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.

Academic journals indicators developed from the information contained in the Scopus database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains.

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.

Abstract A system called QUALIS was implemented in Brazil in 2009, intended to rank graduate programs from different subject areas and promote selected national journals. Since this system uses a complicated suit of criteria (differing among subject areas) to group journals into discrete categories, it could potentially create incentives to publish in low-impact journals ranked highly by QUALIS. Here I assess the influence of the QUALIS journal ranking system on the global impact of Brazilian science. Brazil shows a steeper decrease in the number of citations per document since the implementation of this QUALIS system, compared to the top Latin American countries publishing more scientific articles. All subject areas showed some degree of bias, with social sciences being usually more biased than natural sciences. Lastly, the decrease in the number of citations over time proved steeper in a more biased subject area, suggesting a faster shift towards low-impact journals. Overall, the findings documented here suggest that the QUALIS system has undermined the global impact of Brazilian science, and reinforce a recent recommendation from an official committee evaluating graduate programs to eliminate QUALIS. A system based on impact metrics could avoid introducing distorted incentives, and thereby boost the global impact of Brazilian science.

International Journal of Scientific and Technology Research Acceptance Rate - 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

Research data to accommodate the article "Overlay journals: a study of the current landscape" (https://doi.org/10.1177/09610006221125208)

Identifying the sample of overlay journals was an explorative process (occurring during April 2021 to February 2022). The sample of investigated overlay journals were identified by using the websites of Episciences.org (2021), Scholastica (2021), Free Journal Network (2021), Open Journals (2021), PubPub (2022), and Wikipedia (2021). In total, this study identified 34 overlay journals. Please see the paper for more details about the excluded journal types.

The journal ISSN numbers, manuscript source repositories, first overlay volumes, article volumes, publication languages, peer-review type, licence for published articles, author costs, publisher types, submission policy, and preprint availability policy were observed by inspecting journal editorial policies and submission guidelines found from journal websites. The overlay journals’ ISSN numbers were identified by examining journal websites and cross-checking this information with the Ulrich’s periodicals database (Ulrichsweb, 2021). Journals that published review reports, either with reviewers’ names or anonymously, were classified as operating with open peer-review. Publisher types defined by Laakso and Björk (2013) were used to categorise the findings concerning the publishers. If the journal website did not include publisher information, the editorial board was interpreted to publish the journal.

The Organisation for Economic Co-operation and Development (OECD) field of science classification was used to categorise the journals into different domains of science. The journals’ primary OECD field of sciences were defined by the authors through examining the journal websites.

Whether the journals were indexed in the Directory of Open Access Journals (DOAJ), Scopus, or Clarivate Analytics’ Web of Science Core collection’s journal master list was examined by searching the services with journal ISSN numbers and journal titles.

The identified overlay journals were examined from the viewpoint of both qualitative and quantitative journal metrics. The qualitative metrics comprised the Nordic expert panel rankings of scientific journals, namely the Finnish Publication Forum, the Danish Bibliometric Research Indicator and the Norwegian Register for Scientific Journals, Series and Publishers. Searches were conducted from the web portals of the above services with both ISSN numbers and journal titles. Clarivate Analytics’ Journal Citation Reports database was searched with the use of both ISSN numbers and journal titles to identify whether the journals had a Journal Citation Indicator (JCI), Two-Year Impact Factor (IF) and an Impact Factor ranking (IF rank). The examined Journal Impact Factors and Impact Factor rankings were for the year 2020 (as released in 2021).

Ancient Science of Life Impact Factor 2024-2025 - ResearchHelpDesk - Ancient Science of Life, is the oldest peer-reviewed scientific journal in Ayurveda which publishes full-length original papers and reviews on Ayurveda, allied disciplines and all forms of traditional medicines. The journal provides an interdisciplinary platform for linking traditional knowledge with the latest advancements in science. Preferences are given for contributions that interface Ayurveda with disciplines like Botany, Ethnobotany, Ethnomedicine, Ethnopharmacology, Biology, Biotechnology, Medicinal chemistry, Pharmacology, Cclinical pharmacology, Phytochemistry, Pharmacognosy, Clinical research, Animal experiments and the like. Articles on traditional medicines from the perspective of the history of medicine, medical anthropology, medical sociology, epidemiology and community medicine will also be accepted. Original literary studies covering aspects of linguistics, philology, literary criticism and critical editing of the original writings of Ayurveda and other traditional systems of medicine will also be accepted for publication. Abstracting and Indexing Information The journal is registered with the following abstracting partners: Baidu Scholar, CNKI (China National Knowledge Infrastructure), EBSCO Publishing's Electronic Databases, Ex Libris – Primo Central, Google Scholar, Hinari, Infotrieve, National Science Library, ProQuest, TdNet, Wanfang Data The journal is indexed with, or included in, the following: DOAJ, Emerging Sources Citation Index, Index Copernicus, Indian Science Abstracts, Web of Science

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...

Data and code for manuscript:

David B Resnik, Melissa Morales, Rachel Landrum, Min Shi, Jessica Minnier, Nicole A. Vasilevsky & Robin E. Champieux (2019) Effect of Impact Factor and Discipline on Journal Data Sharing Policies, Accountability in Research, DOI: 10.1080/08989621.2019.1591277

Zenodo pre-print DOI: https://doi.org/10.5281/zenodo.2592682

Data collection utilized three sources:

• 2016 InCites Journal Citations Report
• Directory of Open Access Journal
• Journal websites and author guidelines

The data was collected and analyzed between May 2018 and October 2018.

Data and Code

Data can be found in data/if-discipline-datasharing-policy-rawdata-1.0.0.csv.

Analysis code for tables and figures can be seen in code/analysis_report.md (author of code: Jessica Minnier, OHSU, @jminnier)

European journal of research methods for the behavioral and social sciences Impact Factor 2024-2025 - ResearchHelpDesk - Methodology is the successor of the two journals Metodologia de las Ciencias del Comportamiento and Methods of Psychological Research-Online (MPR-Online). Methodology is the official organ of the European Association of Methodology (EAM), a union of methodologists working in different areas of the social and behavioral sciences (e.g., psychology, sociology, economics, educational and political sciences). The journal provides a platform for interdisciplinary exchange of methodological research and applications in the different fields, including new methodological approaches, review articles, software information, and instructional papers that can be used in teaching. Three main disciplines are covered: data analysis, research methodology, and psychometrics. The articles published in the journal are not only accessible to methodologists but also to more applied researchers in the various disciplines. Abstract & indexing Social Sciences Citation Index (SSCI), Current Contents/Social & Behavioral Sciences (CC/S&BS) (since 2009), PsycINFO, PSYNDEX, ERIH and Scopus.

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.

Objective: To determine the top 100-ranked (by impact factor) clinical journals' policies toward publishing research previously published on preprint servers (preprints).

Design: Cross sectional. Main outcome measures: Editorial guidelines toward preprints, journal rank by impact factor.

Results: 86 (86%) of the journals examined will consider papers previously published as preprints (preprints), 13 (13%) determine their decision on a case-by-case basis, and 1 (1%) does not allow preprints.

Conclusions: We found wide acceptance of publishing preprints in the clinical research community, although researchers may still face uncertainty that their preprints will be accepted by all of their target journals.

Microarray study attributes and data sharing status397 rows, one row for each study that created gene expression microarray data as identified by Ochsner et al. (doi:10.1038/nmeth1208-991). Attributes of each study are included in 23 columns. Dependent variable is called is_data_shared.Piwowar_Metrics2009_rawdata.csvStatistical analysis R scriptStatistical R script for analysis and graphics as presented in the paper.Piwowar_Metrics2009_statistics.R

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to

establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data

Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.
Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review:

Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection.
Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation.

See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

A database from 140 scientific articles (journal and conference papers) from the automotive security domain. In the database, we assigned specific attributes to every article (such as Web of Science Impact Factor or the number of citations). The data set was analyzed by the K-means clustering and decision tree analysis methods to identify and characterize the generated groups of papers.

We did not aim to identify perfectly supplementing categories but to define the relevant research topics of the automotive security domain. Following this, some of the chosen categories may have overlap with other topics, which means that these research categories may be partly laid on common scientific and professional basics. However, all the considered categories can be defined as separate, scientifically significant, and considerably relevant research orientations.

Clinical Data Analytics in Healthcare Market Outlook

The Clinical Data Analytics in Healthcare Market is experiencing a significant surge in demand, with a market size valued at $12 billion in 2023 and projected to reach approximately $35 billion by 2032, expanding at an impressive compound annual growth rate (CAGR) of 12.5%. The driving force behind this robust growth is the increasing need for data-driven decision-making processes in healthcare that enhance operational efficiency and improve patient outcomes. This demand is further fueled by the global shift towards value-based healthcare, which emphasizes the quality of care provided and patient satisfaction over the quantity of services rendered.

A primary growth factor propelling this market is the technological advancements in data processing and storage capacities, allowing healthcare providers to manage and analyze vast amounts of clinical data efficiently. The integration of technologies such as artificial intelligence and machine learning into healthcare data analytics has revolutionized the way data is interpreted, enabling predictive analytics and personalized medicine. These technologies aid in early disease detection and facilitate the creation of tailored treatment plans, which are proving to be more effective than traditional approaches in managing chronic diseases and improving patient care outcomes.

Another significant growth factor is the increasing adoption of electronic health records (EHRs) across healthcare facilities worldwide. EHRs play a crucial role in data collection, providing a comprehensive view of patient histories that is essential for effective data analytics. The widespread implementation of EHRs improves data accuracy and accessibility, which are critical for successful clinical data analytics. Furthermore, healthcare regulations globally are increasingly mandating the digital recording and sharing of patient data, further accelerating the adoption of EHRs and subsequently driving the demand for data analytics solutions.

The growing emphasis on population health management is also a strong catalyst for market growth. As healthcare systems shift towards a more holistic approach to patient care, there is a heightened focus on understanding and managing the health of entire populations. Clinical data analytics provides the tools necessary for identifying health trends and risk factors within populations, allowing healthcare providers to develop targeted interventions and preventive measures. This trend is especially pertinent amid the increasing prevalence of lifestyle-related diseases, which require ongoing monitoring and management to mitigate their impact on healthcare systems.

In the realm of healthcare, operational analytics plays a pivotal role in streamlining processes and enhancing the efficiency of healthcare delivery systems. By leveraging Healthcare Operational Analytics, healthcare organizations can optimize resource allocation, reduce operational costs, and improve patient flow management. This approach enables healthcare providers to identify bottlenecks and inefficiencies within their operations, allowing for data-driven decisions that enhance overall service delivery. As healthcare systems continue to face increasing demands and financial pressures, the adoption of operational analytics becomes essential in maintaining high standards of care while ensuring sustainability and cost-effectiveness.

Regionally, North America dominates the Clinical Data Analytics in Healthcare Market, accounting for the largest market share due to advanced healthcare infrastructure and significant investments in R&D. The region's well-established EHR systems and the presence of major market players spearheading technological innovations further bolster this dominance. However, Asia Pacific is expected to witness the highest growth rate, driven by the rapid adoption of healthcare IT solutions, increasing government initiatives towards digital health transformation, and the growing burden of chronic diseases. Europe follows closely, benefiting from stringent healthcare regulations and a strong focus on improving healthcare outcomes through data analytics.

Component Analysis

The component segment of the Clinical Data Analytics in Healthcare Market is bifurcated into software and services, both integral to the effective deployment of data analytics solutions. Software, the larger of the two segments, encompasses a range of applications designed to

We investigate how textual properties of scientific papers relate to the number of citations they receive. Our main finding is that correlations are nonlinear and affect differently the most cited and typical papers. For instance, we find that, in most journals, short titles correlate positively with citations only for the most cited papers, whereas for typical papers, the correlation is usually negative. Our analysis of six different factors, calculated both at the title and abstract level of 4.3 million papers in over 1500 journals, reveals the number of authors, and the length and complexity of the abstract, as having the strongest (positive) influence on the number of citations.

Mediterranean journal of social sciences Impact Factor 2024-2025 - ResearchHelpDesk - Mediterranean Journal of Social Sciences publishes six issues per year in January, March, May, July, September and November. Social sciences to analyse and critique what’s going on and make choices that shape the future. Social science is not just important for the future but for what’s happening now. Mediterranean Journal of Social Sciences vision is to promote debate, analysis, and critics among the social sciences intellectual scholars. Its particular focus is on understanding how social sciences are embedded in the new millennium, and how the social sciences may help the society to solve the new global problems. Such understanding requires a careful analysis of social context, and a fundamental role to do this is played from all actors and stakeholders: universities, research institutions, governments, media, civil society etc. The important point is that the relationships between social sciences and the dynamics that change our society today are in focus. The term “social sciences” includes relevant disciplines like economics, sociology, psychology, anthropology, communication studies, law, cultural studies, political studies, development studies and other related disciplines. The research data may come from experimental approaches, case studies, interviews, questionnaires, ethnographies, archives etc. Also articles of different kinds will be welcome: reports of empirical research, theoretically orientated analyses, literature reviews, etc. The journal expects that authors write clearly and accessibly for an international audience. No particular theories are favoured.

Fictitious example data that are paper-based (e.g. the citation impact percentile) or respondent-based (e.g. the quality assessment).