Support disciplinary data infrastructures in building networks of boundary objects

From GRDI2020

This is a GRDI recommendation; return to Main Page with all the challenges and recommendations

Context and Challenges

A data infrastructure must allow for the information/knowledge flow between cooperating communities/disciplines to cross syntactic and semantic boundaries without distortions. In addition, it must be able to check the logical consistency of the policies and the quality dimensions adopted by these communities.

A useful means of representing, learning about and transforming knowledge to resolve the consequences that exist at a given boundary is the “boundary object”. The concept of a boundary object, developed by Star, describes information objects that are shared and shareable across different problem-solving contexts: “... both plastic enough to adapt to local needs and constraints of the several parties employing them, yet robust enough to maintain a common identity across sites. They are weakly structured in common use, and become strongly structured in individual site-use. Like a blackboard, a boundary object ‘sits in the middle’ of a group of actors with divergent viewpoints ...”

Here below we have adapted Star’s [9] four categories of boundary objects (repositories, standardised forms and methods, objects or models, and maps of boundaries) to describe the information objects and their use by individuals in the settings present in a multidisciplinary/interdisciplinary environment. In order to overcome a syntactic/semantic/pragmatic boundary, a boundary object should establish a shared syntax, a shared means for representing and specifying differences, and a shared means for representing and specifying dependencies.

• At a syntactic boundary, a boundary object should establish a shared (meta) data model, a shared data language, a shared database, a shared taxonomy, etc.
• At a semantic boundary, a boundary object should provide a concrete means for all individuals to specify and learn about their differences. Examples are a shared ontology, a shared methodology, etc.
• At a pragmatic boundary, a boundary object should facilitate a process wherein individuals can jointly transform their knowledge. Examples are a shared quality framework, a shared policy framework, etc.

In order to work together, “communities of practice” — belonging to the same discipline or to different disciplines—must create a consistent set of boundary objects at syntactic, semantic, and pragmatic boundaries. A data infrastructure that supports the cooperation of communities of practice has to efficiently implement the communities’ sets of boundary objects. It is easy to foresee the development, in the near future, of discipline-specific boundary objects (metadata models, data models, data languages, taxonomies, ontologies, quality and policy frameworks, etc.) in order to allow the interoperation between communities of practice belonging to the same discipline. By interoperation between two “communities of practice”, we mean the ability of their members to exchange meaningful information objects and to use them effectively. In fact, a major effort is currently under way in many disciplines to define such discipline-specific boundary objects. A number of projects are currently being funded by the EU 7FP aiming at developing “disciplinary data infrastructures”. By “disciplinary data infrastructure”, we mean a data infrastructure that implements a consistent number of discipline-specific boundary objects at the syntactic, semantic, and pragmatic boundaries that allow different “communities of practice” (which comprise a given discipline) to work together effectively.

Recommendation

One of the most important features of the future disciplinary data infrastructures will be the efficient implementation of a set of boundary objects defined by the members of the disciplines being supported.