Data types/sources

Fig. 14. Compensation plot for dehydroxylation of kaolinite ( ) and other layer-type silicates (X = montmorillonite, illite and muscovite) data and sources given in Table 11. (Redrawn, with permission, from Advances in Catalysis, ref. 36).

The list of available data sources associated with a specific concept type (source, concept type, flag security, position in the list of displayed Ultra-Links for that source)... 

The detailed design description is translated into source code. Useful descriptions for a module, including expected inputs and outputs, operations to be performed, and expected data types, are often provided in code comments. Source code evaluations, such as code inspections and code walkthroughs, should be conducted to verily compliance with the corresponding design specifications. 

An axis for data type (e.g., source of data on which extrapolations or decisions are based — e.g., laboratory data or field data)... 

In addition to handling large amounts of data generated automatically, the LIMS database must handle data from a number of data sources Instruments, terminals, personal work stations, and other computers. Not only does data exist in several forms but textual information such as header records, comments, reports and other documents must be accommodated. There exist well-defined relationships among the various data types in the laboratory. The dataset relationships must be carefully considered in designing the database. All data in the LIMS must be accessible by key fields such as sample number, method, instrument I.D. or laboratory. It is also necessary to support access of the stored data by ad hoc queries to extract information for correlations, summaries, retrospective studies and special reports. 

Data structures and content must be dehned. Actual data to be loaded into tables, hies, and databases mnst be specihed by reference to their sources. Data dictionaries should be used to describe different data types. Specihc data requirements include ... 

Generate a statistical description (means, trends, variances and correlations) of the reservoir flow field. Doing this requires a rather massive amount of data primary sources are well data, outcrop analogues, seismic profiling and "type functions based bn stratification types and depositional environment. In an ideal case, there should be such a statistical description for every input variable for the reservoir simulator. 

When you activate an embedded chart (by clicking cn it) or switch to a chart sheet, the Worksheet Menu Bar is replaced by the Chart Menu Bar. The first four commands in the Chart menu — Chart Type..., Source Data..., Chart... 

FIGURE 25.3 Physicochemical property distributions of proximately 75,000 biologically active Ugands found in Pfizer s screening data as a function of target type. Source Data courtesy of Barker, Snarey, Groom and Hopkins. 

The requirements for automatic interpretation of SOPs mentioned already leads us to another approach that is a general use for any steps in a laboratory workflow. If we look at the operator-entering data, we have to keep several critical sources of errors in mind typing errors, data type errors, formats errors, and data limit errors. One valuable solution based on expert system technology is a system that verities the data entered by the operator on the basis of rules. 

A relational table has a name, chosen when it is created. Although any name is possible, the name typically reflects the nature or source of the data contained in the table. Each column must also have a name. Consider Table 2.1, called EPA since it was constructed from data provided by the Environmental Protection Agency.2 This table is readily understandable to any chemist. Each row contains information about one compound and each column contains a molecular attribute or property. In order to make it part of a relational database, a minimum of two things must be specified for each column the column name and the column data type. In this example, the column names are Name, Formula, MW, logP, and MP corresponding to the compound name, molecular formula, molecular weight, octanol-water partition coefficient, and melting point. The column name in a relational table is arbitrary but is usually representative of the data contained in the column. 

Data as of January 1 A Exclusive of steam and garden types Source. Department of Agriculture... 

Figure 9. Oxygen isotope compositions of nominally fresh MORE glasses and whole-rocks. Unfilled boxes are data collected using conventional (resistance heated) fluorination methods between 1966 and 1993 filled boxes are data collected only on glass using laser-based methods. Where these two data types overlap, conventional fluorination data are shown as white-outlined boxes. Data sources Taylor (1968), Muehlenbachs and Clayton (1972), Pineau et al. (1976), Kyser et al. (1982), Muehlenbachs and Byerly (1982), Ito et al. (1987), Barrat et al. (1993), Harmon and Hoefs (1995) and references therein, Eiler et al. (2000b), and Eiler and Kitchen (unpublished data).

There are a number of sources of data for failure rates, including Failure Modes and Effects Analysis (FMEA)/Failure Modes and Effects Summary (FMES), product data sheets, failure tests data and accelerated life tests. However, these sources ultimately stem from the following data types ... 

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