research – Laboratory Systems Engineering

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

When we enter into discussions about laboratory automation, computing, and informatics, most of the effort is focused on information management (LIMS, LIS, SDMS), data generation (IDS, LES), and robotics (sample manipulation in preparation for data generation). That’s where the products are and where most of the justification for labs and projects originate.

During the course of laboratory experiments, testing, and research, a lot of information, data, and reports are produced that may or may not be well managed. These materials are a valuable product of lab work and when taken together form the organization’s memory. It’s the history of what has been done, why, what the results were including successes and blind alleys. Ever find yourself remembering that someone did some work on a topic that’s now of interest, and wondering where that stuff is?

Developing an organizational memory as part of the lab’s informatics structure, or being extended into a larger organizational matrix, is an important aspect of realizing a return on investment (ROI) in lab work. By taking steps to make that information resource more usable, the ROI can jump significantly by avoiding duplication of work, using people’s time doing manual searches, and coordinating work from multiple sources.

With the advent of artificial intelligence systems, ELN, and LIMS/LIS, we have the basis for developing an organizational memory system. That is what this article is about

To view the material through the LIMSwiki system, click here. (Note: there is no cost for accessing LIMSwiki material. You simply have to login into your account).

The Application of Informatics to Scientific Work: Laboratory Informatics for Newbies

The purpose of this piece is to introduce people who are not intimately familiar with laboratory work to the basics of laboratory operations and the role that informatics can play in assisting scientists, engineers, and technicians in their efforts. The concepts are important because they provide a functional foundation for understanding lab work and how that work is done in the early part of the twenty-first century (things will change, just wait for it).

Intended audience

This material is intended for anyone who is interested in seeing how modern informatics tools can help those doing scientific work. It will provide an orientation to scientific and laboratory work, as well as the systems that have been developed to make that work more productive. It’s for people coming out of school who have carried out lab experiments but not corporate research projects, for those who need to understand how testing labs work, and for IT professionals who may be faced with supporting computing systems in lab environments. It’s also for those who may be tasked with managing projects to choose, install, and make informatics tools useful.

Figure 1 shows the elements we’ll be discussing in this piece. The treatment of the technical material will be on the lighter side, leaving in-depth subject matter to other works. Instrument data systems will be covered lightly, as any serious discussion becomes lengthy and discipline-specific very quickly; additionally, that material has been covered in other works.

Figure 1. Elements we’ll be covering

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Laboratory Technology Planning and Management: The Practice of Laboratory Systems Engineering

What separates successful advanced laboratories from all the others? It’s largely their ability to meet their goals, with the effective use of resources: people, time, money, equipment, data, and information. The fundamental goals of laboratory work haven’t changed, but they are under increased pressure to do more and do it faster, with a better return on investment (ROI). Laboratory managers have turned to electronic technologies (e.g., computers, networks, robotics, microprocessors, database systems, etc.) to meet those demands. However, without effective planning, technology management, and education, those technologies will only get labs part of the way to meeting their needs. We need to learn how to close the gap between getting part-way there and getting where we need to be. The practice of science has changed; we need to meet that change to be successful.

This document was written to get people thinking more seriously about the technologies used in laboratory work and how those technologies contribute to meeting the challenges labs are facing. There are three primary concerns:

The need for planning and management: When digital components began to be added to lab systems, it was a slow incremental process: integrators and microprocessors grew in capability as the marketplace accepted them. That development gave us the equipment we have now, equipment that can be used in isolation or in a networked, integrated system. In either case, they need attention in their application and management to protect electronic laboratory data, ensure that it can be effectively used, and ensure that the systems and products put in place are both the right ones, and that they fully contribute to improvements in lab operations.
The need for more laboratory systems engineers (LSEs): There is increasing demand for people who have the education and skills needed to accomplish the points above and provide research and testing groups with the support they need.^[a]
The need to collaborate with vendors: In order to develop the best products needed for laboratory work, vendors should be provided more user input. Too often vendors have an idea for a product or modifications to existing products, yet they lack a fully qualified audience to bounce ideas off of. With the planning in the first concern in place, we should be able to approach vendors and say, with confidence, “this is what is needed” and explain why.

If the audience for this work were product manufacturing or production facilities, everything that was being said would have been history. The efficiency and productivity of production operations directly impacts profitability and customer satisfaction; the effort to optimize operations would have been an essential goal. When it comes to laboratory operations, that same level of attention found in production operations must be in place to accelerate laboratory research and testing operations, reducing cost and improving productivity. Aside from a few lab installations in large organizations, this same level of attention isn’t given, as people aren’t educated as to its importance. The purpose of this work is to present ideas of what laboratory technology challenges can be addressed through planning activities using a series of goals.

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Considerations in the Automation of Laboratory Procedures

Scientists have been dealing with the issue of laboratory automation for decades, and during that time the meaning of those words has expanded from the basics of connecting an instrument to a computer, to the possibility of a fully integrated informatics infrastructure beginning with sample preparation and continuing on to the laboratory information management system (LIMS), electronic laboratory notebook (ELN), and beyond. Throughout this evolution there has been one underlying concern: how do we go about doing this?

The answer to that question has changed from a focus on hardware and programming, to today’s need for a lab-wide informatics strategy. We’ve moved from the bits and bytes of assembly language programming to managing terabytes of files and data structures.

The high-end of the problem—the large informatics database systems—has received significant industry-wide attention in the last decade. The stuff on the lab bench, while the target of a lot of individual products, has been less organized and more experimental. Failed or incompletely met promises have to yield to planned successes. How we do it needs to change. This document is about the considerations required when making that change. The haphazard “let’s try this” method has to give way to more engineered solutions and a realistic appraisal of the human issues, as well as the underlying technology management and planning.

Why is this important? Whether you are conducting intense laboratory experiments to produce data and information or making chocolate chip cookies in the kitchen, three things remain important: productivity, the quality of the products, and the cost of running the operation. In any case, if the productivity isn’t high enough, you won’t be able to justify your work; if the quality isn’t there, no one will want what you produce. Conducting laboratory work and making cookies have a lot in common. Your laboratories exist to answer questions. What happens if I do this? What is the purity of this material? What is the structure of this compound? The field of laboratories asking these questions is extensive, basically covering the entire array of lab bench and scientific work, including chemistry, life sciences, physics, and electronics labs. The more efficiently we answer those questions, the more likely it will be that these labs will continue operating and, that you’ll achieve the goals your organization has set. At some point, it comes down to performance against goals and the return on the investment organizations make in lab operations.

This article looks at conditions that need to be met before you embark on the automation of a laboratory process. It comes down to a key factor: is it worth it? What will you gain by doing it, how much effort will it take, and will it significantly improve lab operations?

The material can be access through this link to the LIMSwiki.