Introduction

Question Miner (QM) is a research intelligence tool designed to help scientists extract meaningful research gaps and high-value questions directly from scientific literature.
By analyzing the title, abstract, citation contexts, and reference contexts of a paper, QM identifies signals that often take hours or days of manual reading to discover.

This guide provides a detailed and practical walkthrough on:

• How to use QM effectively
• Why accurate inputs matter
• How QM interprets signals
• What the output means
• How to integrate QM results into your research workflow

The goal is to help you understand QM deeply and use it to accelerate your scientific reasoning.

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Step 1: Choose a High-Quality Paper for Deep Analysis

QM works on any paper, but it yields the most meaningful outputs when the target paper meets the characteristics of high-quality research:

• It identifies an important scientific problem
• It introduces a strong method or insight
• It appears in a reputable venue or is well-cited
• Its references and citations reflect an active research community

These papers tend to have:

• Rich citation contexts (how others cite or critique the work)
• Strong reference contexts (what foundational work it builds on)
• Clear signals of limitations, assumptions, contradictions, or boundary conditions

Manually reviewing all this information can be extremely time-consuming.
QM automates this process—systematically, consistently, and at scale.

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Step 2: Copy the Exact Paper Title

After selecting a suitable paper, simply copy and paste the exact title into QM.

Why the title?

• Academic titles are highly discriminative—even small variations can refer to entirely different papers
• Keyword search can return unrelated documents, introducing noise
• Title-based matching ensures QM reconstructs the correct abstract, context, and scope

Important:
Incomplete or inaccurate titles may lead to the wrong paper being analyzed or may cause the workflow to fail.

This design choice ensures you always analyze the exact paper you intend to study.

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Step 3: QM Begins the Analysis

Once the task is triggered, QM begins reconstructing and analyzing the target paper:

Typical runtime

3 minutes or so, but with natural variability depending on citation density.

Why variability occurs

Citation contexts vary widely:

• Some papers have fewer than 10 citations
• Others may have hundreds or even thousands
• Reference contexts are usually more stable, but still vary by field

How QM handles large citation volumes

QM applies multi-stage filtering:

1. Identifies all available citation contexts
2. Prioritizes high-influence citing papers
3. Applies a maximum exploration limit (approx. 1000 contexts)
4. Ensures citation and reference signals remain balanced

This allows QM to work efficiently while still extracting meaningful signals from information-dense literature.

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Step 4: Review the Output (Including Empty Results)

In some cases, QM may return an empty result.
This is expected and scientifically reasonable—not all papers contain strong or extractable research gaps.

When results are available, they are presented as a structured report.

You can read the results in several ways:

1. Read directly on the results page
2. Use your browser’s “Print to PDF” option
3. Review from your History section
4. Re-open any report using the Re-View button
5. Download the report as a Markdown file (including the full JSON payload for system integration)

If errors occur (e.g., incorrect input, network issues), QM will clearly indicate the cause and suggest next steps.

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Step 5: Understanding the Output Structure

A typical QM result includes components such as:

Research Opportunity:
How can integration frameworks be developed to handle diverse scientific data sources in drug discovery?

Source Context:
Such data can also exist across multiple sources, introducing challenges in factual integration.

Structured Question:
Design frameworks that extend integration capabilities to heterogeneous data sources while preserving core advantages.

Opportunity Level:
High

Innovation Scope:
Field-Level

Cross-boundary Example:
Combining genomic data with chemical interaction databases for enhanced drug discovery insights.

Below is a detailed explanation of each component.

Research Opportunity

This is the main headline.
QM converts the structured question into a research opportunity statement through a Question–Opportunity Converter.

Only signals that pass this transformation become valid opportunities.

Source Context

A short extracted context that explains why the opportunity exists.

Structured Question

A systematic formulation of the research problem, enriched with:

• Scope
• Limitations
• Desired impact

Opportunity Level (High / Medium / Low)

Opportunity Level = Innovation Intensity × Technical Feasibility

Low-level items are usually filtered out automatically.

Innovation Scope

A qualitative category describing the nature of the opportunity:

• Method-level
• Field-level
• Cross-disciplinary
• Application-level
  etc.

Cross-Boundary Example

A concrete example that illustrates how similar opportunities have emerged across domains.

Validated / Failed Labels

Each item undergoes QM’s internal quality filter.

• Validated: clear, specific, well-formed
• Failed: does not meet all quality criteria

Failed items are retained intentionally:

Every identified signal passes through our strict quality screening process.
We retain "Failed" entries to ensure no potentially valuable signal is lost, enabling the researcher’s final judgment.

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Step 6: How to Use the Output in Your Research

QM results integrate into your scientific workflow in several ways:

1. As part of literature review

Use structured opportunities to identify:

• Gaps the paper implicitly signals
• Overlooked assumptions
• Methodological boundaries

2. For research direction planning

Combine QM results with your own domain expertise to:

• Prioritize promising research directions
• Validate or refine your hypotheses
• Avoid redundant paths

3. For preparing research proposals

Structured opportunities map naturally to:

• Significance / problem statements
• Innovation narratives
• Research aims

4. For cross-domain exploration

Cross-boundary examples inspire:

• New analogies
• Technique transplantation
• Hybrid approaches

5. As input to QI (Quest Innovation)

For systematic solution generation:

• Copy the entire structured opportunity
• Paste into QI as the problem input
• Optionally add context or constraints
• Generate multi-angle solutions systematically

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Real-World Scenario

Scenario:
A researcher exploring data integration challenges in computational drug discovery.

QM Output:
High-level opportunity pointing to heterogeneous data integration frameworks.

Usage:
Researcher uses QM to:

• Identify the specific contradiction in current frameworks
• Prioritize the opportunity as High impact
• Forward the opportunity to QI for solution generation

Result:
A structured reasoning chain
→ clear research aims
→ validated innovation direction.

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Conclusion

QM is intentionally simple to use—just paste a title and start the task.
This guide provides deeper insight into how QM works and how to interpret the output effectively.

Key takeaways:

• Use high-quality papers for the best results
• Provide accurate titles to avoid misalignment
• Expect natural output variability
• Interpret structured results for deeper research insights
• Combine QM and QI for complete problem-to-solution workflows

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Ready to get started? Try Question Miner (QM) or Question Innovation (QI) to accelerate your research. Start with 50 free credits and see how AI can transform your workflow.