Effects of seniority, gender and geography on the bibliometric output and collaboration networks of European Research Council (ERC) grant recipients.
Assessing the success and performance of researchers is a difficult task, as their grant output is influenced by a series of factors, including seniority, gender and geographical location of their host institution. In order to assess the effects of these factors, we analysed the publication and citation outputs, using Scopus and Web of Science, and the collaboration networks of European Research Council (ERC) starting (junior) and advanced (senior) grantees. For this study, we used a cohort of 355 grantees from the Life Sciences domain of years 2007-09. While senior grantees had overall greater publication output, junior grantees had a significantly greater pre-post grant award increase in …
Does reviewing experience reduce disagreement in proposals evaluation? Insights from Marie Skłodowska-Curie and COST Actions
Abstract We have limited understanding of why reviewers tend to strongly disagree when scoring the same research proposal. Thus far, research that explored disagreement has focused on the characteristics of the proposal or the applicants, while ignoring the characteristics of the reviewers themselves. This article aims to address this gap by exploring which reviewer characteristics most affect disagreement among reviewers. We present hypotheses regarding the effect of a reviewer’s level of experience in evaluating research proposals for a specific granting scheme, that is, scheme reviewing experience. We test our hypotheses by studying two of the most important research funding programmes i…
Predictors of applying for and winning an ERC Proof-of-Concept grant: An automated machine learning model
Research often fails to be translated into applications because of lack of financial support. The Proof of Concept (PoC) funding scheme from the European Research Council (ERC) supports the early stages of the valorization process of the research conducted by its grantees. This article explores the factors that predict who will apply for ERC grants and which grant proposals will prove successful. By combining information from two datasets of 10,074 ERC grants (representing 8361 individual grantees) and 2186 PoC proposals, and using automated machine learning, we can identify the main predictors of the propensity to apply and to win. Doing so fills a void in the literature on likelihood to a…