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

Abstract

Many theories of human information behavior (HIB) assume that information objects are in text document format. This paper argues that four important HIB theories are insufficient for describing users’ search strategies for research data because of differences between data and text documents. We first review and compare four HIB theories: Bates’ theory of berrypicking, Marchionni’s theory of electronic information search, Dervin’s theory of sense-making, and Meho and Tibbo’s model of social scientist information-seeking. All four theories assume that information-seekers search for text documents. We compare these theories to user search behavior by analyzing the Inter-university Consortium for Political and Social Research’s (ICPSR) search logs. Users took direct, orienting, and scenic paths when searching for research data. We interviewed ICPSR data users (n=20), and they said they needed dataset documentation and contextual information absent from text documents to find data, which suggested ongoing sense-making. However, sense-making alone does not explain the information-seeking behavior we observed. What mattered most to secondary data discovery were information attributes determined by the type of objects that users sought (i.e., data, not documents). We conclude by suggesting an alternative frame for building data discovery tools.