skills.rest
xjtulyc
xjtulycCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

research-impact

Compute field-normalized citation indicators and altmetrics for research impact benchmarking.

33|6|Updated Mar 17, 2026
Search Tags:#research impact#journal ranking#altmetrics#citation normalization#institutional benchmarking#open access advantage
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill research-impact
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-impact
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/21-library-science/research-impact
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill research-impact

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, statsmodels, matplotlib, requests.

What problem does it solve?

This Skill helps you measure and compare research impact across fields and venues by computing field-normalized citation indicators, journal impact metrics, and altmetric attention signals.

Core Features & Use Cases

  • Field-normalized citation indicators: Compute metrics like FWCI/MNCS using field×year citation baselines, plus percentile-based “top 10%” assessments.
  • Journal-level impact analysis: Estimate journal impact factor-like metrics and provide ranking signals for journals, including a simplified eigenfactor-style approach.
  • Altmetrics and online attention: Retrieve or approximate Altmetric scores (with an API lookup option) and analyze how online attention correlates with citations.
  • Institutional benchmarking: Produce Leiden-style indicators using fractional counting and compare research performance across departments or universities.
  • Use Case: You have publication-level data for multiple departments and years; use this Skill to generate a normalized impact report that identifies which groups outperform their field baselines and whether open access or online attention is associated with higher impact.

Quick Start

Use the research-impact skill to compute field-weighted citation impact (FWCI), top-10% percentile indicators, and an altmetrics-vs-citations correlation for your publication dataset.

Frequently Asked Questions about research-impact

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate field-weighted citation impact for a dataset of publications?▼

To calculate field-weighted citation impact (FWCI) and mean normalized citation score (MNCS), you process publication data against field and year citation baselines. This normalizes citations across disciplines, allowing you to identify papers exceeding their field averages.

What is the best way to benchmark institutional research performance across different disciplines?▼

Institutional benchmarking uses Leiden-style performance indicators with fractional counting to compare research performance across departments. This method normalizes disciplinary differences, providing an unbiased comparison of institutional research impact.

How does altmetrics data correlate with traditional citation counts for academic papers?▼

Altmetrics data, retrieved via DOI-based API lookup, measures online attention signals and correlates them with traditional citation counts. This analysis reveals whether broader online visibility translates into higher academic citation impact.

Can I evaluate top 10% percentile research impact using normalized citation indicators?▼

Yes, you can evaluate PP-top10 percentile indicators using field-normalized citation data. This calculation identifies the proportion of publications from an entity that places in the top 10% of citations within its specific field and publication year.

Does Python with pandas and statsmodels support open access advantage analysis?▼

Yes, Python with pandas, numpy, scipy, and statsmodels supports open access advantage analysis. The workflow applies statistical testing to publication datasets to determine if open access publishing correlates with higher normalized citation impact.

Related Skills

Explore More Education & Research Skills→
sky770825sky770825

Data Inspector

Analyze public CSV files from URLs with pandas for structure and statistics.

Community
Basic
KILWA73KILWA73

data-visualization

Generate publication-quality PNG charts from pre-processed numeric data using pandas, matplotlib, and seaborn.

Community
Advanced
RoboSafe-LabRoboSafe-Lab
28

ad-dataset-analysis

Analyzes aviation safety data from crash databases and driving studies using Python libraries like pandas, numpy, scipy, statsmodel.

Official
Advanced
xjtulycxjtulyc
33

openalx-bibliometrics

Retrieve OpenAlex publication metadata and compute bibliometric indices.

Community
Intermediate