The advent of new instruments and methods of data-intensive exploration is facilitating the process of data-intensive scientific discovery and data-driven innovation. These activities are R&D if and only if they meet the five core criteria, in particular, the general requirement that the activity or project should be undertaken in a systematic way – i.e. by clearly identifying the original knowledge gap and focusing specific resources on addressing it. An example is the “Human Genome Project”, which attracted researchers and institutions from 18 countries to co-operate in a 13-year-long research effort to sequence and map out the human DNA code.
Through digitisation, the R&D codification criterion plays a major part in big data projects, as the usability of the data arising from “big data” science projects depends on its ability to convey knowledge about specific phenomena for which the data have been gathered. These data may or may not be made widely accessible or usable for research purposes. The concept of open science commonly refers to efforts to make the output of publicly funded research more widely accessible in digital format to the scientific community, the business sector or society more generally (OECD, 2015). In some cases, efforts to make research data openly accessible to the broad scientific community, including developing specific tools that facilitate the reproducibility of the research, will be an integral part of an R&D project, provided that they are explicitly formulated as such within the R&D project’s objectives and are budgeted. In other cases, these should be treated as separate dissemination efforts and not counted as R&D.
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