Scientific discoveries do not occur in vacuum but rather by connecting existing pieces of knowledge in new and creative ways. Mapping the relation and structure of scientific knowledge is therefore central to our understanding of the dynamics of scientific production. Here we introduce a new approach to generate scientific knowledge maps based on a machine learning approach that, starting from the observed publication patterns of authors, generates an N-dimensional space where it is possible to measure the similarity or distance between different research topics and knowledge domains. We provide an implementation of the proposed approach that considers the American Physical Society publications database and generates a map of the research space in Physics that characterizes the relation among research topics over time. We use this map to measure two indicators, the research capacity fingerprint and the knowledge density, to profile the research activity in physical sciences of more than 400 urban areas across the world. We show that these indicators can be used to analyze and predict the evolution over time of the research capacity and specialization of specific geographical areas. Furthermore we provide an extensive analysis of the relation between socio-economic development indicators and the ability to produce new knowledge for 67 countries, as measured by our approach, highlighting some key correlates of scientific production capacity. The proposed approach is scalable to very large datasets and can be extended to study other disciplines and research areas without having to rely on ad-hoc science classification schemes.

中文翻译：

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映射物理研究空间：一种机器学习方法

科学发现不是凭空发生的，而是通过以新颖和创新的方式联系现有知识的过程而发生的。因此，绘制科学知识的关系和结构对于我们对科学生产动力的理解至关重要。在这里，我们介绍一种基于机器学习方法生成科学知识图谱的新方法，该方法从观察到的作者发表方式开始，生成一个N维空间，可以在其中测量不同研究主题和知识之间的相似性或距离域。我们提供建议方法的实施方案，该方法考虑了美国物理学会的出版物数据库并生成了研究空间图在物理学中描述了研究主题之间的关系。我们使用这张图来衡量两个指标，研究能力指纹和知识密度，以介绍全球400多个城市地区在物理科学领域的研究活动。我们显示这些指标可用于分析和预测特定地理区域的研究能力和专业化程度随时间的演变。此外，根据我们的方法，我们对社会经济发展指标与为67个国家/地区提供新知识的能力之间的关系进行了广泛的分析，强调了科学生产能力的一些关键关联。所提出的方法可扩展到非常大的数据集，并且可以扩展为研究其他学科和研究领域，而不必依赖临时科学分类方案。