Ways of Asking,Ways of Telling

The interpretive understanding that can be derived from interviews is highly influenced by methods of data collection, be they structured or semistructured, ethnographic, clinical, life-history or survey interviews. This article responds to calls for research into the interview process by analyzing data produced by two distinctly different types of interview, a semistructured ethnographic interview and the Structured Clinical Interview for DSM, conducted with participants in the Navajo Healing Project. We examine how the two interview genres shape the context of researcher-respondent interaction and, in turn, influence how patients articulate their lives and their experience in terms of illness, causality, social environment, temporality and self/identity. We discuss the manner in which the two interviews impose narrative constraints on interviewers and respondents, with significant implications for understanding the jointly constructed nature of the interview process. The argument demonstrates both divergence and complementarity in the construction of knowledge by means of these interviewing methods.     

Asking nicely: Best practices for requesting data

Compiling disparate datasets into publicly available composite databases helps natural resource communities explore ecological trends and effectively manage across spatiotemporal scales. Though some studies have reported on the database construction phase, fewer have evaluated the data acquisition and distribution process. To facilitate future data sharing collaborations, Louisiana State University surveyed data providers and requestors to understand the characteristics of effective data requests and sharing. Data providers were largely U.S. natural resource agency personnel, and they reported that unclear data requests, privacy issues, and rigid timelines and formats were the greatest barriers toward providing data, but that they were motivated by improving science and collaboration. Data requestors identified challenges such as evolving needs, standardization issues, and insufficient resources (time and funding) as barriers to compiling data for these types of efforts. In a time of big data, open access, and collaboration, significant scientific advances can be made with effective requests and inclusion of data sets into larger and more powerful databases.