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This dataset contains the dynamic performance of the Mexican Spanish phoneme /be/ by 10 people repeated three times by everyone. Multimodal data was captured using a MS Kinect V2 sensor connected to a laptop. RGB images (24 bits per pixel) and depth images (16 bit per pixel) sequences were captured at 30 frames per second. The synchronized audio of the phoneme was recorded in a WAV file at 44 Khz, 16 bits per sample. For each phoneme performance the following data are available for the mouth region: 90, 256x256 RGB images (24 bits per pixel) in PNG format 90, 256x256 depth images (16 bits per pixel) in PNG format One WAV file at 44 Khz, 16 bits per pixel To facilitate analysis, processing, machine learning and inference methods to be developed the following additional files are provided which are derived from the above raw data: 191 facial landmarks (x,y) extracted with MediaPipe software for each RGB image. The landmarks are stored in a TXT file, with one point (x,y) in each row. 191 facial landmarks (x,y,Z) extracted with MediaPipe software for each depth image. The landmarks are stored in a TXT file, with one point (x,y,Z) in each row. Cloud point (X,Y,Z) corresponding to each depth image obtained from reprojection using the depth camera intrinsic parameters. The cloud is stored as a TXT file, with one point (X,Y,Z) per row. Cloud point (X,Y,Z) corresponding to 191 landmarks of each depth image obtained from reprojection using the depth camera intrinsic parameters. The cloud is stored as a TXT file, with one point (X,Y,Z) per row.

Dataset and codes for paper replication

Replication package for "Bankruptcy Lawyers and Credit Recovery", accepted at Journal of Financial Economics.

Files pertaining to the paper entitled <i>Helobdella</i> leeches of southern Illinois: phylogenetic analyses and comparative methods. Data includes results from Sanger sequencing, images of animals used in phylogenetic analyses, and distance metrics for comparing groups of genetically similar sequences of the cytochrome oxidase 1 gene from leeches.

base de dados do artigo RELAÇÃO ENTRE A ORIENTAÇÃO PARA O MERCADO E A MATURIDADE DO MARKETING DIGITAL NO VAREJO. Autores: Professor Doutor Carlos Marcelo Ardigó - UNIVALI - SC e Evandro Oldra - ll Evandro Oldra - RS

A structured questionnaire was used for data collection, comprising two sections: one covering sociodemographic data and the other addressing latent variables. Sociodemographic variables included the respondents' age, education level, and gender. The latent variables were attitude, subjective norms, perceived behavioral control, purchase intention, and identity. A 7-point Likert scale—ranging from 1 (strongly disagree) to 7 (strongly agree)—was used for all variables. A non-probability purposive sampling method was employed in Baja California, Mexico, collecting 550 data points. The following criteria were applied for sample selection: 1) being over 18 years of age, 2) residing in Baja California, Mexico, and 3) having previously consumed local agri-food products.

Supporting Python and Lean verification files for the manuscript “Complementary-divisor rigidity from proportional valuation truncation” by Nurbek Kenes. The supplement contains exact-arithmetic Python checks, Lean 4.19.0 source files, declaration-level axiom reports, and recorded verification results. The files check local algebraic identities, the two overlap constructions, residual-exponent calculations, the quadratic recurrence and finite-descent components, as well as the finite computational experiments described in Appendix A of the manuscript. The Lean files formalize selected local components of the argument; they do not constitute a formal proof of Theorem 1.1. The asymptotic estimates, the passage from real valuation truncation to the relevant integer hypotheses, the radical estimates, and the full theorem remain part of the conventional mathematical proof in the manuscript.

This dataset contains the dynamic performance of the Mexican Spanish phoneme /a/ by 10 people repeated three times by everyone. Multimodal data was captured using a MS Kinect V2 sensor connected to a laptop. RGB images (24 bits per pixel) and depth images (16 bit per pixel) sequences were captured at 30 frames per second. The synchronized audio of the phoneme was recorded in a WAV file at 44 Khz, 16 bits per sample. For each phoneme performance the following data are available for the mouth region: 90, 256x256 RGB images (24 bits per pixel) in PNG format 90, 256x256 depth images (16 bits per pixel) in PNG format One WAV file at 44 Khz, 16 bits per pixel To facilitate analysis, processing, machine learning and inference methods to be developed the following additional files are provided which are derived from the above raw data: 191 facial landmarks (x,y) extracted with MediaPipe software for each RGB image. The landmarks are stored in a TXT file, with one point (x,y) in each row. 191 facial landmarks (x,y,Z) extracted with MediaPipe software for each depth image. The landmarks are stored in a TXT file, with one point (x,y,Z) in each row. Cloud point (X,Y,Z) corresponding to each depth image obtained from reprojection using the depth camera intrinsic parameters. The cloud is stored as a TXT file, with one point (X,Y,Z) per row. Cloud point (X,Y,Z) corresponding to 191 landmarks of each depth image obtained from reprojection using the depth camera intrinsic parameters. The cloud is stored as a TXT file, with one point (X,Y,Z) per row.

A dataset for the application of the Bayesian AMMI (B-AMMI) model to identify high-yielding and stable barley genotypes

This deposit contains Python code and computational results for arranging twelve sensors on a unit sphere. The study considers angular collision, geometric coverage and the loss of angular diversity when one sensor is removed. An NSGA-II-style variant with uniform parent selection is compared with random search over eight seeds, using 2,440 objective evaluations per method and run. The files include all evaluated configurations, objective values, nondominated archives and the selected compromise design. Additional analyses calculate exact dominated fractions within stated reference boxes and describe variation between paired runs. One Fibonacci layout and eight greedy maximin layouts provide geometric comparisons, with coverage checked on denser grids. The results show a trade-off between diversity and coverage; they do not establish better sensing accuracy or general superiority of the evolutionary method. All configurations and observations were generated computationally. Additional checks include an exact sign test, recomputation of 48 archived objective vectors, and fixed-layout angular resolution sensitivity at 18, 36 and 72 bins. These checks do not constitute independent physical validation or reoptimisation at alternative resolutions.