A review of the literature on citation impact indicators

Citation impact indicators nowadays play an important role in research evaluation, and consequently these indicators have received a lot of attention in the bibliometric and scientometric literature. This paper provides an in-depth review of the literature on citation impact indicators. First, an overview is given of the literature on bibliographic databases that can be used to calculate citation impact indicators (Web of Science, Scopus, and Google Scholar). Next, selected topics in the literature on citation impact indicators are reviewed in detail. The first topic is the selection of publications and citations to be included in the calculation of citation impact indicators. The second topic is the normalization of citation impact indicators, in particular normalization for field differences. Counting methods for dealing with co-authored publications are the third topic, and citation impact indicators for journals are the last topic. The paper concludes by offering some recommendations for future research

Application of machine learning in dementia diagnosis: a systematic literature review

According to the World Health Organization forecast, over 55 million people worldwide have dementia, and about 10 million new cases are detected yearly. Early diagnosis is essential for patients to plan for the future and deal with the disease. Machine Learning algorithms allow us to solve the problems associated with early disease detection. This work attempts to identify the current relevance of the application of machine learning in dementia prediction in the scientific world and suggests open fields for future research. The literature review was conducted by combining bibliometric and content analysis of articles originating in a period of 20 years in the Scopus database. Twenty-seven thousand five hundred twenty papers were identified firstly, of which a limited number focused on machine learning in dementia diagnosis. After the exclusion process, 202 were selected, and 25 were chosen for analysis. The recent increasing interest in the past five years in the theme of machine learning in dementia shows that it is a relevant field for research with still open questions. The methods used to identify dementia or what features are used to identify or predict this disease are explored in this study. The literature review revealed that most studies used magnetic resonance imaging (MRI) and its types as the main feature, accompanied by demographic data such as age, gender, and the mini-mental state examination score (MMSE). Data are usually acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Classification of Alzheimer’s disease is more prevalent than prediction of Mild Cognitive Impairment (MCI) or their combination. The authors preferred machine learning algorithms such as SVM, Ensemble methods, and CNN because of their excellent performance and results in previous studies. However, most use not one machine-learning technique but a combination of techniques. Despite achieving good results in the studies considered, there are new concepts for future investigation declared by the authors and suggestions for improvements by employing promising methods with potentially significant results.info:eu-repo/semantics/publishedVersio

A study assessing the characteristics of big data environments that predict high research impact: application of qualitative and quantitative methods

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible

Is there cross-fertilization in macroeconomics? A quantitative exploration of the interactions between DSGE and macro agent-based models

This paper compares Dynamic Stochastic General Equilibrium (DSGE) and Macro Agent-Based Models(MABMs) by adopting mainly a distant reading perspective. A set of 2,299 papers is retrieved from Scopus byusing keywords related to MABM and DSGE domains. The interactions between the two streams of DSGE andMABM literature are explored by considering a social axis (co-authorship network), and an intellectual axis (citedreferences and bibliographic coupling). The analysis gave results that are neither consistent with a unitarystructure of macroeconomics, nor with a simple dichotomic structure of alternative paradigms and separatedacademics communities. Indeed, the co-authorship network shows that DSGE and MABM form fragmentedcommunities still belonging to two different larger MABM and DSGE communities rather neatly separated.Collaboration insists mainly inside the smaller groups and inside each of the two larger DSGE and MABMcommunities. Moreover, the co-authorship network analysis does not show evidence of systematic collaborationbetween MABM and DSGE authors. From an intellectual point of view, data show that DSGE and MABM articlesrefer to two different sets of bibliographic references. When a measure of paper-similarity is adopted, it appearsthat DSGE literature is fragmented in 4 groups while the MABM articles are clustered together in a unique group.Hence, DSGE approach is less monolithic than at the time of the New Synthesis: indeed, a large and a growingliterature has developed at the margins of the core DSGE approach which includes elements of heterogeneousagent modelling, social interactions, experiments, expectations formation, learning etc. The analysis gave noevidence of cross-fertilization between DSGE and MABM literature whilst it rather suggests a totallydissymmetric influence of DSGE over MABM literature, i.e., only MABM modelers look at DSGE but not vice-versa. The paper questions the capacity of the current dominant approach to benefit from cross-fertilization

Using Q-methodology in environmental sustainability research: A bibliometric analysis and systematic review

Q-methodology is a mixed qualitative-quantitative method used to measure social perspectives on issues relating to sustainability and environmental governance in a systematic, replicable manner. Although it has grown in prominence and use over the past two decades, to date there has not been a comprehensive review of the environmental sustainability Q-methodology literature. Using bibliometric analysis and systematic review, this paper examines the rapid growth in published Q-methodology research on sustainable natural resource management and environmental governance. We analysed and iteratively coded 277 empirical Q-studies published between 2000-2018 to establish research trends, shared gaps, and best practices among environmental social science Q-researchers. We also conducted co-authorship and co-citation analyses to identify research clusters using Q-methodology. We find that, while Q-methodology uses a relatively standardized protocol, considerable heterogeneity persists across such domains as study design, p-set identification, concourse and Q-set development, analysis and interpretation. Further, we identify major reporting gaps among Q-methodology publications where researchers do not fully describe or justify subjective decision-making throughout the research phases. The paper ends with recommendations for improving research reporting and increasing the circulation and uptake of up-to-date Q-methodology practices and innovations.Fil: Sneegas, Gretchen. Texas A&M University; Estados UnidosFil: Beckner, Sydney. Texas A&M University; Estados UnidosFil: Brannstrom, Christian. Texas A&M University; Estados UnidosFil: Jepson, Wendy. Texas A&M University; Estados UnidosFil: Lee, Kyungsun. Texas A&M University; Estados UnidosFil: Seghezzo, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; Argentin

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute