The following project is not the first effort in this direction, in reality, two other projects have carried out efforts in connecting the fields in the previous section. On the broader side, the EURITO project, funded by the European Commission and on the narrower, the more specific side, the AMICa-Pathfinder project at the Technical University of Denmark. In this part of the report, a more in-depth explanation will be given on each one of these initiatives.
This project created and funded by the Community Research and Development Information Service of the European Commission has the high-level goal of: “better integration of evidence on the impact of research and innovation in policy making”. Furthermore, it carries a total contribution of around €1.5M. The project consists of the participation of three institutions: the Fraunhofer research organization (GER), the COTEC innovation foundation (ESP), and the Technical University of Denmark (DK).
This project’s main goal is to bring big data and data analytics to the heart of Research and Innovation(R&I) policy. By first defining the R&I policy user needs and then turning those needs into analytics data pilots in an exploration stage. Moreover, by creating new R&I indicators, and communicating them through interactive visualizations, the project expects to make R&I policy-making more transparent and democratic. Also, the project description includes several considerations about big data and machine learning but argues that there are concerns about representativity, accuracy, and interpretability in what concerns the sources of data.
The success of this project would mean that R&I policies are better informed, better targeted, and that new innovation opportunities could surface. This is because of the open data, code, and knowledge developed alongside the project.
AMICa is a project led and executed by the Engineering System Division at DTU Management Engineering and funded by Climate-KIC. The participating members in this project are Chalmers University, MASH-Biotech, The Nordic Initiative for Sustainable Aviation (NISA) and Novozymes.
The project’s main goal is to facilitate better data-driven decision making, and providing assistance for designing, developing and implementing more sustainable production systems using pre-existing capabilities. With a specific technological target in scope, the project hopes to answer questions such as: are there untapped research gaps? Are there hotspots of unexploited but complementary capabilities? What organizations are unique? An innovative approach is applied to this research problem: AMICa focuses on technological capabilities instead of flows of material and makes use of a complex system view. This complex system view ultimately results in an input-process-output model.
Success for the AMICa project would translate into a fruitful mapping of worldwide industrial capabilities that can support the development of new technologies, products, and services with a positive climate change impact. The first proof of concept utilizes biofuel research as a starting point for this mapping.
More on the technical specifications for this project will be given in Chapter 3.
AMICa and EURITO are two complementary projects in technical terms. In fact, EURITO proposes a theoretical possibility (or idea), and the AMICa project seeks to get closer to the practical applications and implications of such an idea.
By taking biofuel research and trying to map the capabilities of such a field, this thesis aims to provide a proof of concept that is highly modular. By modular, it is meant that the procedures applied to biofuel research can possibly be applied to virtually any field of research, following the complex system approach.
However, there is a need to:
Further understand the how this data can be explored and used by industry and government.
Provide more visualizations with other dimensions.
Show possible applications of Big Data processing tools.
In this context, this project appears as a natural extension of the AMICa pathfinder project, by exploring this biofuel-related data, but also as a source of potential analytical approaches for the EURITO project, given the importance of data-driven insights for policymakers and other institutions.