South-south collaboration on health and development research is a critical mechanism for social and economic progress. It allows sharing and replicating experiences to find a “southern solution” to meet shared health challenges, such as access to adequate HIV/AIDS prevention and treatment. This study aimed to generate evidence on the dynamics of south-south collaboration in HIV/AIDS research, which could ultimately inform stakeholders on the progress and nature of collaboration towards increased research capacities in low- and middle-income countries (LMIC).
METHODS: Bibliometric and social network analysis methods were used to assess the 10-year (2006-2015) scientific contribution of LMIC, through the analysis of scientific publications on HIV/AIDS prevention and/or treatment. Five dimensions oriented the study: knowledge production, co-authorship analysis, research themes mapping, research types classification and funding sources.
RESULTS: Publications involving LMIC have substantially increased overtime, despite small expression of south-south collaboration. Research themes mapping revealed that publication focus varied according to collaborating countries’ income categories, from diagnosis, opportunistic infections and laboratory-based research (LMIC single or LMIC-LMIC) to human behavior and healthcare, drug therapy and mother to child transmission (LMIC-HIC). The analysis of research types showed that south-south collaborations frequently targeted social sciences issues. Funding agencies acknowledged in south-south collaboration also showed diverse focus: LMIC-based funders tended to support basic biomedical research whereas international/HIC-based funders seem to cover predominantly social sciences-oriented research.
CONCLUSIONS: Although the global environment has fostered an increasing participation of LMIC in collaborative learning models, south-south collaboration on HIV/AIDS prevention and/or treatment research seemed to be lower than expected, stressing the need for strategies to foster these partnerships. The evidence presented in this study can be used to strengthen a knowledge platform to inform future policy, planning and funding decisions, contributing to the development of enhanced collaboration and a priority research agenda for LMICs.
Link to FULL-TEXT https://www.ncbi.nlm.nih.gov/pubmed/29490665
Author(s): Bruna Fonseca, Priscila Albuquerque, Ed Noyons, Fabio Zicker
Organization(s): Center for Technological Development in Health (CDTS) at Oswaldo Cruz Foundation (Fiocruz), Leiden University
Source: Global Health
Formal concept analysis (FCA) and concept lattice theory (CLT) are introduced for constructing a network of IDR topics and for evaluating their effectiveness for knowledge structure exploration. We introduced the theory and applications of FCA and CLT, and then proposed a method for interdisciplinary knowledge discovery based on CLT. As an example of empirical analysis, interdisciplinary research (IDR) topics in Information & Library Science (LIS) and Medical Informatics, and in LIS and Geography-Physical, were utilized as empirical fields. Subsequently, we carried out a comparative analysis with two other IDR topic recognition methods.
Findings: The CLT approach is suitable for IDR topic identification and predictions.
Research limitations: IDR topic recognition based on the CLT is not sensitive to the interdisciplinarity of topic terms, since the data can only reflect whether there is a relationship between the discipline and the topic terms. Moreover, the CLT cannot clearly represent a large amounts of concepts.Practical implications: A deeper understanding of the IDR topics was obtained as the structural and hierarchical relationships between them were identified, which can help to get more precise identification and prediction to IDR topics.
Originality/value: IDR topics identification based on CLT have performed well and this theory has several advantages for identifying and predicting IDR topics. First, in a concept lattice, there is a partial order relation between interconnected nodes, and consequently, a complete concept lattice can present hierarchical properties. Second, clustering analysis of IDR topics based on concept lattices can yield clusters that highlight the essential knowledge features and help display the semantic relationship between different IDR topics. Furthermore, the Hasse diagram automatically displays all the IDR topics associated with the different disciplines, thus forming clusters of specific concepts and visually retaining and presenting the associations of IDR topics through multiple inheritance relationships between the concepts.
Author(s): Haiyun Xu, Chao Wang, Kun Dong, Zenghui Yue
Organization(s): Institute of Scientific and Technical Information of China, Qilu University of Technology, Shandong University of Technology, Jining Medical University
Source: Journal of Data and Information Science
Ethanol produced from lignocellulosic biomass of composition is not a new concept. However, the increase in the population and industrial activity in the world, the energy demand and environmental impacts brought to light the need for new energy sources. At the beginning of this new millennium, ethanol and biomass gained notoriety as sources that could sustainably complement the energy matrix and also to mitigate the impacts of fossil energy sources. There are different ways to produce ethanol from biomass, but the most studied technologies are those made through biotechnological routes. In view of the investments on development made over the years, this article aims at analyzing the generation of technologies in the main links of the second generation ethanol production chain through patent applications in order to identify trends that will guide the industry of this biofuel.
Full-text available http://www.revistageintec.net/index.php/revista/article/view/1193
Author(s): Luiz André F. Silva Schlittler, Adelaide Maria de Souza Antunes, Nei Pereira Junior
Organization(s): Faculdade SENAI CETIQT, Instituto Nacional da Propriedade Industrial (INPI), Universidade Federal do Rio de Janeiro
Source: Revista GEINTEC
Technological Convergence (TC) reflects developmental processes that overlap different technological fields. It holds promise to yield outcomes that exceed the sum of its subparts. Measuring emergence for a TC environment can inform innovation management. This paper suggests a novel approach to identify Emergent Topics (ETopics) of the TC environment within a target technology domain using patent information. A non-TC environment is constructed as a comparison group. First, TC is operationalized as a co-classification of a given patent into multiple 4-digit IPC codes (≥2-IPC). We take a set of patents and parse those into three sub-datasets based on the number of IPC codes assigned 1-IPC (Non-TC), 2-IPC and ≥3-IPC. Second, a method is applied to identify emergent terms (ETs) and calculate emergence score for each term in each sub-dataset. Finally, we cluster those ETs using Principal Components Analysis (PCA) to generate a factor map with ETopics. A convergent domain – 3D printing – is selected to present the illustrative results. Results affirm that for 3D printing, emergent topics in TC patents are distinctly different from those in non-TC patents. The number of ETs in the TC environment is increasing annually.
Author(s): Zhinan Wang, Alan L.Porter, Xuefeng Wang, Stephen Carley
Organization(s): Beijing Institute of Technology, Georgia Institute of Technology
Source: Technological Forecasting and Social Change
Knowing the gear of the triple-helix is fundamental to analyze the impact of public policies in the scenario of a country, especially when the variables linked to innovation refer to the chronological production of the facts. In this perspective, an analysis was assembled from intentional samples per regions of Brazil linked to the engineering areas, identifying indices that could demonstrate this evolutionary line, highlighting mainly in their numbers, the quantitative of patents of engineering deposited with and without the relation university-enterprise partnership (EU), with state mapping of the federation, public and private investments in P&D, patents with their respective classifications and scientific production of Engineering indexed to Scopus .It was concluded that from the years of 2005 with the Innovation Law there was a boost in these indices making It possible to understand that the numbers of articles began to scale a greater use for the production of patents, with emphasis on the South and Southeast universities of the country, although It is still a number that needs greater expressiveness for the country’s future.
Author(s): Carlos Tadeu Santana Tatum, Flávio Ferreira da Conceição Franceschi, Letícia-Maria Macedo Tatum, Jonas Pedro Fabris, Suzana Leitão Russo
Organization(s): Federal University of Sergipe
Source: Revista GEINTEC
Current enterprises face organizational and cultural barriers to adopt and harness the potential of strategic emerging technologies. Late adoption of these technologies will affect competitiveness from which it will be hard to recover. Within the frame of technology analysis field, the present work aims at introducing an approach to obtain the characterization of emerging technologies, which facilitates understanding and identifies their potential. This characterization is based on the analysis of scientific activity, to which a set of quantitative methods is applied, namely bibliometrics, text mining, principal component analysis and time series analysis. The outcome is based on obtaining a set of dominant sub-technologies, which are described by means of individual time series, which also allow evolution of the technology as a whole to be forecasted. The approach is applied to the Big Data technology field and the results suggest that sub-technologies such as Mobile Telecommunications and Internet of things will lead this field in the near future.
Author(s): Iñaki Bildosola, Gaizka Garechana Enara Zarrabeitia, Ernesto Cilleruelo
Organizations(s): University of the Basque Country (UPV/EHU)
Source: Central European Journal of Operations Research
Rapid detection technology of food microorganisms is more and more a hot research topic in the field of agricultural, food safety and environmental sciences. In order to understand the current status of world-wide research on rapid detection technology of food microorganisms, research literature during the period of 1925-2018 was retrieved from the Science Citation Index Expanded (SCIE) database and analyzed using bibliometrics. Results showed that rapid microbial detection has entered the rapid growth phase after its forming phase and the initial phase. America enjoys absolute advantages in this field. China, Germany and Spain are also countries with relatively strong competitiveness. CSIC has the largest number of relevant published papers, while the papers from CNRS and INRA are the most influential ones. International Journal of Food Microbiology is the core journal that published the largest number of relevant papers, while Biosensors & Bioelectronics is the most influential relevant journal. Food Science and Technology is the subject of the most importance in this field. Biotechnology & Applied Microbiology and Microbiology are also significantly relevant. Researches in this field focus the most on relevant technologies on Molecular Biology, second on the technologies on Immune method and biosensors rank second, and the least on the technologies on metabolic methods. Sustainability has been seen in a part of the hot research topics, and new hot topics have also emerged. These results provide important insights to the development of rapid detection technology of food microorganisms in our country.
For full-text https://iopscience.iop.org/article/10.1088/1757-899X/439/3/032125/pdf
Author(s): Xiaojing Zhang, L Y Zhou, H G Zheng
Organization(s): Beijing Academy of Agriculture and Forestry Sciences
Source: IOP Conference Series: Materials Science and Engineering
Understanding how a technology is introduced and shared in a society has a strategic value for the planning of technological development and assessing new market opportunities. Among other technologies, microscopy has had a significant role in advancing different fields of science. In Brazil, its use spans from biomedical to engineering areas. Here, we used social network analysis (SNA) to map and quantify the flow of interaction between Brazilian researchers involved in microscopy techniques. The analysis examines co-occurrence of thematic networks and scientific co-authorship in articles published in a ten years window, as retrieved from Scopus database. The results showed an increasing volume of publications using microscopy in Brazil. The two major areas of interest are material and life sciences, which present significant intra-regional interaction. USA, Spain, Germany, Portugal and the United Kingdom are the main partner countries for international scientific collaborations. The share of Brazilian publications applying microscopy follows the global trends, with a slight predominance in health and life sciences. Our results provide a context of the strengths and gaps of the field in Brazil and may help to inform researchers and policy makers for further advancing the field.
Author(s): Priscila C. Albuquerque, Brunade Paula Fonseca e Fonseca, Wendell Girard-Dias, Fabio Zicker, Wanderley de Souza, Kildare Miranda
Organization(s): Fundação Oswaldo Cruz, Universidade Federal do Rio de Janeiro
What are the implications of big data in terms of big impacts? Our research focuses on the question, “How are data analytics involved in policy analysis to create complementary values?” We address this from the perspective of bibliometrics. We initially investigate a set of articles published in Nature and Science, seeking cutting-edge knowledge to sharpen research hypotheses on what data science offers policy analysis. Based on a set of bibliometric models (e.g., topic analysis, scientific evolutionary pathways, and social network analysis), we follow up with studies addressing two aspects: (1) we examine the engagement of data science (including statistical, econometric, and computing approaches) in current policy analyses by analyzing articles published in top-level journals in the areas of political science and public administration; and (2) we examine the development of policy analysis-oriented data analytic models in top-level journals associated with computer science (including both artificial intelligence and information systems). Observations indicate that data science contribution to policy analysis is still an emerging area. Data scientists are moving further than policy analysts, due to technical difficulties in exploiting data analytic models. Integrating artificial intelligence with econometrics is identified as a particularly promising direction.
Author(s): Yi Zhang, Alan L. Porter, Scott Cunningham, Denise Chiavetta, Nils Newman
Organization(s): University of Technology, Georgia Institute of Technology, Delft University of Technology
Source: 2018 Portland International Conference on Management of Engineering and Technology (PICMET)
Global academic exchange and cooperation have become an increasing trend in both academia and industry, but how to quickly and effectively identify potential partners is becoming an urgent problem. This paper proposes a link prediction-based model to help researchers identify partners from a large collection of academic articles in a given technological area. We initially construct a co-authorship network, and take a series of indices based on network and similarity of researchers into consideration. A fitting model of link prediction is then established, in which logistic regression analysis is involved. An empirical study on four journals of informetrics is conducted to demonstrate the reliability of the proposed method.
Author(s): Lu Huang, Yihe Zhu, Yi Zhang, Xiao Zhou, Xiang Jia
Organization(s): Beijing Institute of Technology
Source: 2018 Portland International Conference on Management of Engineering and Technology (PICMET)