Field Sampling & Experimental Records

Reproducible Plate Randomization Planner

Assign named group replicates to a 24- or 96-well plate with a recorded seed. Choose complete-plate randomization or repeated group allocations within row blocks.

Biology · experimental measurements

Generate an inspectable, reproducible well-assignment ledger from the experimental groups you supply.

Private calculations in your browser · explicit inputs and model boundaries
Example preview · Complete-plate randomizationYour reproducible randomized well map

Each marked position has one supplied group replicate. Full names and replicate IDs are listed in the ledger; the map does not imply independent biological units.

  1. 1EnterProvide the known values
  2. 2CalculateResults update automatically
  3. 3VerifyReview the details and units
Try an example

One row: unique group name, replicate count. In row-block mode these counts are repeated within every used row.

Enter values in integer 0–4294967295.

Calculation result

Enter valid values to see the result.

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Understand the relationship

The reasoning behind the result

Randomization changes assignment, not replication

The supplied group counts define the replicates to allocate. The shuffle changes their well positions while preserving every named replicate exactly once. Unassigned positions remain visible rather than receiving invented control roles.

A well is a position in this allocation, not automatically an independent biological unit. The relationship between wells, specimens and experimental units must come from the study design.

Row blocks restrict where assignments can move

Complete-plate mode shuffles all positions and assigns the supplied replicates to those positions. Row-block mode repeats the supplied group allocations in each selected row and shuffles positions separately within each row.

Blocking can keep each group represented across a known nuisance factor, but choosing a row as a block must be scientifically justified. The planner does not correct edge effects or confounding merely by producing a random-looking map.

A seed makes the allocation reproducible

The deterministic generator and Fisher–Yates algorithm use the seed plus the exact order of group input. Reusing both reproduces the same map. Changing the seed or group order may change positions while leaving allocation counts unchanged.

This is a transparent pseudorandom research-planning aid, not a validated clinical randomization service or a concealed treatment-allocation system. It supplies neither blinding nor an immutable audit trail.

Follow the numbers

Three complete row blocks

  1. Enter Control, A and B with two replicates each: six assignments per row.
  2. Use three rows on a 24-well plate. Each row receives six assignments, for eighteen occupied wells total.
  3. Shuffle each row independently with the same recorded generator stream; each group still has six assignments overall.

Rows A–C are occupied, row D remains unassigned, and every used row contains the complete supplied group allocation.

Quick guide

How to use this calculator

  1. Select plate format and complete-plate or row-block allocation.
  2. Enter unique group names and replicate counts; in block mode the entered counts apply to each used row.
  3. Enter and record a seed so the same inputs reproduce the same map.
  4. Inspect every well assignment and save the resulting record before using it in an experiment.

Calculation method

Calculation and interpretation

Generate an inspectable, reproducible well-assignment ledger from the experimental groups you supply.

Fisher–Yates shuffle using a recorded 32-bit pseudorandom seed; allocation counts are preserved exactly

Worked example

Three complete row blocks

Rows A–C are occupied, row D remains unassigned, and every used row contains the complete supplied group allocation.

Fisher–Yates shuffle using a recorded 32-bit pseudorandom seed; allocation counts are preserved exactly

Supported inputs

Precision and limits

Design arithmetic, not design approval

Entered allocations and counts do not establish adequate power, independent experimental units or control of confounding. Randomization must match the actual experiment and analysis; no biological procedure or treatment is selected.

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