In college in the mid-seventies, I met someone who told me his academic field of study was “systems.” I remember having no clue as to what that meant. After graduating, I worked in UC Berkeley’s electrical engineering and computer science department as a technical writer. One day, a grad student showed me a very early prototype of a graphical user interface, which was an astonishing leap from the clunky typing systems then in use. The distance we’ve come since those early days and the rate at which computer technology advances continues to astonish.
This month’s issue looks at a number of technology developments having an ever greater impact on scientific research. The first is the increased use of apps. Our cover story, “A Lab App for That,” discusses not only those apps developed by instrument vendors, but also those designed for management and purchasing support. Author John Joyce also discusses some ethical considerations when using apps, such as “the concept of ‘bring your own device’ (BYOD), which can cut several ways.”
Because of the steady advances in information technology, the dependence of research labs on their IT departments grows. “Once upon a time, lab management’s purchase of technology depended solely on the needs of the scientific work; now an additional set of issues has come into play. As laboratory work becomes more technologically sophisticated, the decision process for new purchases brings in a wider range of concerns and considerations,” says Joe Liscouski in this month’s “Finding a Middle Ground.”
Another situation in which labs and IT departments must work together is when mergers and acquisitions or competitor consolidation require remote or digital collaboration between research entities. In this month’s, “The Challenges of ‘Digital Collaboration,” author Andrew Anderson says, “In order to address some of the scientific collaboration challenges presented by externalization and consolidation, thought leaders in informatics are considering new governance models for instrument-derived laboratory data.”
What discussion of computer technology in the lab would be complete without including “big data?” “Big data are escalating in value in the laboratory, as they have in a variety of other business enterprises that rely on effective generation and management of their data assets,” says Bernard Tulsi in this month’s “Big Data Mining.” As for the problem of making sense of the enormous data sets, the hope is that better statistical and computational methodologies and powerful algorithms will make it possible to do more with what’s collected.
Suffice it to say, all of these “systems” are here to stay. Maybe a box of donuts for your favorite “IT team” might be in order.