation Oncology. Excel file with 17 3D printing fused deposition modeling (FDM) material classes and a total of 70 different materials describing the 3D printing parameters such as vendor, chemical composition, density, 3D printer model, slicer software, extrusion factor, extrusion temperature, bed temperature, printing speed and maximum layer thickness, infill and pattern. For each of these parameters, the resulting radiological properties are given, including printed density, CT number at 80 kV, 100 kV and 140 kV, relative electron density and relative stopping power. The full table is given in the file "2026-FDM Lookup Table.xlsx". To simplify the tables, ABS, PLA and other filaments are separated additionally. How to read and how to use the lookup tables: Material names are given in column A. The largest material classes ABS and PLA have distinct colours (ABS = purple), (PLA = red). The other materials classes are dark grey. For the materials with some modifications such as additives the hue of the color is reduced to a lighter purple, red or grey. Uncertainty from each publication and entry is given as () brackets, while the literature is given with [] brackets. The reference source given in brackets is the same numbering as for the Reference section of the mentioned publication.

This study hypothesizes that underlying structural macroeconomic vulnerabilities—specifically Indonesia's twin deficits and institutional uncertainty surrounding the newly empowered Danantara sovereign wealth fund—leave the Indonesian Rupiah (IDR) uniquely exposed to high-frequency global financial shocks compared to its ASEAN peers. Furthermore, the study hypothesizes that under such crisis conditions, traditional monetary defenses (such as widening interest rate differentials) fail to support the currency, constituting an empirical rejection of uncovered interest rate parity (UIP). Conversely, it is hypothesized that the structural policy frameworks of Malaysia and Singapore provide durable insulation against these same shocks. The provided dataset comprises a monthly macroeconomic panel spanning January 2021 to May 2026 for three ASEAN nations: Indonesia, Malaysia, and Singapore. The raw data was gathered from publicly available central bank and macroeconomic databases, including Bank Indonesia, Bank Negara Malaysia, the Monetary Authority of Singapore, and standard global financial indices. The uploaded materials consist of three files: research.dta: The primary Stata dataset containing the full panel of macroeconomic variables, including bilateral exchange rates against the USD, the United States Dollar Index (DXY), interest rate differentials, and the structural shock dummy variable representing the Q2 2026 market event. Newey data.txt: A plain-text data subset specifically isolated and formatted for the individual time-series Newey-West Heteroskedasticity and Autocorrelation Consistent (HAC) standard error estimations. appendix A.txt: The comprehensive replication code outlining the step-by-step econometric procedures used in the study. Notable Findings and What the Data Shows The data clearly demonstrates an asymmetric currency fracture among the observed ASEAN nations in Q2 2026. Panel fixed-effects estimators (Driscoll and Kraay) and individual time-series (Newey-West) outputs show that the Indonesian Rupiah exhibited a highly significant reaction to the structural shock dummy (coefficient = 0.0108, p < 0.001) and marginal sensitivity to the USD Index. Crucially, the interest rate differential variable proved statistically powerless to explain the IDR's depreciation. In contrast, the data shows the Malaysian Ringgit (MYR) displayed no measurable sensitivity to the same shock, while the Singapore Dollar's (SGD) response metrics indicate orderly central bank management rather than market disorder. How the Data Can Be Interpreted and Used The results should be interpreted as empirical evidence that reserve depletion and reactive interest rate defenses cannot substitute for proactive structural market architecture in emerging markets. The data highlights how Malaysia’s Dynamic Hedging Programme and Singapore’s nominal effective exchange rate (NEER) framework successfully insulate their currencies from the global financial cycle.

Data regarding volleyball players from teams that won medals at one of the last five Olympic Games and their respective participation in youth World Championships.

Research Hypothesis This study hypothesizes that underlying structural macroeconomic vulnerabilities specifically Indonesia's twin deficits and institutional uncertainty surrounding the newly empowered Danantara sovereign wealth fund leave the Indonesian Rupiah (IDR) uniquely exposed to high-frequency global financial shocks compared to its ASEAN peers. Furthermore, the study hypothesizes that under such crisis conditions, traditional monetary defenses (such as widening interest rate differentials) fail to support the currency, constituting an empirical rejection of uncovered interest rate parity (UIP). Conversely, it is hypothesized that the structural policy frameworks of Malaysia and Singapore provide durable insulation against these same shocks. Data Overview and Gathering The provided dataset comprises a monthly macroeconomic panel spanning January 2021 to May 2026 for three ASEAN nations: Indonesia, Malaysia, and Singapore. The raw data was gathered from publicly available central bank and macroeconomic databases, including Bank Indonesia, Bank Negara Malaysia, the Monetary Authority of Singapore, and standard global financial indices. The materials consist: research.dta: The primary Stata dataset containing the full panel of macroeconomic variables, including bilateral exchange rates against the USD, the United States Dollar Index (DXY), interest rate differentials, and the structural shock dummy variable representing the Q2 2026 market event. Newey data.txt: A plain-text data subset specifically isolated and formatted for the individual time-series Newey-West Heteroskedasticity and Autocorrelation Consistent (HAC) standard error estimations. appendix A.txt: The comprehensive replication code outlining the step-by-step econometric procedures used in the study. Notable Findings and What the Data Shows The data clearly demonstrates an asymmetric currency fracture among the observed ASEAN nations in Q2 2026. Panel fixed-effects estimators (Driscoll and Kraay) and individual time-series (Newey-West) outputs show that the Indonesian Rupiah exhibited a highly significant reaction to the structural shock dummy (coefficient = 0.0108, p < 0.001) and marginal sensitivity to the USD Index. Crucially, the interest rate differential variable proved statistically powerless to explain the IDR's depreciation. In contrast, the data shows the Malaysian Ringgit displayed no measurable sensitivity to the same shock, while the Singapore Dollar's response metrics indicate orderly central bank management rather than market disorder. How the Data Can Be Interpreted The results should be interpreted as empirical evidence that reserve depletion and reactive interest rate defenses cannot substitute for proactive structural market architecture in emerging markets. The data highlights how Malaysia’s Dynamic Hedging Programme and Singapore’s nominal effective exchange rate (NEER) framework successfully insulate their currencies from the global financial cycle.