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A game theoretic analysis of research data sharing

机译:研究数据共享的博弈论分析

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摘要

While reusing research data has evident benefits for the scientific community as a whole, decisions to archive and share these data are primarily made by individual researchers. In this paper we analyse, within a game theoretical framework, how sharing and reuse of research data affect individuals who share or do not share their datasets. We construct a model in which there is a cost associated with sharing datasets whereas reusing such sets implies a benefit. In our calculations, conflicting interests appear for researchers. Individual researchers are always better off not sharing and omitting the sharing cost, at the same time both sharing and not sharing researchers are better off if (almost) all researchers share. Namely, the more researchers share, the more benefit can be gained by the reuse of those datasets. We simulated several policy measures to increase benefits for researchers sharing or reusing datasets. Results point out that, although policies should be able to increase the rate of sharing researchers, and increased discoverability and dataset quality could partly compensate for costs, a better measure would be to directly lower the cost for sharing, or even turn it into a (citation-) benefit. Making data available would in that case become the most profitable, and therefore stable, strategy. This means researchers would willingly make their datasets available, and arguably in the best possible way to enable reuse.
机译:尽管重用研究数据对整个科学界都有明显的好处,但归档和共享这些数据的决定主要由单个研究人员做出。在本文中,我们在博弈论的框架内分析了研究数据的共享和重用如何影响共享或不共享数据集的个人。我们构建了一个模型,其中存在与共享数据集相关联的成本,而重用此类集则意味着收益。在我们的计算中,研究人员出现利益冲突。个体研究者总是能够更好地不共享和省略共享成本,同时,如果(几乎)所有研究者都共享,那么共享和不共享研究者的状况都会更好。即,研究人员分享的次数越多,通过重复使用这些数据集可以获得更多的收益。我们模拟了几种政策措施,以增加研究人员共享或重用数据集的收益。结果指出,尽管政策应该能够提高研究人员的共享率,并且增加的可发现性和数据集质量可以部分补偿成本,但更好的措施是直接降低共享成本,甚至将其变成(引文-)收益。在这种情况下,使数据可用将是最有利可图的,因此也是稳定的策略。这意味着研究人员愿意并有可能以最佳方式使数据集可用,以实现重用。

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