exploratory-data-analysis

Exploratory data analysis on scientific data files automatically detects formats across genomics, chemistry, and microscopy to extract structure, metadata, and quality metrics. You point the tool at a standalone file or archive to receive a format-aware assessment.

Yes, automatic format detection extracts metadata and quality metrics from microscopy, spectroscopy, and proteomics files. It applies reference-driven parsing to identify format-specific characteristics and generate validation checks for your scientific datasets.

Format detection supports genomics and proteomics files alongside chemistry, microscopy, and spectroscopy formats. It requires extension-based detection to route files to the appropriate reference-driven parser for accurate structure and metadata extraction.

To generate a markdown report summarizing scientific dataset quality, the tool analyzes your file's metadata and format-specific characteristics. It produces a detailed markdown summary containing findings, identified issues, and recommended next steps for downstream analysis.

Analyzing scientific data archives requires dependencies including numpy, pandas, h5py, biopython, and pillow. These libraries enable the tool to parse diverse scientific formats, extract quality metrics, and perform format-specific data assessments.

The best way to assess readiness for downstream analysis is running an automated EDA assessment. It evaluates your scientific data file's structure, extracts quality metrics, and provides a markdown report with validation findings and recommended next steps.