International academic collaborations cultivate diversity in the research landscape and facilitate multiperspective methods, as the scope of each country's science depends on its needs, history, wealth etc. Moreover the quality of science differ significantly amongst nations\cite{king2004scientific}, which renders international collaborations a potential source to understand the dynamics between countries and their advancements. Analyzing these collaborations can reveal sharing expertise between two countries in different fields, the most well-known institutions of a nation, the overall success of collaborative efforts compared to local ones etc. Such analysis were initially performed using statistical metrics \cite{melin1996studying}, but network analysis has later proven much more expressive \cite{wagner2005mapping,gonzalez2008coauthorship}. In this exploratory analysis, we aim to examine the collaboration patterns between French and US institutions. Towards this, we capitalize on the Microsoft Academic Graph MAG \cite{sinha2015overview}, the largest open bibliographic dataset that contains detailed information for authors, publications and institutions. We use the coordinates of the world map to tally affiliations to France or USA. In cases where the coordinates of an affiliation were absent, we used its Wikipedia url and named entity recognition to identify the country of its address in the Wikipedia page. We need to stress that institute names have been volatile (due to University federations created) in the last decade in France, so this is a best effort trial. The results indicate an intensive and increasing scientific production in with , with certain institutions such as Harvard, MIT and CNRS standing out.
翻译:国际学术协作培养了研究领域的多样性,并促进了多方面的方法,因为每个国家的科学范围取决于其需要、历史、财富等等。此外,各国的科学质量差异很大,这使得国际协作成为了解国家之间动态及其进展的潜在来源。分析这些协作可以揭示出两个国家在不同领域,即一个国家最著名的机构之间分享专门知识的情况,与当地机构相比,协作努力的总体成功情况等等。这种分析最初是利用统计指标(Cite{melin1996研究 ) 进行的,但网络分析后来证明,各国之间的科学质量差异更大得多。在这项探索性分析中,我们的目标是审查法国和美国机构之间的协作模式。为此,我们利用微软学术图MAG\cite{cites in2015overview},这是最大的开放书目库数据集,其中包含了作者、出版物和机构的详细信息。我们利用世界地图的坐标对统计学的坐标,2005年的地图显示,与法国或美国的最佳联系点。我们利用了这十年中最接近的研究所的地址,这是我们最近才了解到的大学的地址。