ExpectedOutcome:
Projects are expected to contribute to the following expected outcomes:
- Structured understanding of the underlying drivers, of concrete and effective interventions - funding, community-based, technical and policy - to increase reproducibility of the results of R&I; and of their benefits;
- Effective solutions, policy-, technical- and practice-based, to increase the reproducibility of R&I results in funding programmes, in communities and in the dissemination of scientific results;
- Greater collaboration, alignment of practices and joint action by stakeholders to increase reproducibility, including but not limited to training, specialised careers and guidelines for best practice.
These outcomes should contribute to medium and long-term impacts:
- Increased proportion of reproducible results from publicly funded R&I;
- Increased re-use of scientific results by research and innovation;
- Greater quality of the scientific production.
Scope:
Reproducibility refers to the possibility for the scientific community to obtain the same results as the originators of a specific finding. As such, reproducibility is core to scientific progress, and there is debate on whether there is a ‘crisis of reproducibility’ in contemporary science. At a time when funding levels for R&I are under scrutiny globally, and societal trust in the outcomes of research and innovation become increasingly essential, there is a need to address inefficiencies in the research process, to avoid useless and costly repetition, to maximise return on investment in R&D&I, to prevent the propagation of mistakes, and to facilitate the translation of results into innovations.
Therefore, this topic aims to fund activities to
a) determine how increased reproducibility generates gains and savings in the R&I process and improve overall performance - alongside the demonstrated positive effects on their quality, integrity and trust-worthiness, and
b) find, experiment and mainstream concrete solutions and best-practices to increase the reproducibility of research funded with European taxpayers’ money, including through the more systematic integration of sex and gender as variables whenever relevant.
Consequently, actions should help understand and promote reproducibility by:
1) creating an open knowledge base of results, methodologies and interventions on the drivers and consequences of reproducibility for the R&I system; and to fill the main gaps in such knowledge;
2) develop, validate, pilot and deploy practices and practical tools for funders, publishers and scientists;
3) promote uptake, greater collaboration, and increased alignment of the activities of stakeholders - scientific and technical communities, publishers and funders among others - to increase reproducibility.
Finally, projects should assist further policy development on reproducibility, based on scoping work by the Commission[1]. While solutions should be applicable to Europe, attention should be paid to reproducibility in global science.
It is expected that the funded action(s) will adhere to best practices in open science and reproducibility (e.g. re-use existing results, fully document the research process), and provide a final reflection based on their own experience at the forefront of reproducibility.
Cross-cutting Priorities:
Digital AgendaArtificial Intelligence
[1]i.e. https://data.europa.eu/doi/10.2777/341654
