light-figure-planning

Paper Figure and Table Design Planning

Positioning

Figures and tables serve the argument, not decoration. Each figure or table should first answer:Which claim does it support? If it were deleted, would the paper be missing a piece?

Planning Process

1. Read through the paper (or outline) + m06 results, and list all claims.
2. Match each claim with the most persuasive form of presentation.
3. Mark priorities:Must-do(supports core contribution) / Can-do(enhancement) / Deletable(redundant or weak).
4. Layout placement: which goes in the main text, which goes in the appendix, and how to group subfigures.

Checklist of Figure and Table Types (select as needed, with suggested tools)

• Concept/Method: framework diagrams, technical roadmap diagrams, model architecture diagrams, algorithm flowcharts, data flow diagrams. → For finely designed editable source, use diagrams.net (XML); for LaTeX-native use, use TikZ; for automatic layout of relations/dependencies, use Graphviz (dot); for embedded sketches in documents, use Mermaid; for life science mechanism diagrams, use BioRender.
• Data: dataset schematic diagrams, sample distributions, annotation examples. → For statistical distributions, use seaborn/matplotlib; for schematic layout, use draw.io/TikZ.
• Quantitative Results: main result comparison figures/tables, ablation figures, parameter sensitivity curves, convergence curves, ROC/PR, confusion matrices, radar charts. → matplotlib (fine layout) + seaborn (statistical convenience); for R use ggplot2; for interactive supplementary material use Plotly/Altair.
• Qualitative Results: real effect figures, model output examples, case studies, failure cases, interpretability visualizations (attention/SHAP/feature maps).
• Tables: metric summary tables, comparison tables, ablation tables, dataset statistics tables, complexity tables.

Tool Selection Quick Reference (write into the "Suggested Tools" field of the planning card)

• Statistical/Quantitative Figures: matplotlib (finest control), seaborn (automatic CI/facets), ggplot2 (R's grammar of graphics), Plotly/Altair (interactive + static export).
• Publication-grade vector graphics, many formula labels: TikZ/PGFPlots, LaTeX-native, seamless fonts and formulas.
• Framework/System Diagrams, requiring editable vector source：diagrams.net（.drawio i.e. XML, can be programmatically batch-modified).
• Relations/Dependencies/Automatic Layout: Graphviz (dot hierarchical; neato/fdp spring; sfdp large graphs).
• Embedded Sketches/Processes in Documents: Mermaid (natively rendered on GitHub/Notion, diagram-as-code).
• Life Science Mechanisms/Experimental Workflows: BioRender (50,000+ verified icons, note publication licensing).
• Final multi-panel assembly/manual vector refinement of already exported figures: Inkscape (free, CLI inkscape in.svg --export-type=pdf,svg, convert text to curves --export-text-to-path) or Adobe Illustrator (industry standard,.ai Private, non-open format, cross-tool PDF/SVG storage). Freely align and assemble heterogeneous panels (mpl-exported PDF + bitmap + text labels) into a single submission figure.

Publication-grade hard specifications (execution is constrained accordingly in the planning card)

• Resolution/format: line art vector (PDF/EPS/SVG) or 600–1200 DPI; bitmap 300–600 DPI, TIFF/PNG,never use JPEG to store scientific data. In matplotlib set pdf.fonttype=42 to avoid Type-3 fonts being rejected.
• Color scheme: for discrete use Okabe-Ito (#E69F00 #56B4E9 #009E73 #F0E442 #0072B2 #D55E00 #CC79A7), for continuous quantities use viridis/plasma/cividis; add redundant encoding beyond color (line style/marker);grayscale + color-blind dual test。
• Font: sans-serif (Arial/Helvetica), at final print size minimum 5 pt, maximum 7 pt (Nature specifications, ✅ curl measured); labels in sentence case, units in parentheses.
• Column width(set figure width in mm according to the target journal; the values below are curl-measured only for Nature, while other publishers' official websites return 403 paywalls to curl/WebFetch, so these are common values from public author guidelines; before submission always defer to the target journal's official website):
  • Nature(✅ curl measured www.nature.com/nature/for-authors/final-submission，HTTP 200): single column 89, double column 183, maximum height 170(leaving space for the caption).
  • Science (AAAS)(column-width numbers use light-figure-drawing figure_export.py JOURNAL_SPECS as the single source of truth; the three-tier system was verified via multiple online sources on 2026-06-11): single column 55(5.5 cm), double column 120(12 cm), full page 183(18.3 cm). Note that Science does not have a "175" tier.
  • Cell Press(⚠️ paywall not measured): single column 85, 1.5 column 114, full page 174. Note that full page is 174, not 178.
  • PLOS(✅ curl measured journals.plos.org/plosone/s/figures, HTTP 200): single column 83, 1.5 column 140, full page 190; accepts only TIFF/EPS, 789–2250px@300dpi, height ≤2625px, text Arial/Times/Symbol 8–12pt, single file <10MB. Column-width numbers use figure_export.py（light-figure-drawing）JOURNAL_SPECS the plos key as the single source of truth.
  • Elsevier(⚠️ paywall not measured, including db01's Computers and Electronics in Agriculture, etc.): minimum 30, single column 90, 1.5 column 140, double column/full page 190。
  • IEEE(⚠️ paywall not measured, IEEEtran double-column layout): single column ≈88.9(3.5 in), double column/full page text width ≈181.9(7.16 in)。
  • MDPI(⚠️ paywall not measured, including db01's Animals): single-column layout body width ≈170, figures are laid out at this width or an integer fraction of it; line art/combination figures are recommended at 1000 DPI, photos ≥300 DPI.
  • Chinese journals(✅ measured from published PDFs on 2026-06-11, pdfplumber word-edge clustering, already recorded in db01 risk_note):Transactions of the Chinese Society of Agricultural Engineering single column≈86, full page≈180(A4 double column);Scientia Agricultura Sinica、Acta Agronomica Sinica single column≈81, full page≈170(A4 double column);Transactions of the Chinese Society for Agricultural Machinery PDF entry blocked by anti-scraping, markto be verified. Chinese journals are not in JOURNAL_SPECS, use journal="custom", custom_width_mm=<db01实测值> escape channel to export figures.
  • Export sizes in mm (e.g., ggplot2 ggsave(w,h,units='mm',dpi=300)、mpl figsize conversion).
• Statistical rigor: must include error bars (SD/SEM/CI, noted in caption), sample size n, significance markers, and plot scatter points where possible.
• Multi-panel: matplotlib GridSpec / subplots; bold uppercase A/B/C labels; consistent style and scale across panels.
  • Anti-redundancy (the core test of a composite figure): each panel must answer aunique scientific question—covering any one panel should create an information gap that cannot be filled from the other panels. Two panels using different graphics to answer the same question is redundancy. Common redundancy traps: ① the same data plotted as an "absolute-value bar" and also as an "absolute-value table"; ② one panel is a subset/superset of another; ③ two ranking plots convey the same ordering information. If redundancy is found → merge, delete, or switch to a perspective that answers a new question (e.g., change the second absolute-value plot to "deviation from baseline").
  • Recommended information progression for multiple panels: overview (panorama) → deviation (deviation/comparison) → relationship (variable relationships), supplementing layer by layer rather than repeating.

db07 lookup discipline and planning card boundaries

• Check db07 canonical first: for general chart patterns check databases/db07-figures/resources_real.md; for advanced/domain-standard charts check databases/db07-figures/figure_advanced_cards.md；figure_cards.md keep only templates + canonical index,do not write entity cards anymore。
• db07 is a reusable chart pattern library: only accumulate cross-project general figure_card. A specific paper's F1/F2, caption, target journal, and export file paths are project-level artifacts; write them to the databases/db09-projects/projects/<project_name>/version_history.md and projects/<project_name>/figures/manifest.mdunder the repository root, and do not stuff them directly back into db07.
• Selection order: first map each claim to an existing canonical figure_type; if there really is no match, then plan to add a new reusable figure_card to resources_real.md or databases/db07-figures/figure_advanced_cards.md, and keep figure_type unique across the whole library.

Planning card for each chart (handed to m11 for execution)

Fill in one per figure/tableplanning card(template templates/figure_plan_card.md). The planning card has two layers, to avoid mistakenly writing project execution fields as db07 schema:

1. db07 base fields (can be accumulated as figure_card)：figure_type, paper_source, research_field, purpose, data_required, layout, color_scheme, annotation_style, caption_style, possible_code_tool, replication_notes, where_to_place_in_paper。
2. Project execution auxiliary fields (only go into planning deliverables/manifest, not as db07 required schema)：figure_id, priority, target_journal, column, caption_draft, output_formats, source_card。

• figure_id: fixed F# named figures,T# named tables (F=Figure, T=Table, e.g., F1/F2/T1), aligned with the [图位 F1]/[表位 T1] placeholders in the m07 paper template, as anchors for figure ↔ figure number ↔ caption.
• source_card (required): record the hit db07 canonical source, e.g., databases/db07-figures/resources_real.md::跨数据集主结果对比(分组柱+误差棒), so m11 can execute according to the card and for later traceability; only when it is confirmed to be a completely new chart pattern, mark source_card: new_canonical_candidate -> databases/db07-figures/resources_real.md(or databases/db07-figures/figure_advanced_cards.md) and write it back to db07 later.
• target_journal: target journal key, take one of the figure_export.py(light-figure-drawing) JOURNAL_SPECS keys:nature/science/cell/plos/ieee/elsevier/mdpi. Determine the physical column width in mm, the minimum font size, and the preferred format; the execution skill passes them directly based on this save_for_journal, avoiding invalid physical dimensions for the entire figure caused by guessing the column width. For non-listed journals (such as Chinese journals), fill in target_journal: custom and add to the planning card custom_width_mm: <mm>(data must have a source: db01 card or measured record,guessing is prohibited), and the execution side uses the save_for_journal(..., journal="custom", custom_width_mm=...) escape channel.
• column: column width tier, take single/double/full/onehalf(must be a key that actually exists for that journal in JOURNAL_SPECS :full only science/mdpi;onehalf only plos/elsevier).target_journal: custom When , column is omitted, and custom_width_mm is used directly as the standard. Recommended tool: choose one of two: heterogeneous panel combinations (code figure + bitmap + text labels) / requiring pixel-by-pixel alignment and free fine-tuning → manual vector editing (Inkscape/Illustrator); same-source, same-type data figures, pursuing reproducibility → code generation (GridSpec/subplots). For conceptual/schematic types, first produce ASCII wireframe + copy + color scheme + icon/chart type suggestions(content-first blueprint), lock down the information architecture, then hand off for execution.

Reviewer-perspective self-check

• Does every core contribution have figure/table support?
• Are there any figures that are "pretty but convey no information"? Delete them.
• Can tables be visualized more intuitively?
• Is the panel-group logic clear (same-type alignment, consistent scale)?Each panel answers a unique scientific question, with no redundancy(covering one panel would lose unique information)?
• Can the caption be understood independently of the main text?
• Schematics are scored on 5 dimensions (0–2 each, total 10):scientific accuracy / clarity and readability / label quality / layout and composition / professionalism; journal-level target ≥8.5.
• 13-point pre-submission checklist: resolution · format · size · readability · colorblind safety · grayscale compatibility · axis labels (including units) · error bars · panel numbering · significance markers · clear legend · consistent fonts · remove decorative elements.

Connection

The planning card is handed to m11 for drawing; synchronize with m07/m08 to ensure text-figure consistency. Project-level style, figure numbers, captions, and export paths are registered in the repository root directory under databases/db09-projects/projects/<project_name>/version_history.md and projects/<project_name>/figures/manifest.md; only new chart patterns reusable across projects are written back to the db07 canonical file. The figure and table style for the full text is maintained by a07.

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For real tool endpoints, API parameters, usage of each plotting library, and known pitfalls, see references.md。