This valuable research tool flourishes in the commercial sector, yet in the academic sector it sits untapped.

In the commercial sector, the impact of laboratory information management systems is pronounced. What began 25 years ago with attempts to computerize a few routine laboratory functions has developed into a technology that provides essential services to a variety of industries. Laboratory information management systems (LIMS) are deployed in virtually all of the Fortune 500 companies that rely on laboratory operations. As the cost of LIMS continues to drop, smaller organizations are able to implement them. This bodes well for an expanding market, at least in the number of product installations if not in absolute dollars. Although LIMS have had a rocky history, with plenty of stories of failed implementation, today LIMS are generally appreciated as a valuable and even necessary tool in the commercial laboratory.

In the academic sector, it’s another story altogether. LIMS have no real presence in the academic scientific world. Although exceptions can be found, as a general rule, LIMS applications are not used in most academic scientific research laboratories (which I’ll refer to as “academic labs”), nor is LIMS commonly taught as a classroom subject. The overwhelming majority of academic research scientists have never even heard of LIMS, and the few who have do not regard LIMS as relevant to their research. In other words, LIMS have developed and flourished outside academia. This disconnect is almost total: Neither academic scientists nor LIMS analysts know—or presumably care—that LIMS have no role in academic science labs.

The Disconnect: Academia
There are two reasons for the disconnect between the commercial and academic use of LIMS: the history of LIMS development and the special characteristics of academic labs (and R&D labs in general). In the early days, LIMS were custom-designed, labor-intensive applications. They were staggeringly expensive, took years to implement, and were often unsatisfactory to end users. But the impetus for LIMS remained: Large organizations wanted automation of routine operations to increase productivity. This impetus never existed in the R&D environment, with its emphasis on flexibility and discovery (and academic labs, of course, represent the extreme R&D environment). Thus, implementation was traditionally limited to production-scale, commercial laboratories that had both the perceived need and the resources to accomplish this. Early-generation LIMS simply did not fit in, literally or metaphorically, with the academic lab.

The culture of graduate schools, in a very general way, is also to blame for this disconnect. In most disciplines, professors take on graduate students not only to teach them how to do independent research in their field, but also to groom them to eventually become professors themselves. Academic scientists follow this model, teaching their students how to be academic scientists. As a result, graduate students are often discouraged from pursuing work in industry, a foreign world to most academic scientists. In fact, new Ph.D.s from academia who cross into industry are often regarded as apostates. In other words, graduate education in science is largely unhooked from the industrial marketplace. This situation is now well recognized, and educational and research organizations are taking steps to correct it, but significant change has yet to take place.

Turning specifically to LIMS, we see a perfect example of this situation: Created, developed, and applied exclusively in the commercial sector, most academic scientists simply know nothing of LIMS. In earlier times and up to the recent past, this of course was not a serious problem, because the data load generated by academic laboratories could be easily managed by modest, conventional means—a PC with a spreadsheet application, for example. Now, with the development of new technologies, such as automated analysis of two-dimensional gels, data are being generated at a rate that is 3 or 4 orders of magnitude greater than even a few years ago. In this new environment, LIMS and other data management systems will be essential. In fact, this need will extend beyond number-crunching to true knowledge management: Electronic laboratory notebooks (ELNs), just now coming into their own as a mature product, will become necessary research tools in the academic lab. Yet few academic scientists have heard of ELNs.

The Disconnect: LIMS Vendors
If academic scientists do not value LIMS, the feeling has been mutual on the part of LIMS vendors, who generally do not perceive academic labs as a potential market worthy of their interest. They see academic labs as too fragmented, small, and poor to afford their LIMS. In fact, compared with vendors’ traditional clients, academic labs are too fragmented, small, and poor! The laboratories of the largest and wealthiest research universities operate as individual fiscal units—even collaborating academic labs act independently. Although academic labs often collectively purchase and share expensive research equipment, “sharing” usually consists of reserving time on a sign-up sheet. In this climate of individualism, it makes no sense to try to implement LIMS at the university level. The alternative would be to try to market LIMS to individual labs, but from an operations point of view, academic labs are “small fry”. Although most LIMS remain out of the financial reach of academic labs, several companies recently initiated academic pricing schedules.

Consequences: Academia
The cultural divide hurts both business and academe. Academic scientists are missing out on a potentially valuable research tool, and LIMS companies are missing the opportunity to establish a firm foundation for their products in the larger scientific community.

The most obvious benefit of LIMS to an academic lab is access to information. While this may surprise academics who have thrived without even knowing about LIMS, the fact is that the sole product of any lab, operating for any reason, is information. Despite the importance of information, academic labs continue to have dismal data storage processes: Raw data are routinely scattered among printouts stapled and folded into notebooks, and spreadsheet files with arcane filenames are hidden in obscure computer folders. Records, if kept at all, are often filed “chronologically by medium”, which is to say, gel radiographs are piled here and EM negatives go in the drawer there. When even just a few people work side-by-side in a lab, it may be impossible for one person to get information from another’s experiments without asking the other’s help.

Academic labs are also faced with another, far more serious impediment to data access: the passing of time. Academic scientists often keep their labs active for decades while technicians, graduate students, and postdoctoral fellows come and go, leaving a mess of disparate data recording techniques and devices. Often, especially when writing manuscripts and grant applications, specific (and now very important) results may be needed that were produced by a postdoc who left the lab two or three years ago.

The data could be in one of many lab notebooks, but finding it may require searching through dozens of notebooks page by page. What if several similar experiments were done, only some of which contributed to the final graph? What if the methods used were so routine to the original investigator that he or she didn’t bother to write them down for the umpteenth time, yet no record remains of where they were written down? What if the calculations used for the final results come under question, yet the original raw data can no longer be found to make new calculations? As anyone who has ever worked in an academic lab knows, such problems are not rare; on the contrary, they are common.

The irony of the situation is that although personnel in academic labs often use state-of-the-art research equipment, their approach to data management has not changed in decades, or in the case of paper note-taking, centuries. Virtually all modern instruments in academic labs allow or demand computer interfacing, which sets the stage for LIMS. The primary purpose of LIMS implementation is not automation, validation, or the other features so useful to production-scale labs—it is integration. Raw data from heterogeneous sources can be brought into a common database that can be accessed by all interested parties.

The absence of LIMS in academic labs also hurts the companies that develop and sell LIMS. It’s not just a question of increasing sales volume, even though academia is an untapped market. A more profound, farther-reaching benefit is the cultivation of a new breed of scientists who regard LIMS as indispensable tools for science, including basic scientific research. The students of today will be the buyers of products in the academic and commercial sectors of tomorrow. Once LIMS are incorporated into academic labs, it will be only a short time before LIMS are taught in the classroom. Students who learn about LIMS on a particular system will be far more likely to order that system when they enter the workforce. This fact is not lost on other software developers, who spend millions to subsidize discounted sales of their products to universities.

The Time Is Right for Change
Although the information revolution has been going on for years, we are poised at a new era in this revolution. Hardware is now cheap and ubiquitous. These developments are bound to have a major impact in the lab, including the academic lab. LIMS-enabled labs will be better able to exploit these developments.

In turn, information has reached a new level of abundance that goes beyond a matter of scale. It is no longer a question of mere access to information: There is so much information that finding the relevant subsets is increasingly difficult. In science, the information overload in genomics exemplifies the problem. In response to the problem, bioinformatics was born. As this trend of information overload continues, LIMS will assume a valid place in scientific informatics, paving the way for academic “respect ability” and the incorporation of LIMS in college science curricula.