Access the world’s most comprehensive academic knowledge with Valyu’s DeepSearch API. Whether you’re conducting literature reviews, validating research, or exploring cutting-edge developments, Valyu provides seamless access to peer-reviewed papers, academic journals, and scholarly datasets.

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Why Academic Search Matters

Academic search on Valyu provides Full Text Academic Search that enables:
  • Literature Reviews - Systematic gathering of scholarly sources on specific topics
  • Research Validation - Cross-referencing findings across multiple academic sources
  • Citation Discovery - Finding relevant papers and their citation networks
  • Trend Analysis - Tracking research developments over time

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Key Academic Features

Enhanced Metadata

Rich Academic Context Get author information, citation counts, DOIs, and publication dates for comprehensive research context.

Proprietary Datasets

Exclusive Academic Access Access closed access academic datasets, journals and books.

Source Prioritization

Quality Assurance Automatically prioritize scholarly databases, academic journals, and research institutions.

Citation Networks

Research Connections Discover related work through citation analysis and reference tracking.

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Available Academic Datasets

Valyu provides access to comprehensive academic and research datasets:
DatasetContentUse Case
valyu/valyu-arxivArXiv preprints and papersLatest research across all fields
valyu/valyu-pubmedMedical and life sciences literatureHealthcare and biomedical research
wiley/wiley-finance-papersFinance and economics papersFinance and economics research
Research Tip: Combine multiple datasets for comprehensive coverage. Check out all available datasets here.

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Quick Start Examples

Get started with scholarly research in minutes: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
"machine learning applications in quantitative finance research",
response_length="large" # Get comprehensive context
)

print(response)

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Target Specific Academic Sources

Focus your search on particular academic datasets and journals: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
"CRISPR gene editing therapeutic applications",
included_sources=[
"valyu/valyu-pubmed", # Medical literature
"valyu/valyu-arxiv", # Latest preprints
"nature.com", # Nature journals
"science.org" # Science journals
],
)

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Recent Research with Date Filtering

Find the latest developments in your field: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
"peer-reviewed studies on climate change mitigation strategies",
included_sources=[
"valyu/valyu-arxiv",
"valyu/valyu-pubmed"
],
start_date="2024-01-01", # Recent research only
response_length="large",
max_num_results=15
)

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Use Cases

  • Literature review: synthesize peer‑reviewed research across journals and preprints
  • Citation discovery: find related work, references, DOIs, citation counts
  • Methods benchmarking: compare methodologies, datasets, and evaluation metrics
  • Trend analysis: track topics, venues, and publication timelines
  • Interdisciplinary research: combine CS, medical, engineering sources

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Advanced Academic Research Patterns

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Interdisciplinary Research

Combine multiple academic domains for comprehensive coverage: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
"artificial intelligence applications in medical diagnosis research",
included_sources=[
"valyu/valyu-arxiv", # CS and AI papers
"valyu/valyu-pubmed", # Medical literature
"ieee.org", # Engineering papers
"acm.org" # Computer science
],
response_length="large"
)

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Historical Research Analysis

Compare research evolution across different time periods: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")

# Early neural network research

early_research = valyu.search(
"neural network architectures and applications",
included_sources=["valyu/valyu-arxiv"],
start_date="1990-01-01",
end_date="2005-12-31"
)

# Modern deep learning research

modern_research = valyu.search(
"neural network architectures and applications",
included_sources=["valyu/valyu-arxiv"],
start_date="2020-01-01"
)

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Use Case Examples

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Medical Research

Access the latest medical literature and clinical studies: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
"immunotherapy cancer treatment efficacy clinical trials",
included_sources=[
"valyu/valyu-pubmed",
"nejm.org",
"thelancet.com",
"clinicaltrials.gov"
],
start_date="2023-01-01",
response_length="large"
)

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Computer Science Research

Find cutting-edge AI and machine learning papers: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
"transformer architecture improvements large language models",
included_sources=[
"valyu/valyu-arxiv",
"openai.com",
"deepmind.com",
"aclweb.org"
],
start_date="2024-01-01",
search_type="proprietary"
)

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Environmental Science

Research climate change and sustainability studies: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
"carbon capture technology effectiveness peer-reviewed studies",
included_sources=[
"valyu/valyu-arxiv",
"nature.com",
"science.org",
"iopscience.iop.org"
],
response_length="large",
max_num_results=20
)

​
Social Sciences

Access social science research and behavioral studies: 
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
"remote work productivity psychological wellbeing studies",
included_sources=[
"valyu/valyu-pubmed",
"psycnet.apa.org",
"springer.com"
],
start_date="2020-01-01", # Post-pandemic research
response_length="large"
)

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Academic Metadata Response

When searching academic content, Valyu returns enhanced metadata: 
{
  "results": [
    {
      "title": "Transformer Architecture for Protein Folding Prediction",
      "authors": ["Dr. Jane Smith", "Dr. John Doe"],
      "citation": "Smith, J., Doe, J. (2024). Nature Biotechnology, 42(3), 123-135",
      "citation_count": 45,
      "doi": "10.1038/s41587-024-12345",
      "publication_date": "2024-03-15",
      "content": "Detailed research content...",
      "references": "1. Previous work citation...",
      "source": "valyu/valyu-arxiv"
    }
  ]
}

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Best Practices

Research Efficiency: Use response_length="large" for academic searches to capture full research context and methodology details.

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Crafting Effective Academic Queries

StrategyExampleWhy It Works
Use Scholarly Language”peer-reviewed studies on neural plasticity”Targets academic content specifically
Include Methodology”randomized controlled trials” or “meta-analysis”Finds specific research approaches
Specify Research Type”empirical studies” or “systematic review”Focuses on particular study designs
Add Field Context”computational biology applications”Narrows to relevant academic domains

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Retrieval Performance Optimisation

  1. Time Filtering: Add date ranges to focus on recent or historical research
  2. Response Length: Set response_length="large" for comprehensive academic context
  3. Source Targeting: Use included_sources to focus on authoritative academic publishers

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Limitations and Considerations

  • Access Restrictions: Some academic content may be behind paywalls or institutional access barriers
  • Publication Lag: Very recent research may not yet be indexed in all academic databases
  • Field Coverage: Source availability varies across different academic disciplines
  • Language Bias: Results may favor English-language publications depending on source selection