Over the last century scientific research has become an increasingly collaborative endeavor. Commentators have pointed to different factors which contribute to this trend, including the specialization of science and growing need for diversity of interest and expertise areas in a scientific team. Very few studies, however, have precisely evaluated how the diversity of interest topics between researchers is related to the emergence of collaboration. Existing theoretical arguments suggest a curvilinear relationship between topic similarity and collaboration: too little similarity can complicate communication and agreement, yet too much overlap can increase competition and limit the potential for synergy. We test this idea using data on six years of publications across all disciplines at a large U.S. research university (approximately 14,300 articles, 12,500 collaborations, and 3400 authors). Employing topic modelling and network statistical models, we analyze the relationship between topic overlap and the likelihood of coauthorship between two researchers while controlling for potential confounders. We find an inverted-U relationship in which the probability of collaboration initially increases with topic similarity, then rapidly declines after peaking at a similarity “sweet spot”. Collaboration is most likely at low-to-moderate levels of topic overlap, which are substantially lower than the average self-similarity of scientists or research groups. These findings – which we replicate for different units of analysis (individuals and groups), genders of collaborators, disciplines, and collaboration types (intra- and interdisciplinary) – support the notion that researchers seek collaborators to augment their scientific and technical human capital. We discuss implications for theories of scientific collaboration and research policy.

在上个世纪，科学研究已成为一项日益协作的努力。评论员指出了促成这一趋势的不同因素，包括科学的专业化以及对科学团队中兴趣和专业领域多样性的日益增长的需求。然而，很少有研究准确评估研究人员之间兴趣主题的多样性与合作的出现有何关系。现有的理论论证表明主题相似性和协作之间存在曲线关系：相似性太少会使沟通和协议复杂化，但过多的重叠会增加竞争并限制协同作用的潜力。我们使用美国一所大型研究型大学所有学科的六年出版物数据（大约 14,300 篇文章，12,500 次合作和 3400 位作者）。采用主题建模和网络统计模型，我们分析了主题重叠与两位研究人员之间合着的可能性之间的关系，同时控制了潜在的混杂因素。我们发现了一种倒 U 型关系，其中协作的概率最初随着主题相似度的增加而增加，然后在相似度“甜蜜点”达到峰值后迅速下降。合作最有可能发生在低到中等程度的主题重叠，这大大低于科学家或研究小组的平均自相似性。这些发现——我们为不同的分析单位（个人和团体）、合作者的性别、学科、和协作类型（学科内和跨学科）——支持研究人员寻求合作者以增加其科技人力资本的观点。我们讨论了对科学合作和研究政策理论的影响。