scholarly journals Learning from retracted papers authored by the highly cited Iran-affiliated researchers: Revisiting research policies and a key message to Clarivate Analytics

2020 ◽  
Author(s):  
Negin Kamali ◽  
Farid Rahimi ◽  
Amin Talebi Bezmin Abadi
Keyword(s):  
Peer Review ◽  
Role Models ◽  
Scientific Literature ◽  
Biomedical Sciences ◽  
Scientific Papers ◽  
Awareness Programs ◽  
Publishing Ethics ◽  
Peer Researchers ◽  
Highly Cited

Abstract Background The scientific literature is anticipated to self-correct with time. An integral part of this self-correction is the retraction notices identifying flawed scientific papers. Prevalence of retractions has been investigated in different countries and different scholarly disciplines, including surgery, biomedical sciences, and engineering. Reportedly retractions have increased with increasing number of papers from Iran. However, reasons underlying retractions of papers authored by the Iran-affiliated highly cited researchers (HCRs) have not been documented.Result Here, we report that 229 of the Iran-affiliated researchers were listed by the Clarivate Analytics as HCRs. In total, 51 retracted papers were authored by the HCRs as documented by the Retraction Watch Database interrogated from 2006 to 2019. Twenty-three of the 229 HCRs (10%) had at least one paper retracted. One of the listed HCRs had 22 papers retracted; 14 of the 23 (60.8%) had only one paper retracted. Of the 51 papers, 43 (84%) had a single retraction reason, whereas eight had multiple reasons. Among the 43 papers, 23 (53%) were retracted due to fake peer-review, eight (19%) were duplications, six (14%) had errors, four (9%) had plagiarism, and two (5%) were labelled as “limited or no information.” Duplication of data, which is easily preventable, amounted to 27%. The time from publication to retraction of the 51 papers ranged from one to 2,483 (mean 856.6) days.Conclusion Any publishing oversight committed by an HCR should not be tolerated because they represent the stakeholders of the scientific literature and stand as role-models for other peer researchers. Future policies supporting the Iranian academia should radically change by implementation of educational and awareness programs on publishing ethics to reduce the retraction rate.

scholarly journals The Quality of Statistical Reporting and Data Presentation in Predatory Dental Journals Was Lower Than in Non-Predatory Journals

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 468
Author(s):  
Pentti Nieminen ◽  
Sergio E. Uribe
Keyword(s):  
Open Access ◽  
Peer Review ◽  
Scientific Journals ◽  
Mean Values ◽  
Data Presentation ◽  
Predatory Journals ◽  
Study Results ◽  
The Mean ◽  
Highly Cited

Proper peer review and quality of published articles are often regarded as signs of reliable scientific journals. The aim of this study was to compare whether the quality of statistical reporting and data presentation differs among articles published in ‘predatory dental journals’ and in other dental journals. We evaluated 50 articles published in ‘predatory open access (OA) journals’ and 100 clinical trials published in legitimate dental journals between 2019 and 2020. The quality of statistical reporting and data presentation of each paper was assessed on a scale from 0 (poor) to 10 (high). The mean (SD) quality score of the statistical reporting and data presentation was 2.5 (1.4) for the predatory OA journals, 4.8 (1.8) for the legitimate OA journals, and 5.6 (1.8) for the more visible dental journals. The mean values differed significantly (p < 0.001). The quality of statistical reporting of clinical studies published in predatory journals was found to be lower than in open access and highly cited journals. This difference in quality is a wake-up call to consume study results critically. Poor statistical reporting indicates wider general lower quality in publications where the authors and journals are less likely to be critiqued by peer review.

scholarly journals Recent applications of quantitative analytical FTIR spectroscopy in pharmaceutical, biomedical, and clinical fields: A brief review

2022 ◽  
Vol 41 (1) ◽  
pp. 21-33
Author(s):  
Khairi Mustafa Fahelelbom ◽  
Abdullah Saleh ◽  
Moawia M. A. Al-Tabakha ◽  
Akram A. Ashames
Keyword(s):  
High Performance Liquid Chromatography ◽  
Fourier Transform ◽  
Ftir Spectroscopy ◽  
High Performance ◽  
Scientific Literature ◽  
Current Status ◽  
Full Potential ◽  
Biomedical Sciences ◽  
The Subject ◽  
Shed Light

Abstract Qualitative Fourier transform infrared (FTIR) spectroscopy has long been established and implemented in a wide variety of fields including pharmaceutical, biomedical, and clinical fields. While the quantitative applications are yet to reach their full potential, this technique is flourishing. It is tempting to shed light on modern engaging and the applicability of analytical quantitative FTIR spectroscopy in the aforementioned fields. More importantly, the credibility, validity, and generality of the application will be thoroughly demonstrated by reviewing the latest published work in the scientific literature. Utilizing FTIR spectroscopy in a quantitative approach in pharmaceutical, biomedical, and interdisciplinary fields has many undeniable advantages over traditional procedures. An insightful account will be undertaken in this regard. The technique will be introduced as an appealing alternative to common methods such as high performance liquid chromatography. It is anticipated that the review will offer researchers an update of the current status and prospect on the subject among the pharmacy and biomedical sciences both in academic and industrial fields.

scholarly journals The Prevalence of Inappropriate Image Duplication in Biomedical Research Publications

mBio ◽  
2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Elisabeth M. Bik ◽  
Arturo Casadevall ◽  
Ferric C. Fang
Keyword(s):  
Biomedical Research ◽  
Scientific Literature ◽  
Scientific Journals ◽  
Marked Variation ◽  
The Past ◽  
Inaccurate Data ◽  
Research Publications ◽  
Scientific Papers ◽  
Actual Prevalence

ABSTRACT Inaccurate data in scientific papers can result from honest error or intentional falsification. This study attempted to determine the percentage of published papers that contain inappropriate image duplication, a specific type of inaccurate data. The images from a total of 20,621 papers published in 40 scientific journals from 1995 to 2014 were visually screened. Overall, 3.8% of published papers contained problematic figures, with at least half exhibiting features suggestive of deliberate manipulation. The prevalence of papers with problematic images has risen markedly during the past decade. Additional papers written by authors of papers with problematic images had an increased likelihood of containing problematic images as well. As this analysis focused only on one type of data, it is likely that the actual prevalence of inaccurate data in the published literature is higher. The marked variation in the frequency of problematic images among journals suggests that journal practices, such as prepublication image screening, influence the quality of the scientific literature.

Contrasted media frames of AI during the COVID-19 pandemic: a content analysis of US and European newspapers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jerome Duberry ◽  
Sabrya Hamidi
Keyword(s):  
Peer Review ◽  
Scientific Literature ◽  
Empirical Studies ◽  
New York Times ◽  
News Article ◽  
Content Type ◽  
Media Frames ◽  
Mainstream Media ◽  
French And English ◽  
English Speaking

PurposeDespite the growing interest in AI, the scientific literature lacks multinational studies that examine how mainstream media depict AI applications. This paper is one of the first empirical studies to explore how French and English-speaking mainstream media portray AI during a pandemic. The purpose of this study is to examine how media define AI and how they frame this technology.Design/methodology/approachThe authors selected five media outlets and extracted all news articles that mentioned AI over a period of 30 days. The authors constituted the sample to ensure a mix of global, national and local media newspapers. The authors included Le Temps (Switzerland), Le Monde (France), The Guardian (United Kingdom), Politico Europe (Europe) and the New York Times (USA). The authors used the NexisUni database to collect the news articles. This resulted in a sample of 54 articles, which the authors then refined to 35 articles mentioning at the same AI and COVID-19. They then manually coded to identify media frames about AI.FindingsAlthough no news article provides a definition of AI, most articles highlight two main characteristics: information processing and adaptability. This paper also shows that the coverage of AI in US newspaper is more optimistic than pessimistic. European newspapers offer a more balanced perspective of the risks and benefits associated with the technology, and highlight its use mainly in the context of the COVID-19. Media framing changes according to the evolution of the pandemic. While the USA were not yet heavily affected by the virus, Europe experienced the peak of the crisis. The authors argue that the framing of AI follows that of the pandemic.Research limitations/implicationsThis study is limited in terms of timeframe (30 days) and media outlets (5). It would be useful to extend this sample to verify the results, and also conduct interviews among journalists to understand their motivations and understanding of AI.Originality/valueDespite the growing interest in AI, the scientific literature lacks multinational studies that examine how mainstream media depict AI applications in society. This paper is one of the first empirical studies to explore how French and English-speaking mainstream media portray AI during a pandemic.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2020-0393

A Thematic Analysis of Leadership Qualities of Women Leaders in Technology

2017 ◽  
pp. 1-15
Author(s):  
Laurie O. Campbell ◽  
Joshua H. Truitt ◽  
Christine P. Herlihy ◽  
Jarrad D. Plante
Keyword(s):  
Women Leaders ◽  
Role Models ◽  
Qualitative Content Analysis ◽  
Gender Disparity ◽  
Technology Leadership ◽  
Leadership Characteristics ◽  
Leadership Roles ◽  
Leadership Qualities ◽  
Women In Technology ◽  
Awareness Programs

There is known gender disparity and inequity of women leaders in technology and STEM fields. A rapid gender decline in these burgeoning fields has sparked a national renewed interest in purposefully attracting and mentoring more women to roles in technology leadership. The gender disparity is not only in attracting young women to consider a technology or STEM career but it is in women staying engaged once they choose a career in these areas. Efforts have been made to improve the sustainability of women in technology leadership roles. Books, articles, and manuscripts have been written, formal and informal meetings and corporate awareness programs have been conducted and mentorship programs abound to attract girls to consider technology as a career choice. Further, identifying women role models has been a strategy employed to promote gender awareness. Within the chapter, the qualitative content analysis study investigates four women roles models and identifies leadership characteristics of these known women leaders in technology. It answers the following questions: What are the leadership characteristics of known women role models in technology? What do these leaders value? How do their differences impact their leadership in the field? Finally, what have they identified as propelling them towards innovation and discovery?

Scientific Papers and Patents on Substances with Unproven Effects

2019 ◽  
Vol 13 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Sergei V. Jargin
Keyword(s):  
Medical Research ◽  
Dietary Supplements ◽  
Scientific Literature ◽  
Evidence Based ◽  
Scientific Papers ◽  
Constructive Discussion

It is evident from reviewing scientific literature that the quality of argumentation in some areas of medical research has deteriorated during the last decades. Publication of a series of questionable reliability has continued without making references to the published criticism; examples are discussed in this review. Another tendency is that drugs without proven efficiency are advertised, corresponding products patented and marketed as evidence-based medications. Professional publications are required to register drugs and dietary supplements to obtain permissions for the practical use; and such papers appeared, sometimes being of questionable reliability. Several examples are discussed in this review when substances without proven effects were patented and introduced into practice being supported by publications of questionable reliability. Some of the topics are not entirely clear; and the arguments provided here can induce a constructive discussion.

scholarly journals KGen: a knowledge graph generator from biomedical scientific literature

2020 ◽  
Vol 20 (S4) ◽  
Author(s):  
Anderson Rossanez ◽  
Julio Cesar dos Reis ◽  
Ricardo da Silva Torres ◽  
Hélène de Ribaupierre
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Processing ◽  
Scientific Community ◽  
Scientific Literature ◽  
Software Tool ◽  
Scientific Texts ◽  
New Knowledge ◽  
Scientific Papers ◽  
Biomedical Texts

Abstract Background Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer’s Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society. Methods In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer’s Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool. Results The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation. Conclusions We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.

Sign in / Sign up

Export Citation Format

Share Document