exploratory-data-analysis

To generate an EDA report for scientific data, provide a data file like CSV, FASTQ, or TIFF. The system identifies the file type, extracts format-specific metadata, assesses data quality, and outputs a structured markdown report with statistical summaries and recommended next steps.

Yes, you can extract metadata automatically from genomics and imaging files. The pipeline detects diverse scientific formats across chemistry, spectroscopy, and imaging, pulling format-specific metadata and generating quality assessments without requiring manual format specification.

Exploratory data analysis works with HDF5 and FASTQ formats, alongside over 200 other scientific data types. It leverages dependencies like biopython and h5py to parse complex structures, extract metadata, and produce markdown summaries for rapid quality assessment.

The best way to assess data quality in scientific datasets is to run an automated EDA pipeline that evaluates structural integrity and computes statistical summaries. It identifies quality control metrics and generates a markdown report detailing potential issues and actionable insights.

You get publication-ready summaries from raw scientific data by inputting the file into the EDA pipeline. It processes the data, extracts relevant metadata, and generates a reproducible markdown report containing structured insights, QC metrics, and downstream analysis recommendations.