Scholar Metrics Analysis

This page covers Google Scholar workflows through SearchAPI, researcher-level Semantic Scholar metrics, and journal impact factors.

SearchAPI Setup

Set the API key once through the environment, or pass api_key=... to each function explicitly:

export SEARCH_API_KEY=YOUR_SEARCHAPI_KEY
export SS_API_KEY=YOUR_SEMANTIC_SCHOLAR_KEY  # Optional enrichment.

The SearchAPI-backed utilities cover the following workflows:

Task	Function	Result
Search Scholar	get_scholar_papers	Paper metadata as a DataFrame
Count citations	get_citations_from_title	Google Scholar citation count
Export a citation	get_bibtex_entry, get_endnote_entry	BibTeX or EndNote text
Find citing papers	get_citing_papers_from_title	A list of Paper objects
Find an author's papers	get_scholar_author_papers	Author-paper metadata as a DataFrame

SearchAPI calls are retried with bounded exponential backoff. The complete outputs below were captured from live calls on 23 September 2026. They reflect live Google Scholar data, so paper order and citation counts can change.

Keyword Paper Search

Search Google Scholar and return paper metadata as a DataFrame:

>>> from paperscraper.scholar import get_scholar_papers
>>> papers = get_scholar_papers(
...     "CMonge",
...     backend="searchapi",
...     search_api_kwargs={"top_k": 1, "num_enrich": 1},
... )
>>> papers.iloc[0].to_dict()
{
    'title': 'Conditional Monge Gap enables generalizable single-cell perturbation modelling',
    'authors': ['Alice Driessen', 'Dhruva Abhijit Rajwade', 'Benedek Harsanyi', 'Marianna Rapsomaniki', 'Jannis Born'],
    'year': 2026,
    'abstract': 'Learning the response of single cells to various treatments offers great potential to enable targeted therapies. In this context, neural optimal transport has emerged as a principled methodological framework because it inherently accommodates the challenges of unpaired data induced by cell destruction during data acquisition. However, most existing optimal transport approaches are incapable of conditioning on different treatment contexts and we still lack methods that show promising generalizability to unseen treatments. Here we propose the Conditional Monge Gap (CMonge), which learns optimal transport maps conditionally on arbitrary covariates.',
    'journal': 'Nature Machine Intelligence',
    'citations': 2,
}

top_k limits the returned rows. num_enrich controls how many rows receive additional author-profile requests for full author, abstract, and journal data.

Paper Citation Counts

Retrieve a Google Scholar citation count from an exact paper title:

>>> from paperscraper.citations import get_citations_from_title
>>> title = "Quantum theory and application of contextual optimal transport"
>>> get_citations_from_title(title, backend="searchapi")
7

The default backend is "auto": it uses SearchAPI when SEARCH_API_KEY is configured, then Semantic Scholar when SS_API_KEY is configured, and otherwise falls back to scholarly, which has limited throughput. An explicit api_key can be passed with a specific backend. Citation counts can differ between providers and between Scholar records for different versions of a paper.

Bibliographic Export

Export a Google Scholar citation through SearchAPI in BibTeX or EndNote format. Both functions accept a title or DOI; DOI inputs are first resolved through Semantic Scholar:

>>> from paperscraper.citations import get_bibtex_entry, get_endnote_entry
>>> title = "Quantum doubly stochastic transformers"
>>> print(get_bibtex_entry(title))
@article{born2026quantum,
  title={Quantum doubly stochastic transformers},
  author={Born, Jannis and Skogh, Filip and Rhrissorrakrai, Kahn and Utro, Filippo and Wagner, Nico and Sobczyk, Aleksandros},
  journal={Advances in Neural Information Processing Systems},
  volume={38},
  pages={70224--70254},
  year={2026}
}
>>> print(get_endnote_entry(title))
%0 Journal Article
%T Quantum doubly stochastic transformers
%A Born, Jannis
%A Skogh, Filip
%A Rhrissorrakrai, Kahn
%A Utro, Filippo
%A Wagner, Nico
%A Sobczyk, Aleksandros
%J Advances in Neural Information Processing Systems
%V 38
%P 70224-70254
%D 2026

Citing Papers

List papers citing a title on Google Scholar:

>>> from paperscraper.citations import get_citing_papers_from_title
>>> title = "Quantum theory and application of contextual optimal transport"
>>> paper = get_citing_papers_from_title(title, max_results=1, full_info=True)[0]
>>> paper.__dict__
{
    'input': 'Advancing single-cell omics and cell-based therapeutics with quantum computing',
    'authors': ['A Bose', 'K Rhrissorrakrai', 'F Utro', 'L Parida'],
    'title': 'Advancing single-cell omics and cell-based therapeutics with quantum computing',
    'doi': '10.1038/s41580-025-00918-0',
}

By default, citing-paper results contain titles. full_info=True additionally resolves authors and available DOIs through Semantic Scholar, using SS_API_KEY unless ss_api_key=... is passed. For larger runs, SS_REQUEST_TIMEOUT, SS_CONCURRENCY_LIMIT, and SS_RATE_LIMIT_DELAY can be tuned through environment variables.

Google Scholar Author Papers

Return papers and associated metadata for a researcher:

>>> from paperscraper.scholar import get_scholar_author_papers
>>> paper = get_scholar_author_papers(
...     "Jannis Born",
...     max_results=25,
...     full_info=True,
... ).iloc[23]
>>> paper.to_dict()
{
    'title': "Regress, Don't Guess--A Regression-like Loss on Number Tokens for Language Models",
    'authors': [
        'Jonas Zausinger',
        'Lars Pennig',
        'Anamarija Kozina',
        'Sean Sdahl',
        'Julian Sikora',
        'Adrian Dendorfer',
        'Timofey Kuznetsov',
        'Mohamad Hagog',
        'Nina Wiedemann',
        'Kacper Chlodny',
        'Vincent Limbach',
        'Anna Ketteler',
        'Thorben Prein',
        'Vishwa Mohan Singh',
        'Michael Morris Danziger',
        'Jannis Born',
    ],
    'publication': 'International Conference on Machine Learning, ICML 2025, 2025',
    'year': 2025,
    'citations': 18,
    'journal': '',
    'date': '2025',
    'volume': '',
    'issue': '',
    'pages': '',
    'publisher': '',
    'description': '',
}

full_info=True adds journal and publication details at the cost of one extra request per paper. Pass author_id to select a specific profile when names are ambiguous.

When no exact profile exists, the function falls back to an author:"name" Scholar search and warns that namesakes may be mixed. This fallback cannot provide full_info. max_results defaults to 30, and explicit smaller or larger integer limits are respected.

Semantic Scholar Author Metrics

Semantic Scholar author pages expose paperCount, citationCount, and hIndex. You can query them by Semantic Scholar Author ID:

from paperscraper.citations.utils import semantic_scholar_requests_get

ssaid = "2062641025"
metrics = semantic_scholar_requests_get(
    f"https://api.semanticscholar.org/graph/v1/author/{ssaid}",
    params={"fields": "name,paperCount,citationCount,hIndex"},
).json()

{
    "authorId": "2062641025",
    "name": "Jannis Born",
    "paperCount": 63,
    "citationCount": 1910,
    "hIndex": 21,
}

Resolve the same author by name:

>>> from paperscraper.citations.utils import author_name_to_ssaid
>>> author_name_to_ssaid("Jannis Born")
("2062641025", "Jannis Born")

Or resolve through ORCID first:

>>> from paperscraper.citations.orcid import orcid_to_author_name
>>> from paperscraper.citations.utils import author_name_to_ssaid
>>> name = orcid_to_author_name("0000-0001-8307-5670")
>>> author_name_to_ssaid(name)
("2062641025", "Jannis Born")

If you need the actual Semantic Scholar paper IDs for an author, use get_papers_for_author:

>>> from paperscraper.citations.utils import get_papers_for_author
>>> paper_ids = get_papers_for_author("2062641025")
>>> len(paper_ids)
63  # Number of papers linked to this Semantic Scholar author record.
>>> paper_ids[0]
'6c245545fcb88df49cf921ba0871b40818665b92'

Citation and paper counts can change as Semantic Scholar updates author records.

Journal Impact Factors

Use Impactor to search journal names, abbreviations, E-ISSNs, or NLM IDs.

>>> from paperscraper.impact import Impactor
>>> impactor = Impactor()
>>> impactor.search("Nat Comms", threshold=85, sort_by="impact")
[
    {"journal": "Nature Computational Science", "factor": 18.3, "score": 88},
    {"journal": "Nature Communications", "factor": 15.7, "score": 94},
    {"journal": "Natural Computing", "factor": 1.6, "score": 88},
]

threshold defaults to 100, which behaves like an exact search. Lower values allow fuzzier matches. sort_by can be "impact", "journal", or "score".

Search by abbreviation, NLM ID, or E-ISSN:

>>> impactor.search("Nat Rev Earth Environ")
[{"journal": "Nature Reviews Earth & Environment", "factor": 71.5, "score": 100}]
>>> impactor.search("101771060")
[{"journal": "Nature Reviews Earth & Environment", "factor": 71.5, "score": 100}]
>>> impactor.search("2662-138X")
[{"journal": "Nature Reviews Earth & Environment", "factor": 71.5, "score": 100}]

Filter by impact factor range:

>>> impactor.search("Neural network", threshold=85, min_impact=1.5, max_impact=20)
[
    {"journal": "IEEE Transactions on Neural Networks and Learning Systems", "factor": 8.9, "score": 93},
    {"journal": "NEURAL NETWORKS", "factor": 6.3, "score": 91},
    {"journal": "Network", "factor": 3.1, "score": 92},
    {"journal": "NETWORK-COMPUTATION IN NEURAL SYSTEMS", "factor": 1.6, "score": 92},
    {"journal": "WORK-A Journal of Prevention Assessment & Rehabilitation", "factor": 1.5, "score": 86},
]

Return all available fields:

>>> impactor.search("quantum information", threshold=90, return_all=True)
[
    {
        "factor": 8.3,
        "jcr": "Q1",
        "nlm_id": "101722857",
        "journal": "npj Quantum Information",
        "issn": ".",
        "zky": ".",
        "journal_abbr": "npj Quantum Inf",
        "eissn": "2056-6387",
        "score": 92,
    },
    {
        "factor": 2.9,
        "jcr": "Q2",
        "nlm_id": "101703749",
        "journal": "Information",
        "issn": ".",
        "zky": ".",
        "journal_abbr": "Information (Basel)",
        "eissn": "2078-2489",
        "score": 95,
    },
    {
        "factor": 1.3,
        "jcr": "Q2",
        "nlm_id": "9877123",
        "journal": "NATION",
        "issn": "0027-8378",
        "zky": ".",
        "journal_abbr": "Nation",
        "eissn": "0027-8378",
        "score": 91,
    },
    {
        "factor": 1.1,
        "jcr": ".",
        "nlm_id": "138060",
        "journal": "Reformation",
        "issn": "1357-4175",
        "zky": ".",
        "journal_abbr": "Reformation",
        "eissn": "1752-0738",
        "score": 90,
    },
]