LII:Organizational Memory and Laboratory Knowledge Management: Its Impact on Laboratory Information Flow and Electronic Notebooks

Title: Organizational Memory and Laboratory Knowledge Management: Its Impact on Laboratory Information Flow and Electronic Notebooks

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

License for content: Creative Commons Attribution-ShareAlike 4.0 International

Publication date: April 2024

Introduction

Beginning in the 1960s, the application of computing to laboratory work was focused on productivity: the reduction of the amount of work and cost needed to generate results, along with an improvement in the return on investment (ROI). This was very much a bottom-up approach, addressing the most labor-intensive issues and moving progressively to higher levels of data and information processing and productivity.

The efforts began with work at Perkin-Elmer, Nelson Analytical, Spectra Physics, Digital Equipment Corporation, and many others on the computer controlled recording and processing of instrument data. Once we learned how to acquire the data, robotic tools were introduced to help process samples and make them ready for introduction into instruments, that with the connection to a computer for data acquisition further increased productivity. That was followed by an emphasis on the storage, management, and analysis of that data through the application of laboratory information management systems (LIMS) and other software. With the recent development of artificial intelligence (AI) systems and large language models (LLMs), we are ready to consider the next stage in automation and system’s application: organizational memory and laboratory knowledge management.

This piece discusses the convergence of a set of technologies and their application to scientific work. The development of software systems like ChatGPT, Gemini, and others means that with a bit of effort the ROI in research and testing can be greatly improved.

The initial interest discussed herein is on the topic of using LLMs to create an effective organizational memory (OM) and how that OM can benefit scientific organizations. Following that, we'll then examine how that potential technology impacts information flow, integration, and productivity, as well as what it could mean for developing electronic laboratory notebooks (ELNs). We’ll also have to extend that discussion to having AI and OM systems work with LIMS, scientific data management systems (SDMS), instrument data systems (IDSs), engineering tools, and field work found in various industries.

This work is not a "how to acquire and implement" article but rather a prompt for "something to think about and pursue" if makes sense within your organization. The idea is the creation of an effective OM (i.e., an extensive document and information database) that fills a gap in scientific and laboratory informatics, one that can be used effectively with an AI tool to search, organize, synthesize, and present material in an immediately applicable way. We need to seriously think about what we want from these systems and what our requirements are for them before the rapid pace of development produces products that need extensive modifications to be useful in scientific, laboratory, field, and engineering work.

Why should you read this?

Most of the products used in scientific work (whether in the lab, field, office, etc.) are designed for a specific application (working with instruments, for example) or adapted from general-purpose tools used in various industries and settings. The ideas discussed here need further development, as do the tools specifically for the needs of the scientific community. Still, that work needs to begin as a community effort to gain possible benefits. We need to guide the development of technologies so that they meet the needs of the scientific community rather than try to adapt them once they are delivered to the general marketplace.

LLM systems have shown rapid development and deployment in almost every facet of industries throughout 2023. Unless something drastic happens, development will only accelerate, given the potential impact on business operations and interest of technology-driven companies. The scientific community needs to not only ensure its unique needs (once they’ve been defined) are included in LLM development and are met, but also that the resultant output reflects empirical rigor.

Acknowledgements

I’d like to thank Gretchen Boria for her help in improving this article and her contributions to it.

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.