Which Python libraries are utilized for analysis?

Depending on the file type, it uses industry standards like Biopython, RDKit, MDAnalysis, Pandas, nd2reader, and cclib to ensure accurate data extraction.

Does it work with large scientific datasets?

Yes, the skill is designed to intelligently sample large files to provide representative analysis and statistical summaries without exhausting memory.

Which scientific domains are supported by this skill?

It supports over 200 formats across chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data.

What is included in the generated EDA report?

The report includes file metadata, structure and dimensions, statistical summaries, quality assessments, key findings, and specific recommendations for preprocessing.

Scientific Data Exploratory Analysis

Name: Scientific Data Exploratory Analysis
Author: youyinnn

byyouyinnn

0•

Data Science & ML

Performs comprehensive exploratory data analysis and generates detailed reports for over 200 scientific file formats.

This skill automates the complex task of initial data exploration across diverse scientific domains including genomics, molecular chemistry, and medical imaging. It features automatic file format detection, deep metadata extraction, and data quality assessment using domain-specific Python libraries. By generating standardized Markdown reports, it helps researchers and data scientists immediately understand dataset characteristics, identify anomalies, and receive actionable recommendations for downstream analysis and visualization.

Key Features

01Domain-specific metadata extraction for chemistry, bioinformatics, and microscopy

02Automatic detection and analysis of 200+ scientific file formats

03Generation of detailed Markdown reports including key findings and next steps

04Smart library recommendations (Biopython, RDKit, etc.) for specialized data handling

05Automated data quality and integrity assessment with statistical summaries

060 GitHub stars

Use Cases

01Verifying sequence integrity and quality scores in genomic FASTQ/FASTA datasets

02Extracting metadata and spatial calibration from multi-dimensional microscopy images

03Assessing chemical structure validity and atomic coordinates in molecular dynamics files

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add youyinnn/skills-collection exploratory-data-analysis

For use in Claude.ai and ChatGPT

Key Features

01Domain-specific metadata extraction for chemistry, bioinformatics, and microscopy

02Automatic detection and analysis of 200+ scientific file formats

03Generation of detailed Markdown reports including key findings and next steps

04Smart library recommendations (Biopython, RDKit, etc.) for specialized data handling

05Automated data quality and integrity assessment with statistical summaries

060 GitHub stars

Use Cases

01Verifying sequence integrity and quality scores in genomic FASTQ/FASTA datasets

02Extracting metadata and spatial calibration from multi-dimensional microscopy images

03Assessing chemical structure validity and atomic coordinates in molecular dynamics files

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add youyinnn/skills-collection exploratory-data-analysis

For use in Claude.ai and ChatGPT