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Purpose The purpose of this paper is to explore the roles of public libraries in the context of Big Data. Design/methodology/approach A mixed method approach was used and had two main data collection phases. A survey of public libraries was used to generate an overview of which professional roles connect public libraries with Big Data. Eight roles were identified, namely, educator, marketer, data organiser, data container, advocator, advisor, developer and organisation server. Semi-structured interviews with library directors and managers were then conducted to gain a deeper understanding of these roles and how they connect to the library’s overall functions. Findings Results of the survey indicated that librarians lack a proper comprehension of and a pragmatic application of Big Data. Their opinions on the eight roles are slightly stronger than neutral. However, they do not demonstrate any strong agreement on these eight roles. In the interviews, the eight roles attained more clear support and are classified into two groups: service-oriented and system-oriented roles. Originality/value As an emerging research field, Big Data is not widely discussed in the library context, especially in public libraries. Therefore, this study fills a research gap between public libraries and Big Data. In addition, Big Data in public libraries could be well managed and readily approached by citizens in undertaking such roles, which entails that public libraries will eventually benefit from the Big Data era.

Big data has been widely discussed. The diverse impacts and potential of big data have been pinpointed and empirically proven. Nevertheless, there is no consensus on the understanding of big data. Big data has been used to refer to different things and its characteristics are not universally accepted either. Therefore, this study aims to generate an overall understanding of big data. The domain of the study is limited to librarianship, because of its unique position in managing and utilising big data. Thus, the aim of this study is to understand big data in librarianship according to how it is defined in that profession. Articles containing definitions of big data were reviewed and 35 definitions were collected. Since the number of analysed definitions is small, a combined method was employed. Both a content analysis and a statistical description of these definitions were conducted. Five aspects are summarised based on the analysis of the collected definitions. These aspects help explicate libraries’ current understanding of big data and librarians’ big data skills.

The Target Article by Lee et al. (2019) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including their inputs and outputs, is also essential to improving the reproducibility of model-based analyses. We outline an ongoing effort (within the context of the Brain Imaging Data Structure community) to develop standards for the sharing of the structure of computational models and their outputs.

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