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.

International Journal of Scientific and Technology Research Impact Factor 2024-2025 - ResearchHelpDesk - IJSTR - International Journal of Scientific & Technology Research is an open access international journal from diverse fields in sciences, engineering, and technologies Open Access that emphasizes new research, development, and applications. Papers reporting original research or extended versions of already published conference/journal papers are all welcomed. Papers for publication are selected through peer review to ensure originality, relevance, and readability. IJSTR ensures a wide indexing policy to make published papers highly visible to the scientific community. IJSTR is part of the eco-friendly community and favors e-publication mode for being an online 'GREEN journal'. IJSTR is an international peer-reviewed, electronic, online journal published monthly. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching, and research in the fields of engineering, science, and technology. Original theoretical work and application-based studies, which contribute to a better understanding of engineering, science, and technological challenges, are encouraged. IJSTR Publication Charges IJSTR covers the costs partially through article processing fees. IJSTR expenses are split among peer review administration and management, production of articles in PDF format, editorial costs, electronic composition and production, journal information system, manuscript management system, electronic archiving, overhead expenses, and administrative costs. Moreover, we are providing research paper publishing in minimum available costing such as there are no charges for rejected articles, no submission charges, and no surcharges based on the figures or supplementary data. IJSTR Publication Indexing IJSTR ​​​​​submit all published papers to indexing partners. Indexing totally depends on the content, indexing partner guidelines, and their indexing procedures. This is the reason sometimes indexing happens immediately and sometimes it takes time. Publication with IJSTR does not guarantee that paper will surely be added indexing partner website. The whole process for including any article (s) in the Scopus database is done by the Scopus team only. Journal or Publication House doesn't have any involvement in the decision whether to accept or reject a paper for the Scopus database and cannot influence the processing time of paper. International Journal of Scientific & Technology Research RG Journal Impact: 0.31 * *This value is calculated using ResearchGate data and is based on average citation counts from work published in this journal. The data used in the calculation may not be exhaustive. RG Journal impact history 2018 / 2019 0.31 2017 0.34 2016 0.33 2015 0.36 2014 0.19 Is Ijstr Scopus indexed? Yes IJSTR - International Journal of Scientific & Technology Research Journal is Scopus indexed. please visit for more details - IJSTR Scoups

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.

Data sharing is crucial to the advancement of science because it facilitates collaboration, transparency, reproducibility, criticism, and re-analysis. Publishers are well-positioned to promote sharing of research data by implementing data sharing policies. While there is an increasing trend toward requiring data sharing, not all journals mandate that data be shared at the time of publication. In this study, we extended previous work to analyze the data sharing policies of 447 journals across several scientific disciplines, including biology, clinical sciences, mathematics, physics, and social sciences. Our results showed that only a small percentage of journals require data sharing as a condition of publication, and that this varies across disciplines and Impact Factors. Both Impact Factors and discipline are associated with the presence of a data sharing policy. Our results suggest that journals with higher Impact Factors are more likely to have data sharing policies; use shared data in peer review; require deposit of specific data types into publicly available data banks; and refer to reproducibility as a rationale for sharing data. Biological science journals are more likely than social science and mathematics journals to require data sharing.

The Journals dataset includes information about all historical Journal titles from Springer Nature, including ones that have been decommissioned.See also: https://scigraph.springernature.com/explorer/datasets/data_at_a_glance/A journal record usually includes information about its publisher, imprint, license model, chief editor, external identifiers, subjects and impact factor when available.Version info:* http://scigraph.downloads.uberresearch.com/archives/current/TIMESTAMP.txt* http://scigraph.downloads.uberresearch.com/archives/current/LICENSE.txt

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.

Supplementary data files associated with this study, which takes a stratified random sample of articles published in 2014 from the top 10 journals in the disciplines of biology, chemistry, mathematics, and physics, as ranked by impact factor. Sampled articles were examined for their reporting of original data or reuse of prior data, and were coded for whether the data was publicly shared or otherwise made available to readers. Other characteristics such as the sharing of software code used for analysis and use of data citation and DOIs for data were examined. The study finds that data sharing practices are still relatively rare in these disciplines’ top journals, but that the disciplines have markedly different practices. Biology shares original data at the highest rate, and physics shares at the lowest rate. Overall, the study finds that only 13% of articles with original data published in 2014 make the data available to others.

The SCImago Journal & Country Rank is a publicly available portal that includes the journals and country scientific indicators developed from the information contained in the Scopus® database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains. Journals can be compared or analysed separately.

💬Also have a look at
💡 COUNTRIES Research & Science Dataset - SCImagoJR
💡 UNIVERSITIES & Research INSTITUTIONS Rank - SCImagoIR

• Journals can be grouped by subject area (27 major thematic areas), subject category (309 specific subject categories) or by country.
• Citation data is drawn from over 34,100 titles from more than 5,000 international publishers
• This platform takes its name from the SCImago Journal Rank (SJR) indicator , developed by SCImago from the widely known algorithm Google PageRank™. This indicator shows the visibility of the journals contained in the Scopus® database from 1996.
• SCImago is a research group from the Consejo Superior de Investigaciones Científicas (CSIC), University of Granada, Extremadura, Carlos III (Madrid) and Alcalá de Henares, dedicated to information analysis, representation and retrieval by means of visualisation techniques.

☢️❓The entire dataset is obtained from public and open-access data of ScimagoJR (SCImago Journal & Country Rank)
ScimagoJR Journal Rank
SCImagoJR About Us

Available indicators:

• SJR (SCImago Journal Rank) indicator: It expresses the average number of weighted citations received in the selected year by the documents published in the selected journal in the three previous years, --i.e. weighted citations received in year X to documents published in the journal in years X-1, X-2 and X-3. See detailed description of SJR (PDF).
• H Index: The h index expresses the journal's number of articles (h) that have received at least h citations. It quantifies both journal scientific productivity and scientific impact and it is also applicable to scientists, countries, etc. (see H-index wikipedia definition)
• Total Documents: Output of the selected period. All types of documents are considered, including citable and non citable documents.
• Total Documents (3years): Published documents in the three previous years (selected year documents are excluded), i.e.when the year X is selected, then X-1, X-2 and X-3 published documents are retrieved. All types of documents are considered, including citable and non citable documents.
• Citable Documents (3 years): Number of citable documents published by a journal in the three previous years (selected year documents are excluded). Exclusively articles, reviews and conference papers are considered. Non-citable Docs. (Available in the graphics) Non-citable documents ratio in the period being considered.
• Total Cites (3years): Number of citations received in the seleted year by a journal to the documents published in the three previous years, --i.e. citations received in year X to documents published in years X-1, X-2 and X-3. All types of documents are considered.
• Cites per Document (2 years): Average citations per document in a 2 year period. It is computed considering the number of citations received by a journal in the current year to the documents published in the two previous years, --i.e. citations received in year X to documents published in years X-1 and X-2.
• Cites per Document (3 years): Average citations per document in a 3 year period. It is computed considering the number of citations received by a journal in the current year to the documents published in the three previous years, --i.e. citations received in year X to documents published in years X-1, X-2 and X-3.
• Self Cites: Number of journal's self-citations in the seleted year to its own documents published in the three previous years, --i.e. self-citations in year X to documents published in years X-1, X-2 and X-3. All types of documents are considered.
• Cited Documents: Number of documents cited at least once in the three previous years, --i.e. years X-1, X-2 and X-3
• Uncited Documents: Number of uncited documents in the three previous years, --i.e. years X-1, X-2 and X-3
• Total References: It includes all the bibliographical references in a journal in the selected period.
• References per Document:Average number of references per document in the selected year.
• % International Collaboration: Document ratio whose affiliation includes more than one country address.

Journals & Ranking

An academic journal or research journal is a periodical publication in which research articles relating to a particular academic discipline is published, according to Wikipedia. Currently, there are more than 25,000 peer-reviewed journals that are indexed in citation index databases such as Scopus and Web of Science. These indexes are ranked on the basis of various metrics such as CiteScore, H-index, etc. The metrics are calculated from yearly citation data of the journal. A lot of efforts are given to make a metric that reflects the journal's quality.

Journal Ranking Dataset

This is a comprehensive dataset on the academic journals coving their metadata information as well as citation, metrics, and ranking information. Detailed data on their subject area is also given in this dataset. The dataset is collected from the following indexing databases: - Scimago Journal Ranking - Scopus - Web of Science Master Journal List

The data is collected by scraping and then it was cleaned, details of which can be found in HERE.

Key Features

• Rank: Overall rank of journal (derived from sorted SJR index).
• Title: Name or title of journal.
• OA: Open Access or not.
• Country: Country of origin.
• SJR-index: A citation index calculated by Scimago.
• CiteScore: A citation index calculated by Scopus.
• H-index: Hirsh index, the largest number h such that at least h articles in that journal were cited at least h times each.
• Best Quartile: Top Q-index or quartile a journal has in any subject area.
• Best Categories: Subject areas with top quartile.
• Best Subject Area: Highest ranking subject area.
• Best Subject Rank: Rank of the highest ranking subject area.
• Total Docs.: Total number of documents of the journal.
• Total Docs. 3y: Total number of documents in the past 3 years.
• Total Refs.: Total number of references of the journal.
• Total Cites 3y: Total number of citations in the past 3 years.
• Citable Docs. 3y: Total number of citable documents in the past 3 years.
• Cites/Doc. 2y: Total number of citations divided by the total number of documents in the past 2 years.
• Refs./Doc.: Total number of references divided by the total number of documents.
• Publisher: Name of the publisher company of the journal.
• Core Collection: Web of Science core collection name.
• Coverage: Starting year of coverage.
• Active: Active or inactive.
• In-Press: Articles in press or not.
• ISO Language Code: Three-letter ISO 639 code for language.
• ASJC Codes: All Science Journal Classification codes for the journal.

Rest of the features provide further details on the journal's subject area or category: - Life Sciences: Top level subject area. - Social Sciences: Top level subject area. - Physical Sciences: Top level subject area. - Health Sciences: Top level subject area. - 1000 General: ASJC main category. - 1100 Agricultural and Biological Sciences: ASJC main category. - 1200 Arts and Humanities: ASJC main category. - 1300 Biochemistry, Genetics and Molecular Biology: ASJC main category. - 1400 Business, Management and Accounting: ASJC main category. - 1500 Chemical Engineering: ASJC main category. - 1600 Chemistry: ASJC main category. - 1700 Computer Science: ASJC main category. - 1800 Decision Sciences: ASJC main category. - 1900 Earth and Planetary Sciences: ASJC main category. - 2000 Economics, Econometrics and Finance: ASJC main category. - 2100 Energy: ASJC main category. - 2200 Engineering: ASJC main category. - 2300 Environmental Science: ASJC main category. - 2400 Immunology and Microbiology: ASJC main category. - 2500 Materials Science: ASJC main category. - 2600 Mathematics: ASJC main category. - 2700 Medicine: ASJC main category. - 2800 Neuroscience: ASJC main category. - 2900 Nursing: ASJC main category. - 3000 Pharmacology, Toxicology and Pharmaceutics: ASJC main category. - 3100 Physics and Astronomy: ASJC main category. - 3200 Psychology: ASJC main category. - 3300 Social Sciences: ASJC main category. - 3400 Veterinary: ASJC main category. - 3500 Dentistry: ASJC main category. - 3600 Health Professions: ASJC main category.

Journal of Animal Science Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Animal Science (JAS) is the premier journal for animal science and serves as the leading source of new knowledge and perspective in this area. JAS publishes more than 500 fully reviewed research articles, invited reviews, technical notes, and letters to the editor each year. Articles published in JAS encompass a broad range of research topics in animal production and fundamental aspects of genetics, nutrition, physiology, and preparation and utilization of animal products. Articles typically report research with beef cattle, companion animals, goats, horses, pigs, and sheep; however, studies involving other farm animals, aquatic and wildlife species, and laboratory animal species that address fundamental questions related to livestock and companion animal biology will be considered for publication. Official Journal of the American Society of Animal Science American Society of Animal Science (ASAS) Mission The American Society of Animal Science fosters the discovery, sharing and application of scientific knowledge concerning the care and responsible use of animals to enhance animal and human health and well-being. These core principles and beliefs are the foundation for ASAS and will guide the implementation of this 5-year strategic plan. Animals are essential to human life and well-being. The care and use of animals are held to the highest standards of integrity and professional ethics. Research and scientific information are communicated in an open, transparent, and dynamic manner. Career development for animal scientists, educators, and producers is essential to the viability of the allied and animal industries. Animal science and the production of animal-sourced foods must continually evolve to meet the needs and values of society. The History of ASAS The American Society of Animal Science (ASAS) celebrated its 100-year anniversary in 2008. During the first 100 years, ASAS broadened membership to more than 7000 members. ASAS developed diverse and dynamic membership programs, and fostered the growth of the premier journal in animal science and the premier animal science meetings. In 2008, the American Society of Animal Science celebrated 100 years of sharing great research and supporting science careers. ASAS was established on July 28, 1908, at Cornell University, Ithaca, New York. A group of animal nutritionists, representing 13 state agricultural experiment stations and the U.S. Department of Agriculture, met during a summer school session and formed a permanent organization with a focus on animal nutrition research. On November 26, 1908, the group met and formed an organization called the American Society of Animal Nutrition. Thirty-three charter members represented 17 state experiment stations, the U.S. Department of Agriculture, and Canada. A constitution was adopted, and 4 committees were established: 1) experiments, 2) terminology, 3) methods of reporting results, and 4) affiliation. The objectives of the new society were: 1. to improve the quality of investigation in animal nutrition, 2. to promote more systematic and better correlated studies of feeding problems, and 3. to facilitate personal interaction between investigators in this field. The first professional papers were presented at the Livestock Exposition Hall in Chicago from November 27–29, 1909. At the business meeting, the membership voted to publish proceedings of the annual meeting representing the first journal publications. During the first year, 100 members joined the society. At the business meeting in 1912, efforts were made to broaden the membership base to include scientists from other disciplines. On November 30, 1915, the name of the society was changed from the American Society of Animal Nutrition to the American Society of Animal Production, and an amendment to the constitution was passed to include members interested in teaching, breeding, and management investigations as well as nutritionists. In addition, a committee on instruction was added. At that time there were 114 members. Growth in membership was almost continuous; by the golden anniversary year in 1958 there were 1,829 members. A second name change was approved at the 53rd annual business meeting in Chicago on November 24, 1961, when the official name became the American Society of Animal Science (ASAS). ASAS expanded to meet the diverse needs of its members by adding sections (Midwest, South, Northeast, and West) with their own meetings. In 1998, ASAS joined forces with its sister societies, the American Dairy Science Association (ADSA) and the Poultry Science Association (PSA), to form the Federation of Animal Science Societies (FASS) to help foster a voice for professional animal scientists. Even with the many changes in ASAS membership, it is unlikely that anyone could have envisioned the growth and change in membership demographics that occurred in the last 10 years. In 1998, about 40% of ASAS membership were also ADSA members. The ASAS membership was more than 75% men. There were few graduate student members, no undergraduate members and only 15% of ASAS members lived outside of the United States. One of the major goals of the 2008 strategic plan was to increase and diversify the ASAS membership. Today, ASAS enjoys a membership of more than 6,000 members, 30% from outside the United States, and almost 50:50 split in gender demographics, approximately 800 graduate students and 1,000 undergraduate student members. In the 2008 ASAS Strategic plan, we recognized that it was time to change one of the most fundamental roles of ASAS in the field of animal sciences and in society. In addition to providing science-based information to ASAS members, ASAS began to build an external voice to communicate and distribute information pertaining to the responsible use of animals in research, teaching, and production. At the direction of the 2008 ASAS Strategic Plan, ASAS initiated an autonomous science policy program governed by ASAS that works with many other groups, created AnimalSmart.com and the accompanying Junior Animal Science program, redesigned and distributed the Image Gallery, added a global scientific magazine Animal Frontiers, created an active and respected Snack and Fact program for Congressional staff in Washington D.C., and ensured an active presence of ASAS members on Capitol Hill. As ASAS moves forward, we need to continue these programs, add to their robustness, and ensure that the programs represent the needs of the global ASAS membership. The 2008, strategic plan helped ASAS grow and diversify its membership. An unintended consequence of this diversification was a change in the ASAS infrastructure. In 2014, ASAS sold its equity shares in FASS as FASS no longer met the growing needs of ASAS. Since 2008, technological advances have also helped change how ASAS communicates internally and externally. For example, ASAS has globalized our communications (i.e., webinars and virtual meetings), taken posters to a new level (i.e., ePosters), created an almost continuous flow of information to the membership worldwide (i.e., Taking Stock), made it possible to push information out in real time, and has diversified our publication portfolio (i.e., Journal of Animal Science, Animal Frontiers, Natural Science Education, and Translational Animal Science) and our publications model (i.e., integration of all journals into a Digital Library, traditional publication, open access, and open review). In addition, technology has helped us add new methods to facilitate scientific communication around the word (i.e., JASEdits). Vision for the Future As ASAS enters its second century, we are changing to adapt to current and future conditions and environments. ASAS will continue to be the world leader as a source of scientific information on the contributions of animals to food and fiber production. We recognize, however, that animals contribute greatly to enhancing the human life and wellbeing in a wide variety of ways, including companionship, recreation, and human aid. Therefore, the broader vision of ASAS is to be a diverse community of professionals recognized as the leading source of new knowledge and perspective on animals that enhance human life and well-being. ASAS facilitates global scientific exchange through innovative and inclusive venues. In the next century, we will look for new opportunities to partner with other professional organizations and non-traditional venues. We are working to become a facilitator of effective interactions among academia, industry, government agencies, and other stakeholders to reach consensus regarding science-based animal issues. Continued leadership in providing a scientific voice of animal science to the broader public is an inherent component in facilitating scientific exchange. To deal with the many contributions of animals to society, we recognize that it is vital for animal science professionals to be trained in a variety of disciplines. To this end, ASAS will provide member services and professional development opportunities in a proactive and accountable manner. Through our meetings, journal, and professional development opportunities, ASAS will be the training ground for future animal scientists. RG Journal Impact: 0.42 * *This value is calculated using ResearchGate data and is based on average citation counts from work published in this journal. The data used in the calculation may not be exhaustive. RG Journal impact history 2020 Available summer 2021 2018 / 2019 0.42 2017 0.30 2016 1.33 2015 2.33 2014 2.48 2013 2.33 2012 2.36 2011 2.35 2010 2.52 2009 2.70 2008 2.74 2007 2.14 2006 1.60 2005 1.36 Additional details Cited half-life 0.00 Immediacy index 0.43 Eigenfactor 0.02 Article influence 0.58 H Index 138 Website http://jas.fass.org Other titles Journal of animal science (Online), Journal of animal science OCLC 41472131

International Journal of Computational Intelligence Systems Impact Factor 2024-2025 - ResearchHelpDesk - The International Journal of Computational Intelligence Systems is an international peer reviewed journal and the official publication of the European Society for Fuzzy Logic and Technologies (EUSFLAT). The journal publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. This is an open access journal, i.e. all articles are immediately and permanently free to read, download, copy & distribute. The journal is published under the CC BY-NC 4.0 user license which defines the permitted 3rd-party reuse of its articles. Aims & Scope The International Journal of Computational Intelligence Systems publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. The core theories of computational intelligence are fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The journal publishes only articles related to the use of computational intelligence and broadly covers the following topics: Autonomous reasoning Bio-informatics Cloud computing Condition monitoring Data science Data mining Data visualization Decision support systems Fault diagnosis Intelligent information retrieval Human-machine interaction and interfaces Image processing Internet and networks Noise analysis Pattern recognition Prediction systems Power (nuclear) safety systems Process and system control Real-time systems Risk analysis and safety-related issues Robotics Signal and image processing IoT and smart environments Systems integration System control System modelling and optimization Telecommunications Time series prediction Warning systems Virtual reality Web intelligence Deep learning

Survey period: 08 April - 08 May, 2014 Top 10 Impact Factor journals in each of 22 categories

Figures https://doi.org/10.6084/m9.figshare.6857273.v1

Article https://doi.org/10.20651/jslis.62.1_20 https://doi.org/10.15068/00158168

The dataset contains all the data produced running the research software for the study:"Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta".

Disclaimer: these results are not considered to be representative, because we have fount that Mega Journals skewed significantly some of the data. The result datasets without Mega Journals are published here.

Description of datasets:

SSH_Publications_in_OC_Meta_and_Open_Access_status.csv: containing information about OpenCitations Meta coverage of ERIH PLUS Journals as well as their Open Access availability. In this dataset, every row holds data for a Journal of ERIH PLUS also covered by OpenCitations Meta database. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

SSH_Publications_by_Discipline.csv: containing information about number of publications per discipline (in addition, number of journals per discipline are also included). The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

SSH_Publications_and_Journals_by_Country: containing information about number of publications and journals per country. The dataset has three columns, the first, labeled "Country", contains single countries of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

result_disciplines.json: the dictionary containing all disciplines as key and a list of related ERIH PLUS venue identifiers as value.

result_countries.json: the dictionary containing all countries as key and a list of related ERIH PLUS venue identifiers as value.

duplicate_omids.csv: a dataset containing the duplicated Journal entries in OpenCitations Meta, structured with two columns: "OC_omid", the internal OC Meta identifier; "issn", the issn values associated to that identifier

eu_data.csv: contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "Original_Title", "Country_of_Publication","ERIH_PLUS_Disciplines", "disc_count", the number of disciplines per Journal.

eu_disciplines_count.csv: containing information about number of publications per discipline and number of journals per discipline of european countries. The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

meta_coverage_eu.csv: contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

us_data.csv: contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "Original_Title", "Country_of_Publication","ERIH_PLUS_Disciplines", "disc_count", the number of disciplines per Journal.

us_disciplines_count.csv: containing information about number of publications per discipline and number of journals per discipline of the United States. The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

meta_coverage_us.csv: contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

Abstract of the research:

Purpose: this study aims to investigate the representation and distribution of Social Science and Humanities (SSH) journals within the OpenCitations Meta database, with a particular emphasis on their Open Access (OA) status, as well as their spread across different disciplines and countries. The underlying premise is that open infrastructures play a pivotal role in promoting transparency, reproducibility, and trust in scientific research. Study Design and Methodology: the study is grounded on the premise that open infrastructures are crucial for ensuring transparency, reproducibility, and fostering trust in scientific research. The research methodology involved the use of secondary data sources, namely the OpenCitations Meta database, the ERIH PLUS bibliographic index, and the DOAJ index. A custom research software was developed in Python to facilitate the processing and analysis of the data. Findings: the results reveal that 78.1% of SSH journals listed in the European Reference Index for the Humanities (ERIH-PLUS) are included in the OpenCitations Meta database. The discipline of Psychology has the highest number of publications. The United States and the United Kingdom are the leading contributors in terms of the number of publications. However, the study also uncovers that only 38% of the SSH journals in the OpenCitations Meta database are OA. Originality: this research adds to the existing body of knowledge by providing insights into the representation of SSH in open bibliographic databases and the role of open access in this domain. The study highlights the necessity for advocating OA practices within SSH and the significance of open data for bibliometric studies. It further encourages additional research into the impact of OA on various facets of citation patterns and the factors leading to disparity across disciplinary representation.

Related resources:

Ghasempouri S., Ghiotto M., & Giacomini S. (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - RESEARCH ARTICLE. https://doi.org/10.5281/zenodo.8263908

Ghasempouri, S., Ghiotto, M., Giacomini, S., (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - DATA MANAGEMENT PLAN (Version 4). Zenodo. https://doi.org/10.5281/zenodo.8174644

Ghasempouri, S., Ghiotto, M., Giacomini, S. (2023e). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - PROTOCOL. V.5. (https://dx.doi.org/10.17504/protocols.io.5jyl8jo1rg2w/v5)

BackgroundSharing 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.Principal FindingsWe 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.SignificanceThis correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.

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.

Academic publishers purport to be arbiters of knowledge, aiming to publish studied that advance the frontiers of their research domain. Yet the effectiveness of journal editors at identifying novel and important research is generally unknown, in part because of the confidential nature of the editorial and peer-review process. Using questionnaires, we evaluated the degree to which journals are effective arbiters of scientific impact in the domain of Ecology, quantified by three key criteria. First, journals discriminated against low-impact manuscripts: the probability of rejection increased as the number of citations gained by the published paper decreased. Second, journals were more likely to publish high-impact manuscripts (those that obtained citations in 90th percentile for their journal) than run-of-the-mill manuscripts; editors were only 23 and 41% as likely to reject an eventual high-impact paper (pre- versus post-review rejection) compared to a run-of-the-mill paper. Third, editors did occasionally reject papers that went on to be highly cited. Error rates were low, however: only 3.8% of rejected papers gained more citations than the median article in the journal that rejected them, and only 9.2% of rejected manuscripts went on to be high-impact papers in the (generally lower impact factor) publishing journal. The effectiveness of scientific arbitration increased with journal prominence, although some highly prominent journals were no more effective than much less prominent ones. We conclude that the academic publishing system, founded on peer review, appropriately recognises the significance of research contained in manuscripts, as measured by the number of citations that manuscripts obtain after publication, even though some errors are made. We therefore recommend that authors reduce publication delays by choosing journals appropriate to the significance of their research.

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

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.

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...