Although beneficial to scientific development, data sharing is still uncommon in many research areas. Various organisations, including funding agencies that endorse open science, aim to increase its uptake. However, estimating the large-scale implications of different policy interventions on data sharing by funding agencies, especially in the context of intense competition among academics, is difficult empirically. Here, we built an agent-based model to simulate the effect of different funding schemes (i.e., highly competitive large grants vs. distributive small grants), and varying intensity of incentives for data sharing on the uptake of data sharing by academic teams strategically adapting to the context. Our results show that more competitive funding schemes may lead to higher rates of data sharing in the short term, but lower rates in the long-term, because the uncertainty associated with competitive funding negatively affects the cost/benefit ratio of data sharing. At the same time, more distributive grants do not allow academic teams to cover the costs and time required for data sharing, limiting uptake. Our findings suggest that without support services and infrastructure to minimise the costs of data sharing and other ancillary conditions (e.g., university policy support, reputational rewards and benefits of data sharing for academic teams), it is unlikely that funding agencies alone can play a leading role for the uptake of data sharing. Therefore, any attempt to reform reward and recognition systems towards open science principles should carefully consider the potential impact of their proposed policies and their long-term side effects.
https://doi.org/10.31222/osf.io/gb4v2
| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |
