Data resource files

All setup data and results are stored in Data Resource Files rather than standard sequential or random access files. This has turned out to be a key feature contributing to a flexible and low maintenance system. These are ASCII files which can be typed to the screen, printed, or edited with a word processor. They contain information organized by variable name, value, and units. The following is an example of a data resource file ... 

Comment This is a sample data resource file [Student I Joe Doe ]... 

Data Resource Files allow the user to access all pertinent data as needed, reducing the need for programmer support or hard code to access specialized variables. The data storage format is very user friendly in that if a user wishes to make changes, he can comment fields as desired and does not need to worry about a formatted sequential file sti ucture. 

The flexibility of data resource files also facilitate dynamic storage so that the user can generate variable names and values during program operation. This is done in the Identify section in which the user can save and later access data under improvised names. 

The only data not stored as Data Resource Files is raw numerical data for curves. This is stored in binary to conserve disk space and speed access but is accessible via editing and ascii conversion options. 

This information is saved under any name desired by the user. It is thereafter accessible through the report generator. If the user frequently saves data, the variable names can be permanently stored and accessed from the identify setup data resource file. 

The goals of flexibility without complication and minimum software maintenance were achieved largely through the use of data resource files, the report generator, and the transform utility. These features are invisible to the casual user who is content with the standard report form and transform setup provided him. The more advanced user, however, can easily access all data, customize reports, and perform unique transformations. 

SAIC provided much of the data used in this book from its proprietary files of previously analyzed and selected information. Since these data were primarily from the nuclear power industry, a literature search and industry survey described in Chapter 4 were conducted to locate other sources of data specific to the process equipment types in the CCPS Taxonomy. Candidate data resources identified through this effort were reviewed, and the appropriate ones were selected. Applicable failure rate data were extracted from them for the CCPS Generic Failure Rate Data Base. The resources that provided failure information are listed in Table 5.1 with data reference numbers used in the data tables to show where the data originated. 

The data points from SAIC s data base were already stored in CARP files. Data extracted from the CPI data resources were also entered into CARP files for storage and organization of the data points by their relevant taxonomy levels. 

The MARS program access resource file data by means of general functions which allow the program to load the necessary resource file into memory, search for variables, edit variables, and re-save the file to disk. Any number of resource files can be loaded into memory at once. Even quite large resource files are loaded rapidly into memory since the data is packed and no assignments are made at that time. The search and replace functions are also so fast that data access time is never perceptible. 

The user is able to customize the transform setup resource file to include as many custom scripts or unit transforms as desired for a particular application. In addition, the user can improvise operations on the fly. If the analyst decides to try plotting the data as a function of a reciprocal log, he takes the log and then reciprocal of the x-axis. If he decides to make this a regular option, he can enter it in the transform setup list under any name desired. Next time the transform option is called he will be able to select that operation by choosing it from the list under the name saved under. 

In the bioinformatics realm, SRS (Sequence Retrieval System) [2] is a popular system, which uses a centralized collection of data resources primarily in flat text file form and, more recently, handles XML (Extensible Markup Language) files as well. Data resources are treated in a federated manner since each is maintained in its original form. However, SRS contains a large number of cross-references between corresponding fields in various data sources, so that keyword searches can be done across them. SRS thus performs more structured searches across the information than what a simple text search provides (such as web indexes perform, for example). Even though the data model implicit in the cross-reference tables is not very deep, SRS provides a useful way for users to browse and do simple queries across a large number of data sources as well as to integrate results from some computational methods. 

The MEPAS shell enables faster, more efficient, and better documented applications. With the addition of the MEPAS shell, fewer resources need to be directed to computer-level details of model implementation. Because the special level of computer expertise (i.e., knowing how to enter data in files to match a FORTRAN format statement) was eliminated, the time for training data entry personnel was greatly reduced. 

Eastman Kodak has many laboratories worldwide, all with their own information departments, but the Information and Computer Technology Division in Rochester, and in particular the Application and Data Resources Unit, is responsible for the 555,000 compounds in the Chemical Registry System (under both MACCS and CAS Registry System software). There are also on file 17000 reactions under REACCS, 190,000 reports and 65,000 patents in a photographic patents index. The Chemical Information Centre holds 1,100,000 index cards, half of them in accession number order and half in molecular formula sequence. 

Reads California Air Resources Board data files. 

Note SAIC has selected some data from resources 8.1 through 8.15 to construct its proprietary data files for use in performing PRAs. Relevant data from these files was used to construct the CCPS Generic Failure Rate Data Base. Accordingly, all usable data points contained in the resources used by SAIC may not be in the Data Tables in this book. 

Recursive procedures demand special attention to flow control and data structures. For instance, the flow control within the procedure must correctly handle an error, say missing file information or inconsistent data, that is discovered several iterations deep. Should the procedure break and return to the previous level only Should it force return to the level of the original invocation of the procedure Should it allow an interactive user a choice of supplying missing data, and if so, on any level of iteration If files are opened within the procedures, should they be closed when a recursive call is needed, or are new channel numbers to be requested, using up system resources ... 

Taken from Mossbauer Effect Data Center (MEDC), Prof John Stevens, University of North Carolina, Asheville, NC, USA, September 2009 for a full list of the nuclear properties for all known Mossbauer isotopes see the MEDC web address http //orgs. unca.edu/medc/Resources.html, or the corresponding pdf file in the CD-ROM of this book An older report [46] states -720 mb the value reported by MEDC is -789 mb... 

Bickerstaff, D. 1998. Alaska resource data file. Lake Clark quadrangle, Alaska. U.S. Geological Survey Open-File Report 1998-359, 109 p. 

Figure 16.2 Data entry screen forAFAR,theAggregated Fate Assessment Resource (SRC/ Fate Pointers file).

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