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'''Title''': ''A Science Student's Guide to Laboratory Informatics''

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

'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]

'''Publication date''': November 2023

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==Introduction==
An undergraduate science education aims to teach people what they need to know to pursue a particular scientific discipline; it emphasizes foundational elements of the discipline. In most cases in current science education, the time allotted to teaching a scientific discipline is often insufficient to address the existing and growing knowledge base and deal with the multidisciplinary aspects of executing [[laboratory]] work in both industrial and academic settings; the focus is primarily on educational topics. Yet employers in science-based industries want to hire people "ready to work," leaving a significant gap between the goals of science education and the background needed to be productive in the workplace. One example of this gap is found in the lack of emphasis on [[laboratory informatics]] in laboratory-adjacent scientific endeavors.

The purpose of this guide is to provide a student with a look at the [[Informatics (academic field)|informatics]] landscape in ''industrial labs''. The guide has two goals: 

# Provide a framework to help the reader understand what they need to know to be both comfortable and effective in an industrial setting, giving them a starting point for learning about the product classes and technologies; and,
# Give an instructor an outline of a survey course should they want to pursue teaching this type of material.

This guide is not intended to provide a textbook-scale level of discussion. It's an annotated map of the laboratory portion of a technological world, identifying critical points of interest and how they relate to one another, while making recommendations for the reader to learn more. Its intent is that in one document you can appreciate what the technologies are, and if you hear their names, you'll be able to understand the technologies' higher-level positioning and function. The details, which are continually developing, will be referenced elsewhere.

Note that this guide references LIMSforum.com on multiple occasions. LIMSforum is an educational forum for laboratory informatics that will ask you to sign in for access to its contents. There is no charge for accessing or using any of the materials; the log-in is for security purposes. Sign-in to LIMSforum can be done with a variety of existing social media accounts, or you can create a new LIMSforum account.

==What's lacking in modern laboratory education?==
The science you’ve learned in school provides a basis for understanding laboratory methods, solving problems, conducting projects/research, and developing methods. It has little to do with the orchestration of industrial lab operations. That role was first filled by paper-based procedures and now firmly falls in the realm of electronic management systems. So why is your laboratory experience in your formal education different from that in industrial labs? After all, both include [[research]], testing, [[chemistry]], [[biotechnology]], pharmaceutical development, [[Materials science|material development]], engineering, etc.

Educational lab work is about understanding principles and techniques, developing skills by executing procedures, and conducting research projects. Industrial work is about producing data, [[information]], materials, and [[Medical device|devices]], some supporting research, others supporting production/manufacturing operations. Those products are subject to both [[Regulatory compliance|regulatory review]] and are subject to internal guidelines. Suppose a regulatory inspector finds fault with a lab's data. In that case, the consequences can range from more detailed inspection and, if warranted, closing the lab or the entire production facility until remedial actions are enacted.

Because of the importance of [[data integrity]] and [[Data quality|quality]], industrial laboratories operate under the requirements, regulations, and standards of a variety of sources, including corporate guidelines, the [[Food and Drug Administration]] (FDA), the Environmental Protection Agency (EPA), the [[International Organization for Standardization]] (ISO), and others. These regulatory and standard-based efforts aim to ensure that the data and information used to make decisions about product quality is maintained and defensible. It all comes down to product [[Quality (business)|quality]] and [[Safety engineering|safety]], so for example, that the acetaminophen capsule you might take has the proper dosage and is free of contamination.

Outside of regulations and standards, even company policy can drive the desire for timely, high-quality analytical results. Corporate guidelines for lab operations ensure that laboratory data and information is well-managed and supportable. Consider a lawsuit brought by a consumer about product quality. Suppose the company can't demonstrate that the data supporting product quality is on solid ground. In that case, they may be fined with significant damages. Seeking to avoid this, the company puts into place enforceable policy and procedures (P&P) and may even put into place a [[quality management system]] (QMS).

As noted, labs are production operations. There is more leeway in research, but service labs (i.e., analytical, physical properties, quality control, contract testing, etc.) are heavily production-oriented, so some refer to the work as "scientific manufacturing" or "scientific production work" because of the heavy reliance on [[Laboratory automation|automation]]. That dependence on automation has led to the adoption of systems such as [[laboratory information management system]]s (LIMS), [[electronic laboratory notebook]]s (ELN), [[scientific data management system]]s (SDMS), instrument data systems (IDS), and robotics to organize and manage the work, and produce results. Some aspects of research, where large volumes of [[Sample (material)|sample]] processing are essential, have the same issues. Yet realistically, how immersed are today's student scientists in the realities of these systems and their use outside of academia? Are they being taught sufficiently about these and other electronic systems that are increasingly finding their way into the modern industrial laboratory?

==Operational models for research and service laboratories==
Before we get too deeply into laboratory informatics concepts, we need to describe the setting where informatics tools are used. Otherwise, the tools won’t make sense. Scientific work, particularly laboratory work, is process-driven at several levels. Organizational processes describe how a business works and how the various departments relate to each other. Laboratories have processes operating at different levels; one may describe how the lab functions and carries out its intended purpose, and others detail how experimental procedures are carried out. Some of these processes—accounting, for example—are largely, with a few exceptions, the same across organizations in differing industries. Others depend on the industry and are the basis for requiring industry experience before hiring people at the mid- and upper levels. Still other activities, such as research, depend on the particular mission of a lab within an organization. A given company may have several different research laboratories directed at different areas of work with only the word “research” and some broad generalizations about what is in common. However, their internal methods of operation can vary widely.

We’ll begin by looking at the working environment. Figure 1 shows the functions we need to consider. That model is based on the author’s experience; however, it fits many applied research groups in different industries whose work is intended to lead to new and improved products. The names of the labs may change to include [[microbiology]], [[toxicology]], electronics, [[Forensic science|forensics]], etc. depending on the industry, but the functional behavior will be similar.


[[File:Fig1 Liscouski SciStudGuideLabInfo23.png|686px]]
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{| border="0" cellpadding="5" cellspacing="0" width="686px"
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  | style="background-color:white; padding-left:10px; padding-right:10px;"| <blockquote>'''Figure 1.''' An organizational structure of an R&D group.</blockquote>
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The labs I was working in supported R&D in polymers and pharmaceuticals because that's what the overarching company had broad interest in. The research labs (left column of Figure 1) were focused on those projects. The other facilities consisted of:

* Analytical research: This lab had four functions: routine chemical analysis in support of the research labs, new method development to support both research and production [[quality control]] (QC), non-routine analytical work to address special projects, and process monitoring that tested the accuracy of the production QC labs (several production facilities were making different products).
* Physical properties research: Similar in function to the analytical lab, this lab measured the physical properties of polymers instead of performing chemical analysis.
* Materials characterization: This group worked with research and special projects looking at the composition of polymers and their properties such as rheology, molecular weight distribution, and other characteristics. 
* Fabrication: The fabrication facility processed experimental polymers into blends, films, and other components that could be further tested in the physical properties lab.

Once an experimental material reached a stage where it was ready for scale-up development, it entered the pilot plant, where production processes were designed and tested to see if the material could be made in larger quantities and still retain its desirable properties (i.e., effectively produced to scale). A dedicated testing lab supported the pilot plant to do raw materials, in-process, and post-production testing. If a product met its goals, it was moved to a production facility for larger-scale testing and eventually commercial production.

===Intra-lab workflows===
Let’s look at each of these support lab groups more closely and examine how their [[workflow]]s relate.

====Analytical research====
The workflows in this lab fell into two categories: routine testing (i.e., the service lab model) and research. In the routine testing portion, samples could come from the research labs, production facilities, and the pilot plant testing lab. The research work could come from salespeople (e.g., “We found this in a sample of a competitive product, what is it?”, “Our customer asked us to analyze this," etc.), customer support trying to solve customer issues, and researchers developing test methods to support research. The methods used for analysis could come from various sources depending on the industry, e.g., standards organizations such as ASTM International (formerly American Society for Testing Materials, but their scope expanded over time), peer-reviewed academic journals, vendors, and intra-organizational sources.

====Physical properties research====
The work here was predominately routine testing (i.e., the service lab model). Although samples could come from a variety of sources, as with analytical research, the test methods were standardized and came from groups like ASTM, and in some cases the customers of the company's products. Standardized procedures were used to compare results to testing by other organizations, including potential customers. Labs like this are today found in a variety of industries, including pharmaceuticals, where the lab might be responsible for tablet uniformity testing, among other things.

====Materials characterization====
As noted, this lab performed work that fell between the analytical and physical properties labs. While their test protocols were standardized within the labs, the nature of the materials they worked on involved individual considerations on how the analysis should be approached and the results interpreted. At one level, they were a service lab and followed that behavior. On another, the execution of testing required more than "just another sample" thinking.

====Fabrication====
The fabrication facility processed materials from a variety of sources: evaluation samples from both the production facility and pilot plant, as well as competitive material evaluation from the research labs. They also did parts fabrication for testing in the physical properties lab. Some physical tests required plastic materials formed into special shapes; for example, tensile bars for tensile strength testing (test bars are stretched to see how they deformed and eventually failed). The sample sizes they worked with ranged from a few pounds to thousands of pounds (e.g., film production).

The pilot plant testing lab did evaluations on scaled-up processing materials. They had to be located within the pilot plant for fast turn-around testing, including on-demand work and routine analysis. They also serviced process chromatographs for in-line testing. Their test procedures came from both the chemical and physical labs as they were responsible for a variety of tests on small samples; anything larger was sent to the analytical research labs. The pilot plant testing lab followed a service lab model.

===On the service lab model and research in general===
The service lab model has been noted several times and is common in most industries. The details of sample types and testing will vary, but the operational behavior will be the same and will work like this:

# Samples are submitted for testing. In many labs, these are done on paper forms listing sample type, testing to be done, whom to bill, and a description of the sample and any unique concerns or issues. In labs with a LIMS, this can be done online by lab personnel or the sample submitter.
# The work is logged in (electronically using a LIMS or manually for paper-based systems), and rush samples are brought to management's attention. Note that in the pilot plant test lab, everything is in a rush as the next steps in the plant’s work may depend upon the results.
# Analysts generate worklists (whether electronic or paper, an ordered representation of sample or specimen locations and what analyses must be performed upon them by a specific instrument and/or analyst using specified procedures) and perform the required analysis, and results are recorded in the LIMS or [[laboratory notebook]]s.
# The work is reviewed and approved for release and, in paper systems, recorded on the submission forms.
# Reports are sent to whoever submitted the work electronically, or via the method the submitter requested.

Work from non-routine samples may be logged in under “special projects,” though it may create the need for additional testing.





==References==
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[[Category:LII:Guides, white papers, and other publications|A Science Student's Guide to Laboratory Informatics]]