Effects of funding on the collaboration and citation in environmental papers and the relationship with nation’s science and technology budgets

Dados de entrada (input), saída (output), impacto e processos são indicadores centrais da produção em Ciência, Tecnologia e Inovação. O input está associado aos investimentos realizados em ciência e tecnologia, podendo variar entre diferentes países e áreas científicas. Assim, o input pode influenciar outros indicadores de impacto. Aqui, avaliamos seu o efeito (número de financiamentos) sobre o processo de colaboração e o número de citações (output) da pesquisa ecológica. Além disso, detalhamos o efeito do número de financiamentos sobre a colaboração e o número de citações por país (baseado na nacionalidade dos autores). Verificamos que a maioria dos artigos publicados tinha algum grau de suporte financeiro, e que a produção de artigos com financiamento aumentou ao longo dos anos. O número de financiamentos teve efeito positivo na colaboração e nas citações, porém observamos que: nos países com maior investimento em ciência e tecnologia, o número de financiamentos impacta positivamente e diretamente a colaboração (número de autores); e nos países com menor investimento em ciência e tecnologia, o número de financiamentos impacta positivamente e diretamente as citações. Nossos resultados demonstram que os indicadores de impacto avaliados têm estrutura integrada e os efeitos em um nível podem afetar outros níveis. Entretanto, o impacto do número de fomentos nos indicadores informétricos pode variar entre os países, portanto esse resultado é importante para o desenvolvimento de políticas nacionais e para futuros estudos informétricos.Input, output, impact, and processes are central indicators of the science, technology, and innovation production. The input is usually associated to investments made in science and technology, and it varies among different countries and scientific fields. Thus, the input can influence other impact indicators. Here, we evaluated the effect of the input data (i.e., number of funding) on process (i.e., collaboration) and output (i.e., number of citation) indicators of ecological research. Moreover, we detailed the effect of the number of funding on the collaboration and number of citations by each country (based on the nationality of authors). We found that most of published papers had some degrees of financial support, and that the production of papers with funding increased over the years. Funding had a positive effect on the collaboration and citation of papers; however, we observed that: in countries with higher investments in Science and Technology, the number of funding impacts positively and directly on the number of authors (collaboration) and in countries with low levels of investments in Science and Technology, the number of funding impacts positively and directly on the number of citations. Our models presented a low predictive power, but similar to other informetric studies. Our results indicated that impact indicators evaluated have an integrated structure, and the effects at one level can affect other levels. Nonetheless, the impact of the number of funding on informetric data can vary among countries; therefore, these results are important to the development of national policies and future informetric studies

Openness and Impact of Leading Scientific Countries

The rapid rise of international collaboration over the past three decades, demonstrated in coauthorship of scientific articles, raises the question of whether countries benefit from cooperative science and how this might be measured. We develop and compare measures to ask this question. For all source publications in 2013, we obtained from Elsevier national-level full and fractional paper counts as well as accompanying field-weighted citation counts. Then we collected information from Elsevier on the percent of all internationally coauthored papers for each country, as well as Organization for Economic Cooperation and Development (OECD) measures of the international mobility of the scientific workforce in 2013, and conducted a principle component analysis that produced an openness index. We added data from the OECD on government budget allocation on research and development (GBARD) for 2011 to tie in the public spending that contributed to the 2013 output. We found that openness among advanced science systems is strongly correlated with impact—the more internationally engaged a nation is in terms of coauthorships and researcher mobility, the higher the impact of scientific work. The results have important implications for policy making around investment, as well as the flows of students, researchers, and technical workers

Contrasting High Scientific Production with Low International Collaboration and Scientific Impact: The Brazilian Case

The article presents an analysis of scientific production and impact among 35 most productive countries in the world. In the period 2000–2016, these countries produced 92% of the world publications. A correlation of international collaboration and scientific impact is shown. Differently from this pattern, Brazil shows high quantitative performance but low scientific impact, which is attributed to its low level of international collaboration. By contrast, instead of a generalized cooperation, as many undeveloped countries do, Brazil uses its internal effort to explore cooperation in a more symmetrical manner. Thus, in several areas, Brazil occupies a prominent position, including technological sectors, enabling it to occupy the eighth world’s economy position. The data confirm that an efficient internal scientific effort combined with well-balanced international cooperation can be more effective to enable countries to achieve higher levels of development in order to meet their technical and socioeconomic challenges. Brazil was able to reach the first step but did not follow the same track concerning higher scientific impact

Why do papers from international collaborations get more citations? A bibliometric analysis of Library and Information Science papers

Scientific activity has become increasingly complex in recent years. The need for international research collaboration has thus become a common pattern in science. In this current landscape, countries face the problem of maintaining their competitiveness while cooperating with other countries to achieve relevant research outputs. In this international context, publications from international collaborations tend to achieve greater scientific impact than those from domestic ones. To design policies that improve the competitiveness of countries and organizations, it thus becomes necessary to understand the factors and mechanisms that influence the benefits and impact of international research. In this regard, the aim of this study is to confirm whether the differences in impact between international and domestic collaborations are affected by their topics and structure. To perform this study, we examined the Library and Information Science category of the Web of Science database between 2015 and 2019. A science mapping analysis approach was used to extract the themes and their structure according to collaboration type and in the whole category (2015–2019). We also looked for differences in these thematic aspects in top countries and in communities of collaborating countries. The results showed that the thematic factor influences the impact of international research, as the themes in this type of collaboration lie at the forefront of the Library and Information Science category (e.g., technologies such as artificial intelligence and social media are found in the category), while domestic collaborations have focused on more well-consolidated themes (e.g., academic libraries and bibliometrics). Organizations, countries, and communities of countries must therefore consider this thematic factor when designing strategies to improve their competitiveness and collaborate.Spanish Government PID2019-105381GA-I00/AEI/10.13039/50110001103

Why do papers from international collaborations get more citations? A bibliometric analysis of Library and Information Science papers

Scientific activity has become increasingly complex in recent years. The need for international research collaboration has thus become a common pattern in science. In this current landscape, countries face the problem of maintaining their competitiveness while cooperating with other countries to achieve relevant research outputs. In this international context, publications from international collaborations tend to achieve greater scientific impact than those from domestic ones. To design policies that improve the competitiveness of countries and organizations, it thus becomes necessary to understand the factors and mechanisms that influence the benefits and impact of international research. In this regard, the aim of this study is to confirm whether the differences in impact between international and domestic collaborations are affected by their topics and structure. To perform this study, we examined the Library and Information Science category of the Web of Science database between 2015 and 2019. A science mapping analysis approach was used to extract the themes and their structure according to collaboration type and in the whole category (2015-2019). We also looked for differences in these thematic aspects in top countries and in communities of collaborating countries. The results showed that the thematic factor influences the impact of international research, as the themes in this type of collaboration lie at the forefront of the Library and Information Science category (e.g., technologies such as artificial intelligence and social media are found in the category), while domestic collaborations have focused on more well-consolidated themes (e.g., academic libraries and bibliometrics). Organizations, countries, and communities of countries must therefore consider this thematic factor when designing strategies to improve their competitiveness and collaborate

Pesquisa médica em hospitais no Brasil : pesquisa, assistência, pós-graduação, produção e colaboração científica

A presente tese tem como objetivo principal analisar a produção científica produzida por hospitais públicos e privados brasileiros selecionados por suas contribuições no atendimento médico-hospitalar, no ensino e destaque na publicação de artigos em periódicos indexados cobrindo o período de 2015 a 2019. Ao todo, 51 hospitais compõem o grupo em estudo destacando-se 32 hospitais integrantes da Rede Ebserh, os cinco hospitais do PROADI-SUS, e outros 14 hospitais públicos e privados de reconhecido desempenho no cenário médico nacional e cujos sites oferecem informações para acesso facilmente disponível. O estudo inclui informações funcionais dos hospitais (ano de fundação, leitos, internações, transplantes, funcionários e médicos) e dados sobre a produção científica individual e sua qualificação (Índice H e Fator de Impacto) e por especialidade médica, cooperação científica entre os hospitais, colaboração internacional nas publicações e, como dados mais recentes (2020-2021) atuação e publicações sobre o SARS-COV-2 e a pandemia da COVID-19.The main objective of this thesis is to analyze the scientific production produced by public and private Brazilian hospitals selected for their contributions in medical-hospital care, teaching and highlight in the publication of articles in indexed journals covering the period from 2015 to 2019. 51 hospitals make up the study group, highlighting 32 hospitals that are part of the Ebserh, the five hospitals of PROADI-SUS, and another 14 public and private hospitals of recognized performance in the national medical scenario and whose websites offer information for easily available access. The study includes functional information from hospitals (foundation year, beds, hospitalizations, transplants, employees and doctors) and data on individual scientific production and its qualification (H-Index and Impact Factor) and by medical specialty, scientific cooperation between hospitals, international collaboration on publications and, as more recent data (2020-2021) performance and publications on SARS-COV-2 and the COVID-19 pandemic

Determinantes político-institucionais na formação de redes de inovação em tecnologias emergentes

O desenvolvimento do sistema de inovação em nanotecnologia no Espaço Europeu de Investigação foi moldado pelas políticas públicas europeias e nacionais. Em particular, os Programas Quadro promovidos pela Comissão Europeia a par de outras instituições europeias, tiveram e continuam ter um papel preponderante no desenvolvimento científico e tecnológico dos Estados-membros. Esta investigação propõe um modelo de análise institucional aos sistemas de inovação europeus, no setor da nanotecnologia, determinado pelas relações e interligações, entre todos os atores institucionais dos sistemas socioeconómicos e políticos. Numa abordagem, sui generis ao neo-institucionalismo, foi possível identificar três dimensões institucionais impactantes dos sistemas de inovação. A implementação de métodos estatísticos baseados na família de grafos aleatórios exponenciais (i.e. Exponential Random Graph Models ou ERGMs), permitiu avaliar o impacto das três dimensões institucionais dos sistemas de inovação. Os resultados empíricos, revelam a importância de determinados por estes fatores institucionais, nas configurações de rede entre os atores, que integram os projetos de inovação do 7º Programa-Quadro em nanotecnologia. Os resultados, demonstram ainda que a introdução de técnicas avançadas em Análise de Redes Sociais, permitem a compreensão dos fenómenos de interação entre atores dos sistemas de inovação e a avaliação das políticas públicas, ao nível institucional.The development of the nanotechnology innovation system in the European Research Area has been shaped by European and national public policies. Particularly, the Framework Programs promoted by the European Commission, alongside other European institutions have had and continue to play a leading role in the scientific and technological development of the EU Member States. This research proposes a model of institutional analysis for European innovation systems in the nanotechnology sector, determined by the relationships and interconnections, among all institutional actors of socioeconomic and polítical systems. In a sui generis approach to neo-institutionalism, it was possible to identify three major institutional dimensions of innovation systems. The deployment of statistical methods based on the Exponential Random Graph Models (ERGMs) allowed us to evaluate the impact of these three institutional dimensions of innovation systems. The empirical results reveal the importance of this institutional factors in the network configurations, between the actors that are part innovation projects of the 7th Framework Program on nanotechnology. The results also show that, the introduction of advanced techniques in Social Network Analysis, allow the understanding the interaction phenomena between actors of innovation systems and the evaluation of public policies at institutional level

Quantitative methods in research evaluation citation indicators, altmetrics, and artificial intelligence

This book critically analyses the value of citation data, altmetrics, and artificial intelligence to support the research evaluation of articles, scholars, departments, universities, countries, and funders. It introduces and discusses indicators that can support research evaluation and analyses their strengths and weaknesses as well as the generic strengths and weaknesses of the use of indicators for research assessment. The book includes evidence of the comparative value of citations and altmetrics in all broad academic fields primarily through comparisons against article level human expert judgements from the UK Research Excellence Framework 2021. It also discusses the potential applications of traditional artificial intelligence and large language models for research evaluation, with large scale evidence for the former. The book concludes that citation data can be informative and helpful in some research fields for some research evaluation purposes but that indicators are never accurate enough to be described as research quality measures. It also argues that AI may be helpful in limited circumstances for some types of research evaluation