The objective of the BIGPROD project is to extend existing econometric approaches to productivity, such as the Crepon-Duguet-Mairesse (CDM) model, with theoretically sound “Big data” measures that are operationalized, validated through pilots and communicated to relevant stakeholders. This is achieved by uncovering the origins of the productivity slowdown in most Western economies. Based on this understanding, we will extend the CDM model to better account for changes in the innovation process and utilize measures enabled by "Big data". The model created will be operationalized using for a random stratified sample of 160,000-200,000 European companies. The operationalization and theoretical framework will be validated using a multi-criteria impact assessment approach. The validation will include three pilots based on issues arising from the research literature on challenges of existing measures of productivity. These are 1) high-technology and digitalization, 2) low-technology and innovation outcomes and 3) services and incumbent entrant dynamics. To allow for full integration with the policy cycle, the project will incorporate a deep stakeholder consultation mitigating the skills gap, creating transparency, enabling stakeholder influence in sources and tools and enabling policymakers being informed on utilizing tools and pilots. The BIGPROD project addresses the work program by enabling the exploitation of "Big data" in productivity analysis with a transparent operationalization and pilots addressing key issues in integrating "Big data" to the policy cycle. The rigorous theoretical approach will enable a better understanding of the "quasi-standstill" of productivity. Using a tested data architecture, now being expanded, the project will address data accuracy and security. The impact assessment will allow us to create stakeholder validated results. Finally, outreach to existing indicator sets, such as observatories, will allow findings to be deployed.

Recent technological developments in data mining, data treatment and data analysis have been rapid and far reaching, offering new dimensions and opportunities for performance analytics in various domains. The introduction of new technologies and initiatives, including open access mechanisms and social media/online media, has been generating increasing volumes of new data on the research domain. Data4Impact aims to capitalise on these developments and utilise big data approaches to improve the monitoring of research and innovation performance and assessment of the societal impact in the Health, Demographic Change and Wellbeing Societal Challenge. The project has he following main objectives: a) define, develop, analyse and disseminate new indicators for assessing the performance of EU and national research and innovation systems; b) explore and collect “big” data on health-related societal challenges at input, throughput, output/result and impact levels; c) employ big data approaches to yield more data on the societal impact of national and EU funding on tackling health-related societal challenges; d) engage stakeholders in the project activities, validate the project results and develop new indicators and tools using a hands-on approach. It will do so by developing a robust conceptual model addressing all key stages of the innovation process; mining large volumes of data on research results and impacts; and analysis of these data using topic modelling, machine learning and other techniques aimed at natural language processing. The Data4Impact consortium possesses specialist knowledge of the health domain & indicator systems, and is uniquely placed to mine data and apply big data approaches thanks to the partner’s long-standing involvement in OA e-infrastructures and big data analytics. Through these objectives, activities and competences Data4Impact addresses both key issues described in the specific CO-CREATION-08-2016-2017 call.

Open Access and Open Scholarship have revolutionized the way scholarly artefacts are evaluated and published, while the introduction of new technologies and media in scientific workflows has changed the “how and to whom” science is communicated, and how stakeholders interact with the scientific community. OpenUP addresses key aspects and challenges of the currently transforming science landscape and aspires to come up with a cohesive framework for the review-disseminate-assess phases of the research life cycle that is fit to support and promote Open Science. Its main objectives are to a) identify and determine ground-breaking mechanisms, processes and tools for peer-review for all types of research results (publications, data, software), b) explore, identify and classify innovative dissemination mechanisms with an outreach aim towards businesses and industry, education, and society as a whole, and c) analyse a set of novel indicators that assess the impact of research results and correlate them of channels of dissemination. It will do so by following a user-centred, evidence-based approach, engaging all stakeholders (researchers, publishers, funders, institutions, industry, public) in an open dialogue through a series of workshops, conferences and training, and validating all interim results via a set of seven pilots involving communities from four research disciplines: life sciences, social sciences, arts & humanities, energy. It will finally produce a set of concrete, practical, validated policy recommendations & guidelines for national and European stakeholders, including EU institutions, a valuable tool in advancing a more open and gender-sensitive science system. OpenUP partners bring expertise and capacity for evaluating and promoting new approaches in support of open science with decade-long experiences in establishing OA e-Infrastructures, excellent skills and innovative approaches for dissemination, impact indicators and policy design and implementation.

The main objective of the RIA PIONEERED is to determine research-informed policy measures and identify pioneering policies and practices to mitigate (intersectional) inequalities in access to, uptake and completion of education, both in formal and informal educational settings. This involves a) mapping emerging and existing sources of educational inequalities over the course of educational careers from early childhood to tertiary education, b) mapping responses to inequalities as pioneering policies and practices from a comparative perspective, and c) synthesising the findings and identifying the most promising tools, pioneering policies and practices within and across European countries. This is achieved by an interdisciplinary and mixed-method strategy including multilevel, intersectional, and life course approaches. The research design relies on four key pillars: A first pillar aims at developing an innovative methodological framework and reviewing the state-of-research. A second pillar centres on a policy analysis on which inequalities and vulnerable groups are addressed by national (or regional) policies, and how polices attempt to reduce inequalities. A third pillar focuses on analyses of new and existing data to identify intersectional inequalities throughout educational careers and drivers of such advantages and disadvantages. A fourth pillar centres on practice research in schools and informal educational settings and analyses the ways through which educational inequalities are tackled by different actors. With these objectives and comprehensive research strategy, PIONEERED thoroughly addresses the challenges identified in the H2020 work programme Europe in a changing world – Inclusive, innovative and reflective societies by providing evidence-based and customised policy tools, specific to various stages of education and target groups in order to enhance access to education among vulnerable children and youth and to reduce educational inequalities.

Research through the decades has demonstrated the existence and persistence of educational inequalities. These inequalities particularly affect vulnerable groups of students such as those with low socioeconomic status or a migratory history, who more frequently show high rates of early school leaving and low school performance. However, there is increasing scientific literature which identifies educational interventions that are effective in compensating for these inequalities, overcoming deterministic analyses in the understanding of the educational trajectories of vulnerable children and youth. Taking this into account, this project will have a double approach. On the one hand, the project will exploit the large volume of existing educational data at the national and regional levels to map and analyse educational inequalities from a longitudinal perspective, identifying key features and trends. For the analysis, retrospective cohort studies will be conducted to understand how educational inequalities have evolved in recent years and how they relate to characteristics of the education received. On the other hand, the existing interventions that compensate for these inequalities will be studied in depth. Longitudinal analysis (quantitative and qualitative) will be performed of the educational trajectories of children and youth who have participated in successful educational interventions already identified by scientific research, to better understand how and to what extent they contribute to improve learning outcomes. With these analyses, this project aims to facilitate that effective interventions can be translated into policies and replicated to advance into more inclusive educational systems with better learning outcomes for all. Additionally, the project will allow a better knowledge of the type of data to collect and how to collect them, to be able to make informed decisions towards overcoming educational inequalities in the short, medium, and long term.