The scientific influence of nations on global scientific and technological development

Capability accumulation patterns across economic, innovation, and knowledge-production activities

The evolution of economic and innovation systems at the national scale is shaped by a complex dynamics related to the multi-layer network connecting countries to the activities in which they are proficient. Each layer represents a different domain, related to the production of knowledge and goods: scientific research, technology innovation, industrial production and trade. Nestedness, a footprint of a complex dynamics, emerges as a persistent feature across these multiple kinds of activities (i.e. network layers). We observe that, in the layers of innovation and trade, the competitiveness of countries correlates unambiguously with their diversification, while the science layer shows some peculiar features. The evolution of the scientific domain leads to an increasingly modular structure, in which the most developed countries become relatively less active in the less advanced scientific fields, where emerging countries acquire prominence. This observation is in line with a capability-based view of the evolution of economic systems, but with a slight twist. Indeed, while the accumulation of specific know-how and skills is a fundamental step towards development, resource constraints force countries to acquire competitiveness in the more complex research fields at the expense of more basic, albeit less visible (or more crowded) ones. This tendency towards a relatively specialized basket of capabilities leads to a trade-off between the need to diversify in order to evolve and the need to allocate resources efficiently. Collaborative patterns among developed countries reduce the necessity to be competitive in the less sophisticated research fields, freeing resources for the more complex ones.

The trickle down from environmental innovation to productive complexity

We study the empirical relationship between green technologies and industrial production at very fine-grained levels by employing Economic Complexity techniques. Firstly, we use patent data on green technology domains as a proxy for competitive green innovation and data on exported products as a proxy for competitive industrial production. Secondly, with the aim of observing how green technological development trickles down into industrial production, we build a bipartite directed network linking single green technologies at time [Formula: see text] to single products at time [Formula: see text] on the basis of their time-lagged co-occurrences in the technological and industrial specialization profiles of countries. Thirdly, we filter the links in the network by employing a maximum entropy null-model. Our results emphasize a strong connection between green technologies and the export of products related to the processing of raw materials, notably crucial for the development of climate change mitigation and adaptation technologies. Furthermore, by looking at the evolution of the network over time, we observe a growing presence of more complex green technologies and high-tech products among the significant links, suggesting an increase in their importance in the network.

Technological Advances to Reduce Apis mellifera Mortality: A Bibliometric Analysis

Bees play a fundamental role in the ecological balance of ecosystems, due to the pollination process they carry out on crops, including the production of honey. However, the mortality of bees is a significant concern; bee mortality can occur for several reasons, such as pesticides, mites, viruses, climate change, pathogens and a reduction in food resources and nests. The honey bee (Apis mellifera) is the most widely used bee for commercial pollination and honey production. Therefore, the main objective is to compare the development of patent families and article publications related to the reduction in A. meliífera mortality. Data on patent families were collected on the Orbit platform, while data on scientific articles were collected on the Scopus database, with a time interval of 1980–2019. Subsequently, the data were analyzed in order to show the main priority countries, main assignees, and main IPC (International Patent Classification) codes, an analysis of the technology life cycle and the correlation between the data of patent families and articles published. The technologies that help to decrease bee mortality showed a technological maturity rate of 27.15% for patent families data and 53.35% for data from articles published in journals. It was noticed that the principal interest regarding the reduction in A. mellifera mortality is focused on universities, mainly in the United States and China.

'All things are in flux': China in global science

Since 1990, a large and dynamic global science system has evolved, based on grass roots collaboration, and resting on the resources, infrastructure and personnel housed by national science systems. Euro-American science systems have become intensively networked in a global duopoly; and many other countries have built national science systems, including a group of large- and middle-sized countries that follow semi-autonomous trajectories based on state investment, intensive national network building, and international engagement, without integrating tightly into the global duopoly. The dual global/national approach pursued by these systems, including China, South Korea, Iran and India, is not always fully understood in papers on science. Nevertheless, China is now the number two science country in the world, the largest producer of papers and number one in parts of STEM physical sciences. The paper investigates the remarkable evolution of China's science funding, output, discipline balance, internationalisation strategy and national and global networking. China has combined global activity and the local/national building of science in positive sum manner, on the ground of the nationally nested science system. The paper also discusses limits of the achievement, noting that while China-US relations have been instrumental in building science, a partial decoupling is occurring and the future is unclear.

Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World

Nowadays, the importance of bibliographic databases (DBs) has increased enormously, as they are the main providers of publication metadata and bibliometric indicators universally used both for research assessment practices and for performing daily tasks. Because the reliability of these tasks firstly depends on the data source, all users of the DBs should be able to choose the most suitable one. Web of Science (WoS) and Scopus are the two main bibliographic DBs. The comprehensive evaluation of the DBs’ coverage is practically impossible without extensive bibliometric analyses or literature reviews, but most DBs users do not have bibliometric competence and/or are not willing to invest additional time for such evaluations. Apart from that, the convenience of the DB’s interface, performance, provided impact indicators and additional tools may also influence the users’ choice. The main goal of this work is to provide all of the potential users with an all-inclusive description of the two main bibliographic DBs by gathering the findings that are presented in the most recent literature and information provided by the owners of the DBs at one place. This overview should aid all stakeholders employing publication and citation data in selecting the most suitable DB.

The Language of Innovation

Predicting innovation is a peculiar problem in data science. Following its definition, an innovation is always a never-seen-before event, leaving no room for traditional supervised learning approaches. Here we propose a strategy to address the problem in the context of innovative patents, by defining innovations as never-seen-before associations of technologies and exploiting self-supervised learning techniques. We think of technological codes present in patents as a vocabulary and the whole technological corpus as written in a specific, evolving language. We leverage such structure with techniques borrowed from Natural Language Processing by embedding technologies in a high dimensional euclidean space where relative positions are representative of learned semantics. Proximity in this space is an effective predictor of specific innovation events, that outperforms a wide range of standard link-prediction metrics. The success of patented innovations follows a complex dynamics characterized by different patterns which we analyze in details with specific examples. The methods proposed in this paper provide a completely new way of understanding and forecasting innovation, by tackling it from a revealing perspective and opening interesting scenarios for a number of applications and further analytic approaches.