According to the EU Regulation No 1291/2013, Title I, Article 2 (6), research infrastructures mean
Facilities, resources and services that are used by the research communities to conduct research and foster innovation in their fields. Where relevant, they may be used beyond research, for example for education or public services. They include major scientific equipment or sets of instruments; knowledge-based resources such as collections, archives or scientific data; e-infrastructures such as data and computing systems and communication networks; and any other infrastructure of a unique nature essential to achieving excellence in research and innovation. Such infrastructures may be ‘single-sited’, ‘virtual’ or ‘distributed’.
The definition proposes that research infrastructures are used by research communities. It seems, thus, that research communities—including individual researchers—are not integral part of research infrastructures, according to this definition. They are not necessary to make a whole complete; essential or fundamental. A research infrastructure may exist whether or not it is used by research communities.
This is at odds with the ENVRI Reference Model (ENVRI RM), where the scientist or researcher is an active role in the Data Use community of the Science Viewpoint. Researchers are also integral part of the Data Use phase of the ENVRI RM research data lifecycle.
Modelling researchers and research communities as integral part of research infrastructures surely makes sense. After all, research communities are key in data processing and analysis, and such processes are critical to the infrastructures’ raison d’être; they add value to the primary data acquired, curated, and published by research infrastructures; they interpret data for their meaning in the context of research investigations.
Yet, one can argue that research communities are not integral part of research infrastructures just as drivers are not integral part of the road network or users are not integral part of the internet. Note that some people are essential to such infrastructures, for instance civil engineers involved in road construction and maintenance; they are necessary to make the whole complete. Drivers are not.
Given that research conducted by research communities is the infrastructures’ raison d’être, I think it is useful to find an overarching concept, one that includes both research communities and research infrastructures as integral parts. Knowledge infrastructure may be such a concept. Paul Edwards (2010, p. 17) defines knowledge infrastructures as
Robust networks of people, artifacts, and institutions that generate, share, and maintain specific knowledge about the human and natural worlds.
The concept of knowledge infrastructure integrates researchers and research communities as well as research infrastructures into networks. Research infrastructures are artifact systems of scientific knowledge infrastructures. Research communities and research infrastructures generate, share, and maintain specific knowledge about the human and natural worlds. In earth and environmental sciences, knowledge is primarily about natural worlds. With their tag line “Knowledge Through Observations,” the Integrated Carbon Observation System succinctly underscores how research infrastructures and science communities are in the business of generating, sharing, and maintaining specific knowledge.
What does this mean for the ENVRI RM? Though it is a reference model for the archetypical environmental research infrastructure, by modelling researchers and research communities as integral part of infrastructures the reference model steps outside the scope of research infrastructures and inside the broader concept of scientific knowledge infrastructures, specifically in earth and environmental science.
So far it is, however, a tentative step. In order to serve as a reference model for scientific knowledge infrastructures, the ENVRI RM must evolve to address, among other concerns, the lifecycle of semantic information in knowledge infrastructures as well as behaviours such as data interpretation.
References
Paul Edwards (2010). A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. ISBN: 9780262013925. MIT Press.