3 resultados para Open Research Data

em Universitat de Girona, Spain


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Los programas de inmersión lingüística han constituido y constituyen dentro del Sistema Educativo catalán la principal forma para que el alumnado de lengua familiar no-catalana aprenda una nueva lengua, el catalán, sin que, en su proceso de aprendizaje, vea mermado ni el desarrollo de su propia lengua ni su rendimiento académico. El éxito de la inmersión lingüística en las décadas anteriores ha sido frecuentemente utilizado como uno de los argumentos orientativos para justificar la política lingüística que se sigue en la escolarización de la infancia extranjera. Sin embargo, los resultados obtenidos por investigaciones recientes parece que no avalan empíricamente dicho argumento. Este artículo analiza dichos resultados y expone, a partir del Plan para la Lengua y Cohesión Social puesto en marcha por el Departamento de Educación de la Generalitat de Cataluña, cuáles son los retos que se presentan a su Sistema Educativo dentro del nuevo marco que supone el aumento de la diversidad cultural y lingüística en la actual sociedad catalana

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Modern methods of compositional data analysis are not well known in biomedical research. Moreover, there appear to be few mathematical and statistical researchers working on compositional biomedical problems. Like the earth and environmental sciences, biomedicine has many problems in which the relevant scienti c information is encoded in the relative abundance of key species or categories. I introduce three problems in cancer research in which analysis of compositions plays an important role. The problems involve 1) the classi cation of serum proteomic pro les for early detection of lung cancer, 2) inference of the relative amounts of di erent tissue types in a diagnostic tumor biopsy, and 3) the subcellular localization of the BRCA1 protein, and it's role in breast cancer patient prognosis. For each of these problems I outline a partial solution. However, none of these problems is \solved". I attempt to identify areas in which additional statistical development is needed with the hope of encouraging more compositional data analysts to become involved in biomedical research

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Compositional data naturally arises from the scientific analysis of the chemical composition of archaeological material such as ceramic and glass artefacts. Data of this type can be explored using a variety of techniques, from standard multivariate methods such as principal components analysis and cluster analysis, to methods based upon the use of log-ratios. The general aim is to identify groups of chemically similar artefacts that could potentially be used to answer questions of provenance. This paper will demonstrate work in progress on the development of a documented library of methods, implemented using the statistical package R, for the analysis of compositional data. R is an open source package that makes available very powerful statistical facilities at no cost. We aim to show how, with the aid of statistical software such as R, traditional exploratory multivariate analysis can easily be used alongside, or in combination with, specialist techniques of compositional data analysis. The library has been developed from a core of basic R functionality, together with purpose-written routines arising from our own research (for example that reported at CoDaWork'03). In addition, we have included other appropriate publicly available techniques and libraries that have been implemented in R by other authors. Available functions range from standard multivariate techniques through to various approaches to log-ratio analysis and zero replacement. We also discuss and demonstrate a small selection of relatively new techniques that have hitherto been little-used in archaeometric applications involving compositional data. The application of the library to the analysis of data arising in archaeometry will be demonstrated; results from different analyses will be compared; and the utility of the various methods discussed