IntroductionAs scientists' means of communication and information behaviors have evolved over the past several decades due to computing and networking developments, the field of library and information science (LIS) has responded by surveying their changing practices. This study continues this type of research but switches focus away from scientists' use of journal articles, the dominant means by which research libraries have supported their investigations, and instead concentrates on scientists' practices related to data. The view of scientists as increasingly energetic generators, managers, and users of large and growing electronic datasets has lately been recognized and promoted at the societal level by funding agencies, academic societies, and large research centers.
Meta-analyses are studies that bring together data or results from multiple independent studies to produce new and over-arching findings. Current data curation systems only partially support meta-analytic research. Some important meta-analytic tasks, such as the selection of relevant studies for review and the integration of research datasets or findings, are not well supported in current data curation systems. To design tools and services that more fully support meta-analyses, we need a better understanding of meta-analytic research. This includes an understanding of both the practices of researchers who perform the analyses and the characteristics of the individual studies that are brought together. In this study, we make an initial contribution to filling this gap by developing a conceptual framework linking meta-analyses with data paths represented in published articles selected for the analysis. The framework focuses on key variables that represent primary/secondary datasets or derived socio-ecological data, contexts of use, and the data transformations that are applied. We introduce the notion of using variables and their relevant information (e.g., metadata and variable relationships) as a type of currency to facilitate synthesis of findings across individual studies and leverage larger bodies of relevant source data produced in small science research. Handling variables in this manner provides an equalizing factor between data from otherwise disparate data-producing communities. We conclude with implications for exploring data integration and synthesis issues as well as system development
This research project explores the role of internships in a new curriculum designed to educate eScience librarians. Experiential learning was identified early on in the IMLS-funded project as a necessary aspect to give students field exposure to informationrelated developments of cyberinfrastructure-enabled science. Nine students were tasked to fill out a daily survey that captured their experience at academic and research libraries, field research stations, and national and discipline-based research centers. Analysis of these accumulated "diary" entries is underway to identify learning outcomes of the eSLib program, particularly two required, data-oriented courses the students mastered in the first year of the program. The analysis will also aggregate and trace longitudinally student skill application throughout the summer. Evaluation of student experience should enhance understanding of the relation between the eScience Librarianship program and what is needed by institutions tasked with managing data produced by computer and network-enabled scientists.
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