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.

Last 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.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company Quartile-Software-Limited.

BackgroundThis paper investigates the sharp increase in media posts and engagement surrounding the initial four months (October 2023–January 2024) of the Israel–Hamas armed conflict, following the inciting incident of a surprise militant attack launched on 7 October 2023. The impetus for documenting the trajectory of social media conversations lies in capturing and cataloging the biggest drivers of engagement, public sentiments and groundswell themes, reflecting the public zeitgeist during a period of uncertainty.ObjectivesFew big data studies have delved into initial public discourse surrounding the escalation of the ongoing conflict. First, we identify the biggest generators of buzz, proxied by spikes in mention-counts; secondly, we identify content trends proxied by quantitative sentiment valence, top keywords and emojis, and qualitatively outline the biggest generators of media engagement via top engagement metrics (likes, reposts).MethodsWe analyse a large corpus of publicly-available content from online platforms (Twitter, Reddit, Tiktok) obtained using academic-level API access, containing search terms: Palestine, Palestinian(s), Israel(i)(s), Gaza, Hamas. Our first research aim utilizes a prominent peaks model (upper-quartile significance threshold of prominence>1,500,000). Our second research aim utilized qualitative analysis on valence, top keywords and emojis, and top themes.ResultsEight prominent peaks were identified, finding that news about violence (e.g., airstrikes, citizen harm), groundswell movements (e.g., international activism like worldwide strikes, protests and marches, awareness movements, and outrage in response to current conditions) and politically-charged happenings (e.g., missile strikes) had the biggest hand in boosting discoursal spikes. Valence scores were generally negative, following a general monthly distribution of negative (59%), neutral (31%), and positive (10%), with main keywords focused on terror, violence, and calls for ceasefire. Qualitatively, we find salient groundswell movements (e.g., e-sims for Gaza, content creator strikes for Palestine, circulation of boycott consumer brand lists, co-option of the watermelon emoji as shorthand for support for the cause) and find that the online space is dominated by a fixation on celebrity opinions on the conflict and the circulation of gory footage.ConclusionsOverall, emergent public chatter worryingly peaks in response to incendiary news about violence, gory footage and celebrity opinions, though discoursal spikes are also slanted toward groundswell movements of goodwill.

We investigated dissolved methane distributions along a 6 km transect crossing active seep sites at 40 m water depth in the central North Sea. These investigations were done under conditions of thermal stratification in summer (July 2013) and homogenous water column in winter (January 2014). Dissolved methane accumulated below the seasonal thermocline in summer with a median concentration of 390 nM, whereas during winter, methane concentrations were typically much lower (median concentration of 22 nM). High-resolution methane analysis using an underwater mass-spectrometer confirmed our summer results and was used to document prevailing stratification over the tidal cycle. We contrast estimates of methane oxidation rates (from 0.1 to 4.0 nM day**-1) using the traditional approach scaled to methane concentrations with microbial turnover time values and suggest that the scaling to concentration may obscure the ecosystem microbial activity when comparing systems with different methane concentrations. Our measured and averaged rate constants (k') were on the order of 0.01 day**-1, equivalent to a turnover time of 100 days, even when summer stratification led to enhanced methane concentrations in the bottom water. Consistent with these observations, we could not detect known methanotrophs and pmoA genes in water samples collected during both seasons. Estimated methane fluxes indicate that horizontal transport is the dominant process dispersing the methane plume. During periods of high wind speed (winter), more methane is lost to the atmosphere than oxidized in the water. Microbial oxidation seems of minor importance throughout the year.

This data set contains surface elevation data over Greenland measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.

An observational, descriptive and cross-sectional study was conducted through a bibliometric analysis of Cuban scientific output on COVID-19, published in journals indexed in Scopus. The following bibliometric indicators were studied: -Number of documents (Ndoc). Total number of documents in which at least one of the authors is affiliated with a Cuban institution. -Percentage of documents (% Ndoc) with respect to the total of the studied articles. -Citations (NCit). Total citations received by articles indexed in Scopus. -Cited articles (Cited doc). Total number of published articles that have been cited at least once according to Scopus. -Citations per document (Cpd). Average number of received citations. -Types of collaboration: ✓No collaboration (NoCollab). Documents in which a national institution appears, regardless of whether more than one author, group or department participates. ✓National collaboration (NC). Documents signed by more than one Cuban institution. ✓International Collaboration (IC). Documents in which the affiliation of their authors includes the address in more than one country. ✓International and National Collaboration (IC & NC). Documents signed by more than one Cuban institution and, at least, one foreign institution. -H-index. This index considers both the number of articles and the citations they receive. An author has an h = x index if he/she has x articles that have been cited at least x times [43]. This indicator is also used to characterize groups (a group of authors, a department, or a country). -Quartiles (Q). According to the SCImago Journal & Country Rank (SJR), the journals indexed in Scopus are placed in quartiles, where those in the first quartile have the highest impact. There are journals that do not appear in the ranking (non-ranked) due to their recent inclusion in the database [44]. -High-quality publications (% Q1). Percentage of publications in journals included in the quartile of maximum visibility. -Articles in Spanish (Ndoc Sp). Articles published in Spanish. -Articles in English (Ndoc Eng). Articles published in English. -Overlap (Ndoc Sp & Eng). Articles published in two languages, in this case, both in Spanish and English. -Scientific leadership (% Lead). Percentage of articles from a country in which the corresponding author belongs to a Cuban institution. These are referred to as lead documents [45]. -% Q1 Lead. Percentage of articles in journals included in the first quartile in which the corresponding author is affiliated with a Cuban institution. -% IC Lead. Percentage of articles in which the authors' affiliation includes the address of more than one country and the corresponding author is affiliated with a Cuban institution. Data collection and processing: To retrieve the publications, Scopus (http://www.scopus.com) was accessed on March 12, 2021, and an advanced search was performed using a filter by country (Cuba), source (journals) and type of articles (article and review). Most of the terms used for the search were extracted from previous bibliometric articles and the PubMed Medical Subject Headings (MeSH) related to the disease included in the MeSH catalog in its 2021 update were also used: COVID-19 vaccines, COVID-19 testing, COVID-19 serological testing and COVID-19 nucleic acid testing. The search strategy we used is shown in Table 1. Search strategy. Operator Field Search term TITLE-ABS-KEY 2019 ncov, 2019 novel coronavirus, 2019 novel coronavirus (2019-ncov), 2019 novel coronavirus disease, 2019 novel coronavirus pneumonia, 2019-nCoV, 2019-novel CoV, coronavirus 2019, coronavirus disease 2019, cov-19, covid, COVID-19, COVID-19 vaccines, COVID-19 testing, COVID-19 serological testing, COVID-19 nucleic acid testing, covid-19 diagnosis, covid-19 pandemic, covid-19 pneumonia, COVID-19 virus infection, covid-2019 epidemic, ncov-2019, new coronavirus, novel coronavirus, novel coronavirus outbreak, novel coronavirus pneumonia, SARS-CoV-2, sars-cov-2 infection, severe acute respiratory syndrome coronavirus 2, Wuhan coronavirus AND SRCTYPE j AND AFFILCOUNTRY Cuba AND LIMIT-TO DOCTYPE, "ar" OR DOCTYPE, "re" Initially, 134 articles with Cuban authorship were retrieved and after normalization, one article related to dramaturgy was eliminated, which had the term COVID-19 in the abstract and was published in the Theatre Journal. Similarly, 45 articles published in English were detected, and after a manual review it was found that six of these had been published in Spanish. In regard to Latin American scientific output, the same filters were used as in the previous strategy and we could obtain information corresponding to Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Uruguay and Venezuela. The SCImago Journal & Country Rank platform (http://www.scimagojr.com) was accessed to know the location of the journals by...

Blockchain data query: contract-txns-quartile

This study investigates the rate of erosion during the 1951-2006 period on the Bykovsky Peninsula, located north-east of the harbour town of Tiksi, north Siberia. Its coastline, which is characterized by the presence of ice-rich sediment (Ice Complex) and the vicinity of the Lena River Delta, retreated at a mean rate of 0.59 m/yr between 1951 and 2006. Total erosion ranged from 434 m of erosion to 92 m of accretion during these 56 years and exhibited large variability (sigma = 45.4). Ninety-seven percent of the rates observed were less than 2 m/yr and 81.6% were less than 1 m/yr. No significant trend in erosion could be recorded despite the study of five temporal subperiods within 1951-2006. Erosion modes and rates actually appear to be strongly dependant on the nature of the backshore material, erosion being stronger along low-lying coastal stretches affected by past or current thermokarst activity. The juxtaposition of wind records monitored at the town of Tiksi and erosion records yielded no significant relationship despite strong record amplitude for both data sets. We explain this poor relationship by the only rough incorporation of sea-ice cover in our storm extraction algorithm, the use of land-based wind records vs. offshore winds, the proximity of the peninsula to the Lena River Delta freshwater and sediment plume and the local topographical constraints on wave development.

This marginality hotspot map of Ethiopia uses the lowest quartile as thresholds for the dimensions of marginality. Again, this map shows how many dimension of marginality - as defined by Gatzweiler et al. (2011) - overlap.

Blockchain data query: Apechain: Quartiles for tx fee and tx number

Blockchain data query: liquidation quartiles

This paper examines whether tobacco expenditure leads to the crowding out or crowding in of different expenditure items in South Africa. We apply genetic matching to expenditure quartiles of the 2010/2011 South African Income and Expenditure Survey. Genetic matching is a more appealing approach for dealing with the endogeneity of tobacco expenditure that often plagues studies using systems of demand equations. Further, genetic matching provides transparent measures of covariate balance giving the analyst objective means of assessing match success. We find that the poorest tobacco consuming households in South Africa consistently allocate smaller budget shares towards food items than non-smoking households. Specifically, we find that dairy, fruits, nuts and oils are displaced in favour of tobacco expenditure in the two poorest quartiles. Unsurprisingly, food items are never displaced for households in the top two quartiles, given these households’ greater access to resources. Like other studies in the literature, we find that tobacco expenditure consistently crowds-in alcohol across all quartiles confirming the strong complementarities between the two.

This dataset presents information from 2016 at the household level; the percentage of households within each Index of Household Advantage and Disadvantage (IHAD) quartile for Statistical Area Level 3 (SA3) 2016 boundaries.

The IHAD is an experimental analytical index developed by the Australian Bureau of Statistics (ABS) that provides a summary measure of relative socio-economic advantage and disadvantage for households. It utilises information from the 2016 Census of Population and Housing.

IHAD quartiles: All households are ordered from lowest to highest disadvantage, the lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided up into four groups, depending on their score.

This data is ABS data (catalogue number: 4198.0) used with permission from the Australian Bureau of Statistics.

For more information please visit the Australian Bureau of Statistics.

Please note:

• AURIN has generated this dataset through aggregating the original SA1 level data (with calculated number of households/quartile) to SA3 level.

• The number of occupied private dwellings, and number of households in each of the IHAD quartiles for each SA3 were calculated by aggregating the values of each of those specified columns from the SA1 dataset. Percentages of households in each of the IHAD quartiles were calculated for each SA3 from these aggregated totals.

• A household is defined as one or more persons, at least one of whom is at least 15 years of age, usually resident in the same private dwelling. All occupants of a dwelling form a household. For Census purposes, the total number of households is equal to the total number of occupied private dwellings (Census of Population and Housing: Census Dictionary, 2016 cat. no. 2901.0).

• IHAD output has been confidentialised to meet ABS requirements. In line with standard ABS procedures to minimise the risk of identifying individuals, a technique has been applied to randomly adjust cell values of the output tables. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

This dataset presents information from 2016 at the household level; the percentage of households within each Index of Household Advantage and Disadvantage (IHAD) quartile for Local Government Area (LGA) 2017 boundaries.

The IHAD is an experimental analytical index developed by the Australian Bureau of Statistics (ABS) that provides a summary measure of relative socio-economic advantage and disadvantage for households. It utilises information from the 2016 Census of Population and Housing.

IHAD quartiles: All households are ordered from lowest to highest disadvantage, the lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided up into four groups, depending on their score.

This data is ABS data (catalogue number: 4198.0) used with permission from the Australian Bureau of Statistics.

For more information please visit the Australian Bureau of Statistics.

Please note:

• AURIN has generated this dataset through aggregating the original SA1 level data (with calculated number of households/quartile) to LGA level.

• Aggregation was achieved through calculating the centroid for each SA1 and assigning it to the LGA it fell within.

• The number of occupied private dwellings, and number of households in each of the IHAD quartiles were calculated for each LGA by aggregating the peviously assigned SA1 values of each of those specified columns from the SA1 dataset. Percentages of households in each of the IHAD quartiles were calculated for each LGA from these aggregated totals.

• A household is defined as one or more persons, at least one of whom is at least 15 years of age, usually resident in the same private dwelling. All occupants of a dwelling form a household. For Census purposes, the total number of households is equal to the total number of occupied private dwellings (Census of Population and Housing: Census Dictionary, 2016 cat. no. 2901.0).

• IHAD output has been confidentialised to meet ABS requirements. In line with standard ABS procedures to minimise the risk of identifying individuals, a technique has been applied to randomly adjust cell values of the output tables. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.