Data, not documents: Moving beyond theories of information-seeking behavior to advance data discovery

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.