Replicability and reproducibility (R&R) are critical for the long-term prosperity of a scientific discipline. In GIScience, researchers have discussed R&R related to different research topics and problems, such as local spatial statistics, digital earth, and metadata (Fotheringham, 2009; Goodchild, 2012; Anselin et al., 2014). This position paper proposes to further support R&R by building benchmarking frameworks in order to facilitate the replication of previous research for effective and effcient comparisons of methods and software tools developed for addressing the same or similar problems. Particularly, this paper will use geoparsing, an important research problem in spatial and textual analysis, as an example to explain the values of such benchmarking frameworks.

中文翻译：

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构建支持可复制性和再现性的基准测试框架：以空间和文本分析为例

可复制性和再现性 (R&R) 对于科学学科的长期繁荣至关重要。在 GIScience 中，研究人员讨论了与不同研究主题和问题相关的 R&R，例如本地空间统计、数字地球和元数据（Fotheringham，2009；Goodchild，2012；Anselin 等，2014）。本立场文件建议通过建立基准框架来进一步支持 R&R，以促进先前研究的复制，以便对为解决相同或类似问题而开发的方法和软件工具进行有效和高效的比较。特别是，本文将使用地理解析这一空间和文本分析中的重要研究问题作为示例来解释此类基准框架的价值。