Data reduction and presentation

Data have been reduced where information is available from more than one source or where it is more meaningful to represent the data in a different way from that used by the original author. The reader should consult the source document if more detailed information is required. 

Where scatter about a mean is reported, this is incorporated by scatter bars on graphs or directly within the tables. 

---

Howe TJ, Mahieu G, Maricahl P, Tabruyn T, Vugts P. Data reduction and presentation in drug discovery. Drug Disc. Today 2007 12 45-53. 

Another time requirement in 2D separations is the time needed for data processing and presentation. With several dozen transfers between dimensions, data reduction, and presentation can be very time-consuming [33]. There is a clear need for speciahzed 2D software, which does all the data acquisition, fraction transfer, valve switching, data reduction, data consoUdadon, and presentation of results. Currently, only one 2D chromatography data system is commercially available [34],... 

Crystal data and details of data collection, data reduction and final refinement are reported in Table 1. The procedure for data collection and processing, which included a correction for scan-truncation effects, were similar to those recently described for syn-l,6 8,13-biscarbonyl[14]annulene [10] and citrinin [11], Figure 1 shows the numbering scheme adopted in the present analysis. 

If the spectrum is satisfactory, you can get to work on it. Can you identify any obvious impurities or solvents that might be present Crossing them off at this stage is a valuable exercise in data reduction and clears the way ahead so that you can concentrate on the important peaks. 

In this section we present the calculation (Kaplan et al, 1982,1983, 1985) of parameters describing the excitations of the P source used by the ITEP group in a series of experiments for measuring the neutrino rest mass. The results of these calculations were used by the ITEP group for data reduction and estimation of mv. 

As compositional analysis has become more routine in archaeological investigations, deficiencies in the numerical techniques used for data reduction and summary have become more apparent. A brief overview of techniques commonly used in the analysis of compositional data is presented as well as an example illustrating how data modeling (as opposed to data summary) can facilitate both the recognition of relevant data structure and inferences from data structure to underlying natural and cultural processes. 

PowderSD Powder3D 1.0 A multi pattern data reduction and graphical presentation software, B. Hinrichsen, R.E. Dinnebier and M. Jansen, 2004 http //www.fkf.mpg.de/xray/ html/powder3d.html Integration of full 2D cones of diffraction under development... 

S. D. Johnston, C. M. Moreton Smith, R. G. Sergeant and D. S. Si via, Rutherford Appleton Laboratory Technical Report RAL TR 1999 031 PowderSD 1.0 A multi pattern data reduction and graphical presentation software,... 

Powder3D 1.0 A multi pattern data reduction and graphical presentation software,... 

Principal components analysis (PGA), a multivariate statistical technique, was used for data reduction and pattern recognition [8]. PGA represents the variation present in many variables using a small number of principal components (PCs). PCA functions by finding a new set of axes, which more efficiently describe the variance in the data. Samples are no longer described by their intensities in... 

The radioactive-tracer technique was perfected for. fluidized-bed application through the development of a computer-aided particle-tracking facility (CAPTF) (Lin, Chen, and Chao, 1985 Moslemian, 1987 Sun, 1989). Considerable effort was expended to develop the efficient photoncounting instrumentation and automated data reduction and processing schemes. In Section 9.2, the principle of radiation detection and a theoretical model of the CAPTF is presented. The instrumentation of the CAPTF and the data reduction schemes are described in Section 9.3. Sample results obtained by the CAPTF are presented in Sections 9.4 and 9.5, and a conclusion is presented in Section 9.6. 

The Committee on Data for Science and Technology (CODATA) has conducted a project to establish internationally agreed values for the thermodynamic properties of key chemical substances. This table presents the final results of the project. Use of these recommended, internally consistent values is encouraged in the analysis of thermodynamic measurements, data reduction, and preparation of other thermodynamic tables. 

While automated data aquisition from laboratory instruments, control of experiments through robotics, automated data reduction and summary analysis have increased productivity in many laboratories, much work remains to be done. Automation from data acquisition through data presentation will be expanded. The manual keying of data will be minimised regardless of whether the source of the data is a laboratory instrument or an online database. Further, experimental design tools will be developed. 

Figure6.10 shows the results obtained when applying the data reduction and image reconstruction algorithms to the baselines from 6.74 to 28.98 m with values of Dynamical Range (DR) 80 to 15, respectively. Spatially (left) the inner gap radius cannot be measured, as expected the interferometric beam is bigger than the radius at these baselines. Spectrally, however, one can extract that different points of the extended source present different levels of continuum.

It has become common practice to present MTDSC results both as heat flow and heat capacity within the same data set. Tjqjically, the greatest source of error is that from the empty-pan baseline for the average signal. Many workers, in order to save time, choose not to make this correction and then, almost by default, the total signal is presented as heat flow rather than heat capacity, because this correction has not been made. This is not necessarily bad practice. The experimenters may well be able to obtain the information they require without this additional calibration step. Consequently, one must take care when reading the literature to ascertain what types of data reduction and calibration have been carried out. The questions that must be asked are as follows. 

However, probabilistic methods can be applied only to discrete ordered monotonous characters. Continuous or unordered, non-monotonous characters can only be analysed after transformation or reduction. This can lead to many additional problems that are beyond the scope of the present chapter (but see (jrzymala-Busse, 2002, for an overview on data reduction and discretization problems). The method used to create probabilistic profiles for species is another major issue since the number and the diversity of strains taken into account can have a profound impact on the basic probabilities and, therefore, on the resulting identifications. 

---