Work

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

Year: 2024

Type: article

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.

Source: Journal of the Association for Information Science and Technology

DOI: 10.1002/asi.24962

Authors: Anthony J. Million, Jeremy York, Sara Lafia, Libby Hemphill

Institution: University of Michigan

Language: English

Topic: Library Science and Information Literacy

Subfield: Library and Information Sciences

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,150

Funder: Division of Social and Economic Sciences