The Hard Look

Managing Science and Research

Managing science and research requires a unique skill set that are not the same as general management skills required for other types of businesses.  General management theory is applicable to science and research management, but not sufficient to cater for the specific requirements of science and research management.  For that purpose we assume in this article that the reader is already familiar with general management principles and approaches.  Our focus here is to look at the specific requirements of science and research management.

An important aspect is understanding what would constitute good science and how to create an environment that would allow the knowledge generation aspect of science and research to flourish.  Important aspects that differ from general management principles are:

Quality assurance often supersedes the process-focused approach in organization generally.  Especially where the problems are not standard and therefore require unique approaches to be solved, it is very difficult to provide consistent quality assurance and performance indicators.
Science and research management requires a careful balance between investment and creating utility for current use.  Unless a considerable effort is made to constantly invest in more capabilities and growth of existing capabilities, management of science and research finds itself over the medium term with an increasingly stale and unproductive scientific research capability.  This requires a financial management approach that does not optimise for short term profit only, but also caters for the capability building of ongoing the investment.
The people performing the science and research work are usually a scarce commodity, and replacing them require considerable investment of both time and money.  For this reason retention and ongoing development of existing experts needs to be a focus in the business model (this is true for all knowledge-intensive innovative environments).
The work environment need to enable innovative and creative work, and facilitate and value team work.  The performance indicators for these are often difficult to define (they might even be intangible).  But giving attention to them and getting them right for the specific type of science and research work is very important for a successful science and research capability.

In addition to all of this there is the aspect of “managing science where it happens”, namely to ensure the scientific work itself is of a good quality and make the best use of the available capabilities.  Usually this is catered for by the various conventions that scientists and researchers of specific disciplines adhere to professionally.

However, the various sciences have a number of differences and commonalities that make maintaining the scientific rigour when work is done in more than one of the major branches of science very difficult.  For this reasons many research capabilities either restrict themselves to only selected branches of science, or they retain the barriers between the various sciences and never really get to an integrated scientific capability that spans across the boundaries of the sciences.  In the complex and highly connected societies we live in that is becoming an increasingly untenable situation.  We need to be able to integrate the sciences to be able to provide relevant and useful new knowledge, utilising the best that science offers.

Using science in an integrated way  unlocks most value in situations like this.  We need to keep in mind that
All the sciences share a common goal to search for the “truth”, or “facts”, or “evidence.  This common goal provides the background against which we are able to identify a number of similarities.
There are some legitimate differences between the sciences that we cannot remove by forcing one approach on all the branches of science.

Accomplishing this is not easy. However, there are two sets of features that are common to all branches of the sciences.  They can be used in all branches of science to ensure that we are able to integrate our scientific work across the traditional branches of the sciences.  They are

The scientific productiveness features:  These are the features of science that facilitate its success in knowledge generation.  Knowledge can be generated in a number of ways, but these science has illustrated over the centuries that where these features are present and used appropriately they facilitate a level of success that is not otherwise possible.
The Scientific Capability Features:  These are the features that describe the way to go about knowledge generation utilising the scientific productivity features.

We have used these two for integrated scientific work in a number of cross-disciplinary applications (mostly to solve complex real life problems in strategic management decision making).  They have proven themselves to add value in the rigor, quality and relevance of cross-disciplinary scientific work.

Developing Expertise Through The Acquisition Of Knowledge
This article presents experts in geoscience, geography, and astronomy using spatial thinking in the process of scientific discovery and explanation. To achieve insights, experts link varied data sources, use their knowledge of processes such as volcanism or evolution, and incorporate their understanding of principles such as thermal equilibrium or biodiversity. Successful researchers reorganize, combine, prioritize, compare, question, and discuss their ideas over extended periods. Experts develop proficiency in their fields over years and often find the methods they use to assess complex displays of data difficult to explain and, therefore, teach to others. Skilled programmers, for example, can inspect a 300-line program and rapidly identify bugs, whereas novices can look at the same 300 lines essentially forever without finding the problem.

Experts specialize in particular aspects of their field. They need time and experience not only to understand the representations used in new aspects of the field, but also to learn the domainspecific principles and ideas to interpret and critique this information. Experts reformulate representations of complex information such as plate movements or crystal configurations and engage in discipline-specific disputes about appropriate ways to reduce data to formats that are maximally open to inspection (cf. work on the human genome, molecular pathways, and electron microscope materials). Each year Science magazine recognizes researchers who create visualizations that are acclaimed by their peers (Bradford et al., 2003). To those outside a particular scientific domain, however, the representations can perplex and confuse as much as inform. Some representations, such as patterns of earthquakes superimposed on the outlines of continents, communicate information that would be difficult to capture in words, whereas others, such as the methods for representing the structure of crystals, can confuse even experts as well as nonexperts. Even ingenious representations, such as modem algebraic systems, have sometimes thwarted as well as hastened scientific discovery.

Experts in one application of spatial thinking, such as architecture, may not find those skills useful in another application of spatial thinking, such as interpreting weather maps, because the representations and their underlying scientific principles are different. Clement, for example, asked expert mathematicians to interpret visual displays of the behavior of springs varying in diameter and flexibility. The mathematicians behaved similarly to students encountering the material about springs for the first time. Lewis and Linn reported similar results when they asked expert chemists and physicists to explain everyday phenomena that exemplify principles that they understand well. One expert, for example, preferred aluminum foil over wool as an insulator because it is a common practice to wrap cold drinks in aluminum. Expertise is, therefore, domain specific. Expertise takes significant time to develop in depth. Learning things is not limited to the scentific area. Instead it also has relations with some other things like speaking a language or using software, including Rosetta Stone English and Rosetta Stone French. If you have a creative mind, you will make all your own differences in the end!

Educators often devise new representations to help novices. Tests of these representations in contexts as diverse as weather maps, molecular models, and the rock cycle have proven humbling. Students cannot readily interpret diagrams and representations, and when they attempt to use them, they often become more, rather than less confused. Students have interpreted representations of heat that use color intensity as implying that heat has mass, for example. Most commonly colored weather maps show only the predicted weather on land rather than showing the weather patterns as extending over the oceans. The maps also show weather only over the United States rather than extending into both Canada and Mexico. Such representations can deter students from thinking about the weather as large-scale, complex systems influenced by differential surface temperatures over land and water.