The goal of this brief paper is to examine what it will take to advance laboratory operations in terms of technical content, data quality, and productivity. Advancements in the past have been incremental, and isolated, the result of an individual’s or group’s work and not part of a broad industry plan. Disjointed, uncoordinated, incremental improvements have to give way to planned, directed methods, such that appropriate standards and products can be developed and mutually beneficial R&D programs instituted. We’ve long since entered a phase where the cost of technology development and implementation is too high to rely on a “let’s try this” approach as the dominant methodology. Making progress in lab technologies is too important to be done without some direction (i.e., deliberate planning). Individual insights, inspiration, and “out of the box” thinking is always valuable; it can inspire a change in direction. But building to a purpose is equally important. This paper revisits past developments in instrument data systems (IDS), looks at issues that need attention as we further venture into the use of integrated informatics systems, and suggests some directions further development can take.
There is a second aspect beyond planning that also deserves attention: education. Yes, there are people who really know what they are doing with instrumental systems and data handling. However, that knowledge base isn’t universal across labs. Many industrial labs and schools have people using instrument data systems with no understanding of what is happening to their data. Others such as Hinshaw and Stevenson et al. have commented on this phenomenon in the past:
Chromatographers go to great lengths to prepare, inject, and separate their samples, but they sometimes do not pay as much attention to the next step: peak detection and measurement … Despite a lot of exposure to computerized data handling, however, many practicing chromatographers do not have a good idea of how a stored chromatogram file—a set of data points arrayed in time—gets translated into a set of peaks with quantitative attributes such as area, height, and amount.[1]
At this point, I noticed that the discussion tipped from an academic recitation of technical needs and possible solutions to a session driven primarily by frustrations. Even today, the instruments are often more sophisticated than the average user, whether he/she is a technician, graduate student, scientist, or principal investigator using chromatography as part of the project. Who is responsible for generating good data? Can the designs be improved to increase data integrity?[2]
We can expect that the same issue holds true for even more demanding individual or combined techniques. Unless lab personnel are well-educated in both the theory and the practice of their work, no amount of automation—including any IDS components—is going to matter in the development of usable data and information.
The IDS entered the laboratory initially as an aid to analysts doing their work. Its primary role was to off-load tedious measurements and calculations, giving analysts more time to inspect and evaluate lab results. The IDS has since morphed from a convenience to a necessity, and then to being a presumed part of an instrument system. That raises two sets of issues that we’ll address here regarding people, technologies, and their intersections:
1. People: Do the users of an IDS understand what is happening to their data once it leaves the instrument and enters the computer? Do they understand the settings that are available and the effect they have on data processing, as well as the potential for compromising the results of the analytical bench work? Are lab personnel educated so that they are effective and competent users of all the technologies used in the course of their work?
2. Technologies: Are the systems we are using up to the task that has been assigned to them as we automate laboratory functions?
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Laboratory Systems Engineering 