Journal Of Management Research And Analysis Impact Factor 2024-2025 - ResearchHelpDesk - Journal of Management Research and Analysis is a Double-Blind Peer Review journal that provides a specialized academic medium and important reference for the encouragement and dissemination of research and practice in management research. JMRA carries theoretical and empirical papers, case studies, research notes, executive experience sharing, and review articles, and it aims at disseminating new knowledge in the field of different domain areas of management, information technology, and related disciplines. It provides a forum for deliberations and exchange of knowledge among academics, industries, researchers, planners and the practitioners who are concerned with the management, financial institutions, public and private organizations, as well as voluntary organizations. Our editorial policy is that the journal serves the profession by publishing significant new scholarly research in management discipline areas that are of the highest quality. Aim & Scope: Journal of Management Research and Analysis (JMRA) is a quarterly, international, refereed journal published with the aim to provide an online publishing platform for the academia, management researchers, and management students to publish their original works. It aims at getting together intellectuals with the dissemination of original research, new ideas and innovations and practical experience in the concerned fields on a common platform. It also aims at understanding, advancing and promoting the emerging global trends in learning and knowledge assimilation of management researches and imparting the same to the benefit of Industry and academia for further improvisation of education systems at national as well as global level and to evolve the participation of student fraternity in the on-going discussion on socially desirable economic, commerce and management issues. JMRA focuses on publishing scholarly articles from the areas of management, management principles, recent inventions in management, company management, financial management, human resources, accounting, marketing, management control systems, supply chain management, operations management, human resource management, economics, commerce, statistics, international business, information technology, environment, risk management, import-export management, logistics management, hospitality management, health and hospital management, globalization and related areas. Journal of Management Research and Analysis seeks original manuscripts that identify, extend, unify, test or apply scientific and multi-disciplinary knowledge concerned to the management field. The following types of papers are considered for publication: 1. Original research works in the above-mentioned fields. 2. Surveys, opinions, abstracts, and essays related to Operations research. 3. Few review papers will be published if the author had done considerable work in that area. 4. Case studies related to the management domain. Indexing Information: Index Copernicus, Google Scholar, UGC, Crossref etc.

It is difficult to determine the influence and impact of journals which are not covered by the ISI databases and Journal Citation Report. However, with the availability of databases such as MyAIS (Malaysian Abstracting and Indexing System), which offers sufficient information to support bibliometric analysis as well as being indexed by Google Scholar which provides citation information, it has become possible to obtain productivity, citation and impact information for non-ISI indexed journals. The bibliometric tool Harzing's Publish and Perish was used to collate citation information from Google scholar. The study examines article productivity, the citations obtained by articles and calculates the impact factor of Medical Journal of Malaysia (MJM) published between 2004 and 2008. MJM is the oldest medical journal in Malaysia and the unit of analysis is 580 articles. The results indicate that once a journal is covered by MyAIS it becomes visible and accessible on the Web because Google Scholarindexes MyAIS. The results show that contributors to MJM were mainly Malaysian (91) and the number of Malaysian-Foreign collaborated papers were very small (28 articles, 4.8). However, citation information from Google scholar indicates that out of the 580 articles, 76.8 (446) have been cited over the 5-year period. The citations were received from both mainstrean foreign as well as Malaysian journals and the top three citors were from China, Malaysia and the United States. In general more citations were received from East Asian countries, Europe, and Southeast Asia. The 2-yearly impact factor calculated for MJM is 0.378 in 2009, 0.367 in 2008, 0.616 in 2007 and 0.456 in 2006. The 5-year impact factor is calculated as 0.577. The results show that although MJM is a Malaysian journal and not ISI indexed its contents have some international significance based on the citations and impact score it receives, indicating the importance of being visible especially in Google scholar.

Supplementary material 1: Database with the values of the impact indicators

This dataset provides supplementary data extracted and processed from the SCImago Journal Rank portal (2023) and the Scopus Discontinued Titles list (February 2025). It includes journal-level metrics such as SJR and h-index, quartile assignments, and subject category information. The files are intended to support exploratory analysis of citation patterns, disciplinary variations, and structural characteristics of journal evaluation systems. The dataset also contains Python code and visual materials used to examine relationships between prestige metrics and cumulative citation indicators.

Contents:

• Scimago Journal Rank 2023.xlsx – full SJR dataset with quartile and h-index data.
• Q1 journals with h-index below 5 (SJR 2023).xlsx – filtered subset of Q1 journals with low citation impact.
• Relationship between journal h-index and SJR 2023.png – visualization of SJR vs h-index by quartile.
• Scopus Discontinued Titles (Feb 2025) – list of discontinued sources from Scopus used for consistency checks.
• Python script for data processing and visualization.

Background This bibliometric analysis examines the top 50 most-cited articles on COVID-19 complications, offering insights into the multifaceted impact of the virus. Since its emergence in Wuhan in December 2019, COVID-19 has evolved into a global health crisis, with over 770 million confirmed cases and 6.9 million deaths as of September 2023. Initially recognized as a respiratory illness causing pneumonia and ARDS, its diverse complications extend to cardiovascular, gastrointestinal, renal, hematological, neurological, endocrinological, ophthalmological, hepatobiliary, and dermatological systems. Methods Identifying the top 50 articles from a pool of 5940 in Scopus, the analysis spans November 2019 to July 2021, employing terms related to COVID-19 and complications. Rigorous review criteria excluded non-relevant studies, basic science research, and animal models. The authors independently reviewed articles, considering factors like title, citations, publication year, journal, impact fa..., A bibliometric analysis of the most cited articles about COVID-19 complications was conducted in July 2021 using all journals indexed in Elsevier’s Scopus and Thomas Reuter’s Web of Science from November 1, 2019 to July 1, 2021. All journals were selected for inclusion regardless of country of origin, language, medical speciality, or electronic availability of articles or abstracts. The terms were combined as follows: (“COVID-19†OR “COVID19†OR “SARS-COV-2†OR “SARSCOV2†OR “SARS 2†OR “Novel coronavirus†OR “2019-nCov†OR “Coronavirus†) AND (“Complication†OR “Long Term Complication†OR “Post-Intensive Care Syndrome†OR “Venous Thromboembolism†OR “Acute Kidney Injury†OR “Acute Liver Injury†OR “Post COVID-19 Syndrome†OR “Acute Cardiac Injury†OR “Cardiac Arrest†OR “Stroke†OR “Embolism†OR “Septic Shock†OR “Disseminated Intravascular Coagulation†OR “Secondary Infection†OR “Blood Clots† OR “Cytokine Release Syndrome†OR “Paediatric Inflammatory Multisystem Syndrome†OR “Vaccine..., , # Data of top 50 most cited articles about COVID-19 and the complications of COVID-19

This dataset contains information about the top 50 most cited articles about COVID-19 and the complications of COVID-19. We have looked into a variety of research and clinical factors for the analysis.

Description of the data and file structure

The data sheet offers a comprehensive analysis of the selected articles. It delves into specifics such as the publication year of the top 50 articles, the journals responsible for publishing them, and the geographical region with the highest number of citations in this elite list. Moreover, the sheet sheds light on the key players involved, including authors and their affiliated departments, in crafting the top 50 most cited articles.

Beyond these fundamental aspects, the data sheet goes on to provide intricate details related to the study types and topics prevalent in the top 50 articles. To enrich the analysis, it incorporates clinical data, capturing...

The use of bibliometric measures in the evaluation of research has increased considerably based on expertise from the growing research field of evaluative citation analysis (ECA). However, mounting criticism of such metrics suggests that the professionalization of bibliometric expertise remains contested. This paper investigates why impact metrics, such as the journal impact factor and the h-index, proliferate even though their legitimacy as a means of professional research assessment is questioned. Our analysis is informed by two relevant sociological theories: Andrew Abbott’s theory of professions and Richard Whitley’s theory of scientific work. These complementary concepts are connected in order to demonstrate that ECA has failed so far to provide scientific authority for professional research assessment. This argument is based on an empirical investigation of the extent of reputational control in the relevant research area. Using three measures of reputational control that are computed from longitudinal inter-organizational networks in ECA (1972–2016), we show that peripheral and isolated actors contribute the same number of novel bibliometric indicators as central actors. In addition, the share of newcomers to the academic sector has remained high. These findings demonstrate that recent methodological debates in ECA have not been accompanied by the formation of an intellectual field in the sociological sense of a reputational organization. Therefore, we conclude that a growing gap exists between an academic sector with little capacity for collective action and increasing demand for routine performance assessment by research organizations and funding agencies. This gap has been filled by database providers. By selecting and distributing research metrics, these commercial providers have gained a powerful role in defining de-facto standards of research excellence without being challenged by expert authority.

International Journal of Data Science and Analytics Impact Factor 2024-2025 - ResearchHelpDesk - International Journal of Data Science and Analytics - Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations.

Background: The assessment of scientific publications is an integral part of the scientific process. Here we investigate three methods of assessing the merit of a scientific paper: subjective post-publication peer review, the number of citations gained by a paper and the impact factor of the journal in which the article was published. Methodology/principle findings: We investigate these methods using two datasets in which subjective post-publication assessments of scientific publications have been made by experts. We find that there are moderate, but statistically significant, correlations between assessor scores, when two assessors have rated the same paper, and between assessor score and the number of citations a paper accrues. However, we show that assessor score depends strongly on the journal in which the paper is published, and that assessors tend to over-rate papers published in journals with high impact factors. If we control for this bias, we find that the correlation between assessor scores and between assessor score and the number of citations is weak, suggesting that scientists have little ability to judge either the intrinsic merit of a paper or its likely impact. We also show that the number of citations a paper receives is an extremely error-prone measure of scientific merit. Finally, we argue that the impact factor is likely to be a poor measure of merit, since it depends on subjective assessment. Conclusions: We conclude that the three measures of scientific merit considered here are poor; in particular subjective assessments are an error-prone, biased and expensive method by which to assess merit. We argue that the impact factor may be the most satisfactory of the methods we have considered, since it is a form of pre-publication review. However, we emphasise that it is likely to be a very error-prone measure of merit that is qualitative, not quantitative.

This is the open data for the preprint "Measuring Back: Bibliodiversity and the Journal Impact Factor brand. A Case study of IF-journals included in the 2021 Journal Citations Report."

Journal of business analytics Impact Factor 2024-2025 - ResearchHelpDesk - Business analytics research focuses on developing new insights and a holistic understanding of an organisation’s business environment to help make timely and accurate decisions, and to survive, innovate and grow. Thus, business analytics draws on the full spectrum of descriptive/diagnostic, predictive and prescriptive analytics in order to make better (i.e., data-driven and evidence-based) decisions to create business value in the broadest sense. The mission of the Journal of Business Analytics Journal (JBA) is to serve the emerging and rapidly growing community of business analytics academics and practitioners. We aim to publish articles that use real-world data and cases to tackle problem situations in a creative and innovative manner. We solicit articles that address an interesting research problem, collect and/or repurpose multiple types of data sets, and develop and evaluate analytics methods and methodologies to help organisations apply business analytics in new and novel ways. Reports of research using qualitative or quantitative approaches are welcomed, as are interdisciplinary and mixed methods approaches. Topics may include: Applications of AI and machine learning methods in business analytics Network science and social network applications for business Social media analytics Statistics and econometrics in business analytics Use of novel data science techniques in business analytics Robotics and autonomous vehicles Methods and methodologies for business analytics development and deployment Organisational factors in business analytics Responsible use of business analytics and AI Ethical and social implications of business analytics and AI Bias and explainability in analytics and AI Our editorial philosophy is to publish papers that contribute to theory and practice. Journal of Business Analytics is indexed in: AIS eLibrary Australian Business Deans Council (ABDC) Journal Quality List British Library CLOCKSS Crossref Ei Compendex (Engineering Village) Google Scholar Microsoft Academic Portico SCImago Scopus Ulrich's Periodicals Directory

Journal of food and drug analysis Impact Factor 2024-2025 - ResearchHelpDesk - Journal of Food and Drug Analysis (JFDA) is the official peer-reviewed open access publication of the Food and Drug Administration of Taiwan. The journal, which was launched in 1993, was recognized with the Taiwan National Science Council's Award of Excellence for 9 years from 1996 to 2004. The Journal of Food and Drug Analysis - JFDA is indexed in SCIE, Medline, Chemical Abstracts, EMBASE, BIOSIS, International Food Information Service (FSTA), Abstracts of Chinese Medicines, Directory of Open Access Journals (DOAJ), Research Alert, Biochemistry & Biophysics Citation Index. The journal aims to provide an international platform for scientists, researchers, and academicians to promote, share, and discuss new findings, current issues, and developments in the different areas of food and drug analysis. The scope of the Journal includes analytical methodologies and biological activities in relation to food, drugs, cosmetics, and traditional Chinese medicine, as well as related disciplines of topical interest to public health professionals. Article types accepted include review articles, original articles, case reports, and research notes. Benefits to authors We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications, and much more.

The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.

For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management.

Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total.

‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term specific data types used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.

The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.

This file contains metadata from the 40 most impactful journals in the field of distance education, selected through a rigorous bibliometric analysis using the SCImago Journal Rank (SJR) indicator provided by SCImago. Two main criteria guided the selection: the first targeted journals ranked in Q1 and Q2 in the specific category of "e-learning" within the social sciences and education area, highlighting publications that demonstrate high impact and relevance in the academic community according to the selected indicator. The second criterion was based on keyword searches in the journal titles, selecting those that include terms like e-learning, online, Distance Education, Technology Learning, Communications in Information, and Information Education and their variants (e.g., plural), also positioned in Q1 or Q2.

The metadata, extracted from the Scopus database, covers publications from the period 2018 to 2022 and includes vital information such as document type, authors' names, article title, journal name, publication year, pages, volume, and issue number. Additionally, each article is identified by a Digital Object Identifier (DOI) and URLs for direct access to the full text online, along with abstracts and keywords. These elements together provide a comprehensive and accessible view of the articles, facilitating bibliometric analyses and related academic research.

This compilation serves as an essential resource for researchers and educators interested in understanding the dynamics and development of the field of distance education, offering a solid foundation for future investigations and the formulation of evidence-based educational policies.

Dataset of metadata analyzed papers, citation counts, normalized authors, ranking terms, thesaurus used in article "Visibility and impact analysis of Revista de Comunicación (Peru) since 2002 to 2019"

1. Neuroscience_Records_Contain_Reference_to_Preprints.Scopus.V3.xlsx

This Excel file contains the titles, DOIs, references, and EIDs of those Neuroscience publications (journal articles, books/book chapters, conference papers, notes, etc.) from 2004 to 2022 that have at least one reference to a preprint. For example, if a Neuroscience journal article has 40 references and one of these references is a preprint, then it's included in this Excel file. These records are retrieved from Scopus through the following query:

REFSRCTITLE ( "OSF Preprints" OR "open science foundation Preprints" OR africarxiv OR agrixiv OR arabixiv OR arxiv OR biohackrxiv OR biorxiv OR bodoarxiv OR cogprints OR eartharxiv OR ecoevorxiv OR ecsarxiv OR edarxiv OR engrxiv OR frenxiv OR "INA-Rxiv" OR indiarxiv OR lawarxiv OR "LIS Scholarship Archive" OR marxiv OR mediarxiv OR metaarxiv OR mindrxiv OR nutrixiv OR paleorxiv OR "Preprints.org" OR psyarxiv OR repec OR socarxiv OR sportrxiv OR "Thesis Commons" OR "CoP preprint" OR "FocUS Archive preprint" OR "PeerJ preprint" OR "Law Archive preprint" OR medrxiv ) AND SUBJAREA ( neur ) AND PUBYEAR < 2023

1. ReferencesToPreprints.V3.txt

References of the publications are split through a Python code (SplitReferences.py) and organized into separate lines in a text file. For example, if a publication has 40 references, all of these 40 references are split into 40 separate lines. After splitting references, those lines containing one of these words/terms ("OSF Preprints" OR "open science foundation preprints" OR africarxiv OR agrixiv OR arabixiv OR arxiv OR biohackrxiv OR biorxiv OR bodoarxiv OR cogprints OR eartharxiv OR ecoevorxiv OR ecsarxiv OR edarxiv OR engrxiv OR frenxiv OR "INA-Rxiv" OR indiarxiv OR lawarxiv OR "LIS Scholarship Archive" OR marxiv OR mediarxiv OR metaarxiv OR mindrxiv OR nutrixiv OR paleorxiv OR "Preprints.org" OR psyarxiv OR repec OR socarxiv OR sportrxiv OR "Thesis Commons" OR "CoP preprint" OR "FocUS Archive preprint" OR "PeerJ preprint" OR "Law Archive preprint" OR medrxiv) are selected (through RetrieveLinesContainingSpeceficString.py) and organized into this text file (ReferencesToPreprints.V3.txt). Each reference contains an EID (separated by ";") in order to specify which publication contains this specific reference.

After this step, through a Python code (AddPreprintServerToEndOfLines.py) the name of a certain preprint was added to the end of each line. For example, if a line (or a reference) contains "biorxiv", the word "biorxiv" will be added to the end of this line after the "@" sign.

The dataset presents content curation practices in top-ranked scientific journals (Q1) within the Communication (COM) and Library and Information Science (LIS) categories, as classified by Scimago Journal Rank. It focuses on how these journals manage and publish content on social media, analyzing indicators such as total and curated publications, topics covered, techniques employed, and types of integration used in their curation strategies. The study aims to identify patterns, methods, and differences between the two categories, providing a comprehensive overview of academic content curation practices. The data was collected from March to April 2024.

Observed maximum changes of citations, articles and Impact Factors.

The public sharing of primary research datasets potentially benefits the research community but is not yet common practice. In this pilot study, we analyzed whether data sharing frequency was associated with funder and publisher requirements, journal impact factor, or investigator experience and impact. Across 397 recent biomedical microarray studies, we found investigators were more likely to publicly share their raw dataset when their study was published in a high-impact journal and when the first or last authors had high levels of career experience and impact. We estimate the USA's National Institutes of Health (NIH) data sharing policy applied to 19% of the studies in our cohort; being subject to the NIH data sharing plan requirement was not found to correlate with increased data sharing behavior in multivariate logistic regression analysis. Studies published in journals that required a database submission accession number as a condition of publication were more likely to share their data, but this trend was not statistically significant. These early results will inform our ongoing larger analysis, and hopefully contribute to the development of more effective data sharing initiatives. Earlier version presented at ASIS&T and ISSI Pre-Conference: Symposium on Informetrics and Scientometrics 2009

In recent years, there have been numerous calls to change research evaluation policies to rely less on journal-level citation metrics such as the Journal Impact Factor (JIF), including two key international initiatives. The San Francisco Declaration on Research Assessment (DORA), developed at the Annual Meeting of the American Society for Cell Biology in 2012, calls for the use of article-level metrics, greater transparency in research evaluation policies and procedures, and consideration of all types of research outputs, not just journal articles.

Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.