Data Explorer

The chemometric basic tools may be divided into the following typologies of study data exploration, modelling, prediction and validation, design of experiments (DOE), process analytical technology (PAT), quantitative structure-activity relationship (QSAR). Details and relevant literature are reported in the following paragraphs. 

Research and development determining chemical kinetic mechanisms and parameters from laboratory or pilot-plant reaction data exploring the effects of different operating conditions for optimization and control studies aiding in scale-up calculations. 

Jones T, Kang 1, Wheeler D et al (2008) CellProfiler analyst data exploration and analysis software for complex image-based screens. BMC Bioinformatics 9 482... 

Michael S. Lajiness Structural and Computational Sciences, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, Indiana, USA Raymond L.H. Lam Department of Data Exploration Sciences, GlaxoSmithKline, King of Prussia, Pennsylvania, USA Barry K. La vine Department of Chemistry, Clarkson University, Potsdam, New York, USA... 

Before pursuing a large series of DFT calculations for a system of interest, numerical data exploring the convergence of the calculations with respect to the number of k points should be obtained. 

In Section 3.3, the background material developed in Section 3.2 is used in a discussion of practical issues involved in the selection of distributions, particularly for models of pesticide ecological risk. The topics discussed include data representativeness, preliminary data exploration, selection of distribution type, estimation of distribution parameters (distribution fitting), and evaluation of distribution fit. 

Like HCA, a is an excellent tool for preliminary data exploration. It is useful for examisiag data sets for expected or unexpected clusters, including the presence c outliers. 

Oliveri et al. (2009) presented the development of an artificial tongue based on cyclic voltammetry at Pt microdisk electrodes for the classification of olive oils according to their geographical origin the measurements are made directly in the oil samples, previously mixed with a proper quantity of a RTIL (room temperature ionic liquid). The pattern recognition techniques applied were PCA for data exploration and fc-NN for classification, validating the results by means of a cross-validation procedure with five cancellation groups. 

Always start pattern recognition by performing a preliminary multivariate data exploration PCA is a perfect tool for this purpose, being useful to visualize structures (groupings, correlation) within the data and to make decisions about the subsequent processing steps. 

The last approach is widely used for practical data exploration and yields a biplot in which PC eigenvalues are plotted against PC numbers (Figure 15.12). Usually the PCs retained are those on the slope of the graph before the decrease in eigenvalues levels off to the right of the plot. In the example presented, three PCs can be retained according to the scree test. 

Record the spectra in reflectron mode using an acceleration voltage of 20 kV, 70% grid voltage and a delay of 1.277 ns. Generate MS spectra by accumulating 2000 laser shots (Fig. 1). Analyze the mass spectra with the Data Explorer Software Version 4.6 (Applied Biosystems GmbH) (see Note 12). 

De Leiva Moreno, J.I., Agnostini, V.N., Caddy, J.F., and Carocci, F. (2000) Is the pelagic-demersal ratio from fishery landings a useful proxy for nutrient availability A preliminary data exploration for the semi-enclosed seas around Europe, ICES. J. Mar. Sci. 57, 1091-1102. 

An Extendable Package of Programs for Data Exploration, Classification and Correlation... 

A data matrix produced by compositional analysis commonly contains 10 or more metric variables (elemental concentrations) determined for an even greater number of observations. The bridge between this multidimensional data matrix and the desired archaeological interpretation is multivariate analysis. The purposes of multivariate analysis are data exploration, hypothesis generation, hypothesis testing, and data reduction. Application of multivariate techniques to data for these purposes entails an assumption that some form of structure exists within the data matrix. The notion of structure is therefore fundamental to compositional investigations. 

Exploratory data analysis is a collection of techniques that search for structure in a data set before calculating any statistic model [Krzanowski, 1988]. Its purpose is to obtain information about the data distribution, about the presence of outliers and clusters, and to disclose relationships and correlations between objects and/or variables. Principal component analysis and cluster analysis are the most well-known techniques for data exploration [Jolliffe, 1986 Jackson, 1991 Basilevsky, 1994]. 

Different extraction functions, for example cutting-, clipping- or iso-planes for interactive data exploration, are implemented in TECK. They are all filtering functions for the simulation data sets that help to reduce the overall visual complexity. Fig. 5.37 shows a user who interacts with simulation data in the CAVE. He is using the iso-surface and cutting-plane functions. 

Data exploration is a scientific exercise where we try to learn things about the data, for example, how the covariates are distributed and how they relate to each other. The exploratory data analysis also defines the population—and thereby the bounds for the validity of the model—and will form the basis for reporting the analysis to others. The data exploration is also important from an error finding point of view since some errors only become apparent when closely studying the data. 

Forina, M., Lanteri, S., and Armanino, C. (2000) Q-RARVUS Release 3.0. An extendable package of programs for data explorative analysis, classification and regression analysis, http //parvus.unige.it (accessed July 10, 2009). 

Partial and total order ranking strategies, which from a mathematical point of view are based on elementary methods of Discrete Mathematics, appear as an attractive and simple tool to perform data analysis. Moreover order ranking strategies seem to be a very useful tool not only to perform data exploration but also to develop order-ranking models, being a possible alternative to conventional QSAR methods. In fact, when data material is characterised by uncertainties, order methods can be used as alternative to statistical methods such as multiple linear regression (MLR), since they do not require specific functional relationship between the independent variables and the dependent variables (responses). 

Partial and total ranking methods have been widely used to perform data exploration, investigate the inter-relationships of objects and/or variables and set priorities. However it appears a very useful tool even for modelling purposes. Mathematical models have become an extremely useful tool in several scientific fields like environmental monitoring, risk assessment, QSAR and QSPR, i.e. in the search for quantitative relationships between the molecular structure and the biological activity/ chemical properties of chemicals. 

---