Scientific Writing

Overview

This is the core skill for the deep research and writing tool—combining AI-driven deep research with well-formatted written outputs. Every document produced is backed by comprehensive literature search and verified citations through the research-lookup skill.

Scientific writing is a process for communicating research with precision and clarity. Write manuscripts using IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, and reporting guidelines (CONSORT/STROBE/PRISMA). Apply this skill for research papers and journal submissions.

Critical Principle: Always write in full paragraphs with flowing prose. Never submit bullet points in the final manuscript. Use a two-stage process: first create section outlines with key points using research-lookup, then convert those outlines into complete paragraphs.

When to Use This Skill

This skill should be used when:

Writing or revising any section of a scientific manuscript (abstract, introduction, methods, results, discussion)
Structuring a research paper using IMRAD or other standard formats
Formatting citations and references in specific styles (APA, AMA, Vancouver, Chicago, IEEE)
Creating, formatting, or improving figures, tables, and data visualizations
Applying study-specific reporting guidelines (CONSORT for trials, STROBE for observational studies, PRISMA for reviews)
Drafting abstracts that meet journal requirements (structured or unstructured)
Preparing manuscripts for submission to specific journals
Improving writing clarity, conciseness, and precision
Ensuring proper use of field-specific terminology and nomenclature
Addressing reviewer comments and revising manuscripts

Visual Enhancement with Scientific Schematics

⚠️ MANDATORY: Every scientific paper MUST include a graphical abstract plus 1-2 additional AI-generated figures using the scientific-schematics skill.

This is not optional. Scientific papers without visual elements are incomplete. Before finalizing any document:

ALWAYS generate a graphical abstract as the first visual element
Generate at minimum ONE additional schematic or diagram using scientific-schematics
Prefer 3-4 total figures for comprehensive papers (graphical abstract + methods flowchart + results visualization + conceptual diagram)

Graphical Abstract (REQUIRED)

Every scientific writeup MUST include a graphical abstract. This is a visual summary of your paper that:

Appears before or immediately after the text abstract
Captures the entire paper’s key message in one image
Is suitable for journal table of contents display
Uses landscape orientation (typically 1200x600px)

Generate the graphical abstract FIRST:

python scripts/generate_schematic.py "Graphical abstract for [paper title]: [brief description showing workflow from input → methods → key findings → conclusions]" -o figures/graphical_abstract.png

Graphical Abstract Requirements:

Content: Visual summary showing workflow, key methods, main findings, and conclusions
Style: Clean, professional, suitable for journal TOC
Elements: Include 3-5 key steps/concepts with connecting arrows or flow
Text: Minimal labels, large readable fonts
Log: [HH:MM:SS] GENERATED: Graphical abstract for paper summary

Additional Figures (GENERATE EXTENSIVELY)

⚠️ CRITICAL: Use BOTH scientific-schematics AND generate-image EXTENSIVELY throughout all documents.

Every document should be richly illustrated. Generate figures liberally - when in doubt, add a visual.

MINIMUM Figure Requirements:

Document Type	Minimum	Recommended
Research Papers	5	6-8
Literature Reviews	4	5-7
Market Research	20	25-30
Presentations	1/slide	1-2/slide
Posters	6	8-10
Grants	4	5-7
Clinical Reports	3	4-6

Use scientific-schematics EXTENSIVELY for technical diagrams:

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

Study design and methodology flowcharts (CONSORT, PRISMA, STROBE)
Conceptual framework diagrams
Experimental workflow illustrations
Data analysis pipeline diagrams
Biological pathway or mechanism diagrams
System architecture visualizations
Neural network architectures
Decision trees, algorithm flowcharts
Comparison matrices, timeline diagrams
Any technical concept that benefits from schematic visualization

Use generate-image EXTENSIVELY for visual content:

python scripts/generate_image.py "your image description" -o figures/output.png

Photorealistic illustrations of concepts
Medical/anatomical illustrations
Environmental/ecological scenes
Equipment and lab setup visualizations
Artistic visualizations, infographics
Cover images, header graphics
Product mockups, prototype visualizations
Any visual that enhances understanding or engagement

The AI will automatically:

Create publication-quality images with proper formatting
Review and refine through multiple iterations
Ensure accessibility (colorblind-friendly, high contrast)
Save outputs in the figures/ directory

When in Doubt, Generate a Figure:

Complex concept → generate a schematic
Data discussion → generate a visualization
Process description → generate a flowchart
Comparison → generate a comparison diagram
Reader benefit → generate a visual

For detailed guidance, refer to the scientific-schematics and generate-image skill documentation.

Core Capabilities

1. Manuscript Structure and Organization

IMRAD Format: Guide papers through the standard Introduction, Methods, Results, And Discussion structure used across most scientific disciplines. This includes:

Introduction: Establish research context, identify gaps, state objectives
Methods: Detail study design, populations, procedures, and analysis approaches
Results: Present findings objectively without interpretation
Discussion: Interpret results, acknowledge limitations, propose future directions

For detailed guidance on IMRAD structure, refer to references/imrad_structure.md.

Alternative Structures: Support discipline-specific formats including:

Review articles (narrative, systematic, scoping)
Case reports and case series
Meta-analyses and pooled analyses
Theoretical/modeling papers
Methods papers and protocols

2. Section-Specific Writing Guidance

Abstract Composition: Craft concise, standalone summaries (100-250 words) that capture the paper’s purpose, methods, results, and conclusions. Support both structured abstracts (with labeled sections) and unstructured single-paragraph formats.

Introduction Development: Build compelling introductions that:

Establish the research problem’s importance
Review relevant literature systematically
Identify knowledge gaps or controversies
State clear research questions or hypotheses
Explain the study’s novelty and significance

Methods Documentation: Ensure reproducibility through:

Detailed participant/sample descriptions
Clear procedural documentation
Statistical methods with justification
Equipment and materials specifications
Ethical approval and consent statements

Results Presentation: Present findings with:

Logical flow from primary to secondary outcomes
Integration with figures and tables
Statistical significance with effect sizes
Objective reporting without interpretation

Discussion Construction: Synthesize findings by:

Relating results to research questions
Comparing with existing literature
Acknowledging limitations honestly
Proposing mechanistic explanations
Suggesting practical implications and future research

3. Citation and Reference Management

Apply citation styles correctly across disciplines. For comprehensive style guides, refer to references/citation_styles.md.

Major Citation Styles:

AMA (American Medical Association): Numbered superscript citations, common in medicine
Vancouver: Numbered citations in square brackets, biomedical standard
APA (American Psychological Association): Author-date in-text citations, common in social sciences
Chicago: Notes-bibliography or author-date, humanities and sciences
IEEE: Numbered square brackets, engineering and computer science

Best Practices:

Cite primary sources when possible
Include recent literature (last 5-10 years for active fields)
Balance citation distribution across introduction and discussion
Verify all citations against original sources
Use reference management software (Zotero, Mendeley, EndNote)

4. Figures and Tables

Create effective data visualizations that enhance comprehension. For detailed best practices, refer to references/figures_tables.md.

When to Use Tables vs. Figures:

Tables: Precise numerical data, complex datasets, multiple variables requiring exact values
Figures: Trends, patterns, relationships, comparisons best understood visually

Design Principles:

Make each table/figure self-explanatory with complete captions
Use consistent formatting and terminology across all display items
Label all axes, columns, and rows with units
Include sample sizes (n) and statistical annotations
Follow the “one table/figure per 1000 words” guideline
Avoid duplicating information between text, tables, and figures

Common Figure Types:

Bar graphs: Comparing discrete categories
Line graphs: Showing trends over time
Scatterplots: Displaying correlations
Box plots: Showing distributions and outliers
Heatmaps: Visualizing matrices and patterns

5. Reporting Guidelines by Study Type

Ensure completeness and transparency by following established reporting standards. For comprehensive guideline details, refer to references/reporting_guidelines.md.

Key Guidelines:

CONSORT: Randomized controlled trials
STROBE: Observational studies (cohort, case-control, cross-sectional)
PRISMA: Systematic reviews and meta-analyses
STARD: Diagnostic accuracy studies
TRIPOD: Prediction model studies
ARRIVE: Animal research
CARE: Case reports
SQUIRE: Quality improvement studies
SPIRIT: Study protocols for clinical trials
CHEERS: Economic evaluations

Each guideline provides checklists ensuring all critical methodological elements are reported.

6. Writing Principles and Style

Apply fundamental scientific writing principles. For detailed guidance, refer to references/writing_principles.md.

Clarity:

Use precise, unambiguous language
Define technical terms and abbreviations at first use
Maintain logical flow within and between paragraphs
Use active voice when appropriate for clarity

Conciseness:

Eliminate redundant words and phrases
Favor shorter sentences (15-20 words average)
Remove unnecessary qualifiers
Respect word limits strictly

Accuracy:

Report exact values with appropriate precision
Use consistent terminology throughout
Distinguish between observations and interpretations
Acknowledge uncertainty appropriately

Objectivity:

Present results without bias
Avoid overstating findings or implications
Acknowledge conflicting evidence
Maintain professional, neutral tone

7. Writing Process: From Outline to Full Paragraphs

CRITICAL: Always write in full paragraphs, never submit bullet points in scientific papers.

Scientific papers must be written in complete, flowing prose. Use this two-stage approach for effective writing:

Stage 1: Create Section Outlines with Key Points

When starting a new section:

Use the research-lookup skill to gather relevant literature and data
Create a structured outline with bullet points marking:
- Main arguments or findings to present
- Key studies to cite
- Data points and statistics to include
- Logical flow and organization
These bullet points serve as scaffolding—they are NOT the final manuscript

Example outline (Introduction section):

- Background: AI in drug discovery gaining traction
  * Cite recent reviews (Smith 2023, Jones 2024)
  * Traditional methods are slow and expensive
- Gap: Limited application to rare diseases
  * Only 2 prior studies (Lee 2022, Chen 2023)
  * Small datasets remain a challenge
- Our approach: Transfer learning from common diseases
  * Novel architecture combining X and Y
- Study objectives: Validate on 3 rare disease datasets

Stage 2: Convert Key Points to Full Paragraphs

Once the outline is complete, expand each bullet point into proper prose:

Transform bullet points into complete sentences with subjects, verbs, and objects
Add transitions between sentences and ideas (however, moreover, in contrast, subsequently)
Integrate citations naturally within sentences, not as lists
Expand with context and explanation that bullet points omit
Ensure logical flow from one sentence to the next within each paragraph
Vary sentence structure to maintain reader engagement

Example conversion to prose:

Artificial intelligence approaches have gained significant traction in drug discovery 
pipelines over the past decade (Smith, 2023; Jones, 2024). While these computational 
methods show promise for accelerating the identification of therapeutic candidates, 
traditional experimental approaches remain slow and resource-intensive, often requiring 
years of laboratory work and substantial financial investment. However, the application 
of AI to rare diseases has been limited, with only two prior studies demonstrating 
proof-of-concept results (Lee, 2022; Chen, 2023). The primary obstacle has been the 
scarcity of training data for conditions affecting small patient populations. 

To address this challenge, we developed a transfer learning approach that leverages 
knowledge from well-characterized common diseases to predict therapeutic targets for 
rare conditions. Our novel neural architecture combines convolutional layers for 
molecular feature extraction with attention mechanisms for protein-ligand interaction 
modeling. The objective of this study was to validate our approach across three 
independent rare disease datasets, assessing both predictive accuracy and biological 
interpretability of the results.

Key Differences Between Outlines and Final Text:

Outline (Planning Stage)	Final Manuscript
Bullet points and fragments	Complete sentences and paragraphs
Telegraphic notes	Full explanations with context
List of citations	Citations integrated into prose
Abbreviated ideas	Developed arguments with transitions
For your eyes only	For publication and peer review

Common Mistakes to Avoid:

❌ Never leave bullet points in the final manuscript
❌ Never submit lists where paragraphs should be
❌ Don’t use numbered or bulleted lists in Results or Discussion sections (except for specific cases like study hypotheses or inclusion criteria)
❌ Don’t write sentence fragments or incomplete thoughts
✅ Do use occasional lists only in Methods (e.g., inclusion/exclusion criteria, materials lists)
✅ Do ensure every section flows as connected prose
✅ Do read paragraphs aloud to check for natural flow

When Lists ARE Acceptable (Limited Cases):

Lists may appear in scientific papers only in specific contexts:

Methods: Inclusion/exclusion criteria, materials and reagents, participant characteristics
Supplementary Materials: Extended protocols, equipment lists, detailed parameters
Never in: Abstract, Introduction, Results, Discussion, Conclusions

Abstract Format Rule:

❌ NEVER use labeled sections (Background:, Methods:, Results:, Conclusions:)
✅ ALWAYS write as flowing paragraph(s) with natural transitions
Exception: Only use structured format if journal explicitly requires it in author guidelines

Integration with Research Lookup:

The research-lookup skill is essential for Stage 1 (creating outlines):

Search for relevant papers using research-lookup
Extract key findings, methods, and data
Organize findings as bullet points in your outline
Then convert the outline to full paragraphs in Stage 2

This two-stage process ensures you:

Gather and organize information systematically
Create logical structure before writing
Produce polished, publication-ready prose
Maintain focus on the narrative flow

8. Professional Report Formatting (Non-Journal Documents)

For research reports, technical reports, white papers, and other professional documents that are NOT journal manuscripts, use the scientific_report.sty LaTeX style package for a polished, professional appearance.

When to Use Professional Report Formatting:

Research reports and technical reports
White papers and policy briefs
Grant reports and progress reports
Industry reports and technical documentation
Internal research summaries
Feasibility studies and project deliverables

When NOT to Use (Use Venue-Specific Formatting Instead):

Journal manuscripts → Use venue-templates skill
Conference papers → Use venue-templates skill
Academic theses → Use institutional templates

The scientific_report.sty Style Package Provides:

Feature	Description
Typography	Helvetica font family for modern, professional appearance
Color Scheme	Professional blues, greens, and accent colors
Box Environments	Colored boxes for key findings, methods, recommendations, limitations
Tables	Alternating row colors, professional headers
Figures	Consistent caption formatting
Scientific Commands	Shortcuts for p-values, effect sizes, confidence intervals

Box Environments for Content Organization:

% Key findings (blue) - for major discoveries
\begin{keyfindings}[Title]
Content with key findings and statistics.
\end{keyfindings}

% Methodology (green) - for methods highlights
\begin{methodology}[Study Design]
Description of methods and procedures.
\end{methodology}

% Recommendations (purple) - for action items
\begin{recommendations}[Clinical Implications]
\begin{enumerate}
    \item Specific recommendation 1
    \item Specific recommendation 2
\end{enumerate}
\end{recommendations}

% Limitations (orange) - for caveats and cautions
\begin{limitations}[Study Limitations]
Description of limitations and their implications.
\end{limitations}

Professional Table Formatting:

\begin{table}[htbp]
\centering
\caption{Results Summary}
\begin{tabular}{@{}lccc@{}}
\toprule
\textbf{Variable} & \textbf{Treatment} & \textbf{Control} & \textbf{p} \\
\midrule
Outcome 1 & \meansd{42.5}{8.3} & \meansd{35.2}{7.9} & <.001\sigthree \\
\rowcolor{tablealt} Outcome 2 & \meansd{3.8}{1.2} & \meansd{3.1}{1.1} & .012\sigone \\
Outcome 3 & \meansd{18.2}{4.5} & \meansd{17.8}{4.2} & .58\signs \\
\bottomrule
\end{tabular}

{\small \siglegend}
\end{table}

Scientific Notation Commands:

Command	Output	Purpose
`\pvalue{0.023}`	p = 0.023	P-values
`\psig{< 0.001}`	p = < 0.001	Significant p-values (bold)
`\CI{0.45}{0.72}`	95% CI [0.45, 0.72]	Confidence intervals
`\effectsize{d}{0.75}`	d = 0.75	Effect sizes
`\samplesize{250}`	n = 250	Sample sizes
`\meansd{42.5}{8.3}`	42.5 ± 8.3	Mean with SD
`\sigone`, `\sigtwo`, `\sigthree`	, , **	Significance stars

Getting Started:

\documentclass[11pt,letterpaper]{report}
\usepackage{scientific_report}

\begin{document}
\makereporttitle
    {Report Title}
    {Subtitle}
    {Author Name}
    {Institution}
    {Date}

% Your content with professional formatting
\end{document}

Compilation: Use XeLaTeX or LuaLaTeX for proper Helvetica font rendering:

xelatex report.tex

For complete documentation, refer to:

assets/scientific_report.sty: The style package
assets/scientific_report_template.tex: Complete template example
assets/REPORT_FORMATTING_GUIDE.md: Quick reference guide
references/professional_report_formatting.md: Comprehensive formatting guide

9. Journal-Specific Formatting

Adapt manuscripts to journal requirements:

Follow author guidelines for structure, length, and format
Apply journal-specific citation styles
Meet figure/table specifications (resolution, file formats, dimensions)
Include required statements (funding, conflicts of interest, data availability, ethical approval)
Adhere to word limits for each section
Format according to template requirements when provided

10. Field-Specific Language and Terminology

Adapt language, terminology, and conventions to match the specific scientific discipline. Each field has established vocabulary, preferred phrasings, and domain-specific conventions that signal expertise and ensure clarity for the target audience.

Identify Field-Specific Linguistic Conventions:

Review terminology used in recent high-impact papers in the target journal
Note field-specific abbreviations, units, and notation systems
Identify preferred terms (e.g., “participants” vs. “subjects,” “compound” vs. “drug,” “specimens” vs. “samples”)
Observe how methods, organisms, or techniques are typically described

Biomedical and Clinical Sciences:

Use precise anatomical and clinical terminology (e.g., “myocardial infarction” not “heart attack” in formal writing)
Follow standardized disease nomenclature (ICD, DSM, SNOMED-CT)
Specify drug names using generic names first, brand names in parentheses if needed
Use “patients” for clinical studies, “participants” for community-based research
Follow Human Genome Variation Society (HGVS) nomenclature for genetic variants
Report lab values with standard units (SI units in most international journals)

Molecular Biology and Genetics:

Use italics for gene symbols (e.g., TP53), regular font for proteins (e.g., p53)
Follow species-specific gene nomenclature (uppercase for human: BRCA1; sentence case for mouse: Brca1)
Specify organism names in full at first mention, then use accepted abbreviations (e.g., Escherichia coli, then E. coli)
Use standard genetic notation (e.g., +/+, +/-, -/- for genotypes)
Employ established terminology for molecular techniques (e.g., “quantitative PCR” or “qPCR,” not “real-time PCR”)

Chemistry and Pharmaceutical Sciences:

Follow IUPAC nomenclature for chemical compounds
Use systematic names for novel compounds, common names for well-known substances
Specify chemical structures using standard notation (e.g., SMILES, InChI for databases)
Report concentrations with appropriate units (mM, μM, nM, or % w/v, v/v)
Describe synthesis routes using accepted reaction nomenclature
Use terms like “bioavailability,” “pharmacokinetics,” “IC50” consistently with field definitions

Ecology and Environmental Sciences:

Use binomial nomenclature for species (italicized: Homo sapiens)
Specify taxonomic authorities at first species mention when relevant
Employ standardized habitat and ecosystem classifications
Use consistent terminology for ecological metrics (e.g., “species richness,” “Shannon diversity index”)
Describe sampling methods with field-standard terms (e.g., “transect,” “quadrat,” “mark-recapture”)

Physics and Engineering:

Follow SI units consistently unless field conventions dictate otherwise
Use standard notation for physical quantities (scalars vs. vectors, tensors)
Employ established terminology for phenomena (e.g., “quantum entanglement,” “laminar flow”)
Specify equipment with model numbers and manufacturers when relevant
Use mathematical notation consistent with field standards (e.g., ℏ for reduced Planck constant)

Neuroscience:

Use standardized brain region nomenclature (e.g., refer to atlases like Allen Brain Atlas)
Specify coordinates for brain regions using established stereotaxic systems
Follow conventions for neural terminology (e.g., “action potential” not “spike” in formal writing)
Use “neural activity,” “neuronal firing,” “brain activation” appropriately based on measurement method
Describe recording techniques with proper specificity (e.g., “whole-cell patch clamp,” “extracellular recording”)

Social and Behavioral Sciences:

Use person-first language when appropriate (e.g., “people with schizophrenia” not “schizophrenics”)
Employ standardized psychological constructs and validated assessment names
Follow APA guidelines for reducing bias in language
Specify theoretical frameworks using established terminology
Use “participants” rather than “subjects” for human research

General Principles:

Match Audience Expertise:

For specialized journals: Use field-specific terminology freely, define only highly specialized or novel terms
For broad-impact journals (e.g., Nature, Science): Define more technical terms, provide context for specialized concepts
For interdisciplinary audiences: Balance precision with accessibility, define terms at first use

Define Technical Terms Strategically:

Define abbreviations at first use: “messenger RNA (mRNA)”
Provide brief explanations for specialized techniques when writing for broader audiences
Avoid over-defining terms well-known to the target audience (signals unfamiliarity with field)
Create a glossary if numerous specialized terms are unavoidable

Maintain Consistency:

Use the same term for the same concept throughout (don’t alternate between “medication,” “drug,” and “pharmaceutical”)
Follow a consistent system for abbreviations (decide on “PCR” or “polymerase chain reaction” after first definition)
Apply the same nomenclature system throughout (especially for genes, species, chemicals)

Avoid Field Mixing Errors:

Don’t use clinical terminology for basic science (e.g., don’t call mice “patients”)
Avoid colloquialisms or overly general terms in place of precise field terminology
Don’t import terminology from adjacent fields without ensuring proper usage

Verify Terminology Usage:

Consult field-specific style guides and nomenclature resources
Check how terms are used in recent papers from the target journal
Use domain-specific databases and ontologies (e.g., Gene Ontology, MeSH terms)
When uncertain, cite a key reference that establishes terminology

11. Common Pitfalls to Avoid

Top Rejection Reasons:

Inappropriate, incomplete, or insufficiently described statistics
Over-interpretation of results or unsupported conclusions
Poorly described methods affecting reproducibility
Small, biased, or inappropriate samples
Poor writing quality or difficult-to-follow text
Inadequate literature review or context
Figures and tables that are unclear or poorly designed
Failure to follow reporting guidelines

Writing Quality Issues:

Mixing tenses inappropriately (use past tense for methods/results, present for established facts)
Excessive jargon or undefined acronyms
Paragraph breaks that disrupt logical flow
Missing transitions between sections
Inconsistent notation or terminology

Workflow for Manuscript Development

Stage 1: Planning

Identify target journal and review author guidelines
Determine applicable reporting guideline (CONSORT, STROBE, etc.)
Outline manuscript structure (usually IMRAD)
Plan figures and tables as the backbone of the paper

Stage 2: Drafting (Use two-stage writing process for each section)

Start with figures and tables (the core data story)
For each section below, follow the two-stage process:
- First: Create outline with bullet points using research-lookup
- Second: Convert bullet points to full paragraphs with flowing prose
Write Methods (often easiest to draft first)
Draft Results (describing figures/tables objectively)
Compose Discussion (interpreting findings)
Write Introduction (setting up the research question)
Craft Abstract (synthesizing the complete story)
Create Title (concise and descriptive)

Remember: Bullet points are for planning only—the final manuscript must be in complete paragraphs.

Stage 3: Revision

Check logical flow and “red thread” throughout
Verify consistency in terminology and notation
Ensure figures/tables are self-explanatory
Confirm adherence to reporting guidelines
Verify all citations are accurate and properly formatted
Check word counts for each section
Proofread for grammar, spelling, and clarity

Stage 4: Final Preparation

Format according to journal requirements
Prepare supplementary materials
Write cover letter highlighting significance
Complete submission checklists
Gather all required statements and forms

Integration with Other Scientific Skills

This skill works effectively with:

Data analysis skills: For generating results to report
Statistical analysis: For determining appropriate statistical presentations
Literature review skills: For contextualizing research
Figure creation tools: For developing publication-quality visualizations
Venue-templates skill: For venue-specific writing styles and formatting (journal manuscripts)
scientific_report.sty: For professional reports, white papers, and technical documents

Professional Reports vs. Journal Manuscripts

Choose the right formatting approach:

Document Type	Formatting Approach
Journal manuscripts	Use `venue-templates` skill
Conference papers	Use `venue-templates` skill
Research reports	Use `scientific_report.sty` (this skill)
White papers	Use `scientific_report.sty` (this skill)
Technical reports	Use `scientific_report.sty` (this skill)
Grant reports	Use `scientific_report.sty` (this skill)

Venue-Specific Writing Styles

Before writing for a specific venue, consult the venue-templates skill for writing style guides:

Different venues have dramatically different writing expectations:

Nature/Science: Accessible, story-driven, broad significance
Cell Press: Mechanistic depth, graphical abstracts, Highlights
Medical journals (NEJM, Lancet): Structured abstracts, evidence language
ML conferences (NeurIPS, ICML): Contribution bullets, ablation studies
CS conferences (CHI, ACL): Field-specific conventions

The venue-templates skill provides:

venue_writing_styles.md: Master style comparison
Venue-specific guides: nature_science_style.md, cell_press_style.md, medical_journal_styles.md, ml_conference_style.md, cs_conference_style.md
reviewer_expectations.md: What reviewers look for at each venue
Writing examples in assets/examples/

Workflow: First use this skill for general scientific writing principles (IMRAD, clarity, citations), then consult venue-templates for venue-specific style adaptation.

References

This skill includes comprehensive reference files covering specific aspects of scientific writing:

references/imrad_structure.md: Detailed guide to IMRAD format and section-specific content
references/citation_styles.md: Complete citation style guides (APA, AMA, Vancouver, Chicago, IEEE)
references/figures_tables.md: Best practices for creating effective data visualizations
references/reporting_guidelines.md: Study-specific reporting standards and checklists
references/writing_principles.md: Core principles of effective scientific communication
references/professional_report_formatting.md: Guide to professional report styling with scientific_report.sty

Assets

This skill includes LaTeX style packages and templates for professional report formatting:

assets/scientific_report.sty: Professional LaTeX style package with Helvetica fonts, colored boxes, and attractive tables
assets/scientific_report_template.tex: Complete report template demonstrating all style features
assets/REPORT_FORMATTING_GUIDE.md: Quick reference guide for the style package

Key Features of scientific_report.sty:

Helvetica font family for modern, professional appearance
Professional color scheme (blues, greens, oranges, purples)
Box environments: keyfindings, methodology, resultsbox, recommendations, limitations, criticalnotice, definition, executivesummary, hypothesis
Tables with alternating row colors and professional headers
Scientific notation commands for p-values, effect sizes, confidence intervals
Professional headers and footers

For venue-specific writing styles (tone, voice, abstract format, reviewer expectations), see the venue-templates skill which provides comprehensive style guides for Nature/Science, Cell Press, medical journals, ML conferences, and CS conferences.

Load these references as needed when working on specific aspects of scientific writing.

Reference: Citation_Styles

Citation Styles Guide

Overview

Citation styles provide standardized formats for acknowledging sources in scientific writing. Different disciplines prefer different styles, and journals typically specify which style to use. The five most common citation styles in science are AMA, Vancouver, APA, Chicago, and IEEE.

Choosing the Right Style

Style	Primary Disciplines	In-Text Format
AMA	Medicine, health sciences	Superscript numbers¹
Vancouver	Biomedical sciences	Numbers in brackets [1]
APA	Psychology, social sciences, education	Author-date (Smith, 2023)
Chicago	Humanities, history, some sciences	Notes-bibliography or author-date
IEEE	Engineering, computer science	Numbers in brackets [1]
ACS	Chemistry	Superscript numbers¹ or (1)
NLM	Life sciences, PubMed	Numbers in brackets [1]

Default recommendation: When in doubt, check the journal’s author guidelines. Most biomedical journals use Vancouver or AMA style.

AMA Style (American Medical Association)

Overview

Used primarily in medical research
Based on the AMA Manual of Style (11th edition, 2020)
Numbered citations appearing as superscripts
References listed numerically in order of appearance

In-Text Citations

Basic format: Superscript numerals outside periods and commas, inside semicolons and colons.

Examples:

Several studies have demonstrated this effect.¹

The results were inconclusive,² although Smith et al³ reported otherwise.

These findings³⁻⁵ suggest a correlation.

One meta-analysis⁶ found significant heterogeneity; however, the pooled effect was significant.⁷

Multiple citations: Use commas or hyphens for ranges

Multiple studies¹,³,⁵⁻⁷ have confirmed this.

Same source cited multiple times: Use the same number throughout

Reference List Format

Journal Articles:

1. Author AA, Author BB, Author CC. Title of article. Journal Name. Year;Volume(Issue):Page range. doi:xx.xxxx

Example:

1. Smith JD, Johnson AB, Williams CD. Effectiveness of cognitive behavioral therapy for anxiety disorders. JAMA Psychiatry. 2023;80(5):456-464. doi:10.1001/jamapsychiatry.2023.0123

Books:

2. Author AA. Book Title. Edition. Publisher; Year.

Book Chapters:

3. Chapter Author AA. Chapter title. In: Editor AA, Editor BB, eds. Book Title. Edition. Publisher; Year:Page range.

Online Resources:

4. Organization Name. Page title. Website name. Published date. Updated date. Accessed date. URL

Special Cases

More than 6 authors: List first 3, then “et al”

Smith JD, Jones AB, Williams CD, et al.

No author: Begin with title

Advance online publication:

Published online Month Day, Year. doi:xx.xxxx

Vancouver Style

Overview

Developed by the International Committee of Medical Journal Editors (ICMJE)
Described in Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals
Also called “author-number style”
Numbered citations in square brackets
References listed numerically

In-Text Citations

Basic format: Numbers in square brackets after the relevant text, before periods and commas.

Examples:

Several studies have shown this effect [1].

The results were inconclusive [2], although Smith et al [3] reported otherwise.

These findings [3-5] suggest a correlation.

Multiple studies [1,3,5-7] have confirmed this.

Reference List Format

Journal Articles:

1. Author AA, Author BB, Author CC. Title of article. Journal Name. Year;Volume(Issue):Page range.

Example:

1. Smith JD, Johnson AB, Williams CD. Effectiveness of cognitive behavioral therapy for anxiety disorders. JAMA Psychiatry. 2023;80(5):456-464.

Books:

2. Author AA, Author BB. Book title. Edition. Place of publication: Publisher; Year.

Book Chapters:

3. Chapter Author AA, Chapter Author BB. Chapter title. In: Editor AA, Editor BB, editors. Book title. Edition. Place: Publisher; Year. p. Page range.

Electronic Sources:

4. Author AA. Title of page [Internet]. Place: Publisher; Date of publication [cited Date of citation]. Available from: URL

Special Cases

More than 6 authors: List first 6, then “et al.”

Journal title abbreviations: Use PubMed/Index Medicus abbreviations

The Journal of the American Medical Association → JAMA
Nature Medicine → Nat Med

No volume or issue: Use year and page numbers only

Article in press: Use “[Epub ahead of print]” notation

APA Style (American Psychological Association)

Overview

Widely used in psychology, education, and social sciences
Based on the Publication Manual of the APA (7th edition, 2020)
Author-date format for in-text citations
References listed alphabetically by author surname

In-Text Citations

Basic format: (Author, Year)

Examples:

One study found significant effects (Smith, 2023).

Smith (2023) found significant effects.

Multiple studies (Jones, 2020; Smith, 2023; Williams, 2024) support this conclusion.

Two authors: Use ”&” in parentheses, “and” in narrative

(Smith & Jones, 2023)
Smith and Jones (2023) demonstrated...

Three or more authors: Use “et al.” after first author

(Smith et al., 2023)
Smith et al. (2023) reported...

Multiple works by same author(s) in same year: Add letters

(Smith, 2023a, 2023b)

Direct quotations: Include page numbers

(Smith, 2023, p. 45)
"Quote text" (Smith, 2023, p. 45).
Smith (2023) stated, "Quote text" (p. 45).

Reference List Format

Journal Articles:

Author, A. A., Author, B. B., & Author, C. C. (Year). Title of article. Journal Name, Volume(Issue), page range. https://doi.org/xx.xxxx

Example:

Smith, J. D., Johnson, A. B., & Williams, C. D. (2023). Effectiveness of cognitive behavioral therapy for anxiety disorders. JAMA Psychiatry, 80(5), 456-464. https://doi.org/10.1001/jamapsychiatry.2023.0123

Books:

Author, A. A. (Year). Book title: Subtitle (Edition). Publisher. https://doi.org/xx.xxxx

Book Chapters:

Chapter Author, A. A., & Chapter Author, B. B. (Year). Chapter title. In E. E. Editor & F. F. Editor (Eds.), Book title (pp. page range). Publisher.

Websites:

Author, A. A. (Year, Month Day). Page title. Website Name. URL

Capitalization Rules

Sentence case for article and book titles (capitalize only first word and proper nouns)
Title case for journal names (capitalize all major words)

Example:

Smith, J. D. (2023). Effects of stress on cognitive performance: A meta-analysis. Journal of Experimental Psychology: General, 152(3), 456-478.

Special Cases

No author: Move title to author position

Title of work. (Year). Journal Name...

No date: Use (n.d.)

Smith, J. D. (n.d.). Title...

Up to 20 authors: List all authors with ”&” before last 21 or more authors: List first 19, then ”…”, then final author

Chicago Style

Overview

Based on The Chicago Manual of Style (17th edition, 2017)
Two systems: Notes-Bibliography and Author-Date
Notes-Bibliography common in humanities
Author-Date common in sciences

Notes-Bibliography System

In-Text: Superscript numbers for footnotes or endnotes

One study demonstrated this effect.¹

Note format:

1. John D. Smith, Alice B. Johnson, and Carol D. Williams, "Effectiveness of Cognitive Behavioral Therapy for Anxiety Disorders," JAMA Psychiatry 80, no. 5 (2023): 456-64.

Bibliography format:

Smith, John D., Alice B. Johnson, and Carol D. Williams. "Effectiveness of Cognitive Behavioral Therapy for Anxiety Disorders." JAMA Psychiatry 80, no. 5 (2023): 456-64.

Author-Date System

In-Text: Similar to APA

(Smith, Johnson, and Williams 2023)
Smith, Johnson, and Williams (2023) found...

Reference list: Similar to APA but with different punctuation

Smith, John D., Alice B. Johnson, and Carol D. Williams. 2023. "Effectiveness of Cognitive Behavioral Therapy for Anxiety Disorders." JAMA Psychiatry 80 (5): 456-64.

Special Features

Full names in bibliography (not just initials)
Uses “and” not ”&”
Different punctuation from APA

IEEE Style

Overview

Used in engineering, computer science, and technology
Published by the Institute of Electrical and Electronics Engineers
Numbered citations in square brackets
References listed numerically

In-Text Citations

Format: Numbers in square brackets

Examples:

Several studies have demonstrated this effect [1].

The algorithm was described by Smith [2] and later improved [3], [4].

Multiple implementations [1]-[4] have been proposed.

Reference List Format

Journal Articles:

[1] A. A. Author, B. B. Author, and C. C. Author, "Title of article," Journal Name, vol. X, no. X, pp. XX-XX, Month Year.

Example:

[1] J. D. Smith, A. B. Johnson, and C. D. Williams, "Effectiveness of cognitive behavioral therapy for anxiety disorders," JAMA Psychiatry, vol. 80, no. 5, pp. 456-464, May 2023.

Books:

[2] A. A. Author, Book Title, Edition. City, State: Publisher, Year.

Conference Papers:

[3] A. A. Author, "Paper title," in Proc. Conference Name, City, State, Year, pp. XX-XX.

Online Sources:

[4] A. A. Author. "Title." Website. URL (accessed Mon. Day, Year).

Special Features

Abbreviated first and middle names
Uses “and” before last author (not comma)
Month abbreviations (Jan., Feb., etc.)
“vol.” and “no.” before volume and issue
“pp.” before page range

Additional Styles

ACS Style (American Chemical Society)

In-Text: Superscript numbers or numbers in parentheses

This reaction has been well studied.¹
This reaction has been well studied (1).

Reference format:

(1) Smith, J. D.; Johnson, A. B.; Williams, C. D. Title of Article. J. Am. Chem. Soc. 2023, 145, 1234-1245.

Features:

Semicolons between authors
Abbreviated journal names
Year in bold
No issue numbers

NLM Style (National Library of Medicine)

Very similar to Vancouver, used by PubMed/MEDLINE

Key differences:

Uses PubMed journal abbreviations
Specific format for electronic publications
PMID or PMCID can be included

Example:

Smith JD, Johnson AB, Williams CD. Effectiveness of cognitive behavioral therapy for anxiety disorders. JAMA Psychiatry. 2023 May;80(5):456-64. doi: 10.1001/jamapsychiatry.2023.0123. PMID: 12345678.

General Citation Best Practices

Across All Styles

When to cite:

Direct quotations
Paraphrased ideas from others
Statistics, data, or figures from other sources
Theories, models, or frameworks developed by others
Information that is not common knowledge

Citation density:

Introduction: Cite liberally to establish context
Methods: Cite when referencing established protocols or instruments
Results: Rarely cite (focus on your own findings)
Discussion: Cite frequently when comparing to prior work

Source quality:

Prefer peer-reviewed journal articles
Cite original sources when possible (not secondary citations)
Use recent sources (within 5-10 years for active fields)
Ensure sources are reputable and relevant

Common mistakes to avoid:

Inconsistent formatting
Missing required elements (DOI, page numbers, etc.)
Citing sources not actually read (citation chaining)
Over-reliance on review articles instead of primary sources
Including uncited references or missing cited references
Incorrect author names or initials
Wrong year of publication
Truncated titles

Managing Citations

Reference Management Software:

Zotero: Free, open-source, browser integration
Mendeley: Free, PDF annotation, social features
EndNote: Commercial, powerful, institutional support
RefWorks: Web-based, institutional subscriptions

Software benefits:

Automatic formatting in multiple styles
In-text citation insertion
Reference list generation
PDF organization
Sharing capabilities

Verifying Citations

Before submission, check:

Every in-text citation has a corresponding reference
Every reference is cited in text
Formatting is consistent throughout
Author names and initials are correct
Titles are accurate
Journal names match required abbreviations
Volume, issue, and page numbers are correct
DOIs are included (when required)
URLs are functional (for web sources)
Citations appear in correct order (numerical styles)

DOI (Digital Object Identifier)

What is a DOI?

A unique alphanumeric string identifying digital content permanently.

Format:

doi:10.1001/jamapsychiatry.2023.0123
or
https://doi.org/10.1001/jamapsychiatry.2023.0123

When to include:

Required by most journals for recent publications
Preferred over URLs because DOIs don’t change
Look up DOIs at https://www.crossref.org/ if not provided

Style-specific formatting:

AMA: doi:10.xxxx/xxxxx
APA: https://doi.org/10.xxxx/xxxxx
Vancouver: Often omitted or added at journal’s discretion
Chicago: https://doi.org/10.xxxx/xxxxx

Quick Reference: Journal Article Format

Style	Format
AMA	Author AA, Author BB. Title of article. Journal. Year;Vol(Iss):pp. doi:xx
Vancouver	Author AA, Author BB. Title of article. Journal. Year;Vol(Iss):pp.
APA	Author, A. A., & Author, B. B. (Year). Title of article. Journal, Vol(Iss), pp. https://doi.org/xx
Chicago A-D	Author, A. A., and B. B. Author. Year. “Title.” Journal Vol (Iss): pp.
IEEE	A. A. Author and B. B. Author, “Title,” Journal, vol. X, no. X, pp. XX-XX, Mon. Year.

Common Abbreviations

Journal Abbreviations

Follow the journal’s specified system (usually Index Medicus or ISO):

The Journal of Biological Chemistry → J Biol Chem
Proceedings of the National Academy of Sciences → Proc Natl Acad Sci USA
Nature Medicine → Nat Med

Month Abbreviations

Jan., Feb., Mar., Apr., May, June, July, Aug., Sept., Oct., Nov., Dec.
Some styles use three-letter abbreviations without periods

Edition Abbreviations

1st ed., 2nd ed., 3rd ed., etc.
Or: 1st edition, 2nd edition

Special Publication Types

Preprints

APA: Author, A. A. (Year). Title [Preprint]. Repository Name. https://doi.org/xx.xxxx

Theses and Dissertations

APA: Author, A. A. (Year). Title [Doctoral dissertation, University Name]. Repository Name. URL

Conference Proceedings

IEEE: A. A. Author, "Title," in Proc. Conf. Name, City, Year, pp. XX-XX.

Software/Code

APA: Author, A. A. (Year). Title (Version X.X) [Computer software]. Publisher. URL

Datasets

APA: Author, A. A. (Year). Title of dataset (Version X) [Data set]. Repository. https://doi.org/xx.xxxx

Transitioning Between Styles

When converting between citation styles:

Use reference management software for automatic conversion
Check these elements that vary by style:
- In-text citation format (numbered vs. author-date)
- Author name format (initials vs. full names)
- Title capitalization (sentence case vs. title case)
- Journal name formatting (abbreviated vs. full)
- Punctuation (periods, commas, semicolons)
- Use of italics and bold
- Order of elements
Manually verify after automatic conversion
Check journal guidelines for specific requirements

Journal-Specific Citation Styles and Requirements

How to Identify a Journal’s Citation Style

Step 1: Check Author Guidelines

Every journal provides author instructions (usually “Instructions for Authors” or “Author Guidelines”)
Citation style is typically specified in “References” or “Citations” section
Look for example references formatted in the journal’s style

Step 2: Review Recent Publications

Examine 3-5 recent articles from your target journal
Note the in-text citation format (numbered vs. author-date)
Compare reference list formatting
Check for journal-specific variations

Step 3: Verify Journal-Specific Variations Some journals use modified versions of standard styles:

Abbreviated vs. full journal names
DOI inclusion requirements
Article titles in title case vs. sentence case
Maximum number of authors before “et al.”

Common Journals and Their Citation Styles

Journal	Citation Style	Key Features
JAMA, JAMA Network journals	AMA	Superscript numbers, abbreviated journal names, no issue numbers
New England Journal of Medicine	Modified Vancouver	Numbered brackets, abbreviated journals, limited authors (3 then et al)
The Lancet	Vancouver	Numbered brackets, PubMed abbreviations
BMJ	Vancouver	Numbered in-text, DOIs required when available
Nature, Nature journals	Nature style (numbered)	Numbered superscripts, abbreviated journals, no article titles in some journals
Science	Science style (numbered)	Numbered in-text, abbreviated format
Cell, Cell Press journals	Cell style (author-year)	Author-date, specific formatting for multiple citations
PLOS journals	Vancouver	Numbered brackets, full journal names in some PLOS journals
Journal of Biological Chemistry	JBC style (numbered)	Numbered in-text, specific abbreviation rules
Psychological journals	APA	Author-date, DOIs required
IEEE journals	IEEE	Numbered brackets, specific format for conference papers
ACS journals	ACS	Superscript or numbered, semicolons between authors

Journal Family Consistency

Journals from the same publisher often share citation styles:

Elsevier journals:

Vary widely; check specific journal
Many use numbered Vancouver-style
Some allow author-date

Springer Nature journals:

Nature journals: Nature style (numbered, abbreviated)
Springer journals: Often numbered or author-date depending on field
BMC journals: Vancouver with full journal names

Wiley journals:

Varies by field
Many biomedical journals use Vancouver
Psychology/social science journals often use APA

American Chemical Society (ACS):

All ACS journals use ACS style
Consistent across Journal of American Chemical Society, Analytical Chemistry, etc.

High-Impact Journal and Conference Preferences

Venue	Field	Citation Preference	Key Features
Nature/Science	Multidisciplinary	Numbered, abbreviated	Space-saving, broad readability
Cell family	Life sciences	Author-date or numbered	Attribution visibility
NEJM/Lancet/JAMA	Medicine	Vancouver/AMA numbered	Medical standard
NeurIPS/ICML/ICLR	Machine Learning	Numbered [1] or (Author, Year)	Varies by conference, check template
CVPR/ICCV/ECCV	Computer Vision	Numbered [1], IEEE-like	Compact format
ACL/EMNLP	NLP	Author-year (ACL style)	Attribution-focused

Adapting Citations for Different Target Journals

When switching journals after desk rejection or withdrawal:

Use reference management software:

Import references into Zotero, Mendeley, or EndNote
Select target journal’s citation style from software library
Regenerate citations and reference list automatically
Manually verify formatting matches journal examples

Key elements to check when converting:

In-text format (switch numbered ↔ author-date)
Journal name abbreviation style
Article title capitalization
Author name format (initials vs. full names)
DOI format and inclusion
Issue number inclusion/exclusion
Page number format

Manual verification essential for:

Preprints and non-standard sources
Software/datasets citations
Conference proceedings
Dissertations and theses

Venue-Specific Evaluation Criteria

Content expectations:

High-impact journals: >50% citations from last 5 years; primary sources preferred
Medical journals: Recent clinical evidence; systematic reviews valued
ML conferences: Recent papers (last 2-3 years); preprints (arXiv) acceptable
Self-citation: Keep <20% across all venues

Format compliance (often automated):

Match venue citation style exactly
All in-text citations have corresponding references
Include DOIs when required (journals) or arXiv IDs (ML conferences)
Use correct abbreviations (PubMed for medical, standard for ML)

ML conference specifics:

NeurIPS/ICML/ICLR: ArXiv preprints widely cited; recent work heavily valued
Page limits strict: Citation formatting affects space
Supplementary material: Can include extended bibliography
Double-blind review: Avoid obvious self-citation patterns during review

Citation Density by Venue Type

Venue Type	Expected Citations	Key Notes
Nature/Science research	30-50	Selective, high-impact citations
Medical journals (RCT)	25-40	Recent clinical evidence
Field-specific journals	30-60	Comprehensive field coverage
ML conferences (8-page)	20-40	Space-limited, recent work
Review articles	100-300+	Comprehensive coverage

ML conference citation practices:

NeurIPS/ICML: 25-40 references typical for 8-page papers
Workshop papers: 15-25 references
ArXiv preprints: Widely accepted and cited
Related work: Concise but comprehensive; often moved to appendix
Recency critical: Cite work from last 1-2 years when relevant

Pre-Submission Citation Checklist

Content:

≥50% citations from last 5-10 years (or 2-3 years for ML conferences)
<20% self-citations; balanced perspectives
Primary sources cited (not citation chains)
All claims supported by appropriate citations

Format:

Style matches venue exactly (check template)
All in-text citations in reference list and vice versa
DOIs/arXiv IDs included as required
Abbreviations match venue style

ML conferences additional:

ArXiv preprints properly formatted
Self-citations anonymized if double-blind review
References fit within page limits

Resources for Citation Styles

Official Manuals

AMA: https://www.amamanualofstyle.com/
Vancouver/ICMJE: http://www.icmje.org/
APA: https://apastyle.apa.org/
Chicago: https://www.chicagomanualofstyle.org/
IEEE: https://ieeeauthorcenter.ieee.org/

Journal-Specific Style Guides

Quick Reference Guides

Purdue OWL: https://owl.purdue.edu/
Citation Machine: https://www.citationmachine.net/
EasyBib: https://www.easybib.com/

Reference Management

Journal Citation Style Databases

Journal Citation Reports (Clarivate): Lists journal citation styles
EndNote style repository: >7000 journal-specific styles
Zotero Style Repository: https://www.zotero.org/styles

Reference: Figures_Tables

Figures and Tables Best Practices

Overview

Figures and tables are essential components of scientific papers, serving to display data patterns, summarize results, and provide evidence for conclusions. Effective visual displays enhance comprehension and can sustain reader interest while illustrating trends, patterns, and relationships not easily conveyed through text alone.

A recent Nature Cell Biology checklist (2025) emphasizes that creating clear and engaging scientific figures is crucial for communicating complex data with clarity, accessibility, and design excellence.

When to Use Tables vs. Figures

Use Tables When:

Presenting precise numerical values that readers need to reference
Comparing exact measurements across multiple variables
Showing detailed statistical outputs
Data cannot be adequately summarized in 1-2 sentences
Readers need access to specific data points
Displaying demographic or baseline characteristics
Presenting multiple related statistical tests

Example use cases:

Baseline participant characteristics (age, sex, diagnosis, etc.)
Detailed statistical model outputs (coefficients, p-values, confidence intervals)
Dose-response data with exact values
Gene expression levels for specific genes
Chemical compositions or concentrations

Use Figures When:

Showing trends over time
Displaying relationships or correlations
Comparing groups visually
Illustrating distributions
Demonstrating patterns not easily seen in numbers
Showing images (microscopy, radiography, etc.)
Displaying workflows, diagrams, or schematics

Example use cases:

Growth curves or time series
Dose-response curves
Scatter plots showing correlations
Bar graphs comparing treatment groups
Histograms showing distributions
Heatmaps displaying patterns across conditions
Microscopy images or Western blots

General Decision Rule

Can the information be conveyed in 1-2 sentences of text?

Yes → Use text only
No, and precise values are needed → Use a table
No, and patterns/trends are most important → Use a figure

Core Design Principles

1. Self-Explanatory Display Items

Each figure or table must stand alone without requiring the main text.

Essential elements:

Complete, descriptive caption
All abbreviations defined (in caption or footnote)
Units of measurement clearly indicated
Sample sizes (n) reported
Statistical significance annotations explained
Legend included (for figures with multiple data series)

Example of self-explanatory caption:

Figure 1. Mean systolic blood pressure (SBP) over 12 weeks in intervention and control groups.
Error bars represent standard error of the mean (SEM). Asterisks indicate significant
differences between groups at each time point (*p < 0.05, **p < 0.01, ***p < 0.001,
two-tailed t-tests). n = 48 per group. BP = blood pressure; SEM = standard error of mean.

2. Avoid Redundancy

Do not duplicate information between text, tables, and figures.

Bad practice:

"Mean age was 45.2 years in Group A and 47.8 years in Group B. Mean BMI was 26.3 in
Group A and 28.1 in Group B. Mean systolic blood pressure was 132 mmHg in Group A..."
[Also shown in Table 1]

Good practice:

"Baseline characteristics were similar between groups (Table 1), with no significant
differences in age, BMI, or blood pressure (all p > 0.15)."
[Details in Table 1]

Key principle: Text should highlight key findings from tables/figures, not repeat all data.

3. Consistency

Maintain uniform formatting across all display items:

Font types and sizes
Color schemes
Terminology and abbreviations
Axis labels and units
Statistical annotation methods
Figure styles (all line graphs should look similar)

Example of inconsistency to avoid:

Figure 1 uses “standard error” while Figure 2 uses “SE”
Figure 1 has blue/red color scheme while Figure 2 uses green/yellow
Table 1 reports p-values as “p = 0.023” while Table 2 uses “p-value = .023”

4. Optimal Quantity

Follow the “one display item per 1000 words” guideline.

Typical manuscript:

3000-4000 words → 3-4 tables/figures total
5000-6000 words → 5-6 tables/figures total

Quality over quantity: A few well-designed, information-rich displays are better than many redundant or poorly designed ones.

5. Clarity and Simplicity

Avoid cluttered or overly complex displays:

Don’t include too many variables in one figure
Use clear, readable fonts (minimum 8-10 pt in final size)
Provide adequate spacing between elements
Use high contrast (especially for color-blind accessibility)
Remove unnecessary grid lines, borders, or decoration
Maximize data-ink ratio (Tufte principle: minimize non-data ink)

Figure Types and When to Use Them

Bar Graphs

Best for:

Comparing discrete categories or groups
Showing counts or frequencies
Displaying mean values with error bars

Design guidelines:

Start y-axis at zero (unless showing small differences in large values)
Order bars logically (by size, alphabetically, or temporally)
Use error bars (SD, SEM, or CI) consistently
Include sample sizes
Avoid 3D effects (they distort perception)

Common mistakes:

Not starting at zero (can exaggerate differences)
Too many categories (consider table instead)
Missing error bars

Example applications:

Mean gene expression across tissue types
Treatment group comparisons
Frequency of adverse events

Line Graphs

Best for:

Showing trends over continuous variables (usually time)
Displaying multiple groups on same axes
Illustrating dose-response relationships

Design guidelines:

Use different line styles or colors for groups
Include data point markers for sparse data
Show error bars or shaded confidence intervals
Label axes clearly with units
Use consistent intervals on x-axis

Common mistakes:

Connecting discrete data points that shouldn’t be connected
Too many lines making graph unreadable
Inconsistent time intervals without indication

Example applications:

Growth curves
Time course experiments
Survival curves (Kaplan-Meier plots)
Pharmacokinetic profiles

Scatter Plots

Best for:

Showing relationships between two continuous variables
Displaying correlations
Identifying outliers

Design guidelines:

Include trend line or regression line with equation and R²
Report correlation coefficient and p-value
Use semi-transparent points if data overlap
Consider logarithmic scales for wide ranges
Mark outliers if relevant

Common mistakes:

Not showing individual data points
Using scatter plots for categorical data
Missing correlation statistics

Example applications:

Correlation between biomarkers
Relationship between dose and response
Method comparison (Bland-Altman plots)

Box Plots (Box-and-Whisker Plots)

Best for:

Showing distributions and spread
Comparing distributions across groups
Identifying outliers

Design guidelines:

Clearly define box elements (median, quartiles, whiskers)
Show or note outliers explicitly
Consider violin plots for small sample sizes
Overlay individual data points when n < 20

Common mistakes:

Not defining what whiskers represent
Using for very small samples without showing raw data
Not marking outliers

Example applications:

Comparing distributions across treatment groups
Showing variability in measurements
Quality control data

Heatmaps

Best for:

Displaying matrices of data
Showing patterns across many conditions
Representing clustering or grouping

Design guidelines:

Use color scales that are perceptually uniform
Include color scale bar with units
Consider hierarchical clustering for rows/columns
Use appropriate color scheme (diverging vs. sequential)
Make axes labels readable

Common mistakes:

Poor color choice (rainbow scales are often misleading)
Too many rows/columns making labels unreadable
No color scale bar

Example applications:

Gene expression across samples
Correlation matrices
Time-series data across multiple variables

Images (Microscopy, Gels, Blots)

Best for:

Showing representative examples
Demonstrating morphology or localization
Presenting gel electrophoresis or Western blots

Design guidelines:

Include scale bars (not magnification in caption)
Show representative images with quantification in separate panel
Label important features with arrows or labels
Ensure adequate resolution (usually 300+ dpi)
Show full, unmanipulated images with cropping noted
Include all relevant controls

Common mistakes:

No scale bar
Over-processed or manipulated images
Cherry-picking best images without quantification
Insufficient resolution

Example applications:

Histological sections
Immunofluorescence
Western blots
Gel electrophoresis

Forest Plots

Best for:

Displaying meta-analysis results
Showing effect sizes with confidence intervals
Comparing multiple studies or subgroups

Design guidelines:

Include point estimates and CI for each study
Show overall pooled estimate clearly
Include line of no effect (typically at 1.0 or 0)
List study details or weights

Example applications:

Meta-analyses
Systematic reviews
Subgroup analyses

Flow Diagrams

Best for:

Study participant flow (CONSORT diagrams)
Systematic review search process (PRISMA diagrams)
Experimental workflows

Design guidelines:

Follow reporting guideline templates (CONSORT, PRISMA)
Use consistent shapes and connectors
Include numbers at each stage
Clearly show inclusions and exclusions

Table Design Guidelines

Structure

Basic anatomy:

Table number and title (above table)
Column headers (with units)
Row labels
Data cells (with appropriate precision)
Footnotes (below table for abbreviations, statistics, notes)

Formatting Best Practices

Column headers:

Use clear, concise labels
Include units in parentheses
Use abbreviations sparingly (define in footnote)

Data presentation:

Align decimal points in columns
Use consistent decimal places (usually 1-2 for means)
Report same precision across rows/columns
Use en-dash (–) for “not applicable”
Use appropriate precision (don’t over-report)

Statistical annotations:

Use superscript letters (ᵃ, ᵇ, ᶜ) or symbols (*, †, ‡) for footnotes
Define p-value thresholds clearly
Report exact p-values when possible (p = 0.032, not p < 0.05)

Footnotes:

Define all abbreviations
Explain statistical tests used
Note any missing data
Indicate data source if not original

Example Table Format

Table 1. Baseline Characteristics of Study Participants

Characteristic          Intervention (n=50)   Control (n=48)    p-value
─────────────────────────────────────────────────────────────────────────
Age, years               45.3 ± 8.2           47.1 ± 9.1        0.28
Male sex, n (%)          28 (56)              25 (52)           0.71
BMI, kg/m²               26.3 ± 3.8           27.1 ± 4.2        0.32
Current smoker, n (%)    12 (24)              15 (31)           0.42
Systolic BP, mmHg        132 ± 15             134 ± 18          0.54
─────────────────────────────────────────────────────────────────────────

Data presented as mean ± SD or n (%). p-values from independent t-tests for
continuous variables and χ² tests for categorical variables. BMI = body mass
index; BP = blood pressure; SD = standard deviation.

Common Table Mistakes

Excessive complexity (too many rows/columns)
Insufficient context (missing units, unclear abbreviations)
Over-precision (reporting 5 decimal places for p-values)
Missing sample sizes
No statistical comparisons when appropriate
Inconsistent formatting across multiple tables
Duplicate information with figures or text

Statistical Presentation in Figures and Tables

Reporting Requirements

For each comparison, report:

Point estimate (mean, median, proportion)
Measure of variability (SD, SEM, CI)
Sample size (n)
Test statistic (t, F, χ², etc.)
p-value (exact when p > 0.001)
Effect size (when appropriate)

Error Bars

Choose the appropriate measure:

Measure	Meaning	When to Use
SD (Standard Deviation)	Variability in the data	Showing data spread
SEM (Standard Error of Mean)	Precision of mean estimate	Showing measurement precision
95% CI (Confidence Interval)	Range likely to contain true mean	Showing statistical significance

Key rule: Always state which measure is shown.

Example caption:

"Error bars represent 95% confidence intervals."
NOT: "Error bars represent standard error."

Recommendation: 95% CI preferred because non-overlapping CIs indicate significant differences.

Significance Indicators

Common notation:

* p < 0.05
** p < 0.01
*** p < 0.001
n.s. or NS = not significant

Alternative: Show exact p-values in table or caption

Best practice: Define significance indicators in every figure caption or table footnote.

Accessibility Considerations

Recommendations:

Use color palettes designed for color-blind accessibility
Don’t rely on color alone (add patterns, shapes, or labels)
Test figures in grayscale
Avoid red-green combinations

Color-blind safe palettes:

Blue-Orange
Purple-Yellow
Colorbrewer2.org palettes
Viridis, Plasma, Inferno (for heatmaps)

High Contrast

Ensure readability:

Dark text on light background (or vice versa)
Avoid low-contrast color combinations (gray on gray)
Use thick enough lines (minimum 0.5-1 pt)
Large enough text (minimum 8-10 pt after scaling)

Screen and Print Compatibility

Design for both media:

Use vector formats when possible (PDF, EPS, SVG)
Minimum 300 dpi for raster images (TIFF, PNG)
Test appearance at final print size
Ensure color figures work in grayscale if printed

Technical Requirements

File Formats

Vector formats (preferred for graphs and diagrams):

PDF: Universal, preserves quality
EPS: Encapsulated PostScript, publishing standard
SVG: Scalable vector graphics, web-friendly

Raster formats (for photos and images):

TIFF: Uncompressed, high quality, large files
PNG: Lossless compression, good for screen
JPEG: Lossy compression, avoid for data figures

Avoid:

Low-resolution screenshots
Figures copied from presentations (usually too low resolution)
Heavily compressed JPEGs (artifacts)

Resolution Requirements

Minimum standards:

Line art (graphs, diagrams): 300-600 dpi
Halftones (photos, grayscale): 300 dpi
Combination (images with labels): 300-600 dpi

Best practice: Create figures at final size and resolution.

Dimensions

Check journal requirements:

Single column: typically 8-9 cm (3-3.5 inches) wide
Double column: typically 17-18 cm (6.5-7 inches) wide
Full page: varies by journal

Recommendation: Design figures to fit single column when possible.

Image Manipulation

Allowed:

Brightness/contrast adjustment applied to entire image
Color balance adjustment
Cropping (with notation)
Rotation

NOT allowed:

Selective editing (e.g., enhancing bands in gels)
Removing background artifacts
Splicing images without clear indication
Any manipulation that obscures, eliminates, or misrepresents data

Ethical requirement: Report all image adjustments in Methods section.

Figure and Table Numbering

Numbering System

Figures:

Number consecutively in order of first mention in text
Use Arabic numerals: Figure 1, Figure 2, Figure 3…
Supplementary figures: Figure S1, Figure S2…

Tables:

Number separately from figures
Use Arabic numerals: Table 1, Table 2, Table 3…
Supplementary tables: Table S1, Table S2…

In-Text References

Format:

"Results are shown in Figure 1."
"Participant characteristics are presented in Table 2."
"Multiple analyses confirmed this finding (Figures 3-5)."

NOT:

"Figure 1 below shows..." (avoid "above" or "below" - pagination may change)
"The figure shows..." (always use specific number)

Captions

Caption Structure

For figures:

Figure 1. [One-sentence title]. [Additional description sentences providing context,
defining abbreviations, explaining panels, describing statistical tests, and noting
sample sizes].

For tables:

Table 1. [Descriptive Title]
[Table contents]
[Footnotes defining abbreviations, statistical methods, and providing additional context]

Caption Content

Essential information:

What is being shown (brief title)
Detailed description of content
Definition of all abbreviations and symbols
Sample sizes
Statistical tests used
Meaning of error bars or annotations
Panel labels explained (if multiple panels)

Example comprehensive caption:

Figure 3. Cognitive performance improves with treatment over 12 weeks. (A) Mean Mini-Mental
State Examination (MMSE) scores at baseline, 6 weeks, and 12 weeks for treatment (blue) and
placebo (gray) groups. (B) Individual participant trajectories for treatment group. Error bars
represent 95% confidence intervals. Asterisks indicate significant between-group differences
(*p < 0.05, **p < 0.01, ***p < 0.001; repeated measures ANOVA with Bonferroni correction).
n = 42 treatment, n = 40 placebo. MMSE scores range from 0-30, with higher scores indicating
better cognitive function.

Journal-Specific Requirements

Before Creating Figures/Tables

Check journal guidelines for:

Preferred file formats
Resolution requirements
Color specifications (RGB vs. CMYK)
Maximum number of display items
Dimension requirements
Font restrictions
Whether to embed figures in manuscript or submit separately

During Submission

Prepare checklist:

All figures/tables numbered correctly
All cited in text in order
Captions complete and self-explanatory
Abbreviations defined
Correct file format and resolution
Appropriate size/dimensions
High enough quality for print
Color-blind friendly (if using color)
Permissions obtained (if adapting from others’ work)

Common Pitfalls to Avoid

Content Issues

Duplication between text, tables, and figures
Insufficient context (unclear what is shown)
Too much information in one display
Missing key information (sample sizes, units, statistics)
Cherry-picking data without showing full picture

Design Issues

Poor color choices (not color-blind friendly)
Inconsistent formatting across displays
Cluttered or busy designs
Too small text at final size
Misleading visualizations (truncated axes, 3D distortions)

Technical Issues

Insufficient resolution (pixelated when printed)
Wrong file format (lossy compression, non-vector graphs)
Improper image manipulation (undeclared editing)
Missing scale bars on images
Figures that don’t work in grayscale (if journal prints in B&W)

Tools for Creating Figures

Graphing Software

R (ggplot2): Highly customizable, publication-quality, reproducible
Python (matplotlib, seaborn): Flexible, programmable, widely used
GraphPad Prism: User-friendly, statistics integrated, common in life sciences
Origin: Advanced graphing, popular in physics/engineering
Excel: Basic graphs, widely available, limited customization
MATLAB: Technical computing, good for complex visualizations

Image Processing

ImageJ/Fiji: Free, powerful, widely used in microscopy
Adobe Photoshop: Professional standard, extensive tools
GIMP: Free alternative to Photoshop
Adobe Illustrator: Vector graphics, figure assembly
Inkscape: Free vector graphics editor

Best Practices for Software Choice

Use tools that produce vector output for graphs
Learn one tool well rather than many superficially
Script your figure generation for reproducibility
Save original data files separately from figure files

Journal-Specific Figure and Table Requirements

Understanding Journal Expectations

Different journals have vastly different requirements for figures and tables. Before creating display items, always consult your target journal’s author guidelines for specific requirements.

Common Journal-Specific Variations

Aspect	Variation by Journal	Example Journals
Number allowed	4-10 display items for research articles	Nature (4-6), PLOS ONE (unlimited), Science (4-5)
File format	TIFF, EPS, PDF, AI, or specific formats	Nature (EPS/PDF for line art), Cell (TIFF preferred)
Resolution	300-1000 dpi depending on type	JAMA (300-600 dpi), Nature (300+ dpi)
Color	RGB vs. CMYK	Print journals: CMYK; Online: RGB
Dimensions	Single vs. double column widths	Nature (89mm or 183mm), Science (specific templates)
Figure legends	Length limits, specific format	Some journals: 150 word max per legend
Table format	Editable vs. image	Most prefer editable tables, not images

Venue-Specific Requirements Summary

Venue Type	Display Limit	Format	Resolution	Key Features
Nature/Science	4-6 main	EPS/PDF/TIFF	300+ dpi	Extended data allowed; multi-panel figures
Medical journals	3-5	TIFF/EPS	300-600 dpi	CONSORT diagrams; conservative design
PLOS ONE	Unlimited	TIFF/EPS/PDF	300+ dpi	Must work in grayscale
ML conferences	4-6 in 8-page limit	PDF (vector preferred)	Print quality	Compact design; info-dense figures

ML Conference Figure Requirements:

NeurIPS/ICML/ICLR:

Figures count toward page limit (typically 8 pages including references)
Vector graphics (PDF) preferred for plots
High information density expected
Supplementary material for additional figures
LaTeX template provided (use neurips_2024.sty or equivalent)
Figures must be legible when printed in grayscale

Computer Vision (CVPR/ICCV/ECCV):

Qualitative results figures critical
Side-by-side comparisons standard
Must show failure cases
Supplementary material for videos/additional examples
Often 6-8 main figures in 8-page papers

Key ML conference figure practices:

Ablation studies: Compact tables/plots showing component contributions
Architecture diagrams: Clear, professional block diagrams
Performance plots: Include error bars/confidence intervals
Qualitative examples: Show diverse, representative samples
Comparison tables: Concise, bold best results

Evaluation Criteria Across Venues

What reviewers check:

Necessity: Each figure/table supports conclusions
Quality: Professional appearance, sufficient resolution
Clarity: Self-explanatory with captions; proper labeling
Statistics: Error bars, sample sizes, significance indicators
Consistency: Formatting uniform across display items

Common rejection reasons:

Poor resolution or image quality
Missing error bars or sample sizes
Unclear or missing labels
Too many figures (exceeds venue limits)
Figures duplicate text information

ML conference specific evaluation:

Ablation studies: Must demonstrate component contributions
Baselines: Comparison with relevant prior work required
Error bars: Confidence intervals/std dev expected
Architecture diagrams: Must be clear and informative
Space efficiency: Information density valued (page limits strict)

Caption/Legend Styles by Venue

Venue Type	Style	Example Features
Nature/Science	Concise	Brief; *P<0.05; minimal methods
Medical	Formal	Title case; 95% CIs; statistical tests spelled out
PLOS/BMC	Detailed	Complete sentences; all abbreviations defined
ML conferences	Technical	Architecture details; hyperparameters; dataset info

ML conference caption example:

Figure 1. Architecture of proposed model. (a) Encoder with 12 transformer layers.
(b) Attention visualization. (c) Performance vs. baseline on ImageNet (error bars:
95% CI over 3 runs).

Technical precision
Hyperparameters when relevant
Dataset/experimental setup details
Compact to save space

Quick Adaptation Guide

When changing venues:

Journal → ML conference: Compress figures; increase information density; add hyperparameters to captions
ML conference → journal: Expand captions; separate dense figures; add more methodological detail
Specialist → broad journal: Simplify; add explanatory panels; define terms in captions
Broad → specialist journal: Add technical detail; use field-standard plot types

Pre-Submission Figure/Table Checklist

Technical (all venues):

Meets format requirements (PDF/EPS/TIFF)
Sufficient resolution (300+ dpi)
Fits venue dimensions/page limits
Self-explanatory captions
All symbols/abbreviations defined
Error bars defined; sample sizes noted

ML conferences additional:

Figures fit in page limit (8-9 pages typical)
Comparison with baselines shown
Ablation studies included
Architecture diagram clear
Legible in grayscale

Checklist for Final Review

Before Submission

For every figure:

For every table:

Overall:

Number of display items within journal limits?
Appropriate number of display items (~1 per 1000 words)?
No duplication between text, figures, and tables?
Consistent formatting across all display items?
All display items necessary (each tells important part of story)?
Visual style matches target journal?
Quality comparable to published examples in journal?

Reference: Imrad_Structure

IMRAD Structure Guide

Overview

IMRAD (Introduction, Methods, Results, And Discussion) is the predominant organizational structure for scientific journal articles of original research. Adopted as the majority format since the 1970s, it is now the standard in medical, health, biological, chemical, engineering, and computer sciences.

Why IMRAD?

The IMRAD structure mirrors the scientific method:

Introduction: What question did you ask?
Methods: How did you study it?
Results: What did you find?
Discussion: What does it mean?

This logical flow makes scientific papers easier to write, read, and evaluate.

Complete Manuscript Components

A full scientific manuscript typically includes these sections in order:

Title
Abstract
Introduction
Methods (also called Materials and Methods, Methodology)
Results
Discussion (sometimes combined with Results)
Conclusion (sometimes part of Discussion)
Acknowledgments
References
Supplementary Materials (if applicable)

Title

Purpose

Attract readers and accurately represent the paper’s content.

Guidelines

Be concise yet descriptive (typically 10-15 words)
Include key variables and the relationship studied
Avoid abbreviations, jargon, and question formats (unless the journal allows)
Make it specific enough to distinguish from other studies
Include key search terms for discoverability

Examples

Good: “Effects of High-Intensity Interval Training on Cardiovascular Function in Older Adults”
Too vague: “Exercise and Health”
Too detailed: “A Randomized Controlled Trial Examining the Effects of High-Intensity Interval Training Compared to Moderate Continuous Training on Cardiovascular Function Measured by VO2 Max in Adults Aged 60-75 Years”

Abstract

Purpose

Provide a complete, standalone summary enabling readers to decide if the full paper is relevant to them.

Format: Flowing Paragraphs (Default)

⚠️ CRITICAL: Write abstracts as flowing paragraphs, NOT with labeled sections.

Most scientific papers use unstructured abstracts written as one or two cohesive paragraphs. This is the standard format for the majority of journals including Nature, Science, Cell, PNAS, and most field-specific journals.

❌ WRONG - Structured abstract with labels:

Background: Hospital-acquired infections remain a major cause of morbidity.
Methods: We conducted a 12-month before-after study...
Results: Post-intervention, surface contamination decreased by 47%...
Conclusions: UV-C disinfection significantly reduced infection rates.

✅ CORRECT - Flowing paragraph style:

Hospital-acquired infections remain a major cause of morbidity, yet optimal 
disinfection strategies remain unclear. We conducted a 12-month before-after 
study in a 500-bed teaching hospital to evaluate UV-C disinfection added to 
standard cleaning protocols. Environmental surfaces were cultured monthly and 
infection rates tracked via surveillance data. Post-intervention, surface 
contamination decreased by 47% (95% CI: 38-56%, p<0.001), and catheter-associated 
urinary tract infections declined from 3.2 to 1.8 per 1000 catheter-days (RR=0.56, 
95% CI: 0.38-0.83, p=0.004). No adverse effects were observed. These findings 
demonstrate that UV-C disinfection significantly reduces environmental contamination 
and infection rates, suggesting it may be a valuable addition to hospital infection 
control programs.

Abstract Structure (as unified paragraph)

While written as flowing prose, the abstract should cover these elements in order:

Context and problem (1-2 sentences): Why the research matters, what gap exists
Study description (1-2 sentences): What was done and how (study design, methods)
Key findings (2-4 sentences): Main results with specific quantitative data
Significance (1-2 sentences): Interpretation, implications, and conclusions

Length

Typically 150-300 words (check journal requirements)
Some journals allow up to 350 words

Key Rules

Write the abstract last (after completing all other sections)
Write as flowing paragraph(s) - no labeled sections
Make it fully understandable without reading the paper
Do not cite references in the abstract
Avoid abbreviations or define them at first use
Use past tense for methods and results, present tense for conclusions
Include key quantitative results with statistical measures
Use transitions to connect sentences naturally

When to Use Structured Abstracts (Exception)

Only use labeled sections (Background/Objective, Methods, Results, Conclusions) when:

The journal explicitly requires structured abstracts in their author guidelines
Common in some medical journals (JAMA, BMJ, Annals of Internal Medicine)
Always check journal requirements before formatting

Even for structured abstracts, write each section as complete sentences, not fragments.

Example: Flowing Paragraph Abstract

Transcriptomic aging clocks offer unique advantages for assessing biological age by 
capturing dynamic cellular states and acute responses to perturbations. Using the 
ARCHS4 database containing uniformly processed RNA-seq data from over 1.2 million 
human samples, we developed deep neural network models to predict chronological age 
from gene expression profiles. Our best-performing model achieved a mean absolute 
error of 4.2 years (R² = 0.89) on held-out test data, substantially outperforming 
traditional machine learning approaches including elastic net regression (MAE = 6.8 
years) and random forests (MAE = 5.9 years). Feature importance analysis identified 
genes enriched in senescence, inflammation, and mitochondrial function pathways as 
the strongest predictors. Cross-tissue validation revealed that lung and blood 
samples yielded the most accurate predictions, while liver showed the highest 
variance. These findings establish deep learning as a powerful approach for 
transcriptomic age prediction and identify candidate biomarkers for biological 
aging assessment.

Introduction

Purpose

Convince readers that the research addresses an important question using an appropriate approach.

Structure and Content

Paragraph 1: The Big Picture

Establish the broad research area
Explain why this topic matters
Use present tense for established facts
Keep it accessible to non-specialists

Paragraphs 2-3: Narrowing Down

Review relevant prior research
Show what is already known
Identify controversies or limitations in existing work
Create a logical progression toward the gap

Paragraph 4: The Gap

Explicitly identify what remains unknown
Explain why this knowledge gap is problematic
Connect the gap to the big picture importance

Final Paragraph: This Study

State the specific research question or hypothesis
Describe the overall approach briefly
Explain how this study addresses the gap
Optional: Preview key findings (some journals discourage this)

Length

Typically 1.5-2 pages (depending on journal)
Usually 4-5 paragraphs
Shorter for letters/brief communications

Verb Tense

Present tense: Established facts (“Exercise improves cardiovascular health”)
Past tense: Previous studies and their findings (“Smith et al. found that…”)
Present/past tense: Your study aims (“This study investigates…” or “This study investigated…”)

Common Mistakes to Avoid

Starting too broad (e.g., “Since the beginning of time…”)
Exhaustive literature review (save for review articles)
Citing irrelevant or outdated references
Failing to identify a clear gap
Weak justification for the study
Not stating a clear research question or hypothesis
Including methods or results (these belong in later sections)

Key Questions to Answer

What do we know about this topic?
What don’t we know? (the gap)
Why does this gap matter?
What did this study aim to find out?

Methods

Purpose

Provide sufficient detail for others to replicate the study and evaluate its validity.

Key Principle

Another expert in the field should be able to repeat your experiment exactly as you performed it.

Standard Subsections

Study Design

State the overall design (e.g., randomized controlled trial, cohort study, cross-sectional survey)
Justify the design choice if not obvious
Mention blinding, randomization, or controls if applicable

Participants/Subjects/Sample

Define the population of interest
Describe inclusion and exclusion criteria precisely
Report sample size and how it was determined (power analysis)
Explain recruitment methods and setting
For animals: specify species, strain, age, sex, housing conditions

Materials and Equipment

List all materials, reagents, and equipment used
Include manufacturer names and locations (in parentheses)
Specify catalog numbers for specialized items
Report software names and versions

Procedures

Describe what was done in chronological order
Include sufficient detail for replication
Use subheadings to organize complex procedures
Specify timing (e.g., “incubated for 2 hours at 37°C”)
For surveys/interviews: describe instruments, validation, administration

Measurements and Outcomes

Define all variables measured
Specify primary and secondary outcomes
Describe measurement instruments and their validity
Include units of measurement

Statistical Analysis

Name all statistical tests used
Justify test selection
State significance level (typically α = 0.05)
Report power analysis for sample size
Name statistical software with version
Describe handling of missing data
Mention adjustments for multiple comparisons if applicable

Ethical Considerations

State IRB/ethics committee approval (with approval number)
Mention informed consent procedures
For human studies: state adherence to Helsinki Declaration
For animal studies: state adherence to relevant guidelines (e.g., ARRIVE)

Length

Typically 2-4 pages
Proportional to study complexity

Verb Tense

Past tense for actions you performed (“We measured…”, “Participants completed…”)
Present tense for established procedures (“PCR amplifies…”, “The questionnaire contains…”)

Common Mistakes

Insufficient detail for replication
Methods appearing for the first time in Results
Including results or discussion
Missing statistical tests
Undefined abbreviations
Lack of ethical approval statement

Results

Purpose

Present the findings objectively without interpretation.

Key Principle

Show, don’t interpret. Save interpretation for the Discussion.

Structure and Content

Opening Paragraph

Describe the participants/sample characteristics
Report recruitment flow (e.g., screened, enrolled, completed)
Consider including a CONSORT-style flow diagram

Subsequent Paragraphs

Present results in logical order (usually primary outcome first)
Follow the order of objectives stated in Introduction
Organize by theme or by chronology, depending on what’s clearest
Reference figures and tables by number

Each Finding Should Include:

The observed result
The direction of the effect
The magnitude of the effect
The statistical significance
The confidence interval

Example: “Mean systolic blood pressure decreased by 12 mmHg in the intervention group compared to 3 mmHg in controls (difference: 9 mmHg, 95% CI: 4-14 mmHg, p=0.002).”

Integration with Figures and Tables

When to Use:

Figures: Trends, patterns, distributions, comparisons, relationships
Tables: Precise values, demographic data, multiple variables

How to Reference:

“Figure 1 shows the distribution of…” (not “Figure 1 below”)
“Table 2 presents baseline characteristics…”
Don’t repeat all table data in text; highlight key findings
Each figure/table should be referenced in text

Figures and Tables Guidelines

Number consecutively in order of mention
Include complete, standalone captions
Define all abbreviations in caption or footnote
Report sample sizes (n)
Indicate statistical significance (*, p-values)
Use consistent formatting

Statistical Reporting

Required Elements:

Test statistic (t, F, χ², etc.)
Degrees of freedom
p-value (exact if p > 0.001, otherwise report as “p < 0.001”)
Effect size and confidence interval
Sample sizes

Example: “Groups differed significantly on test performance (t(48) = 3.21, p = 0.002, Cohen’s d = 0.87, 95% CI: 0.34-1.40).”

Length

Typically 2-4 pages
Roughly equivalent to Methods length

Verb Tense

Past tense for your findings (“The mean was…”, “Participants showed…”)

Common Mistakes

Interpreting results (save for Discussion)
Repeating all table/figure data in text
Presenting new methods
Insufficient statistical detail
Inconsistent units or notation
Not addressing negative or unexpected findings
Selective reporting (all tested hypotheses should be reported)

Organization Strategies

By Objective:

Effect of intervention on primary outcome
Effect of intervention on secondary outcome A
Effect of intervention on secondary outcome B

By Analysis Type:

Descriptive statistics
Univariate analyses
Multivariate analyses

Chronological:

Baseline characteristics
Short-term outcomes (1 month)
Long-term outcomes (6 months)

Discussion

Purpose

Interpret findings, relate them to existing knowledge, acknowledge limitations, and propose future directions.

Structure and Content

Paragraph 1: Summary of Main Findings

Restate the primary objective or hypothesis
Summarize the principal findings in 2-4 sentences
Avoid repeating details from Results
State clearly whether the hypothesis was supported

Paragraphs 2-4: Interpretation in Context

Compare your findings with previous research
Explain agreements and disagreements with prior work
Propose mechanisms or explanations for findings
Discuss unexpected results
Consider alternative explanations
Address whether findings support or refute existing theories

Paragraph 5: Strengths and Limitations

Acknowledge study limitations honestly
Explain how limitations might affect interpretation
Mention study strengths (design, sample, methods)
Avoid generic limitations (“larger sample needed”)—be specific

Paragraph 6: Implications

Clinical implications (for medical research)
Practical applications
Policy implications
Theoretical contributions

Final Paragraph: Conclusions and Future Directions

Summarize the take-home message
Suggest specific future research to address gaps or limitations
End with a strong concluding statement

Length

Typically 3-5 pages
Usually the longest section

Verb Tense

Past tense: Your study findings (“We found that…”, “The results showed…”)
Present tense: Established facts and your interpretations (“This suggests that…”, “These findings indicate…”)
Future tense: Implications and future research (“Future studies should investigate…”)

Discussion Strategies

Comparing to Prior Work:

"Our finding of a 30% reduction in symptoms aligns with Smith et al. (2023), who
reported a 28% reduction using a similar intervention. However, Jones et al. (2022)
found no significant effect, possibly due to their use of a less intensive protocol."

Proposing Mechanisms:

"The observed improvement in cognitive function may result from increased cerebral
blood flow, as evidenced by the concurrent increase in functional MRI signals in the
prefrontal cortex. This interpretation is consistent with the vascular hypothesis of
cognitive enhancement."

Acknowledging Limitations:

"The cross-sectional design prevents causal inference. Additionally, the convenience
sample from a single academic medical center may limit generalizability to community
settings. Self-reported measures may introduce recall bias, though we attempted to
minimize this through structured interviews."

Common Mistakes

Simply repeating results without interpretation
Over-interpreting findings or claiming causation without warrant
Ignoring inconsistent or negative findings
Failing to compare with existing literature
Introducing new data or methods
Generic or superficial discussion of limitations
Overgeneralization beyond the study population
Missing the “so what?”—failing to explain significance

Key Questions to Answer

What do these findings mean?
How do they compare to prior research?
Why might differences exist?
What are alternative explanations?
What are the limitations?
What are the practical implications?
What should future research investigate?

Conclusion

Purpose

Provide a concise summary of key findings and their significance.

Placement

May be a separate section or the final paragraph of Discussion (check journal requirements)

Content

1-2 paragraphs maximum
Restate the main finding(s)
Emphasize the significance or implications
End with a strong, memorable statement
Do NOT introduce new information

Example

This randomized trial demonstrates that a 12-week mindfulness intervention significantly
reduces anxiety symptoms in college students, with effects persisting at 6-month follow-up.
These findings support the integration of mindfulness-based programs into university mental
health services. Given the scalability and cost-effectiveness of group-based mindfulness
training, this approach offers a promising strategy to address the growing mental health
crisis in higher education.

Additional Sections

Acknowledgments

Thank funding sources (with grant numbers)
Acknowledge substantial contributions not qualifying for authorship
Thank those who provided materials, equipment, or assistance
Declare any conflicts of interest

References

Format according to journal style (see citation_styles.md)
Verify all citations are accurate
Ensure all citations appear in text and vice versa
Typical range: 20-50 references for original research

Supplementary Materials

Additional figures, tables, or data sets
Detailed protocols or questionnaires
Video or audio files
Large datasets or code repositories

Tense Usage Summary

Section	Verb Tense
Abstract - Background	Present (established facts) or past (prior studies)
Abstract - Methods	Past
Abstract - Results	Past
Abstract - Conclusions	Present
Introduction - General background	Present
Introduction - Prior studies	Past
Introduction - Your objectives	Present or past
Methods	Past (your actions), present (general procedures)
Results	Past
Discussion - Your findings	Past
Discussion - Interpretations	Present
Discussion - Prior work	Present or past
Conclusion	Present

IMRAD Variations

Combined Results and Discussion

Some journals allow or require this format
Interweaves presentation and interpretation
Each result is presented then immediately discussed
Useful for complex studies with multiple experiments

IMRaD without separate Conclusion

Conclusion integrated into final Discussion paragraph
Common in many journals

Extended IMRAD (ILMRaD)

Adds “Literature Review” as separate section
More common in theses and dissertations

Adapting IMRAD to Different Study Types

Clinical Trials

Add CONSORT flow diagram in Results
Include trial registration number in Methods
Report adverse events in Results

Systematic Reviews/Meta-Analyses

Methods describes search strategy and inclusion criteria
Results includes PRISMA flow diagram and synthesis
May have additional sections (risk of bias assessment)

Case Reports

Introduction: background on the condition
Case Presentation: replaces Methods and Results
Discussion: relates case to literature

Observational Studies

Follow STROBE guidelines
Careful attention to potential confounders in Methods
Discussion addresses causality limitations

Venue-Specific Structure Expectations

Journal vs. Conference Formats

Venue Type	Length	Structure	Methods Placement	Key Focus
Nature/Science	2,000-4,500 words	Modified IMRAD	Supplement	Broad significance
Medical	2,700-3,500 words	Strict IMRAD	Main text	Clinical outcomes
Field journals	3,000-6,000 words	Standard IMRAD	Main text	Technical depth
ML conferences	8-9 pages (~6,000 words)	Intro-Method-Experiments-Conclusion	Main text (concise)	Novel contribution

ML Conference Structure (NeurIPS/ICML/ICLR)

Typical 8-page structure:

Abstract (150-200 words): Problem, method, key results
Introduction (1 page): Motivation, contribution summary, related work overview
Method (2-3 pages): Technical approach, architecture, algorithms
Experiments (2-3 pages): Setup, datasets, baselines, results, ablations
Related Work (0.5-1 page, often in appendix): Detailed literature comparison
Conclusion (0.25-0.5 pages): Summary, limitations, future work
References (within page limit or separate depending on conference)
Appendix/Supplement (unlimited): Additional experiments, proofs, details

Key differences from journals:

Contribution bullets: Often numbered list in intro (e.g., “Our contributions are: (1)… (2)… (3)…”)
No separate Results/Discussion: Integrated in Experiments section
Ablation studies: Critical component showing what matters
Computational requirements: Often required (training time, GPUs, memory)
Code availability: Increasingly expected

Section Length Proportions

Venue	Intro	Methods	Results/Experiments	Discussion/Conclusion
Nature/Science	10%	15%*	40%	35%
Medical (NEJM/JAMA)	10%	25%	30%	35%
Field journals	20%	25%	30%	25%
ML conferences	12-15%	30-35%	40-45%	5-8%

*Methods often in supplement for Nature/Science

Key medical journal features:

NEJM/Lancet/JAMA: Strict IMRAD; clinical focus; structured Discussion; CONSORT/STROBE compliance
Clear primary/secondary outcomes; statistical pre-specification

Key ML conference features:

Numbered contribution list in intro
Method details with pseudocode/equations
Extensive experiments: main results, ablations, analysis
Brief conclusion (limitations noted)
Related work often in appendix

Writing Style by Venue

Venue	Audience	Intro Focus	Methods Detail	Results/Experiments	Discussion/Conclusion
Nature/Science	Non-specialists	Broad significance	Brief, supplement	Story-driven	Broad implications
Medical	Clinicians	Clinical problem	Comprehensive	Primary outcome first	Clinical relevance
Specialized	Experts	Field context	Full technical	By experiment	Mechanistic depth
ML conferences	ML researchers	Novel contribution	Reproducible	Baselines, ablations	Brief, limitations

ML conference emphasis:

Introduction: Clear problem statement; numbered contributions; positioning vs. prior work
Method: Mathematical notation; pseudocode; architecture diagrams; complexity analysis
Experiments: Datasets described; multiple baselines; ablation studies; error analysis
Conclusion: Summary; acknowledged limitations; broader impact (if required)

Evaluation Across Venues

What gets checked:

Fit: Appropriate for venue scope and audience
Length: Within limits (strict for conferences)
Clarity: Writing quality sufficient; claims supported
Reproducibility: Methods enable replication
Completeness: All outcomes reported; limitations acknowledged

Common rejection reasons:

Insufficient significance for venue
Methods lack detail for reproduction
Results don’t support claims
Discussion overstates findings
Page/word limits exceeded (conferences strict)

ML conference specific evaluation:

Clear problem formulation and motivation
Novelty and contribution well-articulated
Baselines comprehensive and fair
Ablation studies demonstrate what works
Code/data availability (increasingly required)
Reproducibility information (seeds, hyperparameters)

Quick Adaptation Guide

Journal → ML conference:

Condense intro; add numbered contributions
Methods: keep concise, add pseudocode
Combine Results+Discussion → Experiments section
Add extensive ablations and baseline comparisons
Brief conclusion with limitations

ML conference → Journal:

Expand introduction with more background
Separate Methods section with full details
Split Experiments into Results and Discussion
Remove contribution numbering
Expand limitations discussion

Specialist → Broad journal:

Simplify intro; emphasize broad significance
Move technical methods to supplement
Story-driven results organization
Lead discussion with implications

Broad → Specialist:

Add detailed literature review
Full methods in main text
Organize results by experiment
Add mechanistic discussion depth

Pre-Submission Structure Checklist

All venues:

Word/page count within limits
Section proportions appropriate
Writing style matches venue
Methods enable reproducibility
Limitations acknowledged

ML conferences add:

Reference: Professional_Report_Formatting

Professional Report Formatting for Scientific Documents

This reference guide covers professional formatting for scientific reports, technical documents, and white papers. Use the scientific_report.sty LaTeX style package for consistent, professional output.

When to Use Professional Report Formatting

Use This Style For:

Research reports - Internal and external research summaries
Technical reports - Detailed technical documentation and analyses
White papers - Position papers and thought leadership documents
Grant reports - Progress reports and final grant reports
Policy briefs - Research-informed policy recommendations
Industry reports - Technical reports for industry audiences
Internal research summaries - Team and stakeholder communications
Feasibility studies - Technical and research feasibility assessments
Project documentation - Research project deliverables

Do NOT Use This Style For:

Journal manuscripts → Use venue-templates skill for journal-specific formatting
Conference papers → Use venue-templates skill for conference requirements
Academic theses/dissertations → Use institutional templates
Peer-reviewed submissions → Follow journal author guidelines

Key Distinction: Professional report formatting prioritizes visual appeal and readability for general audiences, while journal manuscripts must follow strict publisher requirements.

Overview of scientific_report.sty

The scientific_report.sty package provides:

Feature	Description
Typography	Helvetica font family for modern, professional appearance
Color Scheme	Coordinated blues, greens, oranges, and purples
Box Environments	Colored boxes for organizing content types
Tables	Professional styling with alternating rows
Figures	Consistent caption formatting
Headers/Footers	Professional page headers and footers
Scientific Commands	Shortcuts for p-values, effect sizes, statistics

Basic Document Setup

\documentclass[11pt,letterpaper]{report}
\usepackage{scientific_report}

\begin{document}
% Your content here
\end{document}

Compilation: Use XeLaTeX or LuaLaTeX for proper Helvetica font rendering:

xelatex document.tex

Box Environments for Content Organization

Purpose and Usage

Colored boxes help readers quickly identify different types of content. Use them strategically to highlight important information.

Available Box Environments

Environment	Color	Purpose
`keyfindings`	Blue	Major findings, discoveries, key takeaways
`methodology`	Green	Methods, procedures, study design
`resultsbox`	Blue-green	Statistical results, data highlights
`recommendations`	Purple	Recommendations, action items, implications
`limitations`	Orange	Limitations, cautions, caveats
`criticalnotice`	Red	Critical warnings, safety notices
`definition`	Gray	Definitions, notes, supplementary info
`executivesummary`	Blue (shadow)	Executive summaries
`hypothesis`	Light blue	Research hypotheses

Key Findings Box

Use for major findings and important discoveries:

\begin{keyfindings}[Research Highlights]
Our analysis revealed three significant findings:
\begin{enumerate}
    \item Treatment A was 40% more effective than control (\pvalue{0.001})
    \item Effect sizes were clinically meaningful (\effectsize{d}{0.82})
    \item Benefits persisted at 12-month follow-up
\end{enumerate}
\end{keyfindings}

Best Practices:

Use sparingly (1-3 per chapter maximum)
Reserve for genuinely important findings
Include specific numbers and statistics
Write concisely

Methodology Box

Use for highlighting methods and procedures:

\begin{methodology}[Study Design]
This double-blind, randomized controlled trial employed a 2×2 factorial
design. Participants (\samplesize{450}) were randomized to one of four
conditions: (1) Treatment A, (2) Treatment B, (3) Combined A+B, or
(4) Placebo control.
\end{methodology}

Best Practices:

Summarize key methodological features
Use at the start of methods sections
Include sample size and design type
Keep technical but accessible

Results Box

Use for highlighting specific statistical results:

\begin{resultsbox}[Primary Outcome Analysis]
Mixed-effects regression revealed a significant treatment × time
interaction, \effectsize{F(3, 446)}{8.72}, \psig{< 0.001},
$\eta^2_p$ = 0.055, indicating differential improvement across
treatment conditions over the study period.
\end{resultsbox}

Best Practices:

Report complete statistical information
Use scientific notation commands
Include effect sizes alongside p-values
One box per major analysis

Recommendations Box

Use for recommendations and implications:

\begin{recommendations}[Clinical Practice Guidelines]
Based on our findings, we recommend:
\begin{enumerate}
    \item \textbf{Primary recommendation:} Implement screening protocol
        for high-risk populations.
    \item \textbf{Secondary recommendation:} Adjust treatment intensity
        based on baseline severity scores.
    \item \textbf{Monitoring:} Reassess at 3-month intervals.
\end{enumerate}
\end{recommendations}

Best Practices:

Make recommendations specific and actionable
Prioritize with clear labels
Link to supporting evidence
Include implementation guidance

Limitations Box

Use for limitations, caveats, and cautions:

\begin{limitations}[Study Limitations]
Several limitations should be considered:
\begin{itemize}
    \item \textbf{Sample:} Participants were recruited from academic
        medical centers, limiting generalizability to community settings.
    \item \textbf{Design:} The observational design precludes causal
        inference about treatment effects.
    \item \textbf{Attrition:} 15% dropout rate may introduce bias.
\end{itemize}
\end{limitations}

Best Practices:

Be honest and thorough
Explain implications of each limitation
Suggest how future research could address limitations
Don’t over-qualify findings

Critical Notice Box

Use for critical warnings or safety information:

\begin{criticalnotice}[Safety Warning]
\textbf{Contraindication:} This intervention is contraindicated for
patients with [condition]. Monitor for [adverse effects] and discontinue
immediately if [symptoms] occur. Report serious adverse events to [contact].
\end{criticalnotice}

Best Practices:

Reserve for genuinely critical information
Be clear and direct
Include specific actions to take
Provide contact information if relevant

Definition Box

Use for definitions and explanatory notes:

\begin{definition}[Effect Size]
An \textbf{effect size} is a quantitative measure of the magnitude of a
phenomenon. Unlike significance tests, effect sizes are independent of
sample size and allow comparison across studies. Common measures include
Cohen's \textit{d} for mean differences and Pearson's \textit{r} for
correlations.
\end{definition}

Best Practices:

Define technical terms at first use
Keep definitions concise
Include practical interpretation guidance
Use for audience-appropriate terms

Professional Table Formatting

Design Principles

Clean appearance: Use booktabs rules (\toprule, \midrule, \bottomrule)
Alternating rows: Apply \rowcolor{tablealt} to every other row
Clear headers: Bold headers for column identification
Appropriate precision: Report statistics to appropriate decimal places
Complete information: Include sample sizes, units, and notes

Standard Data Table

\begin{table}[htbp]
\centering
\caption{Demographic Characteristics by Treatment Group}
\label{tab:demographics}
\begin{tabular}{@{}lcc@{}}
\toprule
\textbf{Characteristic} & \textbf{Treatment} & \textbf{Control} \\
 & (\samplesize{225}) & (\samplesize{225}) \\
\midrule
Age, years, \meansd{M}{SD} & \meansd{42.3}{12.5} & \meansd{43.1}{11.8} \\
\rowcolor{tablealt} Female, n (\%) & 128 (56.9) & 121 (53.8) \\
Education, years, \meansd{M}{SD} & \meansd{14.2}{2.8} & \meansd{14.5}{2.6} \\
\rowcolor{tablealt} Baseline score, \meansd{M}{SD} & \meansd{52.4}{15.3} & \meansd{51.8}{14.9} \\
\bottomrule
\end{tabular}
\figurenote{No significant differences between groups at baseline (all \textit{p} > .10).}
\end{table}

Results Table with Significance Indicators

\begin{table}[htbp]
\centering
\caption{Treatment Effects on Primary and Secondary Outcomes}
\label{tab:results}
\begin{tabular}{@{}lcccc@{}}
\toprule
\textbf{Outcome} & \textbf{Treatment} & \textbf{Control} & \textbf{Effect} & \textbf{p} \\
 & \meansd{M}{SD} & \meansd{M}{SD} & \textbf{(d)} & \\
\midrule
Primary outcome & \meansd{68.4}{14.2} & \meansd{54.1}{15.8} & 0.95\sigthree & <.001 \\
\rowcolor{tablealt} Secondary A & \meansd{4.2}{1.1} & \meansd{3.5}{1.2} & 0.61\sigtwo & .003 \\
Secondary B & \meansd{22.8}{5.4} & \meansd{21.2}{5.1} & 0.31\sigone & .042 \\
\rowcolor{tablealt} Secondary C & \meansd{8.9}{2.3} & \meansd{8.5}{2.4} & 0.17\signs & .285 \\
\bottomrule
\end{tabular}

\vspace{0.5em}
{\small \siglegend}
\end{table}

Comparison Table with Quality Ratings

\begin{table}[htbp]
\centering
\caption{Evidence Summary by Study}
\label{tab:evidence}
\begin{tabular}{@{}llccc@{}}
\toprule
\textbf{Study} & \textbf{Design} & \textbf{N} & \textbf{Quality} & \textbf{Evidence} \\
\midrule
Smith et al. (2024) & RCT & 450 & \qualityhigh & \evidencestrong \\
\rowcolor{tablealt} Jones et al. (2023) & Cohort & 1,250 & \qualitymedium & \evidencemoderate \\
Chen et al. (2023) & Case-control & 320 & \qualitymedium & \evidencemoderate \\
\rowcolor{tablealt} Lee et al. (2022) & Cross-sectional & 890 & \qualitylow & \evidenceweak \\
\bottomrule
\end{tabular}
\end{table}

Figure and Caption Styling

Caption Formatting

The style package automatically formats captions with:

Blue, bold figure labels
Gray descriptive text
Centered alignment with margins

Standard Figure

\begin{figure}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{../figures/results_comparison.png}
\caption{Comparison of Outcome Scores by Treatment Condition and Time Point}
\label{fig:results}
\end{figure}

Figure with Source Attribution

\begin{figure}[htbp]
\centering
\includegraphics[width=0.85\textwidth]{../figures/trend_analysis.png}
\caption{Trends in Key Metrics Over the Study Period}
\figuresource{Study data collected January--December 2024}
\label{fig:trends}
\end{figure}

Figure with Explanatory Note

\begin{figure}[htbp]
\centering
\includegraphics[width=0.8\textwidth]{../figures/conceptual_model.png}
\caption{Conceptual Model of Hypothesized Relationships}
\figurenote{Solid arrows indicate primary pathways; dashed arrows indicate moderated relationships. Numbers represent standardized coefficients.}
\label{fig:model}
\end{figure}

Color Palette and Visual Hierarchy

Color Usage Guidelines

Color	Use For	Avoid Using For
Primary Blue	Headers, important findings	Warnings, cautions
Science Green	Methods, positive results	Negative findings
Orange	Cautions, limitations	Positive findings
Red	Critical warnings	Routine content
Purple	Recommendations	Findings, methods
Gray	Definitions, notes	Key findings

Visual Hierarchy

Executive summary boxes (shadow effect) - Most prominent
Colored content boxes - High prominence for key content
Tables with color - Medium prominence for data
Body text - Standard prominence
Definition boxes - Lower prominence for supplementary info

Accessibility Considerations

Color palette is designed to be distinguishable for common color vision deficiencies
All boxes have both color AND structural indicators (borders, backgrounds)
Text maintains sufficient contrast ratios
Don’t rely solely on color to convey meaning

Typography Guidelines

Font Specifications

Element	Font	Size	Color
Body text	Helvetica	11pt	Dark gray (#424242)
Chapter titles	Helvetica Bold	Huge	Primary blue (#003366)
Section headings	Helvetica Bold	Large	Primary blue (#003366)
Subsections	Helvetica Bold	large	Secondary blue (#4A90E2)
Subsubsections	Helvetica Bold	normalsize	Dark gray (#424242)

Spacing

Line spacing: 1.15 (for readability)
Paragraph spacing: 0.5em between paragraphs
Page margins: 1 inch on all sides

Best Typography Practices

Consistency: Use the same formatting for similar elements
Hierarchy: Use visual weight to indicate importance
Readability: Adequate spacing and contrast
Professionalism: Avoid mixing fonts or excessive formatting

Scientific Notation Commands Reference

Statistical Reporting

Command	Output	When to Use
`\pvalue{0.023}`	p = 0.023	Report p-values
`\psig{< 0.001}`	p = < 0.001	Significant p-values (bold)
`\CI{0.45}{0.72}`	95% CI [0.45, 0.72]	Confidence intervals
`\effectsize{d}{0.75}`	d = 0.75	Effect sizes
`\samplesize{250}`	n = 250	Sample sizes
`\meansd{42.5}{8.3}`	42.5 ± 8.3	Mean with SD

Significance Indicators

Command	Output	Meaning
`\sigone`	*	p < 0.05
`\sigtwo`	**	p < 0.01
`\sigthree`	***	p < 0.001
`\signs`	ns	not significant
`\siglegend`	Full legend	For table footnotes

Quality and Evidence Ratings

Command	Output	Meaning
`\qualityhigh`	HIGH (green)	High quality
`\qualitymedium`	MEDIUM (orange)	Moderate quality
`\qualitylow`	LOW (red)	Low quality
`\evidencestrong`	Strong (green)	Strong evidence
`\evidencemoderate`	Moderate (orange)	Moderate evidence
`\evidenceweak`	Weak (red)	Weak evidence

Trend Indicators

Command	Symbol	Meaning
`\trendup`	▲ (green)	Increasing trend
`\trenddown`	▼ (red)	Decreasing trend
`\trendflat`	→ (gray)	Stable/no change

Complete LaTeX Examples

Executive Summary Example

\chapter*{Executive Summary}
\addcontentsline{toc}{chapter}{Executive Summary}

\begin{executivesummary}[Report Highlights]
This report presents findings from a comprehensive study of [topic]
involving \samplesize{450} participants across 12 research sites.
The research addressed [key question] using [methodology].
\end{executivesummary}

\subsection*{Key Findings}

\begin{keyfindings}
\begin{enumerate}
    \item The primary intervention demonstrated a large effect
          (\effectsize{d}{0.82}, \psig{< 0.001}).
    \item Benefits were maintained at 12-month follow-up.
    \item Cost-effectiveness analysis supports implementation.
\end{enumerate}
\end{keyfindings}

\subsection*{Recommendations}

\begin{recommendations}
Based on these findings, we recommend:
\begin{enumerate}
    \item Implement the intervention in [settings].
    \item Train practitioners using the standardized protocol.
    \item Monitor outcomes using the validated measures.
\end{enumerate}
\end{recommendations}

Methods Section Example

\chapter{Methods}

\begin{methodology}[Study Overview]
This randomized controlled trial employed a parallel-group design with
1:1 allocation to intervention or control conditions. The study was
conducted across 12 sites between January 2023 and December 2024.
\end{methodology}

\section{Participants}

A total of \samplesize{450} participants were enrolled. Eligibility
criteria were:

\begin{itemize}
    \item Age 18--65 years
    \item Diagnosis of [condition] per [criteria]
    \item No contraindications to [intervention]
\end{itemize}

Table~\ref{tab:participants} presents participant characteristics.

\begin{limitations}[Recruitment Challenges]
Recruitment was slower than anticipated due to [reasons]. The final
sample was 10% below target, which may affect statistical power for
secondary analyses.
\end{limitations}

Results Section Example

\chapter{Results}

\section{Primary Outcome}

\begin{resultsbox}[Primary Analysis]
Mixed-effects regression revealed a significant treatment effect,
\effectsize{F(1, 448)}{42.18}, \psig{< 0.001}, with a large effect
size (\effectsize{d}{0.82}). The treatment group showed significantly
greater improvement (\meansd{16.4}{5.2} points) compared to control
(\meansd{8.1}{4.8} points).
\end{resultsbox}

Figure~\ref{fig:primary} illustrates the treatment effects over time.

\begin{figure}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{../figures/primary_outcome.png}
\caption{Primary Outcome Scores by Treatment Group and Time Point}
\figurenote{Error bars represent 95\% confidence intervals.}
\label{fig:primary}
\end{figure}

\section{Secondary Outcomes}

Results for secondary outcomes are presented in Table~\ref{tab:secondary}.

Discussion Section Example

\chapter{Discussion}

\section{Summary of Findings}

\begin{keyfindings}[Main Conclusions]
\begin{enumerate}
    \item The intervention was highly effective (primary hypothesis
          \highlight{supported})
    \item Effects were clinically meaningful and durable
    \item Evidence strength: \evidencestrong
\end{enumerate}
\end{keyfindings}

\section{Limitations}

\begin{limitations}
Several limitations warrant consideration:
\begin{itemize}
    \item The sample was predominantly [demographic], limiting
          generalizability.
    \item Attrition was higher in the control group (18\% vs. 12\%).
    \item Self-report measures may be subject to response bias.
\end{itemize}
\end{limitations}

\section{Implications}

\begin{recommendations}[Research Implications]
\begin{enumerate}
    \item Replicate in diverse populations
    \item Investigate mechanisms of change
    \item Test implementation strategies
\end{enumerate}
\end{recommendations}

\begin{recommendations}[Practice Implications]
\begin{enumerate}
    \item Adopt the intervention in [settings]
    \item Train providers using standardized protocols
    \item Monitor fidelity and outcomes
\end{enumerate}
\end{recommendations}

Checklist: Professional Report Quality

Before finalizing your report, verify:

Formatting

Using scientific_report.sty package
Compiled with XeLaTeX or LuaLaTeX
Helvetica font rendering correctly
Colors displaying properly

Content Organization

Executive summary present and complete
Key findings highlighted in boxes
Methods clearly described
Results properly formatted with statistics
Limitations acknowledged
Recommendations are specific and actionable

Tables

All tables have captions and labels
Alternating row colors applied
Significance indicators explained
Numbers formatted consistently

Figures

All figures have captions and labels
Sources attributed where appropriate
Resolution sufficient for printing (300 DPI)
Referenced in text

Statistical Reporting

P-values reported appropriately
Effect sizes included
Confidence intervals where relevant
Sample sizes stated

Professional Appearance

Consistent formatting throughout
No orphaned headers or widows
Page breaks at appropriate locations
References complete and formatted

Resources

Files in This Skill

assets/scientific_report.sty - The LaTeX style package
assets/scientific_report_template.tex - Complete report template
assets/REPORT_FORMATTING_GUIDE.md - Quick reference guide

venue-templates - For journal manuscripts and conference papers
scientific-schematics - For generating diagrams and figures
generate-image - For creating illustrations and graphics

External Resources

LaTeX Wikibook - General LaTeX reference
Booktabs Package Documentation - Professional table styling
tcolorbox Package Documentation - Colored box environments

Reference: Reporting_Guidelines

Reporting Guidelines for Scientific Studies

Overview

Reporting guidelines are evidence-based recommendations for what information should be included when reporting specific types of research studies. They provide checklists and flow diagrams to ensure complete, accurate, and transparent reporting, which is essential for readers to assess study validity and for other researchers to replicate the work.

The EQUATOR Network (Enhancing the QUAlity and Transparency Of health Research) maintains a comprehensive library of reporting guidelines. Using appropriate reporting guidelines improves manuscript quality and increases the likelihood of publication acceptance.

Why Use Reporting Guidelines?

Benefits

For authors:

Ensures nothing important is forgotten
Increases acceptance rates
Improves manuscript organization
Reduces reviewer requests for additional information

For readers and reviewers:

Enables critical appraisal of study validity
Facilitates systematic reviews and meta-analyses
Improves understanding of what was actually done

For science:

Enhances reproducibility
Reduces research waste
Improves transparency
Enables better evidence synthesis

When to Use

During study design: Many guidelines include protocol versions (e.g., SPIRIT for trial protocols)
During manuscript drafting: Use checklist to ensure all items are covered
Before submission: Verify adherence and often submit checklist with manuscript
Many journals require: Reporting guideline checklists as part of submission

Major Reporting Guidelines by Study Type

CONSORT - Randomized Controlled Trials

Full name: Consolidated Standards of Reporting Trials

When to use: Any randomized controlled trial (RCT), including pilot and feasibility trials

Latest version: CONSORT 2010 (updated statement)

Key components:

Checklist: 25 items covering title, abstract, introduction, methods, results, discussion
Flow diagram: Participant flow through enrollment, allocation, follow-up, and analysis

Main checklist items:

Title identifies study as randomized trial
Structured abstract
Scientific background and rationale
Specific objectives and hypotheses
Trial design description (parallel, crossover, factorial, etc.)
Eligibility criteria for participants
Settings and locations of data collection
Interventions described in sufficient detail for replication
Primary and secondary outcomes defined
Sample size determination and power calculation
Randomization sequence generation
Allocation concealment mechanism
Blinding implementation
Statistical methods
Participant flow with reasons for dropouts
Recruitment dates and follow-up dates
Baseline characteristics table
Analysis results for each outcome
Harms and adverse events
Trial limitations
Generalizability
Interpretation consistent with results
Trial registration number
Full protocol access
Funding sources

Extensions for specific designs:

CONSORT for cluster randomized trials
CONSORT for non-inferiority and equivalence trials
CONSORT for pragmatic trials
CONSORT for crossover trials
CONSORT for N-of-1 trials
CONSORT for stepped wedge designs

Where to access: http://www.consort-statement.org/

STROBE - Observational Studies

Full name: Strengthening the Reporting of Observational Studies in Epidemiology

When to use: Cohort studies, case-control studies, and cross-sectional studies

Latest version: STROBE 2007 (widely adopted standard)

Key study designs covered:

Cohort: Follow exposed and unexposed groups forward in time
Case-control: Compare exposure history between cases and controls
Cross-sectional: Measure exposure and outcome simultaneously

Main checklist items (22 items):

Title and abstract indicate study design
Background and rationale
Objectives
Study design with rationale
Setting, locations, and dates
Eligibility criteria and selection methods
Variables clearly defined (outcomes, exposures, confounders)
Data sources and measurement methods
Bias management strategies
Study size justification
Handling of quantitative variables
Statistical methods including confounding and interactions
Sensitivity analyses
Participant flow with reasons for non-participation
Descriptive data including follow-up time
Outcome data
Main results with unadjusted and adjusted estimates
Other analyses (subgroups, sensitivity analyses)
Key results summary
Limitations with potential bias discussion
Interpretation and generalizability
Funding sources and role

Extensions:

STROBE-ME (Molecular Epidemiology)
RECORD (Routinely collected health data)
STROBE-RDS (Respondent-driven sampling)

Where to access: https://www.strobe-statement.org/

PRISMA - Systematic Reviews and Meta-Analyses

Full name: Preferred Reporting Items for Systematic Reviews and Meta-Analyses

When to use: Systematic reviews with or without meta-analysis

Latest version: PRISMA 2020 (significant update)

Key components:

Checklist: 27 items covering all sections
Flow diagram: Study selection process

Main sections:

Title: Identify as systematic review/meta-analysis
Abstract: Structured summary
Introduction: Rationale and objectives
Methods:
- Eligibility criteria
- Information sources (databases, dates)
- Search strategy (full strategy for at least one database)
- Selection process
- Data collection process
- Data items extracted
- Risk of bias assessment
- Effect measures
- Synthesis methods
- Reporting bias assessment
- Certainty assessment (e.g., GRADE)
Results:
- Study selection flow diagram
- Study characteristics
- Risk of bias assessment results
- Synthesis results (meta-analysis if applicable)
- Reporting biases
- Certainty of evidence
Discussion:
- Limitations
- Interpretation
- Implications

Extensions:

PRISMA for Abstracts
PRISMA for Protocols (PRISMA-P)
PRISMA for Network Meta-Analyses
PRISMA for Scoping Reviews (PRISMA-ScR)
PRISMA for Individual Patient Data
PRISMA for Diagnostic Test Accuracy
PRISMA for Equity-focused reviews

Where to access: http://www.prisma-statement.org/

SPIRIT - Study Protocols for Clinical Trials

Full name: Standard Protocol Items: Recommendations for Interventional Trials

When to use: Protocols for randomized trials and other planned intervention studies

Latest version: SPIRIT 2013

Purpose: Ensure trial protocols contain complete descriptions before trial begins

Main checklist items (33 items):

Administrative information (title, trial registration, funding)
Introduction (background, rationale, objectives)
Methods: Trial design
- Study setting
- Eligibility criteria
- Interventions in detail
- Outcomes (primary and secondary)
- Participant timeline
- Sample size calculation
- Recruitment strategy
- Allocation and randomization
- Blinding
- Data collection methods
- Data management
- Statistical methods
- Monitoring (data monitoring committee)
- Harms reporting
- Auditing
Ethics and dissemination
- Ethics approval
- Consent procedures
- Confidentiality
- Dissemination plans

Where to access: https://www.spirit-statement.org/

STARD - Diagnostic Accuracy Studies

Full name: Standards for Reporting of Diagnostic Accuracy Studies

When to use: Studies evaluating diagnostic test accuracy

Latest version: STARD 2015

Main checklist items (30 items):

Study design identification
Background information and objectives
Study design description
Participant selection criteria and recruitment
Data collection methods
Index test description and execution
Reference standard description
Rationale for choosing reference standard
Test result definition and cutoffs
Flow of participants with timing
Baseline demographic and clinical characteristics
Cross-tabulation of index test results by reference standard
Estimates of diagnostic accuracy with confidence intervals
Handling of indeterminate results
Adverse events from testing

Flow diagram: Shows participant flow and test results

Where to access: https://www.equator-network.org/reporting-guidelines/stard/

TRIPOD - Prediction Model Studies

Full name: Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis

When to use: Studies developing, validating, or updating prediction models

Latest version: TRIPOD 2015

Types of studies:

Model development only
Model development with validation
External validation of existing model
Model update

Main checklist items (22 items):

Title identifies study as prediction model study
Abstract summarizes key elements
Background and objectives
Data source and participants
Outcome definition
Predictors (candidate and selected)
Sample size justification
Missing data handling
Model building procedure
Model specification (equation or algorithm)
Model performance measures
Risk groups if used
Participant flow diagram
Model development results
Model performance
Model updating if applicable

Where to access: https://www.tripod-statement.org/

ARRIVE - Animal Research

Full name: Animal Research: Reporting of In Vivo Experiments

When to use: All in vivo animal studies

Latest version: ARRIVE 2.0 (2020 update)

Two sets of items:

ARRIVE Essential 10 (minimum requirements):

Study design
Sample size calculation
Inclusion and exclusion criteria
Randomization
Blinding
Outcome measures
Statistical methods
Experimental animals (species, strain, sex, age)
Experimental procedures
Results and interpretation

ARRIVE Recommended Set (additional items for full reporting):

Abstract, background, objectives
Ethics statement
Housing and husbandry
Animal care and monitoring
Interpretation and generalizability
Protocol registration
Data access

Where to access: https://arriveguidelines.org/

CARE - Case Reports

Full name: CAse REport Guidelines

When to use: Case reports and case series

Latest version: CARE 2013

Main checklist items (13 items):

Title with “case report”
Abstract summarizing case
Introduction with case background
Patient information (demographics, primary concern)
Clinical findings
Timeline of events
Diagnostic assessment
Therapeutic intervention
Follow-up and outcomes
Discussion with strengths and limitations
Patient perspective
Informed consent

Where to access: https://www.care-statement.org/

SQUIRE - Quality Improvement Studies

Full name: Standards for QUality Improvement Reporting Excellence

When to use: Healthcare quality improvement reports

Latest version: SQUIRE 2.0 (2015)

Main sections (18 items):

Title and abstract
Introduction (problem description, available knowledge, rationale, objectives)
Methods (context, intervention, study design, measures, analysis, ethical review)
Results (intervention, outcomes)
Discussion (summary, interpretation, limitations, conclusions)
Other information (funding)

Where to access: http://www.squire-statement.org/

CHEERS - Economic Evaluations

Full name: Consolidated Health Economic Evaluation Reporting Standards

When to use: Health economic evaluations

Latest version: CHEERS 2022 (major update from 2013)

Main checklist items (28 items):

Title identification as economic evaluation
Abstract
Background and objectives
Target population and subgroups
Setting and location
Study perspective
Comparators
Time horizon
Discount rate
Selection of outcomes
Measurement of effectiveness
Measurement and valuation of costs
Currency and price adjustments
Choice of model
Assumptions
Analytical methods

Where to access: https://www.equator-network.org/reporting-guidelines/cheers/

SRQR - Qualitative Research

Full name: Standards for Reporting Qualitative Research

When to use: Qualitative and mixed methods research

Latest version: SRQR 2014

Main sections:

Title and abstract
Introduction (problem formulation, purpose)
Methods (qualitative approach, researcher characteristics, context, sampling strategy, ethical issues, data collection, data analysis, trustworthiness)
Results (synthesis and interpretation, links to empirical data)
Discussion (limitations, implications)

Alternative: COREQ (Consolidated criteria for reporting qualitative research) for interviews and focus groups

Where to access: https://www.equator-network.org/reporting-guidelines/srqr/

How to Use Reporting Guidelines

During Study Planning

Identify relevant guideline based on study design
Review checklist items that require planning (e.g., randomization, blinding)
Design study to ensure all required elements will be captured
Consider protocol guidelines (e.g., SPIRIT for trials)

During Manuscript Drafting

Download checklist from guideline website
Work through each item systematically
Note where each item is addressed in manuscript (page/line numbers)
Revise manuscript to include missing items
Use flow diagrams as appropriate

Before Submission

Complete formal checklist with page numbers
Review all items are adequately addressed
Include checklist with submission if journal requires
Note guideline adherence in cover letter or methods

Example Checklist Entry

Item 7: Eligibility criteria for participants, and the settings and locations where the data were collected
Page 6, lines 112-125: "Participants were community-dwelling adults aged 60-85 years with mild cognitive impairment (MCI) as defined by Petersen criteria. Exclusion criteria included dementia diagnosis, major psychiatric disorders, or unstable medical conditions. Recruitment occurred from three memory clinics in Boston, MA, between January 2022 and December 2023."

Finding the Right Guideline

EQUATOR Network Search

Website: https://www.equator-network.org/

How to use:

Select your study design from the wizard
Browse by health research category
Search for specific keywords
Filter by guideline status (development stage)

By Study Design

If your study is a…	Use this guideline
Randomized controlled trial	CONSORT
Cohort, case-control, or cross-sectional study	STROBE
Systematic review or meta-analysis	PRISMA
Protocol for a trial	SPIRIT
Diagnostic accuracy study	STARD
Prediction model study	TRIPOD
Animal study	ARRIVE
Case report	CARE
Quality improvement study	SQUIRE
Economic evaluation	CHEERS
Qualitative research	SRQR or COREQ

Multiple Guidelines

Some studies may require multiple guidelines:

Example 1: Pilot RCT with qualitative component

CONSORT for quantitative arm
SRQR for qualitative component

Example 2: Systematic review of diagnostic tests

PRISMA for review methods
STARD considerations for included studies

Extensions and Adaptations

Many reporting guidelines have extensions for specific contexts:

CONSORT Extensions (examples)

CONSORT for Abstracts: Structured abstracts for RCT reports
CONSORT for Harms: Reporting adverse events
CONSORT-EHEALTH: eHealth interventions
CONSORT-SPI: Social and psychological interventions

PRISMA Extensions (examples)

PRISMA-P: Protocols for systematic reviews
PRISMA for Abstracts: Conference abstracts
PRISMA-NMA: Network meta-analyses
PRISMA-IPD: Individual patient data reviews
PRISMA-S: Search strategies
PRISMA-DTA: Diagnostic test accuracy reviews

STROBE Extensions (examples)

STROBE-ME: Molecular epidemiology
RECORD: Routinely collected health data

Creating Flow Diagrams

CONSORT Flow Diagram

Four stages:

Enrollment: Assessed for eligibility
Allocation: Randomly assigned to groups
Follow-up: Received intervention, lost to follow-up
Analysis: Included in analysis

Example:

Assessed for eligibility (n=250)
    ↓
Excluded (n=50)
  • Did not meet criteria (n=30)
  • Declined to participate (n=15)
  • Other reasons (n=5)
    ↓
Randomized (n=200)
    ├─────────────────┬─────────────────┐
    ↓                 ↓                 ↓
Allocated to       Allocated to      Allocated to
Intervention A     Intervention B     Control
(n=67)            (n=66)            (n=67)
    ↓                 ↓                 ↓
Lost to follow-up  Lost to follow-up  Lost to follow-up
(n=3)             (n=5)             (n=2)
    ↓                 ↓                 ↓
Analyzed          Analyzed          Analyzed
(n=64)            (n=61)            (n=65)

PRISMA Flow Diagram

Stages:

Identification: Records from databases and registers
Screening: Records screened, excluded
Included: Studies included in review and synthesis

New features in PRISMA 2020:

Separate tracking for database and register searches
Tracking of duplicate removal
Clear distinction between reports and studies

Common Mistakes and How to Avoid Them

Mistake 1: Not Using Guidelines at All

Impact: Missing critical information, lower chance of acceptance

Solution: Identify and use appropriate guideline from study planning stage

Mistake 2: Using Guidelines Only After Manuscript is Complete

Impact: May realize key data were not collected or documented

Solution: Review guidelines during study design and data collection

Mistake 3: Incomplete Checklist Completion

Impact: Missed items remain unreported

Solution: Systematically address every single checklist item

Mistake 4: Using Outdated Guidelines

Impact: Missing recent improvements in reporting standards

Solution: Always check for latest version on official guideline website

Mistake 5: Using Wrong Guideline for Study Design

Impact: Important design-specific elements not reported

Solution: Carefully match study design to appropriate guideline

Mistake 6: Not Submitting Checklist When Required

Impact: Editorial desk rejection or delays

Solution: Check journal submission guidelines and include checklist

Mistake 7: Generic Reporting Without Specificity

Impact: Insufficient detail for replication or appraisal

Solution: Provide specific, detailed information for each item

Journal Requirements

Many Journals Now Require:

Statement of adherence to reporting guidelines in Methods
Completed checklist uploaded as supplementary file
Page/line numbers on checklist indicating where items are addressed
Flow diagrams as figures in manuscript

Example Methods Statement:

"This study is reported in accordance with the Strengthening the Reporting of
Observational Studies in Epidemiology (STROBE) statement. A completed STROBE
checklist is provided as Supplementary File 1."

Journals with Strong Requirements:

PLOS journals (require checklists for specific designs)
BMJ (requires CONSORT, PRISMA, and others)
The Lancet (requires adherence statements)
JAMA and JAMA Network journals (require checklists)
Nature portfolio journals (encourage guidelines)

Resources

Official Guideline Websites

EQUATOR Network: https://www.equator-network.org/
CONSORT: http://www.consort-statement.org/
STROBE: https://www.strobe-statement.org/
PRISMA: http://www.prisma-statement.org/
SPIRIT: https://www.spirit-statement.org/
ARRIVE: https://arriveguidelines.org/
CARE: https://www.care-statement.org/

Training Materials

EQUATOR Network provides webinars and training resources
Many guidelines have explanatory papers published in medical journals
Universities often provide workshops on reporting guidelines

Software Tools

Some reference managers can insert reporting guideline citations
Covidence, RevMan for systematic review reporting
PRISMA flow diagram generator: http://prisma.thetacollaborative.ca/

Checklist: Using Reporting Guidelines

Before starting your study:

Identified appropriate reporting guideline(s)
Reviewed checklist items requiring prospective planning
Designed study to capture all required elements
Registered protocol if applicable

During manuscript drafting:

Downloaded latest version of guideline checklist
Systematically addressed each checklist item
Created required flow diagram
Noted where each item is addressed (page/line)

Before submission:

Completed formal checklist with page numbers
Verified all items adequately addressed
Included adherence statement in Methods
Prepared checklist as supplementary file if required
Checked journal-specific requirements
Mentioned guideline adherence in cover letter

Venue-Specific Reporting Requirements

Reporting Standards by Venue Type

Venue Type	Guideline Use	Transparency Requirements
Medical journals	Mandatory (CONSORT, STROBE, etc.)	Checklist required at submission
PLOS/BMC	Mandatory for study types	Checklist uploaded as supplement
Nature/Science	Recommended	Methods completeness emphasized
ML conferences	No formal guidelines	Reproducibility details required

ML Conference Reporting Standards

NeurIPS/ICML/ICLR reproducibility requirements:

Datasets: Names, versions, access methods, preprocessing
Code: Availability statement; GitHub common
Hyperparameters: All settings reported (learning rate, batch size, etc.)
Seeds: Random seeds for reproducibility
Computational resources: GPUs used, training time
Statistical significance: Error bars, confidence intervals, multiple runs
Broader Impact statement (NeurIPS): Societal implications

What to include (typically in appendix):

Complete hyperparameter settings
Training details and convergence criteria
Hardware specifications
Software versions (PyTorch 2.0, etc.)
Dataset splits and any preprocessing
Evaluation metrics and protocols

Enforcement and Evaluation

What gets checked:

Medical journals: Checklist uploaded; adherence statement in Methods; systematic completeness
PLOS/BMC: Mandatory checklists for certain designs; reproducibility emphasized
High-impact: Methods sufficiency for replication (checklist often not required)
ML conferences: Reproducibility checklist (NeurIPS); code availability increasingly expected

Common issues leading to rejection:

Missing required checklists (medical journals)
Insufficient methods detail for reproduction
Missing key information (randomization, blinding, power calculation)
No data/code availability statement when required

Methods statement examples:

Journal (STROBE):

This study followed STROBE reporting guidelines. Checklist provided in Supplement 1.

ML conference (reproducibility):

Code available at github.com/user/project. All hyperparameters in Appendix A.
Training used 4×A100 GPUs (~20 hours). Seeds: {42, 123, 456}.

Pre-Submission Reporting Checklist

For clinical trials (medical journals):

CONSORT checklist complete with page numbers
Trial registration number in abstract and methods
CONSORT flow diagram included
Statistical analysis plan described
Adherence statement in Methods

For observational studies (medical/epidemiology):

For systematic reviews:

PRISMA checklist complete
PRISMA flow diagram included
Protocol registered (PROSPERO)
Search strategy documented
Risk of bias assessment included

For ML conference papers:

All datasets named with versions
Code availability stated (GitHub link if available)
Hyperparameters listed (appendix acceptable)
Random seeds reported
Computational resources specified
Error bars/confidence intervals shown
Broader Impact statement (if required)

Reference: Writing_Principles

Scientific Writing Principles

Overview

Effective scientific writing requires mastering fundamental principles that ensure clarity, precision, and impact. Unlike creative or narrative writing, scientific writing prioritizes accuracy, conciseness, and objectivity. This guide covers the core principles that distinguish good scientific writing from poor writing and provides practical strategies for improvement.

The Three Pillars of Scientific Writing

1. Clarity

Definition: Writing that is immediately understandable to the intended audience without ambiguity or confusion.

Why it matters: Science is complex enough without unclear writing adding confusion. Readers should focus on understanding the science, not deciphering the prose.

Strategies for Clarity

Use precise, unambiguous language:

Poor: "The drug seemed to help quite a few patients."
Better: "The drug reduced symptoms in 68% (32/47) of patients."

Define technical terms at first use:

"We measured brain-derived neurotrophic factor (BDNF), a protein involved in
neuronal survival and plasticity."

Maintain logical flow within and between paragraphs:

Each paragraph should have one main idea
Topic sentence introduces the paragraph’s focus
Supporting sentences develop that focus
Transition sentences connect paragraphs

Use active voice when it improves clarity:

Passive (less clear): "The samples were analyzed by the researchers."
Active (clearer): "Researchers analyzed the samples."

However, passive voice is acceptable and often preferred in Methods when the action is more important than the actor:

"Blood samples were collected at baseline and after 6 weeks."

Break up long, complex sentences:

Poor: "The results of our study, which involved 200 participants recruited from
three hospitals and followed for 12 months with assessments every 4 weeks using
validated questionnaires, showed significant improvements in the intervention
group."

Better: "Our study involved 200 participants recruited from three hospitals.
Participants were followed for 12 months with assessments every 4 weeks using
validated questionnaires. The intervention group showed significant improvements."

Use specific verbs:

Weak: "The study looked at depression in adolescents."
Stronger: "The study examined factors contributing to depression in adolescents."

Common Clarity Problems

Ambiguous pronouns:

Poor: "Group A received the drug and Group B received placebo. They showed
improvement."
(Who is "they"?)

Better: "Group A received the drug and Group B received placebo. The drug-treated
group showed improvement."

Misplaced modifiers:

Poor: "We measured blood pressure in patients using an automated monitor."
(Are the patients using the monitor, or are we?)

Better: "Using an automated monitor, we measured blood pressure in patients."

Unclear referents:

Poor: "The increase in expression was accompanied by decreased proliferation, which
was unexpected."
(What was unexpected—the decrease, the accompaniment, or both?)

Better: "The increase in expression was accompanied by decreased proliferation.
This inverse relationship was unexpected."

2. Conciseness

Definition: Expressing ideas in the fewest words necessary without sacrificing clarity or completeness.

Why it matters: Concise writing respects readers’ time. Every unnecessary word is a missed opportunity for clarity and impact. As the principle states: “We value concise writing because we value time.”

Strategies for Conciseness

Eliminate redundant words and phrases:

Wordy	Concise
”due to the fact that"	"because"
"in order to"	"to"
"it is important to note that”	[delete]
“a total of 50 participants"	"50 participants"
"completely eliminate"	"eliminate"
"has been shown to be"	"is"
"in the event that"	"if"
"at the present time"	"now” or “currently"
"conduct an investigation into"	"investigate"
"give consideration to"	"consider”

Avoid throat-clearing phrases:

Wordy: "It is interesting to note that the results of our study demonstrate that..."
Concise: "Our results demonstrate that..." or "The results show that..."

Use strong verbs instead of noun+verb combinations:

Wordy	Concise
”make a decision"	"decide"
"perform an analysis"	"analyze"
"conduct a study"	"study” or “studied"
"make an assessment"	"assess"
"provide information about"	"inform”

Eliminate unnecessary intensifiers:

Wordy: "The results were very significant."
Concise: "The results were significant." (p-value conveys the degree)

Avoid repeating information unnecessarily:

Redundant: "The results showed that participants in the intervention group, who
received the treatment intervention, had better outcomes."
Concise: "The intervention group had better outcomes."

Favor shorter constructions:

Wordy: "In spite of the fact that the sample size was small..."
Concise: "Although the sample size was small..."

Acceptable Length vs. Unnecessary Length

Not all long sentences are bad:

This detailed sentence is fine: "We analyzed blood samples using liquid
chromatography-tandem mass spectrometry (LC-MS/MS) with a Waters Acquity UPLC
system coupled to a Xevo TQ-S mass spectrometer (Waters Corporation, Milford, MA)."

Why? Because each element is necessary information.

The key question: Can any word be removed without losing meaning or precision? If yes, remove it.

3. Accuracy

Definition: Precise, correct representation of data, methods, and interpretations.

Why it matters: Scientific credibility depends on accuracy. Inaccurate reporting undermines the entire scientific enterprise.

Strategies for Accuracy

Report exact values with appropriate precision:

Poor: "The mean was about 25."
Better: "The mean was 24.7 ± 3.2 (SD)."

Match precision to measurement capability:

Inappropriate: "Mean age was 45.237 years" (implies false precision)
Appropriate: "Mean age was 45.2 years"

Use consistent terminology throughout:

Inconsistent: Introduction calls it "cognitive function," Methods call it "mental
performance," Results call it "intellectual ability."

Consistent: Use "cognitive function" throughout, or define explicitly: "cognitive
function (also termed mental performance)"

Distinguish observations from interpretations:

Observation: "Mean blood pressure decreased from 145 to 132 mmHg (p=0.003)."
Interpretation: "This suggests the intervention effectively lowers blood pressure."

Be specific about uncertainty:

Vague: "There may be some error in these measurements."
Specific: "Measurements have a standard error of ±2.5 mmHg based on instrument
specifications."

Use correct statistical language:

Incorrect: "The correlation was highly significant (p=0.03)."
Correct: "The correlation was statistically significant (p=0.03)."
(p=0.03 is not "highly" significant; that's reserved for p<0.001)

Verify all numbers:

Check that numbers in text match tables/figures
Verify that n values sum correctly
Confirm percentages are correctly calculated
Double-check all statistics

Common Accuracy Problems

Overgeneralization:

Poor: "Exercise prevents depression."
Better: "In our sample, participants randomized to the exercise intervention showed
fewer depressive symptoms than controls (mean difference 3.2 points on the BDI-II,
95% CI: 1.5-4.9, p<0.001)."

Unwarranted causal claims:

Poor (from observational study): "Vitamin D supplementation reduces cancer risk."
Better: "Vitamin D levels were inversely associated with cancer incidence in this
cohort (HR=0.82, 95% CI: 0.71-0.95)."

Imprecise numerical descriptions:

Vague: "Many participants dropped out."
Precise: "15/50 (30%) participants withdrew before study completion."

Additional Key Principles

4. Objectivity

Definition: Presenting information impartially without bias, exaggeration, or unsupported opinion.

Strategies:

Present results without bias:

Biased: "As expected, our superior method performed better."
Objective: "Method A showed higher accuracy than Method B (87% vs. 76%, p=0.02)."

Acknowledge conflicting evidence:

"Our findings contrast with Smith et al. (2022), who reported no significant effect.
This discrepancy may result from differences in intervention intensity or population
characteristics."

Avoid emotional or evaluative language:

Subjective: "The results were disappointing and concerning."
Objective: "The intervention did not significantly reduce symptoms (p=0.42)."

Distinguish fact from speculation:

"The observed decrease in cell viability was accompanied by increased caspase-3
activity, suggesting that apoptosis may be the primary mechanism of cell death."
(Uses "suggesting" and "may be" to indicate interpretation)

5. Consistency

Maintain consistency throughout the manuscript:

Terminology:

Use the same term for the same concept (not synonyms for variety)
Define abbreviations at first use and use consistently thereafter
Use standard nomenclature for genes, proteins, chemicals

Notation:

Statistical notation (p-value format, CI presentation)
Units of measurement
Number formatting (decimal places)

Tense:

Past tense for your specific study actions
Present tense for established facts
See detailed tense guide in IMRAD structure reference

Style:

Follow journal guidelines consistently
Citation format
Heading capitalization
Number vs. word for numerals

6. Logical Organization

Create a clear “red thread” through the manuscript:

Paragraph structure:

Topic sentence (main idea)
Supporting sentences (evidence, explanation)
Concluding/transition sentence (link to next idea)

Section flow:

Each section builds logically on the previous
Questions raised in Introduction are answered in Results
Findings presented in Results are interpreted in Discussion

Signposting:

"First, we examined..."
"Next, we investigated..."
"Finally, we assessed..."

Parallelism:

Not parallel: "Aims were to (1) measure blood pressure, (2) assessment of
cognitive function, and (3) we wanted to evaluate mood."

Parallel: "Aims were to (1) measure blood pressure, (2) assess cognitive
function, and (3) evaluate mood."

Verb Tense in Scientific Writing

General Guidelines

Present tense for:

Established facts and general truths
- “DNA is composed of nucleotides.”
Conclusions you are drawing
- “These findings suggest that…”
Referring to figures and tables
- “Figure 1 shows the distribution…”

Past tense for:

Specific findings from completed research (yours and others’)
- “Smith et al. (2022) found that…”
- “We observed a significant decrease…”
Methods you performed
- “Participants completed questionnaires at baseline.”

Present perfect for:

Recent developments with current relevance
- “Recent studies have demonstrated…”
Research area background
- “Several approaches have been proposed…”

Section-Specific Tense

Section	Primary Tense	Examples
Abstract - Background	Present or present perfect	”Depression affects millions” / “Research has shown…”
Abstract - Methods	Past	”We recruited 100 participants”
Abstract - Results	Past	”The intervention reduced symptoms”
Abstract - Conclusions	Present	”These findings suggest…”
Introduction - Background	Present (facts), present perfect (research)	“Exercise is beneficial” / “Studies have shown…”
Introduction - Gap	Present or present perfect	”However, little is known…”
Introduction - This study	Past or present	”We investigated…” / “This study investigates…”
Methods	Past	”We collected samples…”
Results	Past	”Mean age was 45 years”
Discussion - Your findings	Past	”We found that…”
Discussion - Interpretation	Present	”This suggests…”
Discussion - Prior work	Past or present	”Smith found…” / “Previous work demonstrates…”

Common Writing Pitfalls

1. Jargon Overload

Problem: Excessive use of technical terms without definition

Example:

Poor: "We utilized qRT-PCR to quantify mRNA expression via SYBR-Green-based
fluorescence detection following cDNA synthesis from total RNA using oligo-dT primers."

Better: "We quantified mRNA expression using quantitative reverse transcription PCR
(qRT-PCR). Total RNA was reverse transcribed to complementary DNA (cDNA) using
oligo-dT primers, then amplified with SYBR Green fluorescent detection."

2. Nominalization

Problem: Turning verbs into nouns, making writing heavy and indirect

Examples:

Nominalized	Direct
”give consideration to"	"consider"
"make an assumption"	"assume"
"perform an investigation"	"investigate"
"conduct an examination"	"examine"
"achieve a reduction"	"reduce”

3. Hedging Excessively or Insufficiently

Excessive hedging (sounds uncertain):

"It could perhaps be possible that the intervention might possibly have some effect
on symptoms under certain conditions."

Insufficient hedging (overstates conclusions):

"The intervention cures depression."

Appropriate hedging:

"The intervention significantly reduced depressive symptoms in this sample,
suggesting it may be effective for treating mild to moderate depression."

Hedging words to use appropriately:

Suggests, indicates, implies (not proves, demonstrates for correlational data)
May, might, could (possibilities)
Appears to, seems to (observations needing confirmation)
Likely, probably, possibly (degrees of certainty)

4. Anthropomorphism

Problem: Attributing human characteristics to non-human entities

Examples:

Anthropomorphic	Scientific
”The study wanted to examine…"	"We aimed to examine…” or “The study examined…"
"The data suggest they want…"	"The data suggest that…"
"This paper will prove…"	"This paper demonstrates…"
"Table 1 tells us…"	"Table 1 shows…“

5. Abbreviation Abuse

Problems:

Too many abbreviations burden the reader
Abbreviating terms used only once or twice
Not defining abbreviations at first use

Guidelines:

Only abbreviate terms used ≥3-4 times
Define at first use in abstract (if used in abstract)
Define at first use in main text
Don’t abbreviate in title
Limit to 3-4 new abbreviations per paper when possible
Use standard abbreviations (DNA, RNA, HIV, etc.) without definition

Example:

Poor: "We measured Brain-Derived Neurotrophic Factor (BDNF) at baseline. BDNF
levels were elevated."
(Only used twice, abbreviation unnecessary)

Better: "We measured brain-derived neurotrophic factor at baseline. Levels were
elevated."

Specific Sentence-Level Issues

Dangling Modifiers

Problem:

"After incubating for 2 hours, we measured absorbance."
(The sentence suggests "we" were incubated)

Better: "After incubating samples for 2 hours, we measured absorbance."
Or: "After 2-hour incubation, we measured absorbance."

Misplaced Commas

Common errors:

Between subject and verb:

Wrong: "The participants in the intervention group, showed improvement."
Right: "The participants in the intervention group showed improvement."

In compound predicates:

Wrong: "We measured blood pressure, and recorded symptoms."
Right: "We measured blood pressure and recorded symptoms."
(No comma before "and" when it doesn't join independent clauses)

Pronoun Agreement

Wrong: "Each participant completed their questionnaire."
Right: "Each participant completed his or her questionnaire."
Or better: "Participants completed their questionnaires."

Subject-Verb Agreement

Wrong: "The group of participants were heterogeneous."
Right: "The group of participants was heterogeneous."
(Subject is "group" [singular], not "participants")

But: "The participants were heterogeneous." (Plural subject)

Word Choice

Commonly Confused Words in Scientific Writing

Often Misused	Correct Usage
affect / effect	Affect (verb): influence; Effect (noun): result; Effect (verb): bring about
among / between	Among: three or more; Between: two
continual / continuous	Continual: repeated; Continuous: uninterrupted
data is / data are	Data are (plural); datum is (singular)
fewer / less	Fewer: countable items; Less: continuous quantities
i.e. / e.g.	i.e. (that is): restatement; e.g. (for example): examples
imply / infer	Imply: suggest; Infer: deduce
parameter / variable	Parameter: population value; Variable: measured characteristic
principal / principle	Principal: main; Principle: rule or concept
significant	Reserve for statistical significance, not importance
that / which	That: restrictive clause; Which: nonrestrictive clause

Words to Avoid or Use Carefully

Avoid informal language:

“a lot of” → “many” or “substantial”
“got” → “obtained” or “became”
“showed up” → “appeared” or “was evident”

Avoid vague quantifiers:

“some” → specify how many
“often” → specify frequency
“recently” → specify timeframe

Avoid unnecessary modifiers:

“very significant” → “significant” (p-value shows degree)
“quite large” → “large” or specify size
“rather interesting” → delete or explain why

Numbers and Units

When to Use Numerals vs. Words

Use numerals for:

All numbers ≥10
Numbers with units (5 mg, 3 mL)
Statistical values (p=0.03, t=2.14)
Ages, dates, times
Scores and scales
Percentages (15%)

Use words for:

Numbers <10 when not connected to units (five participants)
Numbers beginning a sentence (spell out or restructure)

Examples:

"Five participants withdrew" OR "There were 5 withdrawals"
(NOT: "5 participants withdrew")

"We tested 15 samples at 3 time points"
"Mean age was 45 years"

Units and Formatting

Guidelines:

Space between number and unit (5 mg, not 5mg)
No period after units (mg not mg.)
Use SI units unless field convention differs
Be consistent in decimal places
Use commas for thousands in text (12,500 not 12500)

Ranges:

Use en-dash (–) for ranges: 15–20 mg
Include unit only after second number: 15–20 mg (not 15 mg–20 mg)

Paragraph Structure

Ideal Paragraph Length

Guidelines:

3-7 sentences typically
One main idea per paragraph
Too short (<2 sentences): may indicate idea needs development or combining
Too long (>10 sentences): may need splitting

Paragraph Coherence

Techniques:

1. Topic sentence:

"Exercise training improves cardiovascular function through multiple mechanisms.
[Following sentences explain these mechanisms]"

2. Transitional phrases:

First, second, third, finally
Furthermore, moreover, in addition
However, nevertheless, conversely
Therefore, thus, consequently
For example, specifically, particularly

3. Repetition of key terms:

"...this mechanism of action. This mechanism may explain..."
(Not: "...this mechanism. This process may explain...")

4. Parallel structure:

"Group A received the drug. Group B received placebo. Group C received no treatment."
(Not: "Group A received the drug. Placebo was given to Group B. No treatment was
provided to the third group.")

Revision Checklist

Content Level

Does every sentence add value?
Are claims supported by data?
Is the logic clear and sound?
Are interpretations warranted by results?

Paragraph Level

Does each paragraph have one main idea?
Are paragraphs in logical order?
Are transitions smooth?
Is there a clear “red thread”?

Sentence Level

Are sentences clear and concise?
Is sentence structure varied?
Are there no dangling modifiers?
Do subjects and verbs agree?

Word Level

Is word choice precise?
Are technical terms defined?
Is terminology consistent?
Are abbreviations necessary and defined?
Are numbers formatted correctly?

Grammar and Mechanics

Is verb tense correct and consistent?
Are commas used correctly?
Do pronouns agree with antecedents?
Is punctuation correct?
Is spelling correct (including technical terms)?

Tools for Improving Writing

Grammar and Style Checkers

Grammarly: Grammar, style, clarity
ProWritingAid: In-depth writing analysis
Hemingway Editor: Readability, simplification
LanguageTool: Open-source grammar checker

Caution: These tools don’t understand scientific writing conventions. Use them as a starting point, not final arbiter.

Readability Metrics

Flesch Reading Ease:

60-70: acceptable for scientific papers
<60: may be too complex

Caution: Don’t sacrifice precision for readability scores designed for general audiences.

Peer Review

Most valuable tool:

Ask colleagues to read and provide feedback
Identify unclear passages
Check logical flow
Verify interpretations are warranted

Additional Resources

Books on Scientific Writing

The Elements of Style by Strunk & White (classic on clear writing)
On Writing Well by William Zinsser
Scientific Writing: A Reader and Writer’s Guide by Jean-Luc Lebrun
How to Write a Scientific Paper by George M. Whitesides
Style: Lessons in Clarity and Grace by Joseph Williams

Online Resources

Academic Phrasebank (University of Manchester): Common academic phrases
Purdue OWL: Grammar, punctuation, style
Nature Masterclasses: Scientific writing courses
WritingCenters: Many universities provide free online resources

University Writing Centers

Most research universities offer:

Individual consultations
Workshops on scientific writing
Online resources and handouts
Support for non-native English speakers

Venue-Specific Writing Styles

Four Major Writing Style Categories

Broad-audience accessible (Nature, Science, PNAS)
Clinical-professional (NEJM, Lancet, JAMA)
Technical-specialist (field-specific journals)
ML conference (NeurIPS, ICML, ICLR, CVPR)

Writing Style Comparison

Aspect	Nature/Science	Medical	Specialized	ML Conference
Sentence length	15-20 words	12-18 words	18-25 words	12-20 words
Vocabulary	Minimal jargon	Clinical terms	Field-specific	Technical + math
Tone	Engaging, significant	Conservative	Formal	Direct, contribution-focused
Key phrases	”Here we show"	"We conducted"	"To elucidate"	"We propose”, “Our contributions”

ML Conference Style:

Characteristics:

Direct, technical language with mathematical notation
Contribution-focused (numbered lists common)
Assumes ML expertise (CNNs, transformers, SGD, etc.)
Emphasizes novelty and performance gains
Pseudocode and equations expected

Example opening (NeurIPS style):

Vision transformers have achieved state-of-the-art performance on image classification,
but their quadratic complexity limits applicability to high-resolution images. We propose
Efficient-ViT, which reduces complexity to O(n log n) while maintaining accuracy. Our
contributions are: (1) a novel sparse attention mechanism, (2) theoretical analysis
showing preserved expressive power, and (3) empirical validation on ImageNet showing
15% speedup with comparable accuracy.

Problem stated with technical context
Solution previewed
Numbered contributions
Quantitative claims

Key Writing Differences

Aspect	Nature/Science	Medical	Specialized	ML Conference
Paragraph length	3-5 sentences	5-7 sentences	6-10 sentences	4-6 sentences
Math/equations	Minimize	Rare	Moderate	Essential
Active voice	Preferred	Mixed	Passive OK	Preferred
Hedging	Moderate	Conservative	Detailed	Minimal (claim gains)
Figure integration	Tight	Systematic	Detailed	Dense, in-page

Evaluation Focus by Venue

Venue	Key Evaluation Criteria
Nature/Science	Accessible to non-specialists? Broad significance clear? Compelling story?
Medical	Clinical relevance apparent? Professional tone? Methods adequate?
Specialized	Technical precision? Field expertise shown? Methods detailed?
ML conferences	Clear contributions? Claims supported by experiments? Reproducible?

Common rejection reasons:

Poor writing quality/unclear prose
Inappropriate style for venue
Overstated claims
Methods insufficient for reproduction
Missing key details (baselines, ablations for ML; statistics for journals)

Quick Style Adaptation Guide

From → To	Key Changes
Journal → ML conference	Add numbered contributions; include equations/pseudocode; emphasize quantitative gains; condense prose
ML conference → Journal	Remove contribution numbering; expand motivation; separate Results/Discussion; reduce equations in main text
Specialist → Broad	Simplify language; emphasize broad implications; explain technical concepts; add context for non-experts
Broad → Specialist	Add technical detail; use field terminology freely; expand mechanistic discussion; cite field literature
Basic science → Clinical	Add patient/clinical context; use clinical language; emphasize outcomes/implications; cite clinical evidence

Pre-Submission Style Checklist

All venues:

Writing style matches 3-5 recent papers from venue
Sentence length appropriate
Technical vocabulary level correct
Tone consistent with venue
No overstated claims

ML conferences add:

Contributions clearly numbered in intro
Mathematical notation correct and consistent
Pseudocode/algorithms included where appropriate
Claims quantified (e.g., “15% faster”, “2.3% accuracy gain”)
Limitations acknowledged

Final Thoughts

Effective scientific writing is a skill developed through practice. Key principles:

Clarity trumps complexity
Conciseness respects readers’ time
Accuracy builds credibility
Objectivity maintains scientific integrity
Consistency aids comprehension
Logical organization guides readers
Journal-specific adaptation maximizes publication success

Remember: The goal is not to impress readers with vocabulary or complexity, but to communicate your science clearly and precisely so readers can understand, evaluate, and build upon your work. Adapt your writing style to match your target journal’s expectations and audience.