Principle:Mbzuai oryx Awesome LLM Post training Publication Count Querying

Knowledge Sources	Semantic Scholar API Docs Bibliometric Analysis Methods
Domains	Bibliometrics, Trend_Analysis
Last Updated	2026-02-08 07:30 GMT

Overview

A bibliometric query strategy that retrieves yearly publication counts for specific research keywords to measure field growth and trend trajectories.

Description

Publication Count Querying is the core data acquisition step in research trend analysis. For each combination of keyword and year, a query is issued to an academic search API requesting only the total count of matching papers. By collecting these counts across a range of years, the pipeline builds a time series that reveals whether a research topic is growing, stable, or declining.

This approach differs from full paper retrieval (as in deep collection) in that it requests minimal data — only the total count — making it efficient for surveying many keywords across many years. The primary challenge is API rate limiting, as the number of queries scales as keywords x years.

Usage

Use this principle when:

• You need to quantify the publication volume for research topics over time
• Full paper metadata is not needed, only aggregate counts
• The analysis spans multiple keywords and a multi-year range
• The academic API supports year-filtered search with total count reporting

Theoretical Basis

The querying strategy generates a matrix of counts:

$C_{k,y}=\text{API.count}(k,y)\mathrm{\forall }k\in \text{Keywords},y\in [Y_{\min },Y_{\max }]$

Where:

• C_k,y is the paper count for keyword k in year y
• The full matrix has dimensions |Keywords| x |Years|

Pseudo-code Logic:

# Abstract count querying algorithm (NOT real implementation)
results = {}
for keyword in keywords:
    counts = []
    for year in year_range:
        count = api.search(keyword, year=year).total_count
        counts.append(count)
        rate_limit_pause()
    results[keyword] = counts

Robust implementations include retry logic with exponential backoff for rate-limited responses.

Related Pages

Implemented By

• Implementation:Mbzuai_oryx_Awesome_LLM_Post_training_Get_Paper_Count