Data discovery

Source discrimination was accomplished by examining a series of two- and three-dimensional plots of the obsidian source data. Discovery of graphical representations which show the clearest picture of inter-source versus intra-source variation, makes possible source discrimination with a high degree of confidence. The greater the number of elements that one can use to reinforce the observed discrimination the smaller becomes the chances for misassignment of artifacts when compared to the obsidian source database. 

There is a strong dependence of advances in enzymology on agriculture, based on both historical and present data. Discovery of enzymes and elucidation of many of their properties were by agricultural chemists. 

Data Discovery, bioinformatics and cheminformatics, and called naive Bayes... 

We evaluated [16] the choice of suitable P2P technology for SCADA systems. Regarding the requirements of interconnected large-scale SCADA systems, structured P2P networks with Distributed Hash Tables (DHT) are appropriate for three reasons, i.e., (i) low routing latencies, (ii) good scalabihty, and (iii) data discovery guarantees in DHTs [16]. 

Spotfire Data discovery and visualization, predictive analytics [56]... 

A very important data mining task is the discovery of characteristic descriptions for subsets of data, which characterize its members and distinguish it from other subsets. Descriptions can, for example, be the output of statistical methods like average or variance. 

U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, Menlo Park, 1996. 

U. Fayyad, G. Grinstein, A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufman Publishers, San Frandsco, USA, 2002. 

Nowadays a broad range of methods is available in the field of chemoinfor-matics. These methods will have a growing impact on drug design. In particular, the discovery of new lead structures and their optimization will profit by virtual saeening [17, 66, 100-103]. The huge amounts of data produced by HTS and combinatorial chemistry enforce the use of database and data mining techniques. 

P. Smyth, From data mining to knowledge discovery An overview, in Advances in Knowledge Discovery and Datamining, U. M. Fayyad,... 

The thermodynamic study of thiazole and of some of its binary mixtures with various solvents has led to the determination of important practical data, but also to the discovery of association phenomena between thiazole and some solvents and of thiazole self-association. 

The discovery of the phenomenon that is now known as extended X-ray absorption fine structure (EXAFS) was made in the 1920s, however, it wasn t until the 1970s that two developments set the foundation for the theory and practice of EXAFS measurements. The first was the demonstration of mathematical algorithms for the analysis of EXAFS data. The second was the advent of intense synchrotron radiation of X-ray wavelengths that immensely facilitated the acquisition of these data. During the past two decades, the use of EXAFS has become firmly established as a practical and powerfiil analytical capability for structure determination. ... 

Evaluate airflow patterns into and within the complaint area. Because of the complexity and variability of airflow patterns, investigators cannot be expected to understand how air moves within the building under all potential operating conditions. However, data on pathways and driving forces can help to locate potential pollutant sources and to understand how contaminants are transported to building occupants. The discovery of unexpected pollutant pathways can show a need to study areas of the building that may be distant from the original complaint area. 

Be sure to remind students that these frequencies are gas phase data and arc thus not the same as the more-faniiliar solution spectra (we will treat solvated systems in Chapter 9). Even so, such gas phase calculations make excellent discovery-based exercises, For example, students may be asked to explain the substituent effects observed tising basic chemistry knowledge. 

L. W. Alvarez (Berkeley) decisive contributions to elementary particle physics, in particular the discovery of a large number of resonance states, made possible by the hydrogen bubble chamber technique and data analysis. 

The data on which Fig. 8.74 is based are for tests carried out in carbonate well-water. McAdam made the further interesting discovery that if mild steel were tested in condenser water and a similar graph constructed, the set of contours corresponded more closely to the right-hand side of Fig. 8.74, i.e. the behaviour of mild steel in condenser water was similar to that of Monel in carbonate water. The apparent universality of this diagram is an interesting observation, but it has not provoked a basic theory of corrosion fatigue. 

A classic example of where definitive experimental data necessitated refinement and extension of a model of drug-receptor interaction involved the discovery of constitutive receptor activity in GPCR systems. The state of the art model before this finding was the ternary complex model for GPCRs, a model that cannot accommodate ligand-independent (constitutive) receptor activity. 

Wienkers, L. C., and Heath, T. G. (2005). Predicting in vivo drug interactions from in vitro drug discovery data. Nature Rev. Drug Discovery 4 825-833. 

Mendeleev s reluctance toward reduction was not widely shared. One of the codiscoverers of the periodic system, the German Lothar Meyer, accepted the possibility of primary matter and supported Prouf s hypothesis. He was also happy to draw curves through numerical data, including his famous plot of atomic volumes that showed such remarkable periodicity that it helped in the acceptance of the periodic system. Nonetheless, prior to Thomson s discovery of the electron, no accepted model of atomic substructure existed to explain the periodic system, and the matter was still very much in dispute. 

Figure 2. Dimitri Mendeleev s discovery of the periodic system in 1869 was quickly followed by controversy over how it should be used, Mendeleev resisted reduction or explanation of the system in terms of atomic structure, and was specifically opposed to attempts to draw curves through points representing numerical data Lothar Meyer, who contributed to the discovery of the periodic system, was not so averse to reduction, however. One of his graphs, shown here in an 1870 publication, plotted atomic volume and was instrumental in the acceptance of the periodic system.

The importance of this discovery lay in the marrying of qualitative chemical properties, such as degree of reactivity, with numerical data on the elements. It suggested that there might be some underlying numerical order that could serve to relate the elements to one another in a systematic way. 

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