The Research Context
Dr. Sarah Chen
Dr. Sarah Chen is a third-year PhD student in Climate Science at Northern University. Her dissertation focuses on Arctic temperature acceleration patterns over the past three decades.
| Attribute | Value |
|---|---|
| Program | PhD, Climate Science |
| Year | Third year |
| Advisor | Prof. James Morrison |
| Funding | NSF Arctic Research Grant |
| Timeline | Dissertation defense in 18 months |
Research Goal
Thesis Question: Have Arctic temperature increases accelerated since 1995, and what factors drive the acceleration?
Sarah’s analysis requires:
- Synthesizing 25+ peer-reviewed papers on Arctic warming
- Analyzing 30 years of temperature data from 12 monitoring stations
- Processing satellite imagery datasets
- Producing reproducible statistical analysis
The Reproducibility Challenge
Sarah’s field has a reproducibility problem. A 2024 meta-analysis found:
| Issue | Prevalence |
|---|---|
| Methods under-specified | 67% of papers |
| Data not preserved | 45% of papers |
| Analysis steps not documented | 72% of papers |
| Results not reproducible | 38% when attempted |
Her advisor emphasizes: “Every finding must trace back to specific data and methodology. Your dissertation defense will include a reproducibility audit.”
Current Research State
Sarah has:
- Downloaded 30 years of temperature data (CSV files, ~2GB)
- Collected 25 papers on Arctic warming patterns
- Rough notes on initial observations
- No systematic approach to tracking analysis
Her pain points:
- Literature notes scattered across Notion, PDFs, and text files
- Analysis scripts in various Jupyter notebooks, unclear dependencies
- Uncertain which findings came from which data version
- No way to know when new data invalidates old conclusions
Why Chant?
Chant addresses each challenge:
| Challenge | Chant Solution |
|---|---|
| Scattered notes | informed_by: links findings to sources |
| Unclear dependencies | depends_on: chains analysis phases |
| Data versioning | origin: tracks input data files |
| Result staleness | Drift detection alerts when inputs change |
| Method documentation | Spec IS the methodology |
Project Setup
Sarah initializes chant in her dissertation repository:
# Initialize chant
chant init --agent claude
# Create directories for research data
mkdir -p data/temperature
mkdir -p data/satellite
mkdir -p papers
mkdir -p analysis
# Create context directory for literature synthesis
mkdir -p .chant/context/arctic-research
Directory structure:
dissertation/
├── .chant/
│ ├── specs/ # Research specs live here
│ ├── context/ # Human-curated summaries
│ │ └── arctic-research/
│ └── config.md
├── data/
│ ├── temperature/ # 30 years of station data
│ └── satellite/ # Imagery datasets
├── papers/ # PDF collection
└── analysis/ # Output: findings, figures
Research Timeline
Sarah plans a four-week research phase:
Week 1 Week 2 Week 3 Week 4
┌──────────┐ ┌───────────────┐ ┌──────────────┐ ┌──────────────┐
│Literature│ │ Data │ │ Pipeline │ │ Write-up & │
│ Review │──>│ Analysis │──>│ Coordination │──>│ Ongoing │
│ (Papers) │ │ (Statistics) │ │ (Driver) │ │ Drift │
└──────────┘ └───────────────┘ └──────────────┘ └──────────────┘
Spec Workflow Preview
Sarah’s research will use these spec types:
| Week | Spec Type | Purpose |
|---|---|---|
| 1 | research with informed_by: | Synthesize 25 papers into themes |
| 2 | research with origin: | Analyze temperature data |
| 3 | driver with members | Coordinate multi-step pipeline |
| 4+ | Drift detection | Alert when new data arrives |
Team Structure
Unlike enterprise scenarios, Sarah works largely alone, but chant’s orchestrator pattern still applies:
- Sarah creates specs, reviews findings, validates methodology
- Chant agents synthesize literature, run statistical analysis
- Git provides version control and audit trail
- Drift detection runs when data files change
What’s Next
With the project initialized, Sarah begins the literature review phase:
Literature Review — Synthesizing 25 papers using research specs with informed_by: