Academic Search Guide

Use Valyu DeepSearch API for academic research

Search peer-reviewed papers, academic journals, and scholarly datasets. Useful for literature reviews, research validation, and exploring developments in any field.

What You Can Do

• Literature reviews - Gather scholarly sources on specific topics
• Research validation - Cross-reference findings across sources
• Citation discovery - Find related papers and citation networks
• Trend analysis - Track research developments over time

Features

Enhanced Metadata

Author info, citation counts, DOIs, and publication dates.

Proprietary Datasets

Access closed-access journals and books.

Source Prioritisation

Automatically prioritise scholarly databases and journals.

Citation Networks

Discover related work through citations and references.

Available Datasets

Dataset	Content	Use Case
valyu/valyu-arxiv	ArXiv preprints and papers	Latest research across all fields
valyu/valyu-pubmed	Medical and life sciences literature	Healthcare and biomedical research
wiley/wiley-finance-papers	Finance and economics papers	Finance and economics research
valyu/valyu-biorxiv	Life sciences preprints from BioRxiv	Latest life sciences research
valyu/valyu-medrxiv	Health and clinical research preprints from medRxiv	Clinical research and medical preprints

Combine multiple datasets for comprehensive coverage. See all available datasets here.

Quick Start

Basic Academic Search

Python

from valyu import Valyu

valyu = Valyu("your-api-key-here")
response = valyu.search(
    "machine learning applications in quantitative finance research",
    response_length="large"
)

print(response)

Target Specific Sources

Python

from valyu import Valyu

valyu = Valyu("your-api-key-here")
response = valyu.search(
    "CRISPR gene editing therapeutic applications",
    included_sources=[
        "valyu/valyu-pubmed",
        "valyu/valyu-arxiv",
        "nature.com",
        "science.org"
    ],
)

Filter by Date

Python

from valyu import Valyu

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

Common Use Cases

• Literature review: Synthesise research across journals and preprints
• Citation discovery: Find related work, references, DOIs, citation counts
• Methods benchmarking: Compare methodologies, datasets, evaluation metrics
• Trend analysis: Track topics, venues, publication timelines
• Interdisciplinary research: Combine CS, medical, engineering sources

Examples

Interdisciplinary Research

Python

from valyu import Valyu

valyu = Valyu("your-api-key-here")
response = valyu.search(
    "artificial intelligence applications in medical diagnosis research",
    included_sources=[
        "valyu/valyu-arxiv",
        "valyu/valyu-pubmed",
        "ieee.org",
        "acm.org"
    ],
    response_length="large"
)

Compare Research Periods

Python

from valyu import Valyu

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

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

Medical Research

Python

from valyu import Valyu

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

Computer Science

Python

from valyu import Valyu

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

Environmental Science

Python

from valyu import Valyu

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

Python

from valyu import Valyu

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

Response Format

Academic searches return 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"
    }
  ]
}

Best Practices

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

Writing Effective Queries

Strategy	Example	Why It Works
Scholarly language	”peer-reviewed studies on neural plasticity”	Targets academic content
Include methodology	”randomised controlled trials” or “meta-analysis”	Finds specific research types
Specify research type	”empirical studies” or “systematic review”	Focuses on particular designs
Add field context	”computational biology applications”	Narrows to relevant domains

Tips

1. Date filtering: Add date ranges to focus on recent or historical research
2. Response length: Use response_length="large" for comprehensive context
3. Source targeting: Use included_sources to focus on specific publishers

Limitations

• Access restrictions: Some content may be behind paywalls
• Publication lag: Very recent research may not be indexed yet
• Field coverage: Source availability varies by discipline
• Language bias: Results may favour English-language publications