The paradox of competition: How funding models could undermine the uptake of data sharing practices
通过基于主体的仿真模型，研究了不同资助方案（如高竞争大额资助与分散小额资助）及激励强度对学术团队数据共享采纳的影响，发现短期高竞争促进共享但长期抑制，而分散资助则相反。
Although it is beneficial to scientific development, data sharing is still uncommon in many research areas. Various organisations, including funding agencies that endorse open science, are working to increase uptake. However, it is difficult to estimate the large-scale implications of different policy interventions on data sharing by funding agencies, especially in the context of intense competition among academics. In this study, we developed an agent-based simulation model to examine the impact of different funding schemes (e.g., highly competitive large grants versus distributive small grants), and the intensity of incentives on the uptake of data sharing by academic teams that adapt their strategy according to the context. Our results show that, in the short term, more competitive funding schemes may lead to higher rates of data sharing, but lower rates in the long term because the uncertainty associated with competitive funding negatively affects the cost/benefit ratio of data sharing. Conversely, more distributive grants imply a drastic reduction in initial uptake compared to more competitive funding schemes because they do not allow academic teams to cover the costs and time required for data sharing. However, they ensure higher long term uptake. Our findings suggest that any attempt to reform reward and recognition systems in line with open science principles must carefully consider the potential impact and long-term side effects of their proposed policies.