text-mining-science
CommunityTurn research papers into structured insights
Education & Research#topic modeling#text mining#tf-idf#trend detection#claim extraction#scientific ner#research literature
Authorxjtulyc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill helps you extract actionable knowledge from scientific literature by identifying topics, scientific entities, claims, and emerging trends within research text corpora.
Core Features & Use Cases
- Topic modeling for scientific corpora: Use LDA or NMF to discover latent themes in abstracts or full-text.
- Scientific information extraction: Extract named entities such as methods, metrics, datasets, genes/chemicals/diseases (via configurable pattern logic) and pull claim-like sentences from paper text.
- Trend and keyword analysis: Combine TF-IDF, RAKE-inspired phrase scoring, and temporal comparisons to detect research fronts across years.
- Similarity and recommendation foundations: Build document-topic representations and keyword vectors that can support literature search and recommendation.
Quick Start
Use the text-mining-science skill on a set of scientific abstracts to produce topic clusters, top keywords, and a timeline of emerging terms.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: text-mining-science Download link: https://github.com/xjtulyc/awesome-rosetta-skills/archive/main.zip#text-mining-science Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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