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CORE participates in new EU funded project on Responsible Research and Innovation - FIT4RRI

Nancy Pontika, Tuesday 11 July 2017 | Annotate
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FIT4RRI is precisely intended to contribute to bridging the gap between RRI and Open Science and promoting viable strategies to render institutional changes in RFPOs (Research Funding and Performing Organizations)

FIT4RRI moves from the assumption that there is a serious gap between the potential role RRI and OS (open science) could play in helping RFPOs (Research Funding and Performing Organizations) to manage the rapid transformation processes affecting science (especially the science-in-society aspects) and the actual impact RRI and OS are having on RFPOs, research sectors and national research systems.

The project will act on 2 key factors:

  1. Enhancing competencies and skills related to RRI and OS through an improvement of the RRI and OS training offer
  2. Institutionally embedding RRI/OS practices and approaches by promoting the diffusion of more advanced governance settings

‘Through FIT4RRI we want to engage hard scientists into responsibility matters and promote RRI and OS as drivers for institutional change in research funding and performing organizations. We look at science as a tool to create bridges towards society’

Andrea Riccio, Project Coordinator UNIROMA1

CORE has two contributions in this project; it will create the platform to host the RRI resources, training tools and events and will also run an RRI experiment with a focus on the Text and Data Mining.

The FIT4RRI project was granted within the Horizon 2020 Program of the European Union after a competitive one stage selection process. The project started with a kick-off meeting in Rome at the Sapienza University on the 12th & 13th of June 2017 and will be funded for three years.

The FIT4RRI partners in the wonderful cloister of San Pietro in Vincoli



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Connecting REpositories by automatically linking Open Access research publications

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Linking semantically similar publications from Open Access repositories using text mining from full-text and representing the relations as Linked Data

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