Metabolism of Public Research Organizations: How Do Laboratories Consume State Subsidies?

Nobel laureates in Physics, Chemistry and Medicine: relation between research funding and citations.. [DOI: 10.21203/rs.3.rs-2907940/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

One of the vital problems in scientometrics is to explore the factors that affect the growth of citations in publications and in general the diffusion of knowledge in science and society. The goal of this study is to analyze the relation between funded and unfunded papers and citations of Nobel Laureates in physics, chemistry and medicine over 2019-2020 period and the same relation in these research fields as a whole to clarify the scientific development. Original results here reveal that in chemistry and medicine, funded papers of Nobel Laureates have higher citations than unfunded papers, vice versa in physics that has high citations in unfunded papers. Instead, when overall research fields of physics, chemistry and medicine are analyzed, funded papers have a higher level of citations than unfunded, with a higher scaling factor in chemistry and medicine. General properties of this study are that: a) funded articles receive more citations than unfunded papers in research fields of physics, chemistry and medicine, generating a high Matthew effect given by a higher accumulation and growth of citations with the growth of papers, b) funding increases the citations of articles in fields oriented to applied research (such as, chemistry and medicine) more than fields oriented to basic research (physics). Overall, then, results here can explain some characteristics of scientific dynamics, showing the critical role of funding to foster citations and diffusion of knowledge, also having potential commercial implications in applied research. Results here can be provide useful information to understand drivers of the scientific development in basic and applied research fields to better allocate financial resources in research fields directed to support a positive scientific and societal impact.

Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency

Scholars and experts argue that future pandemics and/or epidemics are inevitable events, and the problem is not whether they will occur, but when a new health emergency will emerge. In this uncertain scenario, one of the most important questions is an accurate prevention, preparedness and prediction for the next pandemic. The main goal of this study is twofold: first, the clarification of sources and factors that may trigger pandemic threats; second, the examination of prediction models of on-going pandemics, showing pros and cons. Results, based on in-depth systematic review, show the vital role of environmental factors in the spread of Coronavirus Disease 2019 (COVID-19), and many limitations of the epidemiologic models of prediction because of the complex interactions between the new viral agent SARS-CoV-2, environment and society that have generated variants and sub-variants with rapid transmission. The insights here are, whenever possible, to clarify these aspects associated with public health in order to provide lessons learned of health policy that may reduce risks of emergence and diffusion of new pandemics having negative societal impact.

Scientific laws of research funding to support citations and diffusion of knowledge in life science

One of the main problems in scientometrics is to explore the factors that affect the growth of citations in publications to identify best practices of research policy to increase the diffusion of scientific research and knowledge in science and society. The principal purpose of this study is to analyze how research funding affects the citation-based performance of scientific output in vital research fields of life science, which is a critical province (area of knowledge) in science to improve the wellbeing of people. This study uses data from the Scopus database in 2015 (to assess the impact on citations in 2021, after more than 5 years) concerning different disciplines of life science, given by “agricultural and biological sciences”, “biochemistry, genetics, and molecular biology”, “Immunology and microbiology”, “neuroscience” and “pharmacology, toxicology and pharmaceutics”. Results demonstrate that although journals publish un-funded articles more than funded publications in all disciplines of life science, the fraction of total citations in funded papers is higher than the share in the total number of publications. In short, funded documents receive more citations than un-funded papers in all research fields of life science under study. Findings also support that citations of total (funded + un-funded), funded, and un-funded published papers have a power-law distribution in all five research fields of life science. Original results here reveal a general property in scientific development: funded research has a higher scaling potential than un-funded publications. Critical implications of research policy, systematized in a decision-making matrix, suggest that R&D investments in “Neuroscience” can generate a positive impact of scientific results in science and society-in terms of citations-higher than other research fields in medicine. Overall, then, results here can explain some characteristics driving scientific change and help policymakers and scholars to allocate resources towards research fields that facilitate the development and diffusion of scientific research and knowledge in life science for positive societal impact.

Efficiency evaluation of S&T resource allocation using an accurate quantification of the time-lag effect and relation effect: a case study of Chinese research institutes

Efficiency evaluation is a significant means of judging the allocation quality of science and technology (S&T) resource. In reality, the process of S&T resource allocation is accompanied by the time-lag effect and relation effect, and the effect differences among research institutes are obvious. In this study, we attempt to conduct efficiency evaluation of S&T resource allocation in Chinese research institutes using an accurate quantification of the time-lag effect and relation effect. A hybrid model for efficiency evaluation is designed, where the vector autoregression (VAR) model and the output revision model are adopted to quantify the time-lag effect and relation effect, and an extended super-efficiency data envelopment analysis (SE-DEA) model is proposed to conduct efficiency estimation under the quantified effects. Subsequently, a quantitative case study is conducted based on 30 sample institutes in the Chinese Academy of Sciences using statistical data from 1992 to 2014. The results indicate that the time-lag effect of each institute varies with the forms of outputs owing to the difference of individual transformation capability. The relation effect of each institute varies with the allocation proportions of inputs owing to the difference of individual developmental orientation. Compared with other methods, the proposed hybrid model can not only determine a discriminative ranking of the sample institutes, but also clarify the strengths and weaknesses of each institute. These main findings are beneficial for decision makers of sample institutes to realize the overall optimization of S&T resource allocation from the aspects of capability promotion, resource adjustment, and efficiency improvement.