The following project is not the first effort in its 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 Comission and on the narrower, 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 of these initiatives.

EURITO - EU Relevant, Inclusive, Timely, Trusted, and Open Research Innovation Indicators

This project created and funded by the Community Research and Development Information Service of the European Commission has as a high-level goal the “better integration of evidence on the impact of research and innovation in policymaking”.Furthermore, it carries a total contribution of around €1.5M. The project counts with 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.
Finally, the success of this project would mean that R&I policies are better informed, better targeted, and that new innovation opportunities will surface. This is because of the open data, code, and knowledge developed along the project.

AMICa - Advanced Mapping of Industrial Capabilities for Climate

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 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 successful 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 biofuels as a starting point for this mapping.
More on the technical specifications for this project will be given in Chapter 3.

Thesis - Measuring the uniqueness of technological capabilities

It is quite obvious that AMICa serves as a natural extension of the EURITO project. 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 as field, the DTU based project can possibly provide a proof of concept that is highly modular. By modular, it is meant that the procedures applied to biofuels research can possibly be applied to other fields of research.
Some visualizations and practical applications have already been demonstrated by AMICa, such as the Data Exploration Dashboard or the Sankey Diagram visualizations. 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 Machine Learning and 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 to the EURITO project, by the importance of this analysis for policymakers and other institutions.
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