Data standards

In appendix, a formal description of the model can be found, using the Express language, based on the STEP (STandard for the Exchange of Product model data) standardized approach (ISO 10303). 

A.J. Davies, Spectral Data Standard Exchange Formats. CSA23. 

Methods for Calculating Fan Sound Ratingsfrom Eaboratoy Test Data Standard 301, Air Moving and Conditioning Association, Arlington Heights, fll., 1990. 

We are free to choose either K or Kc to report the equilibrium constant of a reaction. However, it is important to remember that calculations of an equilibrium constant from thermodynamic tables of data (standard Gibbs free energies of formation, for instance) and Eq. 8 give K, not Kc. In some cases, we need to know Kc after we have calculated K from thermodynamic data, and so we need to be able to convert between these two constants. 

Raw data is almost always incomplete, being highly dependent on the data production platform and often localized to a platform or regional database. Applications (and processes) generate data. However, applications often use proprietary data types and cannot parse data types from other third-party applications. It is important to consider that there are translation issues plus the host of reasons stated below in the requirements for data standards. 

As stated on the OMG (Object Management) website (http //www.omg. org/), a lack of data standards results in data conversions, loss of information, lack of interoperability, etc. Current standards du jour are XML (Extensible Markup Language) [17], LSID (Life Sciences Identifiers), and now the RDF (Resource Description Framework) from the W3C (World Wide Web Consortium), which is extensible though hard to implement. Substantial work on OO (Object Oriented) modeling of life science data types takes place at the OMG s LSR (Life Sciences Research) group—this is discussed below. 

This is why data standards and knowledge management are so important to information management. 

If you embrace standards, it will avoid technology lock-in and make migration and change easier to deal with. This is why information management and data standards are so important to knowledge management... 

In the approaches to data standards, the authors make no apology for using the OMG s life science research group as a structured approach to building new data standards (as both authors have a wealth of experience in bringing standards to the market via this organization [22, 23]). As only a handful of readers will be conversant with the OMG, here is a brief overview on how the OMG works to deliver standards to the life science community. 

There are many products based on these life sciences standards, such as the aforementioned gene expression standard that is used in Rosetta Merck s Resolver product and the European Bioinformatics Institute s (EBI) Array-Express database. The LECIS (Laboratory Equipment Control Interface Specification) standard is used by Creon as part of their Q-DIS data standard support (note that one of the authors was the finalization task force chairperson for this standard). 

It is therefore easy to see why this current drug safety paradigm, with its lack of standards in data collection and analysis, hinders the analysis of adverse events. Without data standards in place, it is difficult to build practical, reusable tools for systematic safety analysis. With no standard tools, truly standardized analyses cannot occur. Reviewers may forget their initial analytical processes if they are not using standardized data and tools. Comprehensive reproducibility and auditability, therefore, become nearly impossible. In practice, the same data sets and analytical processes cannot be easily reused, even by the same reviewers who produced the original data sets and analyses. Not using standardized tools slows the real-time systematic analysis... 

Data standards and interoperable systems. When interoperability is in place, standard, automated software tools for systematically analyzing the data can be constructed. 

The foundation of any efficient computer-assisted data analysis system is the creation and use of data standards. Data standards consist of standard data file names for each predefined file, standard data elements in each data file, standardized names for each data element, and standard definitions for each data element. 

Traditional analytical methods make extensive use of computers, but typically these methods still require constant restructuring of the data and multiple analytical tools. This endless restructuring wastes time and productivity and also makes the analytical processes difficult to document, audit, and reproduce in real time. This situation also makes it difficult to reconstruct and update analyses in real time when new adverse event data become available or when new questions need to be asked. The application of comprehensive data standards allows the use of integrated, reusable software for analyzing adverse event data. This integration facilitates the reproducibility of the results. 

Although 500,000 individuals were enrolled in clinical trials that were submitted to the FDA during 1990-1995 [10], the lack of a repository of clinical trial data, standardized data, and interoperable systems precludes us from efficiently tapping and reanalyzing these data. This missed opportunity underscores the need for standardization and interoperable systems, as discussed above (see Section 27.4.1 on data standards and interoperable systems). 

The Clinical Data Interchange Standards Consortium (CDISC) is a non-profit group that defines clinical data standards for the pharmaceutical industry. CDISC has developed numerous data models that you should familiarize yourself with. Four of these models are of particular importance to you ... 

The Clinical Data Interchange Standards Consortium (CDISC) and its Submission Data Standards group have provided another way to broadly categorize clinical trial data. 

Because XML is an open standard, many industries are developing open standards for XML data exchange. CDISC is the organization leading XML data standardization for the clinical trial industry. 

Given the same underlying spread of data (standard deviation, s), as more data are gathered, we become more confident of the mean value, x, being an accurate representation of the population mean, x. 

Reference buffer solutions, 14 25-26 Reference data, standard, 15 747 Reference dose (RfD), 25 238, 239 Reference electrodes, 9 571-574 ... 

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