Quick Summary

Your data table needs:

  1. First column: Unit IDs (e.g., Grave_01, Grave_02, …)
  2. Other columns: Numeric counts of grave goods/features
  3. Optional: Coordinates (lat, lng) and age data (adult_count, subadult_count)

Basic Table Structure

For Grave Goods / Count Data

First Column Type columns… … … Optional Optional Optional
Unit ID Count Count Count adult_count subadult_count lat lng
  • Type columns: Numeric counts of grave goods/features
  • Age data: For separate adult/subadult Gini analysis (see below)
  • Coordinates: For submitting results to the map (decimal degrees)
Minimal table structure example
Grave_ID pottery flint_tools beads
G001 2 1 0
G002 0 2 5
G003 1 0 0
G004 3 1 12

For Physical Measures (e.g., House Sizes)

First Column Second Column Optional
Unit ID Size value Other non-numerical metadata
  • Size value: The numeric measurement (m², m³, cm, etc.)
  • Other metadata: Site name, phase, notes, etc. — these columns are ignored in the Gini calculation
House size table structure
House_ID Size_m2
H001 24
H002 45
H003 18
H004 67

Column Naming Best Practices

Use Prefixes for Better Category Bias Control

Adding prefixes before an underscore groups related items together. This gives you control over how the Category Bias simulation treats your data.

Example prefix patterns:

Prefix Meaning Examples
c_ Ceramics c_bowl, c_jug, c_amphora
s_ Stone s_axe, s_scraper, s_blade
f_ Flint/Lithics f_blade, f_core, f_arrowhead
b_ Bone b_awl, b_needle, b_pin
cu_ Copper/Bronze cu_dagger, cu_pin, cu_ring
g_ Gold g_ring, g_pendant
o_ Ornaments o_bead, o_pendant, o_ring

Examples: Good vs. Poor Naming

Column names Why
✓ Good c_bowl, c_jug, c_amphora, s_axe, s_blade, cu_pin Prefixes group related items; Category Bias simulation understands relationships
✗ Poor Bowl, Jug, Amphora, Axe, Blade, Pin No grouping information; every column treated independently

Special High-Count Items

The following item types are automatically recognized and handled specially in the Category Bias simulation (their split potential is limited using asinh(median) instead of raw counts):

  • bead / beads
  • ornament
  • pearl / perle
  • pendant
  • pebble
  • sherd / sherds
  • shell
  • arrowhead

Why? These items often appear in large quantities (e.g., 50 beads in one grave). Without special handling, the category bias simulation might try to split “beads” into the maximum sub-types, which is unrealistic.

Optional Columns

Coordinates: lat and lng

If your data includes coordinates, the app will:

  • Auto-detect them when you upload
  • Calculate the centroid for multi-site datasets
  • Show number of sites and area covered in the map popup
Table with coordinate columns
Grave_ID c_bowl lat lng
G001 1 55.6761 12.5683
G002 0 55.6789 12.5701
G003 2 55.6823 12.5742

Age Data: adult_count and subadult_count

For cemeteries with age information, include these columns to:

  • Calculate separate Gini coefficients for adults and subadults
  • Apply per-capita adjustment for multiple burials
  • Identify grave goods exclusive to subadult graves
Table with age columns (G002 is subadult-only, G004 is mixed)
Grave_ID c_bowl beads adult_count subadult_count
G001 1 0 1 0
G002 0 12 0 1
G003 2 0 2 0
G004 1 3 1 1

Interpretation:

  • adult_count = 1, subadult_count = 0: Single adult burial
  • adult_count = 0, subadult_count = 1: Single child burial
  • adult_count = 2, subadult_count = 0: Double adult burial
  • adult_count = 1, subadult_count = 1: Adult + child burial

File Format

QuantWealth accepts:

  • CSV files (.csv) — semicolon ; or comma , separated
  • Excel files (.xlsx, .xls)

The app auto-detects the separator for CSV files.

Common issues to avoid:

  • Empty rows at the end of the file
  • Text in numeric columns (use 0 or leave empty for absent items)
  • Spaces in column names (use underscores instead: stone_axe not stone axe)
  • Special characters in column names (avoid ä, ø, é, etc.)

Complete Example Table

Here is a well-structured example dataset you can download and try:

Download the example file: quantwealth_example.csv

Upload this file to QuantWealth to see how a well-organized dataset produces clear results.

Complete example dataset (15 graves)
Grave_ID Site adult_count subadult_count lat lng c_bowl c_jug c_amphora s_axe s_blade f_arrowhead cu_pin cu_dagger o_beads o_pendant b_awl
G001 Vikletice 1 0 55.68 12.56 1 0 0 1 0 0 0 0 0 0 0
G002 Vikletice 1 0 55.68 12.57 0 1 0 0 1 3 0 0 0 0 1
G003 Vikletice 1 0 55.68 12.58 2 1 0 0 0 0 0 0 5 1 0
G004 Vikletice 1 0 55.69 12.56 1 0 1 1 2 5 1 0 0 0 0
G005 Vikletice 1 0 55.69 12.57 0 0 0 1 0 2 0 0 0 0 0
G006 Vikletice 0 1 55.69 12.58 0 1 0 0 1 0 0 0 8 1 0
G007 Vikletice 1 0 55.69 12.59 1 1 0 0 0 0 0 0 0 0 1
G008 Vikletice 1 0 55.70 12.56 0 0 0 0 1 0 0 0 0 0 0
G009 Vikletice 2 1 55.70 12.57 3 2 1 1 1 8 2 1 25 3 1
G010 Vikletice 1 0 55.70 12.58 1 0 0 0 0 0 0 0 0 0 0
G011 Vikletice 1 0 55.70 12.59 0 1 0 1 0 2 0 0 0 0 1
G012 Vikletice 1 0 55.71 12.56 0 0 0 0 0 0 0 0 0 0 0
G013 Vikletice 1 0 55.71 12.57 2 1 1 0 0 0 1 0 12 2 0
G014 Vikletice 1 0 55.71 12.58 0 0 0 1 1 4 0 0 0 0 0
G015 Vikletice 0 1 55.71 12.59 1 0 0 0 1 0 0 0 3 0 0

What This Example Demonstrates

  • Prefixes: c_ (ceramics), s_ (stone), f_ (flint), cu_ (copper), o_ (ornaments), b_ (bone)
  • High-count items: o_beads (max 25) and f_arrowhead (max 8) have high counts in some graves
  • Age data: G006 and G015 are subadult-only; G009 is a double burial with one adult and one child
  • Coordinates: 15 graves spread across a small area
  • Wealth variation: G009 is clearly the richest grave (TOT=11); G012 is empty

Expected Results When You Run This Data

Metric Value
Total graves 15
Type columns 11
TOT range 0 – 11
Mean TOT 3.6
Adult graves 13
Subadult-only graves 2 (G006, G015)
Richest grave G009 (TOT = 11)
Empty graves 1 (G012)

Category Bias simulation will find:

  • Lumpable groups: c_ (3 cols), s_ (2 cols), cu_ (2 cols), o_ (2 cols)
  • High-count patterns: o_beads, f_arrowhead (will use IHS transformation to limit splits)

Quick Checklist

Before uploading your data:

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Documentation for QuantWealth Archaeological Inequality Tool Last updated: Januar 2026