LII:Notes on Instrument Data Systems

Title: Notes on Instrument Data Systems

Author for citation: Joe Liscouski, with editorial modifications by Shawn Douglas

License for content: Creative Commons Attribution 4.0 International

Publication date: May 27, 2020

Introduction

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 phenomena 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.

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?

About the author

Initially educated as a chemist, author Joe Liscouski (joe dot liscouski at gmail dot com) is an experienced laboratory automation/computing professional with over forty years of experience in the field, including the design and development of automation systems (both custom and commercial systems), LIMS, robotics and data interchange standards. He also consults on the use of computing in laboratory work. He has held symposia on validation and presented technical material and short courses on laboratory automation and computing in the U.S., Europe, and Japan. He has worked/consulted in pharmaceutical, biotech, polymer, medical, and government laboratories. His current work centers on working with companies to establish planning programs for lab systems, developing effective support groups, and helping people with the application of automation and information technologies in research and quality control environments.