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  1. Abstract

    The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets. Here we present SciSciNet, a large-scale open data lake for the science of science research, covering over 134M scientific publications and millions of external linkages to funding and public uses. We offer detailed documentation of pre-processing steps and analytical choices in constructing the data lake. We further supplement the data lake by computing frequently used measures in the literature, illustrating how researchers may contribute collectively to enriching the data lake. Overall, this data lake serves as an initial but useful resource for the field, by lowering the barrier to entry, reducing duplication of efforts in data processing and measurements, improving the robustness and replicability of empirical claims, and broadening the diversity and representation of ideas in the field.

     
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  5. Abstract

    A central question in the science of science concerns how to develop a quantitative understanding of the evolution and impact of individual careers. Over the course of history, a relatively small fraction of individuals have made disproportionate, profound, and lasting impacts on science and society. Despite a long-standing interest in the careers of scientific elites across diverse disciplines, it remains difficult to collect large-scale career histories that could serve as training sets for systematic empirical and theoretical studies. Here, by combining unstructured data collected from CVs, university websites, and Wikipedia, together with the publication and citation database from Microsoft Academic Graph (MAG), we reconstructed publication histories of nearly all Nobel prize winners from the past century, through both manual curation and algorithmic disambiguation procedures. Data validation shows that the collected dataset presents among the most comprehensive collection of publication records for Nobel laureates currently available. As our quantitative understanding of science deepens, this dataset is expected to have increasing value. It will not only allow us to quantitatively probe novel patterns of productivity, collaboration, and impact governing successful scientific careers, it may also help us unearth the fundamental principles underlying creativity and the genesis of scientific breakthroughs.

     
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