Data Collection

Overview

The worksheet is the central data hub of D.Mike. All measurements, attributes, and intermediate results live here — similar to a spreadsheet in Excel or Minitab. Every analysis module reads its columns from this worksheet.

Columns and column types: Each column has a name, a type (numeric, text, date, boolean), and optionally a unit. The type controls how values are interpreted and which columns analysis modules offer for selection.

Rows: One row represents one observation — e.g. one measured part, one transaction, one event. Empty cells are skipped automatically in most analyses.

Multiple sheets (workbook): A worksheet can contain several tabs — e.g. "Raw data", "Cleaned", "Sample shift A". Use the tab bar at the bottom to add, rename, duplicate, or delete sheets.

Formulas: Cells starting with "=" are formulas (e.g. =A1+B1, =MEAN(A:A)). They are evaluated immediately and recalculated automatically when the worksheet is reopened.

Operation

Entering data

• Click a cell and type — Enter moves down, Tab moves right.
• Select a range and use Ctrl+C / Ctrl+V to copy or paste — also from Excel.
• Click a column header to change name, type, and unit.
• Ctrl+Z / Ctrl+Y for undo and redo.

Import and export

• CSV or XLSX files can be loaded via drag & drop or the import bar.
• Column types are detected automatically on import; correct them manually if needed.
• Export to CSV or XLSX via the export bar — the entire active sheet is written out.

Managing sheets

• Use "+" at the end of the tab bar to add a new sheet.
• Right-click a tab → rename, duplicate, delete.
• Sheets can be reordered by dragging tabs in the tab bar.

Column scan

The column scan checks whether all values in a column belong to the same data type. Right-click a column header → "Column scan" to open a panel with the following information:

• Overview: total count, filled and empty cells.
• Dominant type: the most frequently detected data type (numeric, text, date, time, currency, percent).
• Type distribution: a colored bar and table show the proportion of each detected type.
• Outliers: cells whose type differs from the dominant type are listed individually — with row number and value. Click an entry to jump directly to the affected cell.
• Tip: after a data import, run the column scan on each column first to catch erroneous or mixed entries early.

Pitfalls

Wrong column type: If a numeric column is accidentally set to "text", analysis modules will either not offer it or treat it as a categorical attribute. Check the column type whenever an expected column is missing.

Mixed units in one column: Numbers with different units (mm and cm) in the same column produce wrong statistics. Use exactly one unit per column — fall back to the unit converter module if needed.

Empty rows in the middle of a dataset: Some analyses treat empty rows as "missing" and skip them, others abort. When in doubt, clean the dataset before analysis.

Formulas not updated after data change: Formulas are recalculated on every cell change. If a value still looks stale, reopening the sheet (or F5 in the browser) forces a full recalculation.

Too many sheets: Intermediate snapshots, filtered extracts, and backups in different sheets are useful — but quickly become confusing. Use clear names and delete obsolete sheets.