exploratory-data-analysis

Exploratory data analysis on unknown scientific files is handled by automatically detecting the file type across 200+ formats, extracting metadata, and generating a detailed markdown report with format-specific insights and downstream analysis recommendations.

Yes, you can analyze bioinformatics and chemistry files like FASTQ and PDB. The process detects the format, assesses read quality or structural metadata, and provides specific recommendations such as alignment suggestions for sequencing data.

To assess data quality and extract metadata from microscopy or spectroscopy files, the skill automatically parses the format, evaluates data integrity, and outputs a markdown report detailing quality metrics and format-specific structural insights.

Yes, exploratory data analysis leverages pandas, numpy, biopython, h5py, and pillow to process diverse scientific data files, ensuring comprehensive format support and robust metadata extraction across chemistry, bioinformatics, and imaging domains.

The best way to understand complex scientific data is to generate an automated report that detects file types, calculates quality metrics like GC content, and provides actionable recommendations for downstream steps such as alignment or structural visualization.

While the skill supports over 200 formats including proteomics and metabolomics, it focuses on initial data assessment, metadata extraction, and quality metrics rather than performing the downstream computational analysis itself.