There is a growing demand for digital storage and archiving systems for analytical instrument data. Although there are data archiving methods currently in the market, they are incomplete solutions for long term archiving of data from analytical instruments. In general, these systems offer a centralized method of indexing, storing and retrieving the original binary data files generated by the many proprietary instrument control software packages used throughout the laboratory. When a particular piece of data is retrieved from such a system, it is viewed using that same proprietary software that generated it.

There are two principal problems with this approach:

1. Laboratories typically have many more people that need to access the data than they have computers running the proprietary software required to view it. In some cases, those people may be in a different lab, building or country than the proprietary data station software. It is impractical to deliver copies of proprietary software applications to every person who might need to access the data.

2. Instruments and data systems often have shorter lifetimes than the required retention periods for the actual data they generate. It is very likely that when a critical piece of data is needed some time in the distant future (to demonstrate compliance to a regulator or for a legal defense of a company's intellectual property), the data station software, hardware or even operating system is obsolete or cannot be obtained anymore.

Users must be able to access, view and potentially even reprocess the data in the archive throughout the entire record retention lifetime and beyond. Thus, in order to make data accessible for an undetermined length of time, it must be "normalized" in such a way that it can be easily understood outside the realm of any individual software system.

It is from this understanding that we have proposed a new XML-based data model. GAML.ORG will focus on how XML solves a number of issues related to access and storage of analytical instrument data and will describe the features of the proposed data model in detail.



Last Updated: June 22, 2007

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