2020/07/04 by Yingjie Hu, Hu, Yingjie
Computer Science · Decision Sciences · #Data Quality and Management #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Research Data Management Practices #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2007.01978
openalex publication_date 2020/07/04 · openalex created_date 2020/07/10 · openalex updated_date 2026/07/28
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.