Australian and New Zealand journal of statistics Impact Factor 2024-2025 - ResearchHelpDesk - The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems. In addition, suitable review papers and articles of historical and general interest will be considered. The journal also publishes book reviews on a regular basis. Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Academic Search Elite (EBSCO Publishing) Academic Search Premier (EBSCO Publishing) CompuMath Citation Index (Clarivate Analytics) Current Index to Statistics (ASA/IMS) Journal Citation Reports/Science Edition (Clarivate Analytics) Mathematical Reviews/MathSciNet/Current Mathematical Publications (AMS) RePEc: Research Papers in Economics Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier) Statistical Theory & Method Abstracts (Zentralblatt MATH) ZBMATH (Zentralblatt MATH)

This study estimates the effect of data sharing on the citations of academic articles, using journal policies as a natural experiment. We begin by examining 17 high-impact journals that have adopted the requirement that data from published articles be publicly posted. We match these 17 journals to 13 journals without policy changes and find that empirical articles published just before their change in editorial policy have citation rates with no statistically significant difference from those published shortly after the shift. We then ask whether this null result stems from poor compliance with data sharing policies, and use the data sharing policy changes as instrumental variables to examine more closely two leading journals in economics and political science with relatively strong enforcement of new data policies. We find that articles that make their data available receive 97 additional citations (estimate standard error of 34). We conclude that: a) authors who share data may be rewarded eventually with additional scholarly citations, and b) data-posting policies alone do not increase the impact of articles published in a journal unless those policies are enforced.

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.

BackgroundThere is increasing interest to make primary data from published research publicly available. We aimed to assess the current status of making research data available in highly-cited journals across the scientific literature. Methods and ResultsWe reviewed the first 10 original research papers of 2009 published in the 50 original research journals with the highest impact factor. For each journal we documented the policies related to public availability and sharing of data. Of the 50 journals, 44 (88%) had a statement in their instructions to authors related to public availability and sharing of data. However, there was wide variation in journal requirements, ranging from requiring the sharing of all primary data related to the research to just including a statement in the published manuscript that data can be available on request. Of the 500 assessed papers, 149 (30%) were not subject to any data availability policy. Of the remaining 351 papers that were covered by some data availability policy, 208 papers (59%) did not fully adhere to the data availability instructions of the journals they were published in, most commonly (73%) by not publicly depositing microarray data. The other 143 papers that adhered to the data availability instructions did so by publicly depositing only the specific data type as required, making a statement of willingness to share, or actually sharing all the primary data. Overall, only 47 papers (9%) deposited full primary raw data online. None of the 149 papers not subject to data availability policies made their full primary data publicly available. ConclusionA substantial proportion of original research papers published in high-impact journals are either not subject to any data availability policies, or do not adhere to the data availability instructions in their respective journals. This empiric evaluation highlights opportunities for improvement.

Datasets for publication: 'Measuring the excellence contribution at the journal level: An alternative to Garfield's Impact Factor'.

Overview. Overview of the number of journals, publications, excellent publications and multidisciplinarity for each category considered.

ALL. Journal indicators for all the document types by JCR category.

ALL_JCR. Journal indicators for all the document types by JCR category (only journals indexed in the JCR category are taken into account).

AR. Journal indicators for only articles and reviews by JCR category.

AR_JCR. Journal indicators for only articles and reviews by JCR category (only journals indexed in the JCR category are taken into account).

Journal of Big Data Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material. All articles published by the Journal of Big Data are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. As authors of articles published in the Journal of Big Data you are the copyright holders of your article and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate your article, according to the SpringerOpen copyright and license agreement. For those of you who are US government employees or are prevented from being copyright holders for similar reasons, SpringerOpen can accommodate non-standard copyright lines.

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...

This paper demonstrates the potential problem in using existing economics journal rankings to evaluate the research productivity of scholars by constructing a new ranking of economics journals and articles. Based on 2142 econometrics sample articles published from 2000 to 2005, our ranking results show that the intellectual influence of an econometrics article published in several econometrics/statistics journals is much higher than if it were published in the most prestigious general-interest journal. Given that a study's potential influence is integrated into the submission decision, this suggests a substantial downward bias toward econometricians when existing rankings are used to evaluate their research productivity.

Context

Web Scraped Dataset for Open Access, Indexed and Scopus Indexed Journals from openacessjournal.com!

Accessed on : 21-05-2021

Content

The Main Dataset is : MainOpenAccessJournalsData.csv, (alternate identical file : MainOpenAccessJournalsData.xlsx) Columns : JournalName, ImpactFactor2020, Source, Type, Title, Publisher, ISSN, DoIsbyYear, BackFileDoIs, CurrentDoIs, TotalDoIs, Subjects, ImpactFactor, Journals_Metadata_Paths

Source URLs

Impact Factor List Journals - https://www.openacessjournal.com/impact-factor-list-journals Indexed Journal Lists - https://www.openacessjournal.com/indexed-journals-list Scopus Indexed (SCI) Journals - https://www.openacessjournal.com/blog/scopus-indexed-journals/

View This Notebook for learning how I actually Scraped the Data!

View the Loading Notebook to understand how to load the data.

Inspiration

Research Data is often missing. From this dataset, we can gain that how a Journal can be classified as high scoring or low scoring on the Impact Factor Scale!

Explore A comprehensive index to Artist and influence : the journal of black American cultural history, 1981-1999 through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

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

BackgroundAs statisticians develop new methodological approaches, there are many factors that influence whether others will utilize their work. This paper is a bibliometric study that identifies and quantifies associations between characteristics of new biostatistics methods and their citation counts. Of primary interest was the association between numbers of citations and whether software code was available to the reader.MethodsStatistics journal articles published in 2010 from 35 statistical journals were reviewed by two biostatisticians. Generalized linear mixed models were used to determine which characteristics (author, article, and journal) were independently associated with citation counts (as of April 1, 2017) in other peer-reviewed articles.ResultsOf 722 articles reviewed, 428 were classified as new biostatistics methods. In a multivariable model, for articles that were not freely accessible on the journal’s website, having code available appeared to offer no boost to the number of citations (adjusted rate ratio = 0.96, 95% CI = 0.74 to 1.24, p = 0.74); however, for articles that were freely accessible on the journal’s website, having code available was associated with a 2-fold increase in the number of citations (adjusted rate ratio = 2.01, 95% CI = 1.30 to 3.10, p = 0.002). Higher citation rates were also associated with higher numbers of references, longer articles, SCImago Journal Rank indicator (SJR), and total numbers of publications among authors, with the strongest impact on citation rates coming from SJR (rate ratio = 1.21 for a 1-unit increase in SJR; 95% CI = 1.11 to 1.32).ConclusionThese analyses shed new insight into factors associated with citation rates of articles on new biostatistical methods. Making computer code available to readers is a goal worth striving for that may enhance biostatistics knowledge translation.

Ciccone and Jaroci-ski (American Economic Journal: Macroeconomics 2010; 2: 222-246) show that inference in Bayesian model averaging (BMA) can be highly sensitive to small data perturbations. In particular, they demonstrate that the importance attributed to potential growth determinants varies tremendously over different revisions of international income data. They conclude that agnostic priors appear too sensitive for this strand of growth empirics. In response, we show that the found instability owes much to a specific BMA set-up: first, comparing the same countries over data revisions improves robustness; second, much of the remaining variation can be reduced by applying an evenly agnostic but flexible prior.

Journals sometimes focus the attention of the research community by having a special collection, sometimes an entire special issue, devoted to a single topic. A reasonable question to ask is whether the extra effort of organizing, promoting, and maintaining the special collection is worthwhile. The paper that this data set accompanies examines paper impact in the Journal of Geophysical Research Space Physics, separating the special collection papers from the non-special-collection submissions. The conclusion is that special collections are worth the extra work.

Data sharing is key for replication and re-use in empirical research. Scientific journals can play a central role by establishing data policies and providing technologies. In this study factors of influence for data sharing are analyzed by investigating journal data policies and author behavior in sociology. The websites of 140 journals from sociology were consulted to check their data policy. The results are compared with similar studies from political science and economics. For five selected journals with a broad variety all articles from two years are examined to see if authors really cite and share their data, and which factors are related to this.

Government policies attempting to slow the spread of COVID-19 have reduced access to research laboratories and shifted many scholars to working from home. These disruptions will likely influence submissions to scholarly journals, and affect the time available for editors and reviewers to participate in peer review. In this editorial we examine how journal submissions, and editorial and peer review processes, have been influenced by the pandemic at six journals published by the British Ecological Society (BES). We find no evidence of a change in the geographic pattern of submissions from across the globe. We also find no evidence that submission of manuscripts by women has been more affected by pandemic disruptions than have submissions by men – the proportion of papers authored by women during the COVID period of 2020 has not changed relative to the same period in 2019. Editors handled papers just as quickly, and reviewers have agreed to review just as often, during the pandemic compare...

Health Reports, published by the Health Analysis Division of Statistics Canada, is a peer-reviewed journal of population health and health services research. It is designed for a broad audience that includes health professionals, researchers, policymakers, and the general public. The journal publishes articles of wide interest that contain original and timely analyses of national or provincial/territorial surveys or administrative databases. New articles are published electronically each month.

Health Reports had an impact factor of 2.673 for 2014 and a five-year impact factor of 4.167. All articles are indexed in PubMed. Our online catalogue is free and receives more than 500,000 visits per year. External submissions are welcome.

The Big Data Interagency Working Group (BD IWG) held a workshop, Measuring the Impact of Digital Repositories, on February 28 - March 1, 2017 in Arlington, VA. The aim of the workshop was to identify current assessment metrics, tools, and methodologies that are effective in measuring the impact of digital data repositories, and to identify the assessment issues, obstacles, and tools that require additional research and development (R&D). This workshop brought together leaders from academic, journal, government, and international data repository funders, users, and developers to discuss these issues...

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."

Gene array data files compared gene expression profiles in liver samples from young (21-45 years) and old (69+ years) men and women to determine changes in the expression of xenobiotic metabolism enzymes and transporters. We identified genes that were differentially expressed in males only, females only, or in all individuals between the young and old using microarray. This dataset is associated with the following publication: Corton, J., J. Lee, J. Liu, H. Ren, B. Vallanat, and M. Devito. Determinants of Gene Expression in the Human Liver: Impact of Aging and Sex on Xenobiotic Metabolism. Experimental Gerontology. Elsevier Science Ltd, New York, NY, USA, 169: 111976, (2022).