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.

Overall journal rankings, which are generated with sample articles in different research fields, are commonly used to measure the research productivity of academic economists. In this article, we investigate a growing concern in the profession that the use of the overall journal rankings to evaluate scholars’ relative research productivity may exhibit a downward bias toward researchers in some specialty fields if their respective field journals are under-ranked in the overall journals rankings. To address this concern, we constructed new journal rankings based on the intellectual influence of research in 8 specialty fields using a sample consisting of 26,401 articles published across 60 economics journals from 1998 to 2007. We made various comparisons between the newly constructed journal rankings in specialty fields and the traditional overall journal ranking. Our results show that the overall journal ranking provides a considerably good mapping for the article quality in specialty fields. Supplementary materials for this article are available online.

Version: 6

Date of data collection: May 2025

General description: Publication of datasets according to the FAIR principles could be reached publishing a data paper (and/or a software paper) in data journals as well as in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.

File list:

- data_articles_journal_list_v6.xlsx: full list of 177 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v6.csv: full list of 177 academic journals in which data papers or/and software papers could be published
- readme_v6.txt, with a detailed descritption of the dataset and its variables.

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 6th version
- Information updated: number of journals (17 were added and 4 were deleted), URL, document types associated to a specific journal.
- Information added: diamond journals were identified.

Version: 5

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2023/09/05

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v5.xlsx: full list of 162 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v5.csv: full list of 162 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 5th version
- Information updated: number of journals, URL, document types associated to a specific journal.
163 journals (excel y csv)

Version: 4

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/12/15

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v4.xlsx: full list of 140 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v4.csv: full list of 140 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 4th version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR), Scopus and Web of Science (WOS), Journal Master List.

Version: 3

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/10/28

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v3.xlsx: full list of 124 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_3.csv: full list of 124 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 3rd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR).

Erratum - Data articles in journals Version 3:

Botanical Studies -- ISSN 1999-3110 -- JCR (JIF) Q2
Data -- ISSN 2306-5729 -- JCR (JIF) n/a
Data in Brief -- ISSN 2352-3409 -- JCR (JIF) n/a

Version: 2

Author: Francisco Rubio, Universitat Politècnia de València.

Date of data collection: 2020/06/23

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v2.xlsx: full list of 56 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v2.csv: full list of 56 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 2nd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Scimago Journal and Country Rank (SJR)

Total size: 32 KB

Version 1: Description

This dataset contains a list of journals that publish data articles, code, software articles and database articles.

The search strategy in DOAJ and Ulrichsweb was the search for the word data in the title of the journals.
Acknowledgements:
Xaquín Lores Torres for his invaluable help in preparing this dataset.

This is the data repository for the paper Anytime Ranking on Document-Ordered Indexes by Joel Mackenzie, Matthias Petri, and Alistair Moffat. This paper appeared in ACM TOIS in 2021.

Data of investigation published in the article: "Using Machine Learning for Web Page Classification in Search Engine Optimization" Abstract of the article: This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research.

Data of investigation published in the article "Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus".

Abstract of the article:

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyse and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyse two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, WoS used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

As of December 2023, the English subdomain of Wikipedia had around 6.91 million articles published, being the largest subdomain of the website by number of entries and registered active users. German and French ranked third and fourth, with over 29.6 million and 26.5 million entries. Being the only Asian language figuring among the top 10, Cebuano was the language with the second-most articles on the portal, amassing around 6.11 million entries. However, while most Wikipedia articles in English and other European languages are written by humans, entries in Cebuano are reportedly mostly generated by bots.

This statistic presents a ranking of the most popular manufacturers of childcare articles searched for on the Internet in France between 2017 and 2019, according to the demand index. During this period, the brand Chicco, was the most researched brand online, setting the index basis at 100, while the brand Cybex, reached an index of 64 in comparison.

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

The most viewed English-language article on Wikipedia in 2023 was Deaths in 2024, with a total of 44.4 million views. Political topics also dominated the list, with articles related to the 2024 U.S. presidential election and key political figures like Kamala Harris and Donald Trump ranking among the top ten most viewed pages. Wikipedia's language diversity As of December 2024, the English Wikipedia subdomain contained approximately 6.91 million articles, making it the largest in terms of content and registered active users. Interestingly, the Cebuano language ranked second with around 6.11 million entries, although many of these articles are reportedly generated by bots. German and French followed as the next most populous European language subdomains, each with over 18,000 active users. Compared to the rest of the internet, as of January 2024, English was the primary language for over 52 percent of websites worldwide, far outpacing Spanish at 5.5 percent and German at 4.8 percent. Global traffic to Wikipedia.org Hosted by the Wikimedia Foundation, Wikipedia.org saw around 4.4 billion unique global visits in March 2024, a slight decrease from 4.6 billion visitors in January. In addition, as of January 2024, Wikipedia ranked amongst the top ten websites with the most referring subnets worldwide.

Data collected during conjoint analysis experiments conducted as part of the *metrics-project. Detailed information on the experiments' background, conduction and results can be found in the following article from the Journal of the Association for Information Science and Technology: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24445

General descriptionThis dataset contains 5884 Romanian online news articles regarding climate change. The articles are written in Romanian and published between 2008 and 2022 by 13 different Romanian online news outlets. Each data entry has an "id", "date publish", "main text", "source", "url", and "title".Data collectionThe dataset is a byproduct of a larger crawling effort aimed at extracting online news articles published by the top five most visited online news outlets from three business-related categories: News and Analysis, Economy and Finance, and General News. A reputed Romanian online audience ranking website was used to determine the categories and audience ratings (https://www.brat.ro/). The present dataset was obtained by selecting only articles containing the "climate change" or "climate changes" keywords. As such, there are articles that talk primarily about climate change and articles that talk adjacently about climate change.Errors Due to crawling errors, some titles are erroneous and there are 1476 items with missing data in the "date publish" field. Additionally, it is worth noticing that a manual analysis of 200 random articles between 2009 and 2019 revealed that 3.5% of the articles were not related to climate change. A probable cause is the use of text from the "recommended articles" section.A de-duplication procedure was conducted on items with duplicate titles published by the same source. However, duplicates are still present due to similar articles published by different online news outlets. An analysis based on the title of the articles suggested that there are 4% remaining duplicates. However, these were not remove as the "title" field has some erroneous text. If regarded appropriate, a de-duplication procedure using only the title of the articles can be conducted.

Data Access: The data in the research collection provided may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use it only for research purposes. Due to these restrictions, the collection is not open data. Please download the Agreement at Data Sharing Agreement and send the signed form to fakenewstask@gmail.com .

Citation

Please cite our work as

@article{shahi2021overview,
 title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection},
 author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas},
 journal={Working Notes of CLEF},
 year={2021}
}

Problem Definition: Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in English.

Subtask 3A: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. The training data will be released in batches and roughly about 900 articles with the respective label. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. Our definitions for the categories are as follows:

• False - The main claim made in an article is untrue.

• Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.

• True - This rating indicates that the primary elements of the main claim are demonstrably true.

• Other- An article that cannot be categorised as true, false, or partially false due to lack of evidence about its claims. This category includes articles in dispute and unproven articles.

Subtask 3B: Topical Domain Classification of News Articles (English) Fact-checkers require background expertise to identify the truthfulness of an article. The categorisation will help to automate the sampling process from a stream of data. Given the text of a news article, determine the topical domain of the article (English). This is a classification problem. The task is to categorise fake news articles into six topical categories like health, election, crime, climate, election, education. This task will be offered for a subset of the data of Subtask 3A.

Input Data

The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:

Task 3a

• ID- Unique identifier of the news article
• Title- Title of the news article
• text- Text mentioned inside the news article
• our rating - class of the news article as false, partially false, true, other

Task 3b

• public_id- Unique identifier of the news article
• Title- Title of the news article
• text- Text mentioned inside the news article
• domain - domain of the given news article(applicable only for task B)

Output data format

Task 3a

• public_id- Unique identifier of the news article
• predicted_rating- predicted class

Sample File

public_id, predicted_rating
1, false
2, true

Task 3b

• public_id- Unique identifier of the news article
• predicted_domain- predicted domain

Sample file

public_id, predicted_domain
1, health
2, crime

Additional data for Training

To train your model, the participant can use additional data with a similar format; some datasets are available over the web. We don't provide the background truth for those datasets. For testing, we will not use any articles from other datasets. Some of the possible source:

• Fakenews Classification Datasets
• Fake News Detection Challenge KDD 2020
• FakeNewsNet

IMPORTANT!

1. Fake news article used for task 3b is a subset of task 3a.
2. We have used the data from 2010 to 2021, and the content of fake news is mixed up with several topics like election, COVID-19 etc.

Evaluation Metrics

This task is evaluated as a classification task. We will use the F1-macro measure for the ranking of teams. There is a limit of 5 runs (total and not per day), and only one person from a team is allowed to submit runs.

Submission Link: https://competitions.codalab.org/competitions/31238

Related Work

• Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.https://arxiv.org/pdf/2010.00502.pdf
• G. K. Shahi and D. Nandini, “FakeCovid – a multilingualcross-domain fact check news dataset for covid-19,” inWorkshop Proceedings of the 14th International AAAIConference on Web and Social Media, 2020. http://workshop-proceedings.icwsm.org/abstract?id=2020_14
• Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104

This statistic presents a ranking of the most popular online stores in Norway in the sports and outdoor segment in 2018, sorted by annual net e-commerce sales. For more information please visit ecommerceDB.com.In 2018, market leader xxl.no generated 51,1 million U.S. dollars via the sale of sports and outdoor articles in Norway. The online store gsport.no was ranked second with a revenue of 21,4 million U.S. dollars.

Total-Current-Assets Time Series for New York Times Company. The New York Times Company, together with its subsidiaries, creates, collects, and distributes news and information worldwide. The company operates through two segments, The New York Times Group and The Athletic. It offers The New York Times (The Times) through company's mobile application, website, printed newspaper, and associated content, such as podcast. The company offers The Athletic, a sports media product; Cooking, a recipe product; Games, a puzzle games product; and Audio, an audio product. In addition, it offers a portfolio of advertising products and services to advertisers, such as luxury goods, technology, and financial companies, to promote products, services or brands on digital platforms in the form of display ads, audio and video, in print in the form of column-inch ads, and at live events; and Wirecutter, a product review and recommendation product. Further, the company licenses content to digital aggregators in the business, professional, academic and library markets, and third-party digital platforms; articles, graphics, and photographs, including newspapers, magazines, and websites; and for use in television, films, and books, as well as provide rights to reprint articles, and create and sell new digests. Additionally, it engages in commercial printing and distribution for third parties; and operates the NYTimes.com website. The company was founded in 1851 and is headquartered in New York, New York.

Conservation and Society Impact Factor 2024-2025 - ResearchHelpDesk - Conservation & Society Aim Conservation & Society is a peer-reviewed, interdisciplinary, open-access journal, dedicated to the advancement of the theory and practice of conservation. It aims to serve as a bridge between conservation practitioners from a wide array of disciplines and therefore seeks to disseminate work presented in an integrative and simple manner that is accessible to individuals from disciplines ranging from the natural and social sciences to the humanities. Conservation & Society Scope The journal draws on both natural and social sciences and covers basic and applied research in areas including but not restricted to political ecology, human-wildlife conflicts, decentralised conservation, conservation policy, ecosystem structure and functioning, systematics, community and species ecology, behavioural ecology, landscape ecology, restoration ecology and conservation biology. Readership The journal is of interest to academics, researchers, teachers, naturalists, policy makers, planners, resource managers and media professionals. About the Journal Conservation & Society (www.conservationandsociety.org) is a peer-reviewed interdisciplinary open access journal dedicated to the advancement of the theory and practice of conservation. The journal draws on both natural and social sciences, and covers basic and applied research in areas including but not restricted to political ecology, environmental history, anthropology and sociology, human ecology, conservation policy and governance, human-wildlife conflicts, ecosystem structure and functioning, systematics, community and species ecology, behavioural ecology, landscape ecology, restoration ecology, and conservation biology. The journal publishes submitted and commissioned articles, debates and discussions, editorials, book reviews, comments and notes, and reader feedback. Conservation & Society is of interest to academics, researchers, teachers, naturalists, policymakers, planners, and resource managers. It aims to serve as a bridge between conservation practitioners from a wide array of disciplines and therefore seeks to disseminate work presented in an integrative and simple manner that is accessible to individuals from disciplines ranging from the natural and social sciences to the humanities. The journal accepts articles addressing conservation issues the world over, with a focus on developing countries. 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, Indian Science Abstracts, Scimago Journal Ranking, SCOPUS, Science Citation Index Expanded, Web of Science SJR Report Year SJR 2012 0.263 2013 0.779 2014 0.986 2015 0.815 2016 0.765 2017 0.811 2018 0.797 2019 0.779 2020 1.040 Cites Year Value Self Cites 2007 0 Self Cites 2008 8 Self Cites 2009 4 Self Cites 2010 14 Self Cites 2011 10 Self Cites 2012 15 Self Cites 2013 18 Self Cites 2014 11 Self Cites 2015 12 Self Cites 2016 6 Self Cites 2017 10 Self Cites 2018 10 Self Cites 2019 9 Self Cites 2020 15 Total Cites 2007 0 Total Cites 2008 20 Total Cites 2009 54 Total Cites 2010 135 Total Cites 2011 77 Total Cites 2012 140 Total Cites 2013 161 Total Cites 2014 184 Total Cites 2015 218 Total Cites 2016 163 Total Cites 2017 193 Total Cites 2018 223 Total Cites 2019 261 Total Cites 2020 324 Cites per document Year Value Cites / Doc. (4 years) 2007 0.000 Cites / Doc. (4 years) 2008 0.741 Cites / Doc. (4 years) 2009 1.000 Cites / Doc. (4 years) 2010 1.646 Cites / Doc. (4 years) 2011 1.613 Cites / Doc. (4 years) 2012 1.584 Cites / Doc. (4 years) 2013 1.880 Cites / Doc. (4 years) 2014 2.475 Cites / Doc. (4 years) 2015 2.214 Cites / Doc. (4 years) 2016 1.896 Cites / Doc. (4 years) 2017 2.235 Cites / Doc. (4 years) 2018 2.061 Cites / Doc. (4 years) 2019 2.234 Cites / Doc. (4 years) 2020 2.946 Cites / Doc. (3 years) 2007 0.000 Cites / Doc. (3 years) 2008 0.741 Cites / Doc. (3 years) 2009 1.000 Cites / Doc. (3 years) 2010 1.646 Cites / Doc. (3 years) 2011 0.917 Cites / Doc. (3 years) 2012 1.628 Cites / Doc. (3 years) 2013 1.809 Cites / Doc. (3 years) 2014 2.067 Cites / Doc. (3 years) 2015 2.247 Cites / Doc. (3 years) 2016 1.583 Cites / Doc. (3 years) 2017 1.804 Cites / Doc. (3 years) 2018 2.027 Cites / Doc. (3 years) 2019 2.231 Cites / Doc. (3 years) 2020 2.817 Cites / Doc. (2 years) 2007 0.000 Cites / Doc. (2 years) 2008 0.741 Cites / Doc. (2 years) 2009 1.000 Cites / Doc. (2 years) 2010 0.764 Cites / Doc. (2 years) 2011 1.018 Cites / Doc. (2 years) 2012 1.586 Cites / Doc. (2 years) 2013 1.750 Cites / Doc. (2 years) 2014 1.850 Cites / Doc. (2 years) 2015 1.636 Cites / Doc. (2 years) 2016 0.959 Cites / Doc. (2 years) 2017 1.714 Cites / Doc. (2 years) 2018 2.000 Cites / Doc. (2 years) 2019 2.131 Cites / Doc. (2 years) 2020 3.120 Year International Collaboration 2007 37.04 2008 29.63 2009 32.14 2010 24.14 2011 37.93 2012 58.06 2013 31.03 2014 37.84 2015 37.84 2016 21.21 2017 37.50 2018 45.45 2019 32.26 2020 44.12

This statistic presents a ranking of the most popular online stores in Spain in the sports and outdoor segment in 2018, sorted by annual net e-commerce sales. For more information please visit ecommerceDB.com.In 2018, market leader decathlon.es generated 62 million U.S. dollars via the sale of sports and outdoor articles in Spain. The online store adidas.es was ranked second with a revenue of 49,6 million U.S. dollars.

This statistic presents a ranking of the most popular online stores in Greater China in the fashion segment in 2018, sorted by annual net e-commerce sales. For more information please visit ecommerceDB.com.In 2018, market leader jd.com generated 9,2 billion U.S. dollars via the sale of fashion articles in Greater China. The online store vip.com was ranked second with a revenue of 6,1 billion U.S. dollars.

Indian journal of public health Impact Factor 2024-2025 - ResearchHelpDesk - Indian Journal of Public Health is a peer-reviewed international journal published Quarterly by the Indian Public Health Association. It is indexed/abstracted by the major international indexing systems like Index Medicus/MEDLINE, SCOPUS, PUBMED, etc. The journal allows free access (Open Access) to its contents and permits authors to self-archive the final accepted version of the articles. The journal’s full text is available online at www.ijph.in. 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, Indian Science Abstracts, IndMed, MEDLINE/Index Medicus, Scimago Journal Ranking, SCOPUS, Web of Science

About the NUDA Dataset
Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address bias in news articles is to automatically detect and indicate it through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. To facilitate the data-gathering process, we introduce NewsUnravel, a news-reading web application leveraging an initially tested feedback mechanism to collect reader feedback on machine-generated bias highlights within news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31% and improving classifier performance by 2.49%. As the first human-in-the-loop application for media bias, NewsUnravel shows that a user-centric approach to media bias data collection can return reliable data while being scalable and evaluated as easy to use. NewsUnravel demonstrates that feedback mechanisms are a promising strategy to reduce data collection expenses, fluidly adapt to changes in language, and enhance evaluators' diversity.

General

This dataset was created through user feedback on automatically generated bias highlights on news articles on the website NewsUnravel made by ANON. Its goal is to improve the detection of linguistic media bias for analysis and to indicate it to the public. Support came from ANON. None of the funders played any role in the dataset creation process or publication-related decisions.

The dataset consists of text, namely biased sentences with binary bias labels (processed, biased or not biased) as well as metadata about the article. It includes all feedback that was given. The single ratings (unprocessed) used to create the labels with correlating User IDs are included.

For training, this dataset was combined with the BABE dataset. All data is completely anonymous. Some sentences might be offensive or triggering as they were taken from biased or more extreme news sources. The dataset does not identify sub-populations or can be considered sensitive to them, nor is it possible to identify individuals.

Description of the Data Files

This repository contains the datasets for the anonymous NewsUnravel submission. The tables contain the following data:

NUDAdataset.csv: the NUDA dataset with 310 new sentences with bias labels
Statistics.png: contains all Umami statistics for NewsUnravel's usage data
Feedback.csv: holds the participantID of a single feedback with the sentence ID (contentId), the bias rating, and provided reasons
Content.csv: holds the participant ID of a rating with the sentence ID (contentId) of a rated sentence and the bias rating, and reason, if given
Article.csv: holds the article ID, title, source, article metadata, article topic, and bias amount in %
Participant.csv: holds the participant IDs and data processing consent

Collection Process

Data was collected through interactions with the Feedback Mechanism on NewsUnravel. A news article was displayed with automatically generated bias highlights. Each highlight could be selected, and readers were able to agree or disagree with the automatic label. Through a majority vote, labels were generated from those feedback interactions. Spammers were excluded through a spam detection approach.

Readers came to our website voluntarily through posts on LinkedIn and social media as well as posts on university boards. The data collection period lasted for one week, from March 4th to March 11th (2023). The landing page informed them about the goal and the data processing. After being informed, they could proceed to the article overview.

So far, the dataset has been used on top of BABE to train a linguistic bias classifier, adopting hyperparameter configurations from BABE with a pre-trained model from Hugging Face.
The dataset will be open source. On acceptance, a link with all details and contact information will be provided. No third parties are involved.

The dataset will not be maintained as it captures the first test of NewsUnravel at a specific point in time. However, new datasets will arise from further iterations. Those will be linked in the repository. Please cite the NewsUnravel paper if you use the dataset and contact us if you're interested in more information or joining the project.