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SciVal Metric: Publications in Journal Quartiles
Last updated on November 15, 2024
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Back to all metricsPublications in Journal Quartiles in SciVal indicates the extent to which an entity’s publications have been published in the selected journal quartiles: how many publications are in the quartile 1, 2, 3 or 4 rated journals indexed by Scopus?
The journal quartiles are defined by the journal metrics CiteScore, SNIP (Source-Normalized Impact per Paper) or SJR (SCImago Journal Rank). This means that the data universe is the set of items indexed by Scopus that have a journal metric and so can be organized into percentiles; this excludes publications in stand-alone books and trade publications, which do not have journal metrics.
A journal in Scopus can have different journal quartile metrics for each subject area in which it’s allocated. If no subject area filter is selected, SciVal always uses the highest quartile value.
All items indexed by Scopus that have a CiteScore, SNIP or SJR value form the data universe used to generate this metric:
- The CiteScore, SNIP or SJR percentiles are used to calculate each quartile. Quartile one (Q1) is the top 25th percentile, quartile two (Q2) are the 26 – 50th percentiles, quartile three (Q3) are the 51 – 75th percentiles and quartile four (Q4) are the 76 – 100th percentiles.
- For journal quartiles, anywhere between 99-75th percentile is classified as being in quartile 1 (the top 25%). For example, a journal with a CiteScore Percentile of 77% will be in Q1 and a journal with CiteScore Percentile 49% will be in Q3.
- These thresholds are calculated separately for CiteScore, SNIP and SJR, not once for a combination of both journal metrics.
- For CiteScore, the percentage thresholds are taken directly from the CiteScore Percentile values that are calculated by Scopus. A journal receives a CiteScore Percentile for each ASJC in which it’s categorized. SciVal always uses the highest relevant CiteScore Percentile, which is dictated by the subject area filter.
- SciVal uses these journal metric thresholds to calculate the number of an entity’s publications within indexed items that fall within each percentile range.
- Indexed items have multiple journal metric values, for distinct years. Which one is used in assigning publications to journal metrics thresholds?
- The journal metric value matching the publication year of an item of scholarly output is used as far as possible.
- The first journal metric values for SNIP and SJR are available for 1999. For scholarly output published in the range 1996-1999, the journal metric value for 1999 is used.
- The first journal metric values for CiteScore are available for 2011. For scholarly output published in the range 1996–2010, the CiteScore Percentile value for 2011 is used. If no 2011 metrics are available for the journal, then no value is displayed.
- Current year journal metric values are published during the course of the following year. For recent items whose journal metric values have not yet been published, the previous year’s journal metric values are used until the current year’s become available.
- A publication may be counted in a Journal Quartile without itself ever having been cited. The citations received by an individual publication are irrelevant for this metric, which is based only on citations received by a journal or conference proceedings.
- SNIP and SJR are both field-normalized journal metrics, meaning that this metric can be used to compare the presence of publications in journals of entities working in different disciplines. The CiteScore Percentile allows a similar comparison across disciplines.
Publications in Journal Quartiles is a:
- Citation Impact metric
- "Power Metric" when the "Total value" option is selected, but not when the "Percentage" option is selected
SciVal often displays Publications in Journal Quartiles in a chart or table with years. These years are always the years in which items were published, and do not refer to the years in which citations were received.
- Benchmark entities even if they have different sizes and disciplinary profiles:
- It is advised to select the “Percentage” option when comparing entities of different sizes, to normalize for this variable.
- SNIP and SJR are field-normalized metrics. CiteScore is not a field-normalized metric, but the CiteScore Percentile (used for the calculation of the Publications in Journal Quartiles) is. Selecting one of these journal metrics will inherently account for differences in the behavior of academics between fields.
- Choose quartile one (Q1) to showcase the presence of publications in journals that are likely to be perceived as the most prestigious in the world
- Choose quartile four (Q4) to view the publications in journals that are likely to be perceived as the least prestigious in the world
- Incorporate peer review into a metric, since it is the judgment of experts in the field that determines whether a publication is accepted by a particular journal or not
- Avoid the “dip” in recent years seen with metrics like Citation Count and Citations per Publication
- Investigate performance very early in a new strategy, or for an early-career researcher, since journal data underpin this metric, and not the citations received by individual publications themselves which would require time to accumulate
- The objective is to judge an entity’s publications based on their actual performance, rather than based on the average of a journal:
- A publication may appear in a journal with a very high CiteScore, SNIP or SJR value, and not itself receive any citations; even the most highly cited journals in the world contain publications that have never been cited.
- A publication may be very influential and have received many citations, without being published in a journal with a high CiteScore, SNIP or SJR value; journals which do not rank very highly in the data universe may still contain very highly cited publications.
- Benchmarking the performance of a Researcher who is the editor of a journal in the top quartile, since they can publish multiple editorials which all fall into this quartile. In this situation, it is advised to use the Publication Type filter to exclude editorials and ensure that the types of publications in the data sets being compared are consistent.
- Entities are small and there may be gaps in their output within the Scopus coverage. A single missing publication from a small data set will have a significant negative impact on apparent performance. Although it is relatively unlikely that publications in such prominent journals are not indexed by Scopus, we advise users to be vigilant and to bear this possible limitation in mind.
- Citation Count, Citations per Publication, Field-Weighted Citation Impact, and Outputs in Top Citation Percentiles which rely on the citations received by an entity’s publications themselves, and not on the average performance of the journal
- Outputs in Top Citation Percentiles, which indicates the extent to which an entity’s publications are present in the most-cited percentiles of the data universe, but depends on the citations received by the publications themselves. This is a logical partner metric.
- The set of all other “Power Metrics” whose value tends to increase as the entity becomes bigger: Scholarly Output, Subject Area Count, Scopus Source Title Count, Citation Count, Cited Publications (“Total value”), Number of Citing Countries, Collaboration (“Total value”), Academic-Corporate Collaboration (“Total value”), Outputs in Top Citation Percentiles (“Total value”), and h-indices
- The set of all other “time-independent metrics”, which provide useful, reliable information immediately upon publication and do not rely on the passing of time for useful data to accumulate: Scholarly Output, Subject Area Count, Scopus Source Title Count, Collaboration, and Academic-Corporate Collaboration
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