SOURCE-LINKED INTELLIGENCE

improving Reproducibility In SciencE

CORDIS · observation · Publication date unknown

plementation of interventions intended to improve reproducibility. iRISE brings together qualitative and quantitative expertise, from academia and SMEs, including meta-science, statistics, economics, artificial intelligence, research ethics and integrity, quality assurance, and project management. iRISE proposes the development of a general framework for diagnosing and addressing reproducibility problems using analytical and computational modelling, simulations and meta-studies. Data on existing interventions will be systematically curated and evaluated, and stakeholders will be consulted to collaboratively identify practices and tools that should be prioritised for implementation. iRISE proposes to conduct empirical studies of both technical and practice-based solutions to increase reproducibility. Across all iRISE activities, the influences of research culture will be investigated, with a focus on mainstreaming systematic integration of equity, diversity and inclusion practices. A comprehensive Stakeholder Forum will be engaged to provide advice, and iRISE will commit to open and r

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recordType

award

status

SIGNED

region

EU

value

1446771.25

unit

EUR