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.

Social scientists increasingly share data so others can evaluate, replicate, and extend their research. To understand the process of data discovery as a precursor to data use, we study prospective users’ interactions with archived data. We gathered data for 98,000 user sessions initiated at a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). Our data reflect four years (2012-16) of users’ interactions with archival resources, including a data catalog, study-level metadata, variables, and publications that cite nearly 10,000 datasets. We constructed a network of user interactions linking website landing (e.g., site entrances) to exit pages, from which we identified three types of paths that users take through the research data archive: direct, orienting, and scenic. We also interpreted points of failure (e.g., drop-offs) and recurring behaviors (e.g., sensemaking) that support or impede data discovery along search paths. We articulate strategies that users adopt as they navigate data search and suggest ways to enhance the accessibility of data, metadata, and the systems that organize each.

Abstract This paper presents findings from an interview study of research data managers in academic data archives. Our study examined policies and professional autonomy with a focus on dilemmas encountered in everyday work by data managers. We found that dilemmas arose at every stage of the research data lifecycle, and legacy data presents particularly vexing challenges. The iFields' emphasis on knowledge organization and representation provides insight into how data, used by scientists, are used to create knowledge. The iFields' disciplinary emphasis also encompasses the sociotechnical complexity of dilemmas that we found arise in research data management. Therefore, we posit that iSchools are positioned to contribute to data science education by teaching about ethics and infrastructure used to collect, organize, and disseminate data through problem‐based learning.