Does this skill require specific Python libraries?

Yes, depending on the file type, the skill may recommend or use libraries such as Biopython, RDKit, MDAnalysis, or nd2reader to perform deep analysis.

What is included in the generated EDA report?

Reports include file properties, format-specific metadata, statistical summaries, data quality assessments, key findings, and specific recommendations for visualization and next-step analysis.

What scientific file formats are supported?

The skill supports over 200 formats across chemistry (.pdb, .sdf), bioinformatics (.fasta, .bam), microscopy (.nd2, .czi), spectroscopy (.mzML), and general data formats (.hdf5, .parquet).

Can it handle very large scientific datasets?

The skill is designed to handle large files by implementing smart sampling techniques and efficient metadata extraction to provide a representative overview without exhausting memory.

Scientific Exploratory Data Analysis

Name: Scientific Exploratory Data Analysis
Author: Weiwei-Mao

byWeiwei-Mao

Ciencia de Datos y ML

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

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This skill equips Claude with the specialized knowledge to analyze complex scientific datasets across chemistry, bioinformatics, microscopy, and more. It automatically detects file types, extracts domain-specific metadata, and performs quality assessments to generate structured markdown reports. By providing statistical summaries and actionable recommendations for downstream analysis, it streamlines the research workflow for scientists and data engineers working with diverse formats like PDB, FASTQ, ND2, and HDF5.

Características Principales

Data quality assessment and statistical integrity profiling
Automatic detection and analysis of 200+ scientific file formats
Domain-specific metadata extraction for chemistry, genomics, and imaging
Recommendations for preprocessing, visualization, and specialized tools
Automated generation of detailed Markdown analysis reports
0 GitHub stars

Casos de Uso

Extracting structural metadata from molecular files for computational chemistry
Summarizing contents and quality metrics for large bioinformatics datasets
Automating initial data exploration and reporting for multi-dimensional microscopy imaging

Acerca de

Características Principales

Data quality assessment and statistical integrity profiling
Automatic detection and analysis of 200+ scientific file formats
Domain-specific metadata extraction for chemistry, genomics, and imaging
Recommendations for preprocessing, visualization, and specialized tools
Automated generation of detailed Markdown analysis reports
0 GitHub stars

Casos de Uso

Extracting structural metadata from molecular files for computational chemistry
Summarizing contents and quality metrics for large bioinformatics datasets
Automating initial data exploration and reporting for multi-dimensional microscopy imaging