Big Data approaches to estimating the impact of EU research funding on innovation development

EU will spend around € 80 billion in supporting research through H2020 instrument alone. Such significant amounts of money dedicated to supporting research projects naturally beg the question: What impacts resulted from this EU funding? Answering this question is hard, especially keeping in mind that EU-funded researchers already struggle with significant amounts of paperwork and are burdened by various periodic surveys. In this paper, we propose a method to estimate the impact of EU funding on innovation development using non-invasive data acquisition methods, which do not further contribute to the EU survey fatigue. The method utilizes supervised machine-learning techniques to infer data on the innovation outputs of the EU-funded companies from publicly available data on their websites. Gathered information is then compared to the control group of enterprises from the same geographical area and economic sector. The panel of target and control group of enterprises is analyzed through time to uncover potential lagged/ long-term effects of the EU funding. The paper is structured as follows: the first part presents an overview of the need to estimate the impact of EU (research) funding and the methodological challenges associated with it. Second part presents the methodology and study design. The third part presents the preliminary results and their implications, while the forth – discusses outstanding challenges and the next steps

Big Data approaches to estimating the impact of EU research funding on innovation development

EU will spend around € 80 billion in supporting research through H2020 instrument alone. Such significant amounts of money dedicated to supporting research projects naturally beg the question: What impacts resulted from this EU funding? Answering this question is hard, especially keeping in mind that EU-funded researchers already struggle with significant amounts of paperwork and are burdened by various periodic surveys. In this paper, we propose a method to estimate the impact of EU funding on innovation development using non-invasive data acquisition methods, which do not further contribute to the EU survey fatigue. The method utilizes supervised machine-learning techniques to infer data on the innovation outputs of the EU-funded companies from publicly available data on their websites. Gathered information is then compared to the control group of enterprises from the same geographical area and economic sector. The panel of target and control group of enterprises is analyzed through time to uncover potential lagged/ long-term effects of the EU funding. The paper is structured as follows: the first part presents an overview of the need to estimate the impact of EU (research) funding and the methodological challenges associated with it. Second part presents the methodology and study design. The third part presents the preliminary results and their implications, while the forth – discusses outstanding challenges and the next steps

Deliverable D3.1– Practices, evaluation and mapping: Methods, tools and user needs:OPENing UP new methods, indicators and tools for peer review, impact measurement and dissemination of research results

This report demonstrates how alternative peer review tools and methods are instrumental in further shaping the communication of scholarly results towards open science. The analysis is based on the examination of various review methods (peer commentary, post-publication peer review, decoupled review, portable or cascading review) and review tools and services (publishing platforms, repository-based, independent reviews). Besides the differences in operation and functionality, these new workflows and services combine common features of network-based solutions and collaborative research applications with varying degrees of openness (e.g. regarding participation, identities and/or reports). They, therefore, represent good examples of open science, in terms of transparency and networking among researchers