Data search strategy for science and technology emergence: A scalable and evolutionary query for nanotechnology tracking

Abstract

Nanotechnology, like other emerging technologies that increasingly characterize the dynamic of our era, makes specific demands on datamining to track and interpret efficiently what is happening, through publications and other scientific output. We here propose and describe a strategy based on an automated lexical modular methodology to overcome rapidly evolving content and classification problems, which may otherwise accommodate poor quality of data and expert bias, with potential dire consequences for interpretation, decision and strategy. The proposed methodology is based on an initial nanostring enriched and screened by eight subfields, automatically identified and defined through the journal inter-citation network density displayed in the initial core nanodataset. Relevant keywords linked to each subfield are then tested for their specificity and relevance before being sequentially incorporated to build a modular query. We then, as a first test, compare the database constructed using this methodology for years 2003 and 2005 with those obtained by other approaches previously used to cover and explore the nanotechnology dynamic. Finally, using the inherent transparency, portablity and replicability of our methodology, we offer, in order to help our initial query evolve and develop, a set of evaluation processes for tests by researchers in the nano field, other scientometric teams and intelligence experts involved in decision-making processes.