Data, not documents: Moving beyond theories of information‐seeking behavior to advance data discovery
本文指出四种主流信息行为理论因假设用户搜寻文本文档，不足以描述数据搜索行为；通过分析ICPSR的Google Analytics数据和20名用户访谈，发现用户采用直接、风景和定向路径搜索数据，且需要数据集文档和上下文信息，最终提出构建以用户为中心的数据发现工具的新框架。
Abstract Many theories of human information behavior (HIB) assume that information objects are in text document format. This paper argues four important HIB theories are insufficient for describing users' search strategies for data because of assumptions about the attributes of objects that users seek. We first review and compare four HIB theories: Bates' berrypicking , Marchionni's electronic information search , Dervin's sense‐making , and Meho and Tibbo's social scientist information‐seeking . All four theories assume that information‐seekers search for text documents. Next, we compare these theories to search behavior by analyzing Google Analytics data from the Inter‐university Consortium for Political and Social Research (ICPSR). Users took direct, scenic, and orienting paths when searching for data. We also interviewed ICPSR users ( n = 20), and they said they needed dataset documentation and contextual information to find data. However, Dervin's sense‐making alone cannot explain the information‐seeking behaviors that we observed. Instead, what mattered most were object attributes determined by the type of information that users sought (i.e., data, not documents). We conclude by suggesting an alternative frame for building user‐centered data discovery tools.