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The final aim of the quantitative approach is not just to explain a particular set of developments but to understand broader processes of change and make a theoretical contribution to some facet of historical understanding. Charles D. Tupper, in Data Architecture , Quantification of the amount of data on the database is critical in making decisions about the choice of platform and DBMS. Three factors will affect the population of the database after its initial installation and population: growth, purge, and archive.

Growth is affected by two things. These account for the steady increase in size of a normal database that is keeping track of its data over time. The second way growth is affected is the number of bytes changed with the update of the variable-length fields.

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The first can have a profound impact on the database if there are many tables with long rows. While the second of these two effects of growth seems trivial, let me remind you of the 4,byte variable fields seen so commonly nowadays that will be used for comments on the database once the history starts rolling in. These Text and Comments fields tend to be landmines that can cause explosions when least expected. Allow for them, because they will happen. While this is a desirable thing, remember that the population has to be at maximum before the purge or archive will resize the database population.

This does not apply to logically deleted rows, where the row is marked but never removed. These logically deleted rows are just status marked and have no effect on the population of the database. In fact, they may sometimes inhibit active efficient processing from taking place due to the exception logic that must be put into the application programs to bypass them. Logical deletes and purging will be covered in the next chapter. Data archive is another way in which the database population is affected. If an archive sweep based on data is run, then the diminishment counts of the database should be applied to the annual calculation of the database population.

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If it is a single-point archive, such as every third year, this should be documented as a cyclical data archive of the database so proper DASD can be allocated as the cycle continues. Alberta Tomassini, Quantification is the passage that transforms a set of NMR spectra into a data matrix. Figure 3. Strategy for NMR-based metabolomic analysis: pattern recognition red route and targeted analysis blue route. This latter strategy is performed on the whole set of spectra at once and requires a series of automatic, or semi-automatic steps that can include peak alignment [22,23] and then discretization by subdividing the spectral range in small fractions bucketing or binning.

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The first, more obvious binning procedure divided the spectra in regular intervals of equal size: this can lead to unreliable results if peaks are not perfectly aligned and do not have similar linewidths in each spectrum. More sophisticated algorithms have been developed [24,25] to address this issue and are included in the most popular NMR analysis software packages.

The result is a data matrix that embeds all the information included in the spectra, but whose variables spectral intervals do not have a direct metabolic meaning: multivariate data analysis is then applied for variable reduction to unravel the structural and metabolic relationships subtended by the spectra. Both targeted analysis and pattern recognition method require the assignment of the resonance signals to their source metabolites in work flow Figure 3.

This procedure needs the operator's skill, which can be based on a large set of 2D NMR techniques bidimensional homonuclear spectroscopy like correlation spectroscopy and total correlation spectroscopy experiments or bidimensional heteronuclear spectroscopy like heteronuclear single quantum coherence or heteronuclear multiple bond correlation , on a number of either publicly available [26—28] or proprietary databases, on reference spectra, and on literature data. Some effort has been done to automate this process [29,30].

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  4. One more available tool is given by Statistical Total Correlation Spectroscopy [31] : variables from the unsupervised matrix are correlated pairwise and the correlation coefficients are arranged in a correlation matrix. The strongest correlations are due to signals belonging to the same molecules.

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    Table of contents Introduction. II Descriptive Statistics. Selection procedures. V Inferential Statistics for more than two Characters. Multivariate analysis of variance. Downloads Datasets Download.