Data Mining and Knowledge Discovery Template
Get your Data Mining and Knowledge Discovery paper right the first time: a ready-made structure with the right abstract and citations, so editors see your science, not formatting slips.
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How to Use the Data Mining and Knowledge Discovery Template | A Step-by-Step Guide
Authored by Sumalatha Gangadhar | Published on 10 August, 2026
Submitting to Data Mining and Knowledge Discovery goes smoothly when your draft already matches the journal's own rules. Editors screen new manuscripts against the Data Mining and Knowledge Discovery guide for authors before peer review, so formatting, structure, and required statements should be right on the first draft rather than repaired during upload. This template sets that structure up for you.
What is the Data Mining and Knowledge Discovery Template?
It is a manuscript shell aligned to Data Mining and Knowledge Discovery. It covers front matter, the abstract the journal expects, a body organized to the journal's article types, and a reference list in the journal's required style. Confirm current scope and article types on the Data Mining and Knowledge Discovery journal page before you draft.
What's actually in a Data Mining Knowledge Discovery submission:
Prepare the title, authors, affiliations, and ORCID iDs. Write the abstract to the journal's rule and word limit, organize the body to the article type, and apply the required reference style from the first citation. Complete every declaration (ethics, competing interests, funding, data availability) the Data Mining and Knowledge Discovery guide for authors lists, and cite figures and tables in order.
What separates a clean submission from one that gets kicked back:
The common failure modes are formatting or reference style that does not match the guide for authors, missing required statements, and an incomplete portal checklist. Clear all three against the Data Mining and Knowledge Discovery guide for authors before you submit.
What does this Data Mining and Knowledge Discovery Template include?
The shell mirrors what editorial staff check first: article-type fit, the abstract rules, required declarations, and the journal's reference style. For more detail see the author resources. When your draft is ready, upload it through the journal's submission system.
Steps to Use This Template with SciSpace Agent
- Click Start Writing on this page. The SciSpace Agent prompt screen opens with the template loaded.
- Use the loaded template, or upload a version your group already uses for Data Mining Knowledge Discovery.
- Edit the prompt with your topic and article type, and note any requirement from the Data Mining and Knowledge Discovery guide for authors.
- Run the prompt. The agent drafts into the correct structure and reference style.
- Use View Outputs to verify required statements and formatting are present.
- Export and submit through the journal's submission system.
Data Mining and Knowledge Discovery Template Structure at a Glance
| Element | Data Mining Knowledge Discovery requirement |
|---|---|
| Abstract | 150 to 250 words. The abstract should not contain any undefined abbreviations or unspecified references. No specification of structured vs unstructured format provided in the guidelines |
| Main text | - No specific word or page limits stated for main text by article type. The guidelines only mention that the journal does not impose 'length restrictions' as conferences do |
| Declarations | Ethics approval/consent as applicable; competing interests; funding; data availability |
| Figures/tables | Publication-ready; cited in order |
| Supplemental | Upload separately when used |
| Keywords | 4 to 6 keywords which can be used for indexing purposes. No specification of MeSH or other controlled vocabulary requirement |
| References | Author-date citation style (name and year in parentheses). Examples: (Thompson 1990), (Becker and Seligman 1996). Reference list entries should be alphabetized by last names of first author. DOIs should be included as full DOI links |
| Peer review | Single-blind peer review (authors' identities are disclosed to anonymous reviewers, as confirmed by the ECML-PKDD journal track which partners with this journal) |
| Submission | submission.springernature.com/new-submission/10618/3 |
Quick Tips for Using the Data Mining and Knowledge Discovery Template
Read the Data Mining and Knowledge Discovery guide for authors once before drafting and once before upload. Draft the abstract after results are stable. Set the reference style before adding citations. Keep a cover letter that states your contribution and fit to Data Mining and Knowledge Discovery in under a page.
Best Practices
Skim two or three recent Data Mining Knowledge Discovery papers to calibrate depth and figure style. Make the novelty clear in the first two pages. Deposit data and code when the policy on the Data Mining and Knowledge Discovery journal page requires it. Disclose any preprint or conference version with identifiers in the cover letter.
Before You Submit: Final Check
Do one focused pass against the Data Mining and Knowledge Discovery guide for authors: scope, length, reference style, required declarations, and figure quality. Then submit through the journal's submission system.
