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scholar-evaluation

Evaluate researcher portfolios using structured metrics and field-normalized citation analysis.

6|2|Updated Jun 11, 2026
Search Tags:#research evaluation#portfolio review#metrics analysis#academic career#scholarship assessment
One-click install
npx skills add https://github.com/pradyumnasagar/open-research-skills --skill scholar-evaluation-pradyumnasagar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scholar-evaluation
Source: https://github.com/pradyumnasagar/open-research-skills/tree/main/skills/peer-review/scholar-evaluation
Command: npx skills add https://github.com/pradyumnasagar/open-research-skills --skill scholar-evaluation-pradyumnasagar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, scikit-learn, numpy, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a framework for evaluating a researcher's portfolio, ensuring fair and comprehensive assessment for tenure, awards, recruitment, or promotion.

Core Features & Use Cases

  • Structured Evaluation: Offers a comprehensive guide for evaluating CVs, publications, citations, funding, mentorship, and service.
  • Field-Normalized Metrics: Utilizes field-normalized citation metrics and provides guidance on interpreting and comparing them across different fields.
  • Customizable: Tailored to the specific needs of tenure and promotion committees, award evaluations, recruitment processes, and internal review.

Quick Start

Run the scholar-evaluation skill to begin the evaluation process for a researcher's portfolio.

Frequently Asked Questions about scholar-evaluation

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

FAQPage Schema
How do I evaluate a researcher's portfolio for tenure and promotion objectively?▼

Field-normalized citation metrics allow fair comparison of a researcher's publications across different academic disciplines. This method adjusts for varying citation practices by field, enabling accurate evaluation of academic career impact during scholarship assessment.

What data do I need to provide for a comprehensive academic career evaluation?▼

Python libraries such as pandas, scikit-learn, numpy, and scipy perform the data extraction and metrics analysis for portfolio review. They process raw academic data to calculate structured evaluation scores for recruitment or award assessments.

Can I customize the metrics analysis for a specific award evaluation committee?▼

Yes, the metrics analysis is customizable to fit specific award evaluation committees or recruitment processes. You can tailor the structured evaluation framework to weigh CV, publications, funding, mentorship, and service criteria according to your internal review needs.

What's the best way to compare scholarship assessment metrics across different fields?▼

The best way to compare scholarship assessment metrics across different fields is using field-normalized citation analysis. This technique neutralizes disciplinary citation biases, providing a standardized baseline for evaluating researcher portfolios fairly.

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