Research Lookup Elite

Search, retrieve, and synthesize academic research across multiple databases and sources. This skill covers multi-database search strategies, citation extraction, abstract analysis, research trend identification, and building automated literature monitoring workflows.

When to Use This Skill

Choose Research Lookup Elite when you need to:

• Search for academic papers across PubMed, OpenAlex, Crossref, and Google Scholar simultaneously
• Build comprehensive research bibliographies for grant proposals or reviews
• Monitor new publications on specific topics with automated alerts
• Extract and synthesize key findings from collections of papers

Consider alternatives when:

• You need systematic review methodology specifically (use Literature Review Complete)
• You need full-text article analysis (use publisher APIs or institutional access tools)
• You need citation network visualization (use VOSviewer or Gephi)

Quick Start

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pip install pyalex habanero scholarly requests pandas

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from pyalex import Works
import pandas as pd

# Search OpenAlex for recent papers
works = (
    Works()
    .search("transformer attention mechanism")
    .filter(publication_year=2024, type="article")
    .sort(cited_by_count="desc")
    .get(per_page=20)
)

papers = []
for w in works:
    papers.append({
        "title": w["title"],
        "year": w["publication_year"],
        "citations": w["cited_by_count"],
        "doi": w.get("doi", ""),
        "source": w.get("primary_location", {}).get("source", {}).get("display_name", "Unknown")
    })

df = pd.DataFrame(papers)
print(df[["title", "citations", "source"]].head(10))

Core Concepts

Search Databases

Database	Coverage	Best For
OpenAlex	240M+ works	Broad academic search, bibliometrics
PubMed	35M+ biomedical	Life sciences, clinical research
Crossref	140M+ DOIs	Cross-disciplinary DOI metadata
Semantic Scholar	200M+ papers	AI-enhanced relevance ranking
Google Scholar	Broadest coverage	Discovering grey literature

Multi-Database Search

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from pyalex import Works
from habanero import Crossref
import requests
import pandas as pd

class ResearchLookup:
    def __init__(self, email=None):
        self.email = email
        self.cr = Crossref(mailto=email) if email else Crossref()

    def search_openalex(self, query, max_results=50):
        works = (
            Works()
            .search(query)
            .filter(type="article")
            .sort(cited_by_count="desc")
            .get(per_page=min(max_results, 200))
        )
        return [{
            "title": w["title"], "doi": w.get("doi", ""),
            "year": w["publication_year"],
            "citations": w["cited_by_count"],
            "source": "OpenAlex"
        } for w in works]

    def search_crossref(self, query, max_results=50):
        results = self.cr.works(
            query=query, sort="relevance", limit=max_results
        )
        return [{
            "title": item.get("title", [""])[0],
            "doi": item.get("DOI", ""),
            "year": item.get("published-print", {}).get("date-parts", [[None]])[0][0],
            "citations": item.get("is-referenced-by-count", 0),
            "source": "Crossref"
        } for item in results["message"]["items"]]

    def unified_search(self, query, max_results=50):
        """Search across multiple databases and deduplicate."""
        all_results = []
        all_results.extend(self.search_openalex(query, max_results))
        all_results.extend(self.search_crossref(query, max_results))

        # Deduplicate by DOI
        seen_dois = set()
        unique = []
        for r in all_results:
            doi = r.get("doi", "")
            if doi and doi in seen_dois:
                continue
            if doi:
                seen_dois.add(doi)
            unique.append(r)

        df = pd.DataFrame(unique)
        return df.sort_values("citations", ascending=False).reset_index(drop=True)

lookup = ResearchLookup(email="researcher@university.edu")
results = lookup.unified_search("graph neural networks drug discovery")
print(f"Found {len(results)} unique papers")
print(results[["title", "citations", "year"]].head(10))

Configuration

Parameter	Description	Default
databases	Databases to search	["openalex", "crossref"]
max_results	Maximum results per database	50
sort_by	Sort criterion (citations, date, relevance)	"citations"
date_filter	Publication year range	All years
type_filter	Publication types to include	["article"]
dedup_method	Deduplication strategy	"doi"

Best Practices

1. Search multiple databases for comprehensive coverage — No single database indexes everything. OpenAlex has broad coverage but may miss recent preprints. PubMed focuses on biomedical literature. Crossref provides reliable DOI-based metadata. Cross-referencing catches papers that any single source misses.

2. Deduplicate by DOI, then by fuzzy title match — Papers appear in multiple databases with slightly different metadata. Match on DOI first (most reliable), then fall back to title similarity (threshold > 0.9) for entries without DOIs.

3. Use citation count with a year-relative perspective — A 2024 paper with 10 citations may be more impactful than a 2015 paper with 50. Calculate citations-per-year or normalize by field average when comparing impact across time periods.

4. Save search queries for reproducibility — Document the exact search terms, filters, databases, and date of each search. This is essential for systematic reviews and enables others to replicate your literature search.

5. Set up automated monitoring for active topics — For ongoing research areas, schedule weekly or monthly searches with the same query and track new papers that appear. This keeps you current without manual effort.

Common Issues

Too many irrelevant results — Broad queries return noise. Add field-specific terms, use phrase matching ("exact phrase"), and apply type filters (article only, excluding preprints or datasets). Iteratively refine your query based on the first page of results.

Citation counts vary between databases — OpenAlex, Crossref, and Google Scholar report different citation counts for the same paper because they index different citing sources. Use one database consistently for comparative analysis rather than mixing citation counts across sources.

Rate limiting on bulk searches — All academic APIs have rate limits. Set email for polite pool access, add time.sleep(0.2) between requests, and cache results locally. For very large analyses, download database snapshots instead of querying the API.