Foundations of Data Science's 2026 Journal Metrics Score is 2.2, ranking Q2 in Computational Theory and Mathematics and 3 other subfields. Two-year citation average from OpenAlex data, updated August 2026.
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Journal Metrics Score 2026 · 2021–2024 window citedness 1.98.
Not available — there are not enough recent PubMed records with both received and accepted dates.
No journal-level APC is available in the current OpenAlex snapshot. Cost is excluded from scoring.
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Each spoke is a 0–100 percentile within a comparable journal cohort—not a percentage grade or a single black-box rating.
14 of 126 works · same type, year & subfield
0 works · same type, year & subfield
Below field-year expectation
of 159 topic-associated OpenAlex sources by lifetime output
FWCI compares papers with the same publication year, type, and field. 1.0 is the world average; above 1.0 is above expectation. This journal's median is 0.50, so its typical recent paper runs about 50% below its field benchmark.
11.1% of recent papers rank in the global top 10% after field normalization. This is easier to compare across specialties than raw citation totals.
Volume is separated from citation accumulation so newer papers are not presented as weaker simply because they are newer.
OpenAlex source records · 2019–2025
126 OpenAlex works · 2021–2024
OpenAlex topics show the research communities this journal serves.
Candidates share at least one OpenAlex subfield (or field, if too few do) with this journal's main research topics, ignoring generic catch-all topics, and are ordered by topic overlap, then current Journal Metrics Score.
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| Category | Quartile |
|---|---|
| Computational Theory and Mathematics | Q2 |
| Artificial Intelligence | Q2 |
| Statistical and Nonlinear Physics | Q2 |
| Radiology, Nuclear Medicine and Imaging | Q2 |
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Source: the current OpenAlex source record. OpenAlex works can include multiple work types. Fully Open Access is a source-level flag. Recent OA Works is summed from OpenAlex's counts_by_year series (up to the latest ten years); it is not a lifetime OA percentage. Any APC shown here is an indicative OpenAlex value—verify current charges on the journal's official website.
Foundations of Data Science's 2026 Journal Metrics Score is 2.2, from the August 2026 data release. The score is a two-year citation average 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.
Foundations of Data Science's best quartile is Q2. By OpenAlex subfield: Q2 in Computational Theory and Mathematics; Q2 in Artificial Intelligence; Q2 in Statistical and Nonlinear Physics; Q2 in Radiology, Nuclear Medicine and Imaging. Quartiles rank journals by Journal Metrics Score within each subfield.
No. Journal Metrics computes the score independently from OpenAlex open data. Other providers use different citation databases, document types, and counting rules, so their published figures for Foundations of Data Science will not match this score exactly. Compare journals using one source at a time.
Comparable journals include Siam Review, Multiscale Modeling & Simulation, Siam Journal On Mathematics of Data Science, and Formal Aspects of Computing. Compare their scores, acceptance times, and costs in the recommended alternatives on this page before choosing where to submit.