Patterns Journal: Scopus Indexing, Scope, Submission & Author Guide
Patterns is a peer-reviewed, gold open-access data-science journal from Cell Press, an Elsevier imprint. It was launched in 2020 and is designed for computational and data-intensive work across disciplines rather than for one scientific domain alone. The journal’s official positioning emphasizes data science broadly, including methods, algorithms, infrastructure, datasets, software, responsible use of data, and research that connects data-intensive approaches with practical, scientific, policy, or societal questions.
For researchers checking whether Patterns is Scopus indexed, the supplied source record identifies Scopus Source ID 21101028388. The ISSN Portal also lists Patterns as covered by Scopus. Its online ISSN is 2666-3899. Before using any journal for a university, doctoral, promotion, funding, or accreditation requirement, researchers should still verify the current source status and coverage directly in the applicable database because indexing status and metrics can change.
Quick Answer: What Is Patterns?
Patterns is a multidisciplinary data-science journal published by Cell Press/Elsevier. It focuses on substantial advances in the way data are generated, organized, analyzed, interpreted, shared, governed, and reused across computational, physical, life, social, and other research settings. Its breadth means that a manuscript can originate in medicine, biology, engineering, materials science, climate research, social science, policy, or another domain, provided the data-science contribution is sufficiently clear and important.
It is a fully open-access journal. Elsevier currently lists a standard APC of USD 4,900 excluding taxes and reports journal-level timelines of 5 days to first decision and 134 days from submission to acceptance. Those figures are publisher-reported averages or indicators, not promises for a particular paper. The journal is selective, so authors should evaluate intellectual fit—not only indexing or speed—before submitting.
Key Takeaways
- Publisher: Cell Press, an Elsevier imprint.
- ISSN: 2666-3899 (online).
- Scopus: the supplied source record is 21101028388; current status should be reconfirmed in Scopus.
- Scope: broad, cross-disciplinary data science, including methods, infrastructure, datasets, software, AI/ML, responsible data practice, and data-driven decision making.
- Open access: gold open access; Elsevier currently lists a USD 4,900 APC excluding taxes.
- Article mix: recent issues include research articles, reviews, perspectives, resources, descriptors, and People of Data features.
- Fit matters: the strongest submissions usually make a data-science contribution that is useful beyond one narrow application.
- No acceptance guarantee: professional editing or submission support can improve readiness, but editorial decisions remain solely with the journal.
Journal Information at a Glance
| Field | Journal information |
|---|---|
| Journal name | Patterns |
| Source type | Journal |
| Scopus Source ID | 21101028388 |
| ISSN / EISSN | 2666-3899 (online) |
| Publisher | Cell Press / Elsevier |
| Country | United States (ISSN record) |
| Language | English |
| First published | 2020 |
| Publication model | Online, open access |
| Primary subject focus | Broad data science; information systems, statistics, AI, and computer-science applications are among Elsevier-listed subject areas |
| Scopus status | Indexed; verify current status in Scopus |
| CiteScore / SJR / SNIP / quartile | Verify the latest reporting year in Scopus; no numeric value is reproduced here without a current official source snapshot |
| Impact Factor | Verify current Journal Citation Reports status/value where required |
| Open access | Yes |
| APC | USD 4,900 excluding taxes (Elsevier Journal Insights; verify at submission) |
| First-decision timeline | 5 days (publisher-reported journal insight) |
| Submission-to-acceptance timeline | 134 days (publisher-reported journal insight) |
| Acceptance rate | Not publicly verified in the authoritative sources reviewed |
What This Page Covers
- journal identity, publisher, ISSN and Scopus source information
- aims, scope, subject areas and likely manuscript fit
- article types visible in recent official issues
- open-access model, APC and publisher-reported timelines
- indexing and responsible use of journal metrics
- practical manuscript and submission-readiness steps
- peer-review and publication-ethics expectations
- how Contentxprtz can ethically support authors without promising acceptance
Methodology and Source Note
This profile was prepared from the supplied Scopus source record together with authoritative journal and publishing sources, including Cell Press/Elsevier journal information, ScienceDirect issues and journal insights, the ISSN Portal, and DOAJ. Journal fees, indexing, editorial teams, timelines, policies, and metrics can change. Authors should therefore reconfirm time-sensitive information on official sources immediately before submission.
Journal Aims and Scope
Patterns describes data science in the broadest sense. Its remit is not limited to one branch of machine learning or to conventional computer-science papers. Instead, it seeks computational and data-heavy research from many fields, alongside work on the ethics, philosophy, policy, and practical consequences of data-driven systems. The journal’s launch materials emphasize collaboration across computational, physical, life, and social sciences and the humanities.
A central theme is the development and use of data-science infrastructure, tools, services, algorithms, and methodologies. That can include artificial intelligence and machine learning, statistical modeling, visualization, data management, interoperable research resources, computational workflows, scalable analysis, data curation, reproducibility, and methods that make data more useful across disciplines. The journal also provides space for research on how decisions based on data affect people, institutions, environments, and policy.
For a prospective author, the practical implication is that domain relevance alone is not enough. A paper about a medical dataset, for example, should explain what the data-science contribution adds beyond one clinical use case. Likewise, a new algorithm should be positioned in terms of its conceptual or practical advance, validated appropriately, and explained so readers outside the narrow application domain can understand why it matters.
Subjects and Research Themes
Elsevier Journal Insights associates Patterns with Information Systems and Management; Statistics, Probability and Uncertainty; Artificial Intelligence; and Computer Science Applications. The actual published content is wider than those labels suggest because the journal deliberately follows data science into multiple research areas.
- machine learning, artificial intelligence, interpretable and responsible AI
- statistics, uncertainty, inference and computational modeling
- data visualization, clustering and dimensionality reduction
- scientific software, reusable tools and research infrastructure
- data curation, management, FAIR-oriented workflows and repositories
- biomedical, clinical, omics and health-data applications
- materials, manufacturing, engineering and physical-science data
- climate, environment, sustainability and policy analytics
- social, behavioral and human-centered data science
- ethics, governance, accountability and consequences of data-driven systems
Article Types and Content Formats
Recent official ScienceDirect issues show that Patterns publishes several content formats. Research Articles form a major part of the journal. Recent issues also contain Reviews, Perspectives, Resources, Descriptors and People of Data content. The exact requirements for each category may differ, and a format visible in the journal should not be assumed to be open to unsolicited submission in every case.
For authors, the safest approach is to choose the article type only after checking the current guide for authors and comparing the manuscript with recently published examples. A reusable dataset, software package or research-data-management toolkit may be better framed as a resource-oriented contribution than as a conventional hypothesis-driven research article. A synthesis of an emerging field may fit a review format, while broader conceptual or policy arguments may align with perspective-style content if the journal currently accepts such submissions.
Indexing, Scopus Status and Scholarly Visibility
The ISSN Portal lists Patterns as covered by Scopus, Web of Science, PubMed, DOAJ, Crossref and other discovery or registration services. Elsevier Journal Insights specifically lists Scopus and DOAJ and also identifies PubMed Central (PMC), SJR and SNIP in its abstracting/indexing section. The supplied Scopus Source ID is 21101028388.
Researchers should distinguish database inclusion from journal quality claims. Scopus indexing confirms discoverability within Scopus under the database’s current coverage, but it does not mean that every institution applies the same eligibility rule. Some universities require a particular quartile, subject category, coverage year, or current active status. Always check the latest Scopus source page and your institution’s written policy rather than relying on screenshots or third-party ranking sites.
Journal Metrics: CiteScore, SJR, SNIP and Quartile
Journal metrics are time-specific and category-specific. CiteScore is a Scopus journal-level citation metric based on a four-year window. SJR weights citations by the prestige of the citing source, while SNIP normalizes citation impact for differences between fields. Quartiles derive from rank positions within subject categories and can differ across categories.
The accessible Elsevier Patterns insights page reviewed for this article did not expose current numeric CiteScore, SJR, SNIP, quartile, or Impact Factor values in a way that could be responsibly reproduced as a current 2026 fact. Accordingly, this guide does not substitute third-party values. If a metric matters for a PhD, promotion, funding or institutional decision, open the current Scopus source profile and, where relevant, Journal Citation Reports, record the reporting year and category, and retain evidence of the value used.
Open Access and Article Processing Charge
Patterns is a gold open-access journal. Elsevier currently lists an APC of USD 4,900 excluding taxes. DOAJ also records publication charges and notes that a waiver policy exists. The amount an author actually pays can depend on institutional agreements, funding arrangements, waivers, geographic programs, taxes, and publisher policies in force at the time of acceptance.
Authors should not treat an APC as a payment for acceptance. The publication charge is part of the open-access business model and is separate from editorial evaluation. A manuscript can be rejected regardless of an author’s ability to pay, and payment does not replace peer review or scientific standards.
Peer Review and Editorial Assessment
Cell Press describes Patterns as publishing peer-reviewed research and emphasizes fair, rigorous review supported by professional editors and expert input. Elsevier Journal Insights currently reports a median or journal-level indicator of 5 days from submission to first decision and 134 days from submission to acceptance. Those numbers should be understood as historical journal indicators, not a guaranteed schedule.
In practical terms, authors should expect an initial editorial assessment of scope, novelty, significance and presentation. Manuscripts selected for external review are evaluated by subject experts. Revision requests may involve scientific clarification, additional validation, improved data or code documentation, statistical explanation, methodological transparency, or clearer framing of the contribution. The final decision remains with the journal’s editors.
Who Should Consider Submitting?
Patterns may be particularly relevant to researchers whose main contribution sits at the intersection of data science and another discipline. This can include computational scientists, data scientists, AI and machine-learning researchers, statisticians, bioinformaticians, research-software engineers, data stewards, digital-health researchers, materials informaticians, climate-data researchers, social-data scientists, and interdisciplinary teams.
A PhD scholar should consider the journal only when the manuscript’s contribution aligns with the journal’s level of selectivity and cross-disciplinary relevance. The fact that a thesis chapter uses machine learning is not by itself a reason to choose Patterns. A stronger fit exists when the work introduces a meaningful method, resource, generalizable workflow, data-centric insight, or responsible-data contribution that can matter to readers beyond the immediate dataset or case study.
How to Evaluate Journal Fit Before Submission
- Read the aims and scope. Identify the specific part of the journal remit that your manuscript addresses.
- Review recent issues. Compare your topic, method, evidence level, article type and breadth with several recent Patterns papers.
- State the data-science advance in one paragraph. If the contribution can only be described as an application of an established method to a new dataset, the fit may be weak unless the application yields an unusually important or transferable insight.
- Test cross-disciplinary value. Ask whether a reader outside your originating field can reuse the method, learn from the resource, or apply the conceptual insight.
- Check reproducibility. Make sure methods, data provenance, preprocessing, model evaluation, uncertainty, code and limitations are reported at a level suitable for independent scrutiny.
- Verify current formal requirements. Article type, declarations, file formats, data availability, figure specifications and other details can change.
Common Reasons a Manuscript May Be Unsuitable
Although only the editors can decide suitability, several practical warning signs can be identified from the journal’s broad but selective positioning. A manuscript may struggle if its contribution is mainly routine application of a standard algorithm, if the data-science novelty is weak, if the results are too narrowly local without transferable insight, if validation is insufficient, or if the manuscript does not explain why the work matters to a broad data-science audience.
Weak reproducibility can also undermine a submission. Missing descriptions of preprocessing, inadequate baselines, data leakage, unclear train/test separation, inappropriate statistical comparisons, unsupported claims of generalization, undocumented code dependencies, or inaccessible data without a justified reason can make it difficult for reviewers to assess the work. Authors should address these issues before submission rather than relying on peer review to identify them.
Manuscript and Submission Readiness
As a practical publication-readiness step, prepare the manuscript around a clear research question and a precise statement of contribution. The title and abstract should accurately describe what was done and avoid inflated claims. The introduction should establish the data-science problem, explain limitations in existing approaches and show why the proposed work is needed. Methods should be sufficiently detailed for expert evaluation, and results should distinguish exploratory findings from confirmatory evidence.
For computational work, report datasets, inclusion/exclusion criteria, preprocessing, model architecture or algorithm details, hyperparameter decisions, evaluation metrics, baselines, uncertainty, sensitivity checks and failure cases where appropriate. Explain access restrictions transparently. If code or data can be shared, use persistent and appropriately documented repositories. If they cannot be shared, state the reason and provide the strongest reproducibility pathway permitted by ethics, privacy, licensing or contractual constraints.
Before uploading files, verify authorship order and contributions, affiliations, corresponding-author details, funding, competing interests, permissions, ethics approvals where relevant, data and code availability, figure legibility, table accuracy, reference completeness and any declarations required by Elsevier. The current guide for authors should be treated as the controlling source for formatting and submission requirements.
Publication Ethics
Authors should follow Elsevier and Cell Press policies on originality, authorship, competing interests, research integrity, duplicate submission, citation practice, data integrity and use of generative AI or AI-assisted tools. A manuscript should not be under simultaneous consideration elsewhere. Contributors should be credited appropriately, and all listed authors should take responsibility for the work consistent with the publisher’s authorship requirements.
Image manipulation, fabricated or selectively altered data, invented references, undisclosed conflicts, plagiarism, redundant publication and deceptive authorship practices are incompatible with ethical submission. Where AI-assisted tools are used in manuscript preparation, authors should check the publisher’s current disclosure policy and remain responsible for accuracy, originality, confidentiality and final content.
How Contentxprtz Can Support Publication Readiness
Contentxprtz can support authors who already have legitimate research and need help presenting it clearly and professionally. Relevant services may include academic editing, manuscript editing, language polishing, proofreading, journal-format checks, reference and citation consistency, manuscript assessment, cover-letter editing, reviewer-response editing, thesis-to-article preparation and publication-readiness review.
Our role is preparatory and editorial. We can help an author improve clarity, structure, consistency, journal alignment and compliance with stated instructions, but we cannot influence editorial decisions or guarantee publication. The research design, data, analysis, interpretation, authorship, declarations and final submission remain the responsibility of the researchers.
Explore ethical journal publication support from Contentxprtz
Authoritative Sources to Recheck Before Submission
- Patterns on ScienceDirect
- Elsevier Journal Insights for Patterns
- Scopus source profile: 21101028388
- ISSN Portal record for 2666-3899
- DOAJ record for Patterns
Frequently Asked Questions About Patterns
Is Patterns indexed in Scopus?
Yes. The supplied Scopus Source Record ID is 21101028388, and authoritative ISSN and Elsevier records identify Patterns as covered by Scopus. Because database coverage and source status can change, authors should still open the current Scopus source profile before using the journal for an institutional or PhD requirement.
What is the ISSN of Patterns?
Patterns is an online journal with ISSN 2666-3899. The ISSN Portal lists the key title as Patterns (New York), identifies Cell Press/Elsevier as the publisher, and records publication from 2020 onward.
Who publishes Patterns?
Patterns is published by Cell Press, an Elsevier imprint. Official Cell Press information describes it as a gold open-access journal focused on data science across disciplines.
What is the main scope of Patterns?
Patterns covers data science in a deliberately broad sense. It publishes computational and data-heavy work from many disciplines and also considers questions involving data ethics, philosophy, policy, infrastructure, methods, software, datasets, and the wider effects of data-driven decisions.
What article types appear in Patterns?
Recent official ScienceDirect issues show research articles, reviews, perspectives, resources, descriptors, and People of Data features. The appropriate format depends on the contribution, so authors should confirm the current article-type instructions before preparing a submission.
Is Patterns an open-access journal?
Yes. Elsevier lists Patterns as open access, and DOAJ records it as an open-access journal. Published articles are made openly available under the license options offered by the journal.
What is the APC for Patterns?
Elsevier Journal Insights currently lists an article publishing charge of USD 4,900 excluding taxes. Waivers, agreements, institutional arrangements, geographic programs, or publisher policies may change the amount paid, so authors should verify the fee during submission.
How long does Patterns take to make a decision?
Elsevier Journal Insights currently reports 5 days from submission to first decision and 134 days from submission to acceptance. These are publisher-reported journal timelines, not guarantees for an individual manuscript; complex review or revision cycles can take longer.
What is the acceptance rate of Patterns?
A verified official acceptance rate was not found in the authoritative sources reviewed for this profile. Authors should avoid relying on unofficial acceptance-rate estimates when assessing the journal.
What is the current CiteScore, SJR, SNIP, quartile, or Impact Factor?
Patterns is indexed in Scopus and official Elsevier information lists SJR and SNIP among its abstracting and indexing information, but the accessible journal-insights snapshot reviewed for this article did not expose current numeric values. Authors should verify the latest reporting year and subject category directly in Scopus and, where applicable, Journal Citation Reports before citing a metric.
What kind of manuscript is a strong fit for Patterns?
A strong candidate usually has a clear data-science contribution that matters beyond a narrow application: a reusable method, data resource, computational approach, cross-disciplinary solution, responsible-data insight, or analysis with broader methodological significance. The journal describes itself as highly selective, so technical correctness alone may not establish fit.
Does a domain-specific study fit Patterns?
Potentially. Patterns explicitly welcomes data-heavy work from many research domains, but the manuscript should explain why the data-science advance, method, resource, workflow, or insight is valuable beyond the immediate domain. Authors should compare their paper with recent Patterns articles before submission.
What should authors prepare before submitting?
Authors should verify the current guide for authors, select the correct article type, prepare a concise and accurate title and abstract, explain the data-science contribution clearly, provide reproducible methods and appropriate data/code availability information, check figures and tables, disclose funding and competing interests, and ensure references and authorship information are accurate.
Does Contentxprtz guarantee publication in Patterns?
No. Contentxprtz can assist with editing, proofreading, manuscript assessment, formatting, reference consistency, cover-letter refinement, journal-fit review, and reviewer-response editing. Editorial assessment, peer review, acceptance, rejection, and publication decisions remain entirely with Patterns and its publisher.
Where should I verify the latest Patterns requirements?
Use the official Patterns pages on ScienceDirect/Cell Press, the current Elsevier guide for authors, the Scopus source profile for Source ID 21101028388, the ISSN Portal, and DOAJ. Recheck these sources immediately before submission because fees, timelines, editorial information, indexing, and instructions can change.
Summary
Patterns is a Cell Press/Elsevier gold open-access journal for broad, cross-disciplinary data science. The supplied Scopus Source ID is 21101028388 and the online ISSN is 2666-3899. Its scope extends from algorithms and AI to datasets, software, research infrastructure, data management, ethics and data-driven policy or practice. Elsevier currently lists a USD 4,900 APC excluding taxes and journal-level timelines of 5 days to first decision and 134 days from submission to acceptance.
Researchers should evaluate the journal on fit, selectivity, article type, reproducibility expectations and institutional requirements rather than on indexing alone. Current metric values, quartiles, fees and submission instructions should be verified directly from official sources at the time of submission. Contentxprtz may help authors improve manuscript clarity and readiness, but acceptance and publication decisions remain entirely with Patterns.
