Journal Metrics Analysis

Impact Factors 2026: What Changed Across 19,208 Journals

The JCR 2026 edition was released June 17, 2026 and uses 2025 citation data. This article keeps our editorial commentary on that release; the live figures below come from the 2026 Journal Metrics Score, computed from OpenAlex data — not from JCR.

MZ
Dr. Meng Zhao|Physician-Scientist · Founder, LabCat AI
Published: June 2026Updated: August 202618 min readJournal Metrics Analysis

When Clarivate published the 2026 Journal Citation Reports on June 17, 2026, the release moved upward for most journals. In our June comparison of the two JCR editions, 19,208 titles had exact values in both releases: about three in five increased, roughly one in four declined, and the median rose from 1.8 to 1.9. Those numbers describe the JCR release itself and are reported here as editorial commentary.

Journal Metrics no longer displays Journal Citation Reports data. What follows instead is a look at the same journal landscape through the metric this site now computes and publishes: the 2026 Journal Metrics Score.

About the data on this page

Every chart and table below shows the Journal Metrics Score, which is computed from OpenAlex data and is not the Journal Impact Factor. The Journal Metrics Score is the two-year mean citedness of a journal, computed from OpenAlex (CC0) data: citations received in the most recent complete year to works published in the two prior years, divided by the number of those works. Quartiles rank journals within each OpenAlex subfield by score.

Data source: OpenAlex (CC0)

The 2026 Journal Metrics Score landscape

Of 22,643 journals in the current dataset, 22,340 have a computable 2026 score. The median is 1.4 while the arithmetic mean is 2.18. That gap matters: a small number of exceptional values pull the mean upward, so the median is the clearer description of the typical journal.

Scored journals

22,340

Of 22,643 journal records

Median score

1.4

Middle half runs 0.52.7

Arithmetic mean

2.18

Sensitive to high outliers

90th percentile

4.8

Nine in ten journals score below this

How journals divide into subfield quartiles

Quartiles rank journals within each OpenAlex subfield by score, and each journal is labeled with the best quartile it reaches in any of its subfields. Because most journals belong to several subfields, the best-quartile view skews upward: 8,042 journals (36.0%) reach Q1 somewhere, which is why this chart should be read as “best placement,” not a strict 25/25/25/25 split.

Best subfield quartile across all scored journals, from the Journal Metrics Score computed from OpenAlex data. Counts and percentages are repeated below.
Best subfield quartile by Journal Metrics Score
QuartileJournalsShare
Q18,04236.0%
Q26,05127.1%
Q34,78421.4%
Q43,39015.2%
N/A730.3%

Most journals score in the low single digits

Citation averages form a strongly right-skewed distribution, and the Journal Metrics Score is no exception. The largest group of journals scores below 1.0, and only 2.0% of scored titles reach 10.0 or higher. This shape is why comparing a raw score across unrelated fields says very little.

Distribution of 2026 Journal Metrics Scores. The dashed line marks the bin containing the median score.
View distribution data
Journal Metrics Score distribution
Score rangeJournalsShare
<1.08,60138.5%
1.0–1.95,38024.1%
2.0–2.93,32614.9%
3.0–4.92,96713.3%
5.0–9.91,6287.3%
≥10.04382.0%

The highest Journal Metrics Scores

Ranking by the 2026 score puts one journal far outside the rest of the distribution, so we show it separately to keep the common axis readable. Extreme values in any two-year citation average usually come from a very small publication denominator — a title that publishes few citable items while accumulating citations — and can also reflect quirks in the underlying OpenAlex source records. Treat the top of this list as a description of the metric, not a quality ranking.

Highest 2026 score · shown separately

823.0

Highest 2026 Journal Metrics Scores after the separately displayed outlier. “Highest” describes the metric only; it is not a judgment of manuscript fit, editorial quality, or likely article citations.

Two widely searched journals

PLOS ONE and Scientific Reports illustrate where two large multidisciplinary journals sit in the score distribution. Their values come from the same canonical dataset as every chart above.

These are Journal Metrics Scores computed from OpenAlex data, not Journal Impact Factors; the two metrics use different source data and cannot be compared decimal-for-decimal.

What these scores do—and do not—mean

A higher Journal Metrics Score says that citations to a journal's recent works increased relative to its output. It does not tell you why that happened, whether your manuscript belongs there, or how often your eventual article will be cited.

  • Compare journals within the same subfield before comparing raw values across fields.
  • Use subfield quartiles when you need field-normalized context.
  • Check scope, readership, indexing, fees, and author instructions alongside any metric.
  • Remember that a two-year citation average is volatile for small journals.
  • Choose for audience and manuscript fit first; use the score as supporting evidence.

Methodology and limitations

Source and inclusion

We analyzed the canonical Journal Metrics dataset for the August 2026 snapshot. A journal enters the analysis when it has a computable 2026 Journal Metrics Score; 303 records without a score were excluded.

The metric

The Journal Metrics Score is the two-year mean citedness of a journal, computed from OpenAlex (CC0) data: citations received in the most recent complete year to works published in the two prior years, divided by the number of those works. Quartiles rank journals within each OpenAlex subfield by score.

Calculations

The mean is the arithmetic average; the median is the middle observation after sorting. Quartile shares use each journal's best quartile across its OpenAlex subfields.

Interpretive limits

This is a descriptive snapshot of one release across disciplines. It is not the Journal Impact Factor, cannot identify causes, and does not predict the influence of an individual paper.

Further reading

MZ

Written by Dr. Meng Zhao

Physician-Scientist · Founder, LabCat AI

MD · Former Neurosurgeon · Medical AI Researcher

Dr. Meng Zhao is a former neurosurgeon turned medical-AI researcher. After years in the operating room, he moved into applied AI for clinical workflows and now leads LabCat AI, a medical-AI company working on decision support and research tooling for clinicians. He built Journal Metrics as a free resource for researchers who need reliable journal metrics without paid database subscriptions.

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