Project description
Open data for scientific research optimisation
The inability to reproduce important scientific studies and research can have a negative impact on social sciences, resulting in a remarkable slowdown of scientific work. Scientists argue that only by improved transparency and spirit of collaboration is it possible to face the problem of the reproducibility crisis. Open data could play an instrumental role in this process if the associated individual costs weren’t too high and their effectiveness was certain. The EU-funded OPTIMISE project will address the problem by combining social and natural sciences methodology to estimate whether open data can offer high-quality data that improve scientific research. It will investigate the factors behind decisions of open data adoption focusing on the fields of ecology and evolution.
Objective
Science is facing a reproducibility crisis, whereby many research results, including landmark studies, cannot be independently reproduced. As a consequence, scientific progress is slowed and entire research fields can be misguided. Finding a meaningful solution to this crisis requires increasing transparency and collaboration among researchers to ‘OPTIMISE’ how we conduct science. I will study the role and importance of open data as a means of achieving this goal. Making research data openly accessible to other scientists and the public has many societal benefits, including validating research results and accelerating discoveries. However, open data is controversial among researchers, mainly because of perceived individual costs. Furthermore, we lack empirical research on the efficacy of open data practices at resolving the reproducibility crisis. By combining approaches in social and natural sciences, this action will address this knowledge gap in an interdisciplinary fashion via two overarching objectives: A) Assess whether open data policies result in high-quality data sharing and reduce poor scientific practices; B) Investigate the barriers and motivations behind decisions to adopt open data practices. I will focus on the field of ecology and evolution (my background) and examine: 1) the efficacy of editorial policies mandating open data, 2) the influence of open data practices on the quality of research results, 3) challenges and solutions to sharing sensitive data, 4) barriers to good open data practices, and 5) individual motivations for sharing data. The data to answer these questions are readily collectable and reciprocal knowledge transfer will directly benefit both the hosts and the candidate. Deliverables will help elucidate the barriers and benefits of open science practices to improve research transparency, reproducibility and discovery. These goals support H2020’s objective to facilitate innovation and growth while maintaining scientific integrity.
Fields of science
Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
2000 NEUCHATEL
Switzerland