Scientific knowledge production in European regions: patterns of growth, diversity and complexity

Heimeriks, Gaston; Li, Deyu; Lamers, Wout S.; Meijer, Ingeborg; Yegros-Yegros, Alfredo

doi:10.1080/09654313.2019.1645814

Cited by 7 publications

(5 citation statements)

References 47 publications

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“…We analyze this by constructing a spatial–temporal bipartite network with time: One type of nodes in this bipartite network represent cities and the other type of nodes represent time periods. Therefore, unlike the region-subfields network ( 32 , 44 ), our network itself contains temporal information. We group time by decade, so a time period node represents 10 years.…”

Section: Resultsmentioning

confidence: 99%

Quantification of the spatial–temporal patterns of great ideas

Zhang

Zeng

2023

PNAS Nexus

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Quantitative understanding of the process of knowledge creation is crucial for accelerating the advance of science. Recent years have witnessed a great effort to address this issue by studying the publication data of scientific journals, leading to a variety of surprising discoveries at both individual level and disciplinary level. However, before scientific journals appeared on a large scale and became the mainstream for publishing research results, there are also intellectual achievements that have changed the world, which have usually become classic and are now referred to as the great ideas of great people. So far, little is known about the general law of their birth. In this paper, we reference Wikipedia and academic history books to collect 2001 magnum opuses as representations of great ideas, covering nine disciplines. Using the year and place of publication of these magnum opuses, we show that the birth of great ideas is very concentrated in geography, and more concentrated than other human activities such as contemporary knowledge production. We construct a spatial-temporal bipartite network to study the similarity of output structures between different historical periods and discover the existence of a Great Transformation around the 1870s, which may be associated with the rise of the United States in academia. Finally, we re-rank cities and historical periods by employing an iterative approach to study cities’ leadership and historical periods’ prosperity.

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Section: Resultsmentioning

confidence: 99%

Quantification of the spatial–temporal patterns of great ideas

Zhang

Zeng

2023

PNAS Nexus

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“…Given such a variety of setups in regional knowledge production systems, there cannot be implemented a unified "one-size-fits-all" approach, method, or tools to knowledge management. A place-sensitive territorial development policy should be in place (Heimeriks et al, 2019). Meanwhile, the aggregated regional data is heavily used for administrative purposes at the federal, regional, and city levels.…”

Section: Discussionmentioning

confidence: 99%

“…However, there are limitations related to the propensity of patenting in hightech or knowledge-intensive industries. Scientific publications, on the other hand, provide a rich data on knowledge that is not yet commercially exploited making it an increasingly popular source of S&T information (Heimeriks et al, 2019).…”

Section: Measuring the Spatial Patterns Of The Knowledge Production Processmentioning

confidence: 99%

Three-Dimensional Assessment of the Knowledge Production System: Region-City-Organization

Mikhaylov¹,

Mikhaylova²,

Hvaley³

2021

EJKM

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Conceptualization of the region as an integral territorial system of knowledge production has formed a widely used research strategy for innovation studies within regional boundaries. Regional level studies are supported by detailed innovation statistics, which is unavailable for smaller administrative-territorial units, such as municipalities or settlements. The development of spatial scientometrics gave impetus for a new round of research on knowledge and innovation geography with a closer approximation in the context of cities and urban agglomerations. The scope of recent research also includes individual organizations that generate new knowledge or innovation. Despite the topic prominence, the entire array of studies is fragmented, and connections between different levels are not established: region – city – organization. Whereas this is critically important for the implementation of an effective innovation policy. In this regard, in this study, we test the hypothesis that the aggregate data obscures a wide variety of knowledge nodes, which are represented by a dominant knowledge centre. In the case of the region, such centres are often the largest cities, and in the case of cities – the largest organizations. The research design is focused on assessing the knowledge production at a multiscale level – organization, city and region, using the method of spatial scientometrics. The example of the Russian Federation illustrates well the territorial and institutional diversity in the distribution of knowledge production centres of different levels due to its great length and complexity of the structure of the national innovation system. This fact determines the high degree of heterogeneity of the Russian innovation space at the interregional, intercity and inter-organizational levels. The research results show a strong correlation between the knowledge profiles of regions and their primary knowledge-generating cities (KGCs). In cases of a strong central-peripheral structure of the regional knowledge production system, the regional profile completely coincides with the profile of its primary KGC. The knowledge capacity of second-tier cities remains hidden. At the city level, the identified trend is exacerbated. The absence of a pronounced leader among knowledge-intensive organizations (KIOs) against organizational diversity leads to a strong blur of the effectiveness of the knowledge production capabilities of a city. The example of Khabarovsk shows that the research profile of a city in a given situation may not repeat the most productive KIO, but, on the contrary, a weak one. Thus, the three-dimensional region-city-organization approach captures local specifics and organizational diversity, encompassing the entire set of elements of a regional knowledge production system. The study concludes with recommendations for a knowledge management policy at a tiered level.

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“…Генерация научных знаний -важный фактор инновационного развития (Heimeriks et al, 2019). Отдельное изучение получил аспект включенности разных типов городов в научное пространство России.…”

Section: методика исследованияunclassified

Does the coastal location of municipalities influence their innovation development?

Mikhaylova¹,

Gorochnaya²,

Gumenyuk³

et al. 2021

Vestnik SPbSU. Earth Sciences

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The coastal regions are increasingly in the focus of contemporary academic research as economically favorable territories with high innovative potential. The coastalization factor gets individual attention, the influence of which is registered in various countries of the world as a tendency of the population and economic activity to concentrate in the coastal zone. However, there is significant heterogeneity between the coastal areas, due to natural and climatic features and affecting their economic development. This paper focuses on assessing the differences in the readiness of the coastal regions of the European part of Russia to the innovation economy, taking into account their geographical location (northern, northwestern, southern). The study is conducted at the level of municipalities across 6 regions of Russia: Murmansk, Arkhangelsk, Leningrad, Kaliningrad, Rostov regions, and Krasnodar Krai. The research methodology included an assessment of 4 most important components of an innovative economy: human capital, business environment, knowledge production, and technological equipment. The results of the study showed a high degree of spatial divergence in terms of the level of readiness for the development of an innovative economy both between the regions of the sample and within them in the inter-municipal context. It is revealed that the concentration of innovative potential within the coastal region is largely the result of the cross-influence of the agglomeration and the coastalization factors. In the case of their complementary influence on coastal municipalities, the latter are characterized by a relatively higher level of readiness for the development of an innovative economy than inland ones. This is true for both the northern, northwestern, and southern coastal regions of the European part of Russia.

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Scientific knowledge production in European regions: patterns of growth, diversity and complexity

Cited by 7 publications

References 47 publications

Quantification of the spatial–temporal patterns of great ideas

Quantification of the spatial–temporal patterns of great ideas

Three-Dimensional Assessment of the Knowledge Production System: Region-City-Organization

Does the coastal location of municipalities influence their innovation development?

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