Impact Of Scientific Publications Research Articles

ChatGPT revisited: Using ChatGPT-4 for finding references and editing language in medical scientific articles

The attainment of academic superiority relies heavily upon the accessibility of scholarly resources and the expression of research findings through faultless language usage. Although modern tools, such as the Publish or Perish software program, are proficient in sourcing academic papers based on specific keywords, they often fall short of extracting comprehensive content, including crucial references. The challenge of linguistic precision remains a prominent issue, particularly for research papers composed by non-native English speakers who may encounter word usage errors. This manuscript serves a twofold purpose: firstly, it reassesses the effectiveness of ChatGPT-4 in the context of retrieving pertinent references tailored to specific research topics. Secondly, it introduces a suite of language editing services that are skilled in rectifying word usage errors, ensuring the refined presentation of research outcomes. The article also provides practical guidelines for formulating precise queries to mitigate the risks of erroneous language usage and the inclusion of spurious references. In the ever-evolving realm of academic discourse, leveraging the potential of advanced AI, such as ChatGPT-4, can significantly enhance the quality and impact of scientific publications.

Bioprospecção como estratégia para a conservação e uso sustentável da Flora Brasileira

Abstract In Brazil, research with natural products had a strong impulse when FAPESP supported the creation of the Laboratory of Chemistry of Natural Products of the Institute of Chemistry of USP (1966). In 1999, FAPESP launched the Research Program in the Characterization, Conservation, Restoration and Sustainable Use of Biodiversity (BIOTA-FAPESP), which intensified the sustainable exploitation of biodiversity, and which evolved to form the Biota Network for Bioprospection and Bioassays (BIOprospecTA), which integrates groups from all over the country, optimizing the use of the skills already installed for the bioprospecting of microorganisms, plants, invertebrates, vertebrates and marine organisms. Of the 104 projects related to plant sciences, 35 carried out bioprospection of Brazilian flora, belonging to the areas of Chemistry, Botany, Genetics, Plant Physiology, Plant Morphology, Plant (Chemo)taxonomy, Ecosystem Ecology, Plant Genetics. Physical Sciences, Forest Resources, Forestry Engineering, Agronomy, leading to thousands of publications, engagement of hundreds of students and a deeper understanding of natural products in different biological models through macromolecules analysis aided by computational and spectrometric strategies, in addition to pharmacological evaluations. The development of omics approaches led to a more comprehensive view of the chemical profile of an organism, and enabled integrated and concomitant studies of several samples, and faster annotation of known molecules, through the use of hyphenated and chemometric techniques, and molecular networking. This also helped to overcome the lack of information on the safety and efficacy of herbal preparations, in projects dealing with the standardization of herbal products, according to international standards. The BIOTA-FAPESP program has also focused on environmental aspects, in accordance with the principles of Green Chemistry and has had positive effects on international collaboration, on the number and impact of scientific publications and on partnership with companies, a crucial step to add value and expand the production chain of bioproducts. Also, the compilation, systematization and sharing of data were contemplated with the creation of the NUBBEDB database, of free access, and that integrates with international databases (ACD/labs, American Chemical Society – ACS), helping researchers and companies in the development from different areas of science, technology, strengthening the bioeconomy and subsidizing public policies.

Data analysis tools for the study of scientific citations


 
 
 This research work made a study of scientific citations, with the aim of identifying aspects that may influence the citations of a higher educational institution. We analyzed 219 records of publications of the National University of Chimborazo and 10304 records of manuscripts from Ecuador. This work had a qualitative approach and a systemic design. As a result, it was found that the impact of scientific publications is reflected by the number of citations that have the documents published by the higher education institutions; in this sense, publications with larger citations are not related to the number of authors or volume of the published magazine, but they are supported by a quality research and correspond mostly to applied sciences.
 
 

The Two Pandemics.

This article reflects on the importance and the impact of scientific publications in the midst of a global health crisis. It aims to raise awareness about the responsibility of accepting manuscripts in such sensitive times and is intended to motivate the production of high-quality papers through a critical vision.

Can citation metrics predict the true impact of scientific papers?

Bibliometric quantification is frequently used as metrics for the evaluation of the scientific performance of researchers and institutions. The researchers' merit is usually assessed by the analysis of quantitative parameters such as the number of publications, the impact factor of journals, the total number of citations, or the h-index, although the limitations in translating these indicators into the impact of the outcome of scientific production are a matter of harsh criticism. To assess, based on factual evidences, the validity of traditional bibliometric analyses to conclude on the impact of papers to advance the state of the art, we carried out an innovative methodology on selected publications (test set). This methodology is based on identifying those citations of the test set papers that truly embed the methods, concepts, or hypotheses to build new knowledge and formulate conclusions. The results show that the percentage of citations that reflect the real impact of the papers of the test set has an average value of 12.4% of total citations and is not related to the impact factor of the journal where the test set papers were published. In conclusion, our analysis demonstrates factually, using experimental data, the total failure of using quantitative bulk citation analyses to conclude on the scientific impact of publications. Only a careful analysis of how the work described in papers was embedded on the subsequent work and/or conclusions of others can tell about the real contribution of a published work to the development of new knowledge and advancement of science.

Modelos semânticos para dados bibliográficos de publicações científicas disponibilizados como Linked Data

Esta pesquisa aborda a oportunidade de aumento do impacto de publicações científicas com a disponibilização dos dados de seus registros bibliográficos como Linked Data. Objetiva investigar quais são as melhores práticas para tal, bem como delinear um modelo semântico para dados bibliográficos de publicações científicas. A metodologia é exploratória e descritiva. Como resultado, apresenta um conjunto de declarações RDF, que estruturam o modelo semântico. Conclui que a representação bibliográfica das publicações científicas no Linked Data valoriza a coexistência de vários vocabulários para descrição em um mesmo registro.

Interdisciplinary Comparison of Scientific Impact of Publications Using the Citation-Ratio

The commonly used indexes for evaluating the scientific impact of publications and individual researchers do not allow accurate comparison between disciplines with varying citation frequencies. The Citation-Ratio (CR) was developed to measure impact of an individual publication and allow field-normalised comparison. The CR equals the total number of citations of a publication divided by the median of citations of its references and was tested for the top 5% of the most-cited publications of 13 selected disciplines in sciences, social sciences and humanities. Each publication had a CR = 0 until it was firstly cited. At CR = 1 the number of citations equalled the median of citations of the references. CRs of the most-cited publications mostly ranged between 1 and 10 and were not significantly different across the selected disciplines. In contrast, the total number of citations of the same publications were significantly different across disciplines. One of the advantages of the CR is that it can be calculated for any publication as long as it has references (e.g. books, book chapters, reports, and symposium contributions).

Being highly productive in the Biomedical Sciences: A qualitative study of motivation and habits of high-throughput researchers

Background: Quantity, quality, and impact of scientific publications are used to assess national, institutional, and individual levels of research productivity. While the importance of quality research is stressed among the medical research community, minimal research has been conducted on analyzing which factors affect research productivity. Current literature assesses the quality of research institutions rather than that of individual researchers; there is also no research on the difference between high-impact researchers and other researchers. This study, conducted in 2015, sought to investigate the underlying reason for high-throughput authors' success by understanding their similar habits and motivations leading to high productivity.
 Methods: The authors conducted a qualitative study via interviews of high-throughput researchers from around the world. Semi-structured interview scripts guided the interviews in accordance to the grounded theory method for qualitative studies. Broad themes from preliminary interviews were identified and explored in subsequent interviews.
 Results: Qualitative analysis of participant interviews identified eight major themes: “Writing habits,” “Writing strategy,” “Previous training and writing experience,” “Major driver,” “Balancing volume and impact of publications,” “Ideal and non-ideal conditions,” “Timelines,” and “Role of networking on high-throughput productivity.” These themes are not exclusive nor required qualities of high-throughput researchers but highlight similarities and broadly unifying characteristics of these researchers.
 Conclusion:This study identified the common qualities and attitudes of high-throughput researchers. We found common factors in most individuals that can be considered markers of high productivity.

Recent Trends in Research with Human Pluripotent Stem Cells: Impact of Research and Use of Cell Lines in Experimental Research and Clinical Trials.

SummaryThe human pluripotent stem cell (hPSC) research landscape is rapidly evolving. To assess possible novel trends in hPSC usage, we analyzed experimental hPSC research published from 2014 to 2016 and compared our data with those of earlier periods. The number of papers describing experimental work involving hPSCs increased further with clear differences in the scientific impact of publications from different countries. Our results confirm the leading position of US-based hPSC research, although to a lesser degree than observed previously. Our data reveal that research into human induced pluripotent stem cells alone surpassed human embryonic stem cell (hESC) research by 2015 and rapidly grew after that. We also report on continuing and even slightly growing research activities in the hESC field as well as on a generally declining rate of the generation of new hESC lines. An increasing portion of new hESC lines represents disease-specific and clinical-grade cell lines. The previously noted usage of only a few early established hESC lines in the vast majority of scientific work is sustained. We also provide a comprehensive overview on clinical trials on the basis of hPSCs. We find that the vast majority of those trials are based on hESC-derived cell products that were generated from an only limited number of relatively old cell lines.

Author-team diversity and the impact of scientific publications: Evidence from physics research at a national science lab

In the second half of the 20th century, scientific research in physics, chemistry, and engineering began to focus on the use of large government-funded laboratories. This shift toward so-called big science also brought about a concomitant change in scientific work itself, with a sustained trend toward the use of highly specialized scientific teams, elevating the role of team characteristics on scientific outputs. The actual impact of scientific knowledge is commonly measured by how often peer-reviewed publications are, in turn, cited by other researchers. Therefore, how characteristics such as author team seniority, affiliation diversity, and size affect the overall impact of team publications was examined. Citation information and author demographics were reviewed for 123 articles published in Physical Review Letters from 2004 to 2006 and written by 476 scientists who used the National High Magnetic Field Laboratory's facilities. Correlation analysis indicated that author teams that were more multi-institutional and had homogeneous seniority tended to have more senior scientists. In addition, the analysis suggests that more mixed seniority author teams were likely to be less institutionally dispersed. Quantile regression was used to examine the relationships between author-team characteristics and publication impact. The analysis indicated that both weighted average seniority and average seniority had a negative relationship with the number of citations the publication received. Furthermore, the analysis also showed a positive relationship between first-author seniority and the number of citations, and a negative relationship between the number of authors and the number of citations.

Estadísticas para repositorios: sistema métrico de datos en <i>Digital.CSIC</i>

Open access to information has brought about a multiplication of data that can be used to carry out statistical analysis on the impact of scientific publications, thus paving the way for new metrics models for scholarly communication. In May 2010 institutional repository Digital.CSIC launched a new statistics model to address CSIC structural complexity as well as requests by CSIC libraries to generate statistical reports by centres. Thanks to these newly developed statistics, it will be possible to undertake both internal follow-up activities and external promotional and advocacy efforts which will show cost-benefit relationship at Digital.CSIC more clearly alongside the impact of CSIC science that is openly available through Digital.CSIC . The article explains the architecture behind statistics a la carte developed by Digital.CSIC and needs and challenges that are to be addressed.

Impact of Scientific Publication [President's Message

Publishing is vital to scientists and engineers for disseminating research results to the scientific community and discoveries to the general public. In this digital age with an ever-growing amount of information available on the Internet, it has become an increasingly difficult choice for authors to choose journals and for editors to choose articles.

Cruciate ligament: density-equalizing mapping and scientometrics as a measure of the current scientific evaluation

The topic “cruciate ligament” is of increasing interest due to improved diagnostic methods and the necessity to cure “modern” leisure time sports injuries. Publications have increased strongly since the 1990s. In this respect, it is difficult for a scientist to obtain an overview of a topic he/she is interested in. Scientometrics is a relatively new method to evaluate research efforts, to detect single areas of interest and to quantify the scientific impact of publications according to a particular topic. By comparing quantitative (i.e. number of published items) and qualitative factors (i.e. h-index, Impact-Factor or Citation rate), it is possible to evaluate this analysis methodically. In the period 1903–2007, a number of 9,806 published items were included in the ISI-Web referring to “cruciate ligament”. The publications were published by 59 countries demonstrating the USA, Germany and the UK as the most productive ones. The “American Journal of Sports Medicine” was determined to be the most prolific journal and “WOO, SLY” as the most prolific author dealing with “cruciate ligament”. The USA, Sweden, Finland and Norway take a leading position in citation per item rankings. In this respect, further analysis of the citations revealed the tendency of a disproportional high self-citation rate for some countries (e.g. Sweden, Finland). Self-citations lead to high average citations rates and distort further qualitative parameters (i.e. Impact-Factor, h-index). In this respect, the value of these parameters should be considered from a critical point of view. Furthermore, it might be useful to establish parameters which limit co-authorship and exclude self-citations to obtain more sustainable results.

Comparing the scientific impact of conference and journal publications in computer science

The impact of scientific publications is often estimated by the number of citations they receive, i.e. how frequently they are referenced by other publications. Since publications have associated authors, originating institutions and publication venues (e.g. journals, conference proceedings) citations have also been used to compare their scientific impact. For instance, one commonly considered indicator of the quality of a journal is its impact factor [1]. The impact factors are published yearly by Thomson ISI in the Journal Citation Report (JCR) by counting the citations from articles of thousands of journals. However, research results in computer science are often published in high-quality conferences which are not covered by the JCR citation databases [2]. Other commercial citation data sources such as Elsevier Scopus also focus on journals and contain comparatively few conference publications. Hence these data sources cover only a fraction of quality scientific publications in computer science. Furthermore, they miss many citations even for journal articles since all references to them are not captured which originate from conference papers or other papers not included in the publication database. Several recent system developments capture citation numbers for both journal and conference publications especially in computer science, e.g. Citeseer, the ACM Digital Library, Microsoft Libra (Libra) and Google Scholar (GS). For example, Libra holds more than 900,000 computer science publications and more than 3.5 million citations to them as of December 2007. As shown in Table 1, the majority of papers appeared in conferences and workshops, not in journals. Furthermore, the total number of citations is higher for conferences and workshops than for journals. While there are many workshops and conferences with comparatively little scientific impact the top-cited conferences are highly significant and need to be considered for a meaningful citation analysis in computer science. For example, in the Libra dataset the average number of citations per paper is similar for the 100 most cited conference series than for the 100 most cited journals. These 200 venues account for 78% of all citations. In [4] we used cleaned citation data from GS for an in-depth citation analysis for database research, a subfield of computer science research. We analyzed all publications over a period of 10 years (1994–2003) which appeared in top database conferences and top database journals. It turned out that the two top conferences (ACM Sigmod, VLDB) not only publish many more papers than the top journals (ACM TODS, VLDB Journal) but that they receive many more citations in total and per paper. The original study used GS data from August 2005. We recently confirmed the findings with GS citation data from December 2007.

The effect of university–industry collaboration on the scientific impact of publications: the Canadian case, 1980–2005

Previous research on university-industry collaboration in Canada, using mean impact factors as a proxy, concluded that the scientific impact of such research is not inferior to that of university research. Using field-normalized impact factors and citation counts, this paper re-examines the Canadian case. It shows that, when impact factors are field-normalized, university-industry papers are published, on average, in journals with lower impact factors than papers originating from universities only. However, field-normalized citation values reveal the opposite: the average scientific impact of university-industry papers is significantly above that of both university-only papers and industry-only papers. Collaboration with industries is, thus, far from detrimental to the scientific impact of university research and even increases it significantly.

Measuring the impact of scientific publications. The case of the biomedical sciences

The bibliometric indicators currently used to assess scientific production have a serious flaw: a notable bias is produced when different subfields are compared. In this paper we demonstrate the existence of this bias using the impact factor (IF) indicator. The impact factor is related to the quality of a published article, but only when each specific subfield is taken separately: only 15.6% of the subfields we studied were found to have homogeneous means. The bias involved can be very misleading when bibliometric estimators are used as a basis for assigning research funds. To improve this situation, we propose a new estimator, the RPU, based on a normalization of the impact factor that minimizes bias and permits comparison among subfields. The RPU of a journal is calculated with the formula: RPU=10(1-exp (-IF/x)), where IF is the impact factor of the journal and x the mean IF for the subfield in which the journal belongs. The RPU retains the advantages of the impact factor: simplicity of calculation, immediacy and objectivity, and increases homogeneous subfields from 15.6% to 93.7%.

The comparative impact of scientific publications and journals: Methods of measurement and graphical display

A method is presented to display the comparative impact of scientific publications relative to their ‘environment’ (e.g., journals). Furthermore, the method gives a new approach to the establishment of a journal's impact as measured by received citations. Moreover, in this impact measurement a differentiation between various types of publications (editorials and letters, ‘normal’ papers, reviews, etc.) can be made. It is argued that the method presented is more useful for library and research evaluation policies than the ISI impact factor.

Differences in Impact of Scientific Publications: Some Indices Derived from a Citation Analysis

A mathematical model is introduced which describes citing as a probabilistic process, while taking into account that in general the probability for a paper to be cited decreases with age. With the aid of the model it is possible to ascertain what differences in numbers of citations are to be considered statistically significant (i.e. not due to mere chance). Significant differences are supposed to reflect differences in 'impact' (i.e. differences in the overall judgement of the paper by the scientific community). The 'impact' of a paper is thought not only to be determined by its scientific significance, but also by such extrinsic factors as locale of the author, prestige of the journal in which the paper is published, and so on. A parameter is also introduced which measures the decay rate of citations. This parameter may be useful in making quantitative comparisons between the 'dynamics' of various scientific specialties. Finally, indices are proposed to measure the dissemination of scientific work.