exploratory-data-analysis

Automated exploratory data analysis identifies file types, extracts format-specific metadata, and generates a Markdown report with statistical summaries and quality assessments for scientific data files. It supports over 200 formats in chemistry, biology, imaging, and spectroscopy.

Yes, batch metadata extraction and data quality assessment are supported for scientific files. The analyzer detects formats across batches, applies category-aware extraction, and outputs a comprehensive Markdown report detailing quality and downstream recommendations.

Yes, the exploratory data analysis tool works with HDF5 files and biological data formats using dependencies like h5py and biopython. It automatically detects formats in proteomics, metabolomics, and general scientific data to extract metadata and generate reports.

The best way to generate an EDA report for spectroscopy and imaging data is using an automated analyzer that detects the file format, extracts metadata using libraries like Pillow, assesses data quality, and outputs a Markdown report with downstream recommendations.

Yes, you need Python with pandas, numpy, and other specified dependencies installed to run automated scientific data exploration. These libraries enable the analyzer to load references, extract metadata, and generate the EDA report.

The analysis relies on local reference guides under references/ and optional components in scripts/ and assets/ to detect over 200 scientific formats. If a file format is unrecognized or lacks a specific reference, the metadata extraction and quality assessment may be limited.