Mapping Climate Change Research via Open Repositories & AI: advantages and limitations for an evidence-based R&D policy-making

In the last few years, several initiatives have been starting to offer access to research outputs data and metadata in an open fashion. The platforms developed by those initiatives are opening up scientific production to the wider public and they can be an invaluable asset for evidence-based policy-making in Science, Technology and Innovation (STI). These resources can indeed facilitate knowledge discovery and help identify available R&D assets and relevant actors within specific research niches of interest. Ideally, to gain a comprehensive view of entire STI ecosystems, the information provided by each of these resources should be combined and analysed accordingly. To ensure so, at least a certain degree of interoperability should be guaranteed across data sources, so that data could be better aggregated and complemented and that evidence provided towards policy-making is more complete and reliable. Here, we study whether this is the case for the case of mapping Climate Action research in the whole Denmark STI ecosystem, by using 4 popular open access STI data sources, namely OpenAire, Open Alex, CORDIS and Kohesio.

Mapping STI ecosystems via Open Data: overcoming the limitations of conflicting taxonomies. A case study for Climate Change Research in Denmark

Science, Technology and Innovation (STI) decision-makers often need to have a clear vision of what is researched and by whom to design effective policies. Such a vision is provided by effective and comprehensive mappings of the research activities carried out within their institutional boundaries. A major challenge to be faced in this context is the difficulty in accessing the relevant data and in combining information coming from different sources: indeed, traditionally, STI data has been confined within closed data sources and, when available, it is categorised with different taxonomies. Here, we present a proof-of-concept study of the use of Open Resources to map the research landscape on the Sustainable Development Goal (SDG) 13-Climate Action, for an entire country, Denmark, and we map it on the 25 ERC panels.

INODE: Building an End-to-End Data Exploration System in Practice

A full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process, by being reactive and anticipative both for data discovery and for data linking. Such systems are a real opportunity for our community to cater to users with different domain and data science expertise. We introduce INODE -- an end-to-end data exploration system -- that leverages, on the one hand, Machine Learning and, on the other hand, semantics for the purpose of Data Management (DM). Our vision is to develop a classic unified, comprehensive platform that provides extensive access to open datasets, and we demonstrate it in three significant use cases in the fields of Cancer Biomarker Reearch, Research and Innovation Policy Making, and Astrophysics. INODE offers sustainable services in (a) data modeling and linking, (b) integrated query processing using natural language, (c) guidance, and (d) data exploration through visualization, thus facilitating the user in discovering new insights. We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public. Finally, we briefly illustrate how this work paves the way for new research opportunities in DM.