Some diverse indicators used to measure the innovation process are considered. They include those with an aggregate, and often national, focus, and rely on data from scientific publications, patents and R&D expenditures, etc. Others have a firm-level perspective, relying primarily on surveys or case studies. Also included are indicators derived from specialized databases, or consensual agreements reached through foresight exercises. There is an obvious need for greater integration of the various approaches to capture more effectively the richness of available data and better reflect the reality of innovation. The focus for such integration could be in the area of technology strategy, which integrates the diverse scientific, technological, and innovation activities of firms within their operating environments; improved capacity to measure it has implications for policy-makers, managers and researchers.
Despite an increasing policy interest in biotechnology in Europe, there is a lack of internationally comparable data on national performance in biotechnology. This paper presents a performance analysis of 14 Member States using science technology and economic indicators. A strong relationship between scientific and commercial performance was found. Four performance clusters were identified: the best-performing countries Denmark, Sweden and Finland; the second-best performers The Netherlands, the United Kingdom, Belgium and Germany; cluster three with Austria, France and Ireland, performing below the European median performance value, and the Mediterranean countries Italy, Spain, Portugal and Greece performing weakly as measured by all indicators.
The emergence of open science and new data practices is changing the way research is done. Opportunities to access data through purpose built platforms and repositories, combined with emerging data and meta-data curation practices are expanding data availability in many fields. This paper presents a conceptual framework for studying scientific research careers, motivated by opportunities to link empirical datasets to construct new analyses that address remaining and emerging knowledge gaps. The research career conceptual framework (RCCF) emerges from a review of relevant theories and empirical findings regarding research careers. The paper reviews existing models and develops a typology of research careers. It also compiles a list of variables drawn from the literature on research careers. Two preliminary demonstrations of linking datasets to address empirical questions are outlined. The final discussion advocates an approach to emerging data opportunities that combines theories and models with empirical research questions as being superior to an approach that produces ad hoc explanations on the basis of 'data fishing' exercises.
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