如何确定广泛的研究主题和领域的根源？以气候变化研究为例介绍RPYS抽样方法
How to identify the roots of broad research topics and fields? The introduction of RPYS sampling using the example of climate change research
摘要：
自从引入参考出版年光谱（RPYS）方法和相应的程序CRExplorer以来，已经发表了许多研究，揭示了主题，领域和研究人员的历史根源。到目前为止，该方法的应用受到用于运行CRExplorer的计算机的可用内存的限制。因此，许多用户无法为更广泛的研究领域或主题执行RPYS。在这项研究中，我们提出了各种抽样方法来解决这个问题：随机抽样，系统抽样和整群抽样。我们介绍了CRExplorer的脚本语言，它可以用来从人口数据集中提取许多样本。基于气候变化研究的大量出版物数据集，我们比较了使用人口数据的RPYS结果与使用不同采样技术的RPYS结果。从我们与完整的RPYS（人口谱图）的比较中，我们得出结论，集群抽样表现最差，系统抽样表现最好。随机抽样也表现得很好，但不如系统抽样。因此，这项研究证明了应用RPYS的抽样方法的成果。
英文摘要：
Since the introduction of the reference publication year spectroscopy (RPYS) method and the corresponding program CRExplorer, many studies have been published revealing the historical roots of topics, fields, and researchers. The application of the method was restricted up to now by the available memory of the computer used for running the CRExplorer. Thus, many users could not perform RPYS for broader research fields or topics. In this study, we present various sampling methods to solve this problem: random, systematic, and cluster sampling. We introduce the script language of the CRExplorer which can be used to draw many samples from the population dataset. Based on a large dataset of publications from climate change research, we compare RPYS results using population data with RPYS results using different sampling techniques. From our comparison with the full RPYS (population spectrogram), we conclude that the cluster sampling performs worst and the systematic sampling performs best. The random sampling also performs very well but not as well as the systematic sampling. The study therefore demonstrates the fruitfulness of the sampling approach for applying RPYS.
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