Cerqueti Roy, Iovanella Antonio, Mattera Raffaele
Department of Social and Economic Sciences, Sapienza University of Rome, P.le A. Moro, 5, 00185 Rome, Italy.
GRANEM, Université d'Angers, Angers, France.
Ann Oper Res. 2023 May 5:1-29. doi: 10.1007/s10479-023-05321-6.
This paper treats a well-established public evaluation problem, which is the analysis of the funded research projects. We specifically deal with the collection of the research actions funded by the European Union over the 7th Framework Programme for Research and Technological Development and Horizon 2020. The reference period is 2007-2020. The study is developed through three methodological steps. First, we consider the networked scientific institutions by stating a link between two organizations when they are partners in the same funded project. In doing so, we build yearly complex networks. We compute four nodal centrality measures with relevant, informative content for each of them. Second, we implement a rank-size procedure on each network and each centrality measure by testing four meaningful classes of parametric curves to fit the ranked data. At the end of such a step, we derive the best fit curve and the calibrated parameters. Third, we perform a clustering procedure based on the best-fit curves of the ranked data for identifying regularities and deviations among years of research and scientific institutions. The joint employment of the three methodological approaches allows a clear view of the research activity in Europe in recent years.
本文探讨了一个既定的公众评估问题，即对获得资助的研究项目进行分析。我们具体处理的是欧盟在第七个研究与技术发展框架计划以及“地平线2020”下资助的研究行动的收集情况。参考时间段为2007年至2020年。该研究通过三个方法步骤展开。首先，我们通过确定两个组织在同一个获得资助的项目中为合作伙伴时的联系来考量网络化的科研机构。在此过程中，我们构建年度复杂网络。我们为每个网络计算四种具有相关且信息丰富内容的节点中心性度量。其次，我们对每个网络和每种中心性度量实施秩规模程序，通过测试四类有意义的参数曲线来拟合排序后的数据。在这一步骤结束时，我们得出最佳拟合曲线和校准参数。第三，我们基于排序后数据的最佳拟合曲线执行聚类程序，以识别多年来研究和科研机构之间的规律与偏差。这三种方法的联合运用让我们能够清晰地了解近年来欧洲的研究活动。
