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Results behind AI Aware's published accuracy figures, covering the best result from each detector between 14 August and 7 November 2026. Text: 98.6% accuracy (AUROC 0.998) on 10,334 held-out texts from 30 AI models across 14 types of writing. Images and video: 99.4% accuracy (AUROC 0.9996) on 43,106 held-out images from 28 generators, with 0.39% of real photos wrongly flagged. Audio: 97.6% accuracy (AUROC 0.995) on the BRSpeech-DF validation set. The CSV files give summary metrics and breakdowns by type of writing, text length, AI model, image generator and source of real photos. The PDF explains the method. See 00-README.txt for a list of files. Full results and method: https://aiaware.io/ai-detector-accuracy-technical-results Comparison with other detectors: https://aiaware.io/most-accurate-ai-detector

This dataset holds the data behind the article "Matrix and modifier contributions to single-pass copper capacity in reactive Jatropha polyurethane/polyethersulfone–chitosan/ZIF-8 membranes" (Zeng, Zainal Abidin, Omar, Harun; Universiti Putra Malaysia). Asymmetric membranes were cast by nonsolvent-induced phase separation from N,N-dimethylformamide dopes in which a Jatropha-derived polyurethane (JPU) precursor carrying residual isocyanate (NCO) was blended 50/50 with polyethersulfone (PES), with chitosan and ZIF-8 as modifiers. A 2 x 2 x 2 matrix x chitosan x ZIF-8 factorial (all eight cells cast and filtered in each of three blocks, in independently randomised orders) and its replication on a second PES lot, four modifier-bearing formulations, a same-session JPU/PES versus JPU/PES/C/Z campaign, a randomised 2 x 2 matrix x modifier stack series and NCO-capped precursor controls were characterised and tested for single-pass Cu capture at 50-150 L m-2 h-1. Version 5 corresponds to the revised article. Changes from version 4: - Supporting Information condensed: record-level, register and acquisition-condition tables now sit in the workbook as Data Tables D1-D15 (sheets T_D1-T_D15); T_ sheets hold Tables 1-5 and S1-S47 as reported, with sheet names and cross-references renumbered. - TABLE_INDEX record-sheet column re-aligned with the renumbered tables. - reproduce_factorial.py: table references updated; both scripts read 03_Supporting_Data.xlsx by default. Files: - 03_Supporting_Data.xlsx: START_HERE lists the sheet groups; TABLE_INDEX maps every table to its sheet and record sheets; SHEET_INDEX describes each sheet; PROVENANCE gives the source of each data family; REPRODUCE maps reported values to records and computations. T_ sheets hold the tables as reported (T_D1-T_D15: Data Tables D1-D15); A_, B_ and C_ sheets hold run-, coupon- and point-level records and measurement arrays. - reproduce_factorial.py and reproduce_pes_factorial.py (Python 3 with numpy, pandas, scipy and openpyxl): python reproduce_factorial.py 03_Supporting_Data.xlsx Reference data of the unmodified JPU/PES (50/50 DMF) matrix are those of Zeng et al., J. Membr. Sci. 762 (2027) 126309 (https://doi.org/10.1016/j.memsci.2026.126309), deposited at https://doi.org/10.17632/wn47krc8p6.5.

Fortran 90 implementation of four time integrators (SSP-RK3, ILW3, classical RK4, ILW4) sharing the same mapped-WENO spatial operator of sixth or eighth order, for the porous medium equation, the Buckley–Leverett equation and strongly degenerate diffusion, in one and two space dimensions. The archive contains the source modules, 36 self-contained programs (nine benchmark examples × four schemes), the JSON files with the timings and differences reported in the paper, and the scripts that post-process the output.

This dataset was generated by a research team at the Institute of Oceanology, Chinese Academy of Sciences, using the independently developed ROMS-based MPIOS and its subregional models. Daily mean output from the parent model was used to prescribe the boundary conditions for the nested model, and output for December 30 of the final parent-model year was used to initialize the nested simulation. Atmospheric forcing was derived from COADS, and tidal forcing was obtained from TPXO8, incorporating 10 tidal constituents: M2, S2, N2, K2, K1, O1, P1, Q1, Mf, and Mm. The model was first integrated under climatological forcing until it reached climatological equilibrium, followed by an additional five-year integration. Monthly mean fields from the final simulation year were archived. Spatial correlations between the simulated annual mean sea surface height, sea surface temperature, and sea surface salinity and their satellite-observed counterparts all exceeded 0.99, with root-mean-square errors of 0.0538 m, 0.5435 °C, and 0.3591 PSU, respectively. The model successfully reproduced the transport, seasonal variability, and vertical structure of major currents, including the Kuroshio in the East China Sea, and flows through key passages. The simulated amplitudes and phases of the M2 and K1 tidal constituents also agreed closely with observations, supporting the use of this dataset for studies of regional circulation and its underlying dynamical mechanisms. The dataset consists of 12 files named ECS_1x30_monthXX_crop, where XX denotes the month. Each file contains longitude and latitude coordinates, two-dimensional sea surface height, and three-dimensional temperature, salinity, and velocity fields.Temporal coverage: Model climatological monthly means. Spatial coverage: 116°–136°E, 21°S–41°N.

This dataset provides a multimodal collection of static Indian Sign Language (ISL) gestures representing 79 sign classes, comprising 26 English letters (A–Z), 43 Marathi characters (अ–ज्ञ), and 10 numerical digits (0–9). Images were collected from four participants under controlled indoor conditions using iPhone smartphones (iPhone 15 and iPhone XS Max) against a black background with 1:1 aspect-ratio framing. The released dataset contains 4,740 image files organized into three aligned visual modalities: RGB photographs, MediaPipe-generated skeletal landmark overlay images, and skin-segmented binary masks. Each modality contains 1,580 images (20 samples per class), derived from 1,425 unique photographic captures. To maintain class balance, 155 entries per modality were generated through cyclic duplication; all duplicated samples are explicitly identified in the accompanying metadata files. The processing workflow standardizes images to a resolution of 1280 × 1280 pixels, generates skeletal landmark overlays using MediaPipe Hands, and produces skin-segmented representations through HSV-based thresholding and morphological filtering. The dataset distributes rendered image representations only and does not include numerical landmark-coordinate files. The aligned modalities enable comparative studies of RGB appearance, skeletal hand geometry, and segmented hand shape for static-sign recognition tasks. Potential applications include machine-learning model development, multimodal gesture classification, and benchmarking of visual representations for ISL recognition. Users should note that the dataset was collected from a limited number of participants under controlled acquisition conditions and does not include dynamic sign sequences, which may restrict generalization to signer-independent or continuous-sign recognition scenarios.

The uploaded raw data correspond to all main figures (Figures 1–5) in the manuscript. The supplementary data include the raw data underlying Supplementary Figures 1–3 and the primer sequence. The dataset contains the complete, unfiltered raw data, with no data points removed or excluded.

Titles of journals with the most published articles on patient navigation

This deposit contains the complete analysis code and the aggregated intermediate results underlying the article "The cost of an outage is more unequal than the outage: utility disruption and household borrowing in a monthly panel from Uzbekistan". The study uses 82 monthly rounds of the Listening to the Citizens of Uzbekistan (L2CU) household telephone panel, covering 121,618 household-months on 2,499 households, to separate exposure to utility disruption from its financial cost. Contents: scripts/ — five R scripts. Three produce the estimates (diagnostics, estimation, mechanisms), one produces the figures, and one generates the LaTeX tables directly from the result files so that no number in the article is transcribed by hand. results/ — 51 CSV files holding every aggregated quantity reported in the article, including the coefficients, standard errors and sample sizes behind each table and figure. figures/ — the six figures of the article in PDF. tables/ — the LaTeX emitted by the table-generating script. README.txt — how to reproduce, which script produces which output, a map from each table and figure of the article to its source file, and notes on two deliberate specification choices. The household microdata are NOT included. The analysis uses the public-use file of the L2CU survey, distributed by the World Bank Microdata Library under Licensed Data terms that do not permit redistribution by third parties. Any researcher may obtain the same file directly from that source by accepting the same conditions. In accordance with the licence, no cell derived from fewer than ten households appears in the article or in the files deposited here. Software: R (>= 4.2) with haven, dplyr, tidyr, ggplot2 and patchwork.

GAMS and Python/Gurobi code and case-study input data to reproduce all results (Tables 6–8, Figures 3–8). See README.txt.

This dataset supports the study “Dual-frequency superposition-based vibration communication and an associated carrier-screening method for coupled tubing strings.” The study tests the hypothesis that periodic couplings, finite tubing-string length, and the finite spectral bandwidth of short symbols jointly cause strong frequency selectivity, so carriers selected only from short-string response peaks may become weak when the number of tubing–coupling periods changes. The dataset contains: (1) a cleaned Code Composer Studio 12.4 project for the Texas Instruments TMS320F28335, with Example_2833xAdcSoc.c as the main source file; (2) MATLAB_Online_Decoder.m for SCI reception, frame reconstruction, online decoding, display, and storage of 15 predefined decimal-value groups; (3) Unified_Periodic_Response_Data.csv, containing processed finite-period response data for N = 1, 3, 6, and 9; and (4) Full_Band_Carrier_Robustness_Results.csv, containing spectrally weighted responses and carrier-screening metrics. The finite-period data were obtained from frequency-domain models of coupled tubing strings and arranged on a common frequency grid. The carrier-screening results were calculated from the effective response covered by the main lobe of a finite-duration symbol. For each carrier, G_worst is the minimum effective response across N = 1, 3, 6, and 9, R is the maximum-minus-minimum response range, and S_lambda = G_worst - lambda R is the robustness score. Higher G_worst and S_lambda indicate better worst-case response and overall robustness, while a smaller R indicates lower sensitivity to period count. The data reveal recurring broadband low-response regions that generally become more pronounced as period count increases. Under the evaluated 26 ms symbol condition, the results identify weak-carrier regions and provide candidate replacement frequencies ranked by worst-case response and cross-period stability. These rankings are screening results only. They do not directly represent communication capacity, delay, intersymbol interference, dual-frequency balance, or decoding accuracy. The DSP code generates dual-frequency waveforms, outputs them through the DAC5620C, acquires vibration-response samples through the ADC, and transfers 64-sample blocks through SCI-A at 468750 baud using the synchronization header A5 5A 3C C3. MATLAB performs online reception and decoding. Reuse with different tubing lengths, coupling counts, sensor mounting conditions, amplifier settings, or acquisition chains requires recalibration of carrier frequencies, equalization coefficients, and decision thresholds. The complete original raw ADC waveform archive is no longer retained. The available processed response data, carrier-screening results, DSP source code, MATLAB program, and README are provided to support interpretation and partial reproduction of the reported workflow. Version 2 adds C40-Greedy, C80-DP and ISI hardware records, processing scripts and a README.