On Formalization and Representation in Collaborative Research

Introduction

This should start asking why do people collaborate?

Most of the scientific research conventional iconography depicts scientists working in isolation in their laboratory. Can it still be like this?

The answer is of course not. As a general trend, scientific topics moved from narrow domain specific problems to systemic issues, where by systems we mean complex sets of interacting and cross dependent mechanisms resulting in complex phenomena.

At the same time, technologies available in scientific research made a huge leap forward triggering high specialization so that no scientist, anymore, can cover all the needed topics and activities.

Full encyclopedic knowledge coverage by a single person is not possible anymore.

Collaborative research is invoked then to overcome such limitations. Researches get together, bringing their contributions to understand and solve complex issues and problems that they would not be able to address alone.

In addition to this, a peculiar loop exists between collaborative science and large scientific facilities such as, for example: satellites, particle colliders, telescopes and the like. If on one hand scientists need such big facilities for their work, on the other hand, the need to share the costs of such infrastructures triggers collaborative science (Georghiou, 1998).

Collaborative research brings in the picture big promises but also problems. High specialization of researchers can result, during teamwork, in possible lacks of understanding and difficulties in handling data in a consistent way.

This is only seldom addressed.

Current trends in collaborative science support mostly focus on the technical issues related to the consolidation of data to a central facility, where it can be processed using high quality resources and from where data can be retrieved remotely, many times and by multiple researchers.

This, of course, is a very respectable goal, but considering the above mentioned issues is unlikely to be enough.

On the contrary, we think necessary to pave the road to a new vision of collaborative science that aims to bridge the gaps between scientific domains and ways of thinking. But first we should start from a strict definition of what we mean by collaboration.

The terms collaboration, cooperation and coordination are often used interchangeably. Actually, this is not correct.

Cooperation means working together, using possibly the same resources, but separating goals in order that each partner achieves its own. The main motivation in participating in a cooperation is that partners gain something that would not be available should they go alone.

Collaboration has the fundamental difference that all the parties work towards the construction of a common goal.

In the first case the cognitive overlap among partners is limited, being each partner activities linked to their “separated” goal. In the second case a common cognitive space must be created, maintained and referred to by all partners. Such as in the old medieval maps, where dangerous areas had to be highlighted, we can say “Hic sunt leones” (here are the lions).

To understand how to address such problems we need to start from the very beginning.