exploratory-data-analysis

Automated exploratory data analysis detects scientific data file formats by extension and content, then applies domain-specific processing for genomics and microscopy to generate structured Markdown reports. You provide a file path, and the tool routes the data to the appropriate analysis category.

Automatic file type detection for scientific data analysis inspects file extensions and internal content to classify data into categories like general scientific formats, genomics, or microscopy. This classification enables targeted, domain-aware analysis instead of generic processing.

To generate Markdown reports from HDF5 and imaging data, provide the file path to the automated analysis tool. It uses dependencies like h5py and Pillow to extract metadata, analyze the content, and output a structured report with file information and reference summaries.

Yes, exploratory data analysis utilizes pandas and numpy for general scientific data processing, and biopython specifically for genomics formats. These dependencies enable the tool to parse, analyze, and extract metadata across various scientific data categories.

Yes, you can use this for extracting metadata from general scientific formats. The analysis process identifies file characteristics, applies format-specific extraction logic, and returns structured metadata alongside a Markdown report for downstream interpretation.

Automated scientific data exploration is limited to supported general scientific, genomics, and microscopy formats recognized by the file detection logic. It produces metadata and Markdown reports for initial interpretation but does not replace specialized downstream statistical modeling.