Several recently published papers in Decision Support Systems discussed issues related to data quality in Information Systems research. In this short research note, I build on the work introduced in these papers and document two data quality issues discovered in a large open dataset commonly used in research. Inside Airbnb (IA) collects data from places and reviews as posted by users of Airbnb.com. Visitors can effortlessly download data collected by IA for several locations around the globe. While the dataset is widely used in academic research, no thorough investigation of the dataset and its validity has been conducted. This note examines the dataset and explains an issue of incorrect data added to the dataset. Findings suggest that this issue can be attributed to systemic errors in the data collection process. The results suggest that the use of unverified open datasets can be problematic, although the discoveries presented in this work may not be significant enough to challenge all published research that used the IA dataset. Additionally, findings indicate that the incorrect data happens because of a new feature implemented by Airbnb. Thus, unless changes are made, it is likely that the consequences of this issue will only become more severe. Finally, this note explores why reproducibility is a problem when two different releases of the dataset are compared.

决策支持系统中最近发表的几篇论文讨论了信息系统研究中与数据质量有关的问题。在这份简短的研究笔记中，我以这些论文介绍的工作为基础，并记录了在研究中常用的大型开放数据集中发现的两个数据质量问题。Inside Airbnb（IA）从Airbnb.com用户发布的地点和评论中收集数据。访客可以毫不费力地下载IA收集的全球多个位置的数据。尽管该数据集已广泛用于学术研究，但尚未对数据集及其有效性进行彻底的调查。本说明检查数据集并解释了将错误的数据添加到数据集中的问题。调查结果表明，此问题可归因于数据收集过程中的系统性错误。结果表明，未验证的开放数据集的使用可能会有问题，尽管这项工作中提出的发现可能不足以挑战所有使用IA数据集的已发表研究。此外，调查结果表明，由于Airbnb实施了一项新功能，因此发生了错误的数据。因此，除非进行更改，否则此问题的后果可能只会变得更加严重。最后，本说明探讨了在比较两个不同版本的数据集时，为什么重现性会成为问题。因此，除非进行更改，否则此问题的后果可能只会变得更加严重。最后，本说明探讨了在比较两个不同版本的数据集时，为什么重现性会成为问题。因此，除非进行更改，否则此问题的后果可能只会变得更加严重。最后，本说明探讨了在比较两个不同版本的数据集时，为什么重现性会成为问题。