Research Lookup

Research Lookup is a community skill for finding and retrieving academic research papers, covering literature search, citation discovery, abstract analysis, full-text access, and reference management for academic and professional research workflows.

What Is This?

Overview

Research Lookup provides tools for discovering and accessing academic publications through scholarly databases and APIs. It covers literature search that queries databases like PubMed, Semantic Scholar, and CrossRef using keywords, authors, and date filters, citation discovery that traces reference chains to find related and influential papers, abstract analysis that extracts key findings and methodology from paper abstracts, full-text access that retrieves available open-access papers and preprints, and reference management that organizes found papers with metadata for bibliography generation. The skill helps researchers find relevant literature efficiently.

Who Should Use This

This skill serves academic researchers conducting literature reviews, graduate students exploring new research areas, and professionals who need evidence-based references for reports and publications.

Why Use It?

Problems It Solves

Searching multiple academic databases separately for the same topic is repetitive and time-consuming. Identifying the most influential papers in a field requires tracing citation networks across databases. Extracting structured information from abstracts for comparison requires manual reading of many candidates. Building reference lists with correct citation formats needs metadata collection from multiple sources.

Core Highlights

Multi-database searcher queries multiple scholarly sources in parallel. Citation tracer follows reference chains to discover related work. Abstract extractor pulls key findings from paper summaries. Reference builder formats citations in standard academic styles.

How to Use It?

Basic Usage

import requests

BASE = ('https://api'
  '.semanticscholar.org'
  '/graph/v1')

def search_papers(
  query: str,
  limit: int = 10
) -> list:
  url = f'{BASE}/paper'\
    f'/search'
  resp = requests.get(
    url, params={
      'query': query,
      'limit': limit,
      'fields': 'title,'
        'year,citationCount,'
        'abstract'})
  resp.raise_for_status()
  return resp.json()\
    .get('data', [])

def get_citations(
  paper_id: str
) -> list:
  url = (f'{BASE}/paper'
    f'/{paper_id}'
    f'/citations')
  resp = requests.get(
    url, params={
      'fields': 'title,'
        'year',
      'limit': 20})
  resp.raise_for_status()
  return resp.json()\
    .get('data', [])

papers = search_papers(
  'transformer attention')
for p in papers[:5]:
  print(
    f'{p["citationCount"]}'
    f' cites: {p["title"]}')

Real-World Examples

import requests

class LitReview:
  BASE = (
    'https://api'
    '.semanticscholar.org'
    '/graph/v1')

  def __init__(
    self, topic: str
  ):
    self.topic = topic
    self.papers = []

  def search(
    self,
    limit: int = 50
  ):
    resp = requests.get(
      f'{self.BASE}'
      f'/paper/search',
      params={
        'query':
          self.topic,
        'limit': limit,
        'fields': 'title,'
          'year,authors,'
          'citationCount,'
          'abstract'})
    self.papers = (
      resp.json()
        .get('data', []))
    return self

  def top_cited(
    self, n: int = 10
  ) -> list:
    return sorted(
      self.papers,
      key=lambda p:
        p.get(
          'citationCount',
          0),
      reverse=True)[:n]

  def by_year(
    self,
    start: int,
    end: int
  ) -> list:
    return [
      p for p in
        self.papers
      if start <= p.get(
        'year', 0) <= end]

review = LitReview(
  'large language models')
review.search(limit=100)
for p in review\
  .top_cited(5):
  print(
    f'{p["year"]} - '
    f'{p["title"]}')

Advanced Tips

Sort search results by citation count to identify the most influential papers in a research area quickly. Use citation traversal to find seminal papers that are referenced by many recent publications. Combine results from multiple databases to increase coverage since different databases index different journals and conferences.

When to Use It?

Use Cases

Conduct a literature review by finding the most cited papers on a specific research topic. Discover recent publications by a specific author or research group. Build a reference list with metadata for a grant proposal or manuscript.

Related Topics

Academic research, literature review, Semantic Scholar, PubMed, citations, paper search, and reference management.

Important Notes

Requirements

HTTP client library for REST API access to scholarly databases. API keys for services that require authentication such as Semantic Scholar for higher rate limits. Internet connectivity for querying remote scholarly databases.

Usage Recommendations

Do: use specific search terms and filters to narrow results to relevant papers. Verify paper details against the original source before citing since metadata may contain errors. Combine keyword search with citation traversal for comprehensive literature coverage.

Don't: rely on a single database for literature review since coverage varies across providers. Use citation count alone as a quality metric since older papers accumulate more citations regardless of relevance. Download full-text papers without checking access permissions and copyright restrictions.

Limitations

Scholarly database APIs have rate limits that restrict the volume of queries per time period. Abstract-level analysis may miss important details found only in full-text papers. Citation data may lag behind actual publication dates by several weeks or months.