References data sets

Geometry level at whieh the strueture is optimized higher-order eon-elation method(s) for estimating higher-order eorrelation effeets thermo level at which the thermodynamieal eoneetions are ealeulated [vibrational seale factor] MAD Mean Absolute Deviation for reference data set in kcal/mol. 

The baseline data set must be updated each time the machine is repaired, rebuilt, or when major maintenance is performed. Even when best practices are used, machinery cannot be restored to as-new condition when major maintenance is performed. Therefore, a new baseline or reference data set must be established following these events. 

A series of baseline or reference data sets should be taken for each machine-train included in a predictive-maintenance program. These data sets are necessary for future use as a reference point for trends, time traces, and FFT signatures that are collected over time. Such baseline data sets must be representative of the normal... 

M. G. Cox and P. M. Harris, Design and use of reference data sets for testing scientific software, Analytica Chimica Acta, 380, 339-351 (1999). 

B. P. Butler, M. G. Cox, S. L. R. Ellison, and W. A. Hardcastle, Statistics Software Qualification (Reference Data Sets), The Royal Society of Chemistry, ISBN 0-85404-422-1. 

Fig. 2.19. Various sets of analytical data (A) Hard reference data set, mR(x). (B) Hard test data set, mT(x), which is slightly shifted compared with (A). (C) Fuzzy set of test data, mT(x) = exp (—(x — a)2/b2). (D) Intersection mT R(x) of test data and reference data which is empty in this case. (E) Intersection of fuzzed test data and reference data with a membership value of about 0.8 in this case...

Zadeh [1975] extended the classical set theory to the so-called fuzzy set theory, introducing membership functions that can take on any value between 0 and 1. As illustrated by the intersection of the (hard) reference data set (A) and the fuzzed test data set (C), the intersection (E) shows an agreement of about 80%. Details on application of fuzzy set theory in analytical chemistry can be found in Blaffert [1984], Otto and Bandemer [ 1986a,b] and Otto et al. [1992],... 

Vertzoni et al. (30) recently clarified the applicability of the similarity factor, the difference factor, and the Rescigno index in the comparison of cumulative data sets. Although all these indices should be used with caution (because inclusion of too many data points in the plateau region will lead to the outcome that the profiles are more similar and because the cutoff time per percentage dissolved is empirically chosen and not based on theory), all can be useful for comparing two cumulative data sets. When the measurement error is low, i.e., the data have low variability, mean profiles can be used and any one of these indices could be used. Selection depends on the nature of the difference one wishes to estimate and the existence of a reference data set. When data are more variable, index evaluation must be done on a confidence interval basis and selection of the appropriate index, depends on the number of the replications per data set in addition to the type of difference one wishes to estimate. When a large number of replications per data set are available (e.g., 12), construction of nonparametric or bootstrap confidence intervals of the similarity factor appears to be the most reliable of the three methods, provided that the plateau level is 100. With a restricted number of replications per data set (e.g., three), any of the three indices can be used, provided either non-parametric or bootstrap confidence intervals are determined (30). 

Using the data with built-in error generated in the previous section (six replications per data set), for every c value, two test data sets (b = 0.5 and b = 1.5) were separately compared with a reference data set (b = 1). The estimated total amount dissolved (W0) of the test and the reference data sets were compared by constructing confidence intervals at the 0.05 level for their mean differences. Estimated shape parameter, c, and scale parameter, b, of the test and the reference... 

The asterisk denotes that the difference factor, f (31), and of the Rescigno index, (32), have been adjusted to apply to non-cumulative data T and R denote the test and the reference data set, respectively and i is usually set equal to 1 or 2 (30,32). 

The second difference relates to the definition of a cutoff time point for the evaluation of the difference factor and the Rescigno index. When cumulative data are available, evaluation of the difference factor or the Rescigno index usually requires a reference data set in order to define the cutoff time point for index evaluation (30). For the evaluation of fl and the , i.e., when the difference factor and the Rescigno index are evaluated from non-cumulative data, this difficulty does not exist, provided that the release process has been monitored up to the end (i.e., until dissolution of the drug is complete). At this point, it is worth mentioning that a similar conclusion cannot be drawn for the similarity factor (31) because application of this index to non-cumulative data is set apart by the careful scaling procedure required, in addition to the existence of a reference data set. The reason is that this index can continue to change even after dissolution of both products is complete. 

Note The shape parameter was the same for the test and the reference data sets. In all cases b reference — 1 ... 

Before the methodology can be implemented, some data must be available. There must be replicate data sets from which experimental errors may be estimated. If the reactions are catalytic, it Is highly recommended that the data include at least three different batches of catalyst (not 3 samples of the same batch). If the catalyst is experimental (either proprietary or from a vendor), it 1s also highly recommended that the reference data set include six different batches of catalyst. This is to identify unacceptably large variations in the catalyst and to reduce the possibility of formulating a set of rate equations which do not represent the catalyst batches which can be reproduced. The reference data set must also include results at two or more temperatures. 

Saccharide Topology Gaucher et al. (23) Reference data set containing set of all possible... 

In the absence of reliable predictive methods for assessing potential immunological cross-reactivity, a comparative sequence analysis approach can be used to identify whether or not known epitopes from human proteins exist in a candidate vaccine antigen. The Immune Epitope Database (IEDB) provides a comprehensive reference data set for known epitopes.78 When screening potential vaccine candidates, the presence of known human-derived epitopes can be ruled out by comparison against the IEDB (www.iedb.org). 

Table 11.1 shows the sea surface area of the grid cells, computed from the RANGS shoreline polygons (Feistel, 1999) with about 100 m resolution, on the WGS-84 ellipsoid (NIMA, 2000). Table 11.2 shows the vertically integrated water volume beneath these surface elements, computed from the topography of Seifert et al. (2001). A complete list of all particular cell volumina is provided in the Digital Supplement of this book, together with the reference data sets used for this purpose (compare Chapter 20). 

Gotland Basin, Stolpe Furrow regular grids 2.5 Nautical soundings Reissmann (1999) reference data sets... 

A comparison of the DCS value estimated using in silica, HTS and rat in situ perfusion estimated for a given subset of clinical candidates is given in Table 1 based on internal historical data. As indicated, in silica data predicted DCS class correctly in 74% of cases and HTS data in 81% of cases using rat in situ perfusion as the reference data set. In many cases, errors were associated with the misclassification of DCS Class I or III with class II or IV (permeability errors). Misclassifications based on solubility errors were less common. Also some very poorly soluble Class II compounds can masquerade as Class IV candidates based on precipitation and adsorption to the hlters and plastics associated with the permeability apparatus. 

Describing the diversity of a data collection with a unique measure is almost impossible. Such a measure would depend on its relationship to a generally valid reference data set, which is hard to define. In fact, the terms similarity and diversity can have quite different meanings in chemical investigations. In the simplest case, similarity concerns structural features, which are, in fact, easy to determine. Similarity in a more general chemical context typically includes additional properties and is in most cases hard to describe as an individual feature. 

---