Bibliometria

A package for bibliometric analysis of journals.

This package provides tools for retrieving journal information and comparing metrics,
combining the data from Scopus (SCImago Journal Rank) and Web of Science.

Installation

pip install bibliometria

import bibliometria as bm

# or import the functions directly
from bibliometria import get_sjr, get_wos, title_matches, title_best_match, journal_metrics, journal_info

Usage

Data

The package contains two built-in datasets with SJR and WoS data that can be downloaded from this repository or via internal loading functions:

import bibliometria as bm

sjr = bm.get_sjr()
>>> pd.DataFrame

wos = bm.get_wos()
>>> pd.DataFrame

Main functions

The package exposes four main functions for working with journal data.

---

title_matches

title_matches(title_query, limit=10, score_cutoff=60)
>>> pd.DataFrame

Fuzzy-searches a journal by title across SJR and WoS and returns a DataFrame of the top candidate matches with similarity scores and basic metadata (title, ISSN/eISSN, SJR, quartiles, etc.).

---

title_best_match

title_best_match(title_query) 
>>> pd.Series

Returns the single best fuzzy match for a journal title as a pandas Series with similarity score and metadata, or None if no suitable match is found.

---

journal_metrics

journal_metrics(query, query_type="title") 
>>> pd.Series

Retrieve core bibliometric indicators for a journal, using either:

• query_type="title" – fuzzy match by journal title, or
• query_type="issn" – exact match by ISSN / eISSN

The returned Series contains a small set of metrics such as:

• sjr, sjr_best_quartile, h_index (from SJR)
• wos_quartile, wos_jif, wos_jif_5_year (from WoS)

If the journal is not found, an “empty” Series with all fields set to None is returned, and a warning is emitted.

---

journal_info

journal_info(query, query_type="title") 
>>> pd.DataFrame

Return a single-row DataFrame with all available fields for a journal from both SJR and WoS,
merged into one record. Supports the same lookup modes as journal_metrics:

• query_type="title" – fuzzy title match
• query_type="issn" – exact ISSN / eISSN match

The result also includes a few metadata columns describing the lookup:

• query, query_type, source_primary, matched_title, match_score

Interactive examples

You can explore example outputs in the notebook:

Contribution

This package is in the testing status. To report a bug or suggest an improvement, you can open an issue or contact us directly.

Authors: Vladislava Termus, Alexandra Pogozheva