ON-MERRIT targets an equitable scientific system that rewards based on merit rather than the “Matthew Effect” of cumulative advantage. Responsible Research and Innovation (RRI), including elements like Open Science and Gender Equality, promises to fundamentally transform scholarship to bring greater transparency and participation to research processes, and increase the impact of outputs. Yet just making processes open will not per se drive re-use or participation unless also accompanied by the capacity (in terms of knowledge, skills, motivation and technological readiness) to do so. Absorptive capacity and ability to capitalize on knowledge resources vary considerably amongst business, researchers and the general public. Those in possession of such capacities are at an advantage, with the effect that RRI’s agenda of inclusivity is put at risk by conditions of “cumulative advantage” (“Matthew Effect”). Recognising this key threat to RRI, ON-MERRIT’s transdisciplinary consortium deploys a cutting-edge combination of qualitative (surveys, questionnaires, interviews, focus groups, case-studies) and computational (scientometrics, social network analysis, predictive analytics, text and data mining) methods that use stakeholder participation and co-design to engage researchers, industry, policy-makers and citizens in examining the extent of the Matthew Effect in key RRI elements (Public Engagement, Gender, Open Access/Open Science and Governance). Selected research questions focus on disciplinary contexts of particular importance for the UN Sustainable Development Goals (Agriculture, Climate, and Health). ON-MERRIT then synthesises this evidence to make evidence-based policy recommendations on how Research Performing and Funding Organisations and others should amend policies, indicators and incentives to address and/or mitigate these effects, thus breaking new ground to broaden the SWAFS knowledge-base and show the way ahead for equitable RRI.

The goal of AFEL (Analytics for Everyday Learning) is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieve in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) online social environments; 2) creating methods for the analysis of such informal learning data, based on combining visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported; 4) evaluate all the former items in real world large scale applications and platforms.

More than half of the persons with spinal cord injuries (SCI) are suffering from impairments of both hands, which results in a tremendous decrease of quality of life (QoL) and represents a major barrier for inclusion in society. Functional restoration is possible with neuroprostheses based on functional electrical stimulation (FES). However, current systems are non-intelligent, non-intuitive open loop systems without sensory feedback. MoreGrasp aims at developing a multi-adaptive, multimodal user interface including brain-computer interfaces (BCIs) for intuitive control of a semi-autonomous motor and sensory grasp neuroprosthesis to support activities of daily living in individuals with SCI. With such a system a bilateral grasp restoration may become reality. The multimodal interfaces will be based on non-invasive BCIs for decoding of movements intentions with gel-less electrodes and wireless amplifiers. The neuroprosthesis will include FES electrode arrays and different sensors to allow for implementation of predefined or autonomously learned sequences. MoreGrasp will consequently follow the concept of the user-centered design by providing a scalable, modular, user-specific neuroprosthesis together with personalized EEG recording technology. Novel multimodal software architectures including interoperability standards will be defined to integrate neuroprostheses into the field of assistive technology. Long-term end user studies will demonstrate the reliability, usefulness and impact on QoL of the MoreGrasp technology. A web-based service infrastructure including a discussion forum will be set up for assessing user priorities and screening of users’ status. The evaluation of the training and patterns of use will allow for user modeling to identify factors for successful use. The highly interdisciplinary MoreGrasp consortium consists of members from universities, industry and rehabilitation centers, which have a long history of successful cooperation.