Suchergebnisse

Science and Public Policy Vol.42 Nr.4, 549-566 ; The integration of national research systems is one of the central objectives of European research policies. Yet the epistemic objectives of this project have been poorly defined, and scant attention has been paid to whether political, social and financial integration of the European Research Area (ERA) is accompanied by epistemic integration. We discuss the conceptual framework and methodological practices to monitor research integration, and conclude that most of them, such as research collaboration, are only partial indicators of it. To augment existing approaches with an analysis of epistemic integration, we analyse the geographical sources of knowledge of Finnish research in the period 1995-2010. We show a broad shift towards a European knowledge base, demonstrating epistemic integration into the ERA, and that Finnish researchers are, paradoxically, sourcing knowledge from an increasingly distributed system of European knowledge hubs. As policy implications, we recommend clarifying the ERA's epistemic objectives and redefining its strategy of 'reducing fragmentation'.

Purpose
– Motivated with the ever growing number of bibliometric trend extrapolation studies, the purpose of this paper is to demonstrate through two technologies how the selection of an upper limit of growth affects the correlation and causality of technology development measured with bibliometric data.



Design/methodology/approach
– The paper uses Gompertz and Fisher-Pry curves to model the technological development of white light emitting diodes and flash memory, and show with extrapolation results from several bibliometric sources how a typical bias is caused in trend extrapolations.



Findings
– The paper shows how drastic an effect the decision to set an upper bound has on trend extrapolations, to be used as a reference for applications. The paper recommends carefully examining the interconnection of actual development and bibliometric activity.



Originality/value
– Despite increasing interest in modelling technological data using this method, reports rarely discuss basic assumptions and their effects on outcomes. Since trend extrapolations are applied more widely in different disciplines, the basic limitations of methods should be explicitly expressed.

This paper demonstrates a method to transform and link textual information scraped from companies' websites to the scientific body of knowledge. The method illustrates the benefit of Natural Language Processing (NLP) in creating links between established economic classification systems with novel and agile constructs that new data sources enable. Therefore, we experimented on the European classification of economic activities (known as NACE) on sectoral and company levels. We established a connection with Microsoft Academic Graph hierarchical topic modeling based on companies' website content. Central to the operationalization of our method are a web scraping process, NLP and a data transformation/linkage procedure. The method contains three main steps: data source identification, raw data retrieval, and data preparation and transformation. These steps are applied to two distinct data sources.

This article presents data on companies' innovative behavior measured at the firm-level based on web scraped firm-level data derived from medium-high and high-technology companies in the European Union and the United Kingdom. The data are retrieved from individual company websites and contains in total data on 96,921 companies. The data provide information on various aspects of innovation, most significantly the research and development orientation of the company at the company and product level, the company's collaborative activities, company's products, and use of standards. In addition to the web scraped data, the dataset aggregates a variety firm-level indicators including patenting activities. In total, the dataset includes 21 variables with unique identifiers which enables connecting to other databases such as financial data.

This article presents data on companies' innovative behavior measured at the firm-level based on web scraped firm-level data derived from medium-high and high-technology companies in the European Union and the United Kingdom. The data are retrieved from individual company websites and contains in total data on 96,921 companies. The data provide information on various aspects of innovation, most significantly the research and development orientation of the company at the company and product level, the company's collaborative activities, company's products, and use of standards. In addition to the web scraped data, the dataset aggregates a variety firm-level indicators including patenting activities. In total, the dataset includes 21 variables with unique identifiers which enables connecting to other databases such as financial data.