Rapidata/rapidata-vs-prolific

Rapidata vs. Prolific: the same 300 questions on both platforms

In July 2026 we asked the crowds of two annotation platforms — Prolific and Rapidata — the same 300 two-image questions and graded every response against known correct answers. This dataset is the complete raw material of that comparison: the questions, the images, and all 18,007 individual responses from both crowds.

Each question is a short instruction and two images, one of which is correct. 92 questions are simple object recognition ("Select the image that shows a Snake."); 208 target one of six attention skills: counting, absence, search, spatial relations, object state, and attribute binding. Nothing requires special knowledge — a question is only ever hard because it takes a careful look.

How the two crowds compare

	Prolific	Rapidata
People	150	5,103
Answers per question	10	50
Total answers	3,000	15,007
Collection time	~66 minutes	~2 minutes

Graded one answer at a time, Prolific's crowd is more accurate: 98.0% against Rapidata's 91.6%. In practice, labels are made by majority vote over several answers per question, and there the two crowds converge:

Prolific's majority labels all 300 questions correctly from seven responses per question. Rapidata reaches 99.9% at nine responses and labels all 300 correctly from 23. At full depth, neither crowd got a single question majority-wrong.

Rapidata's answers cost $4 per 1,000 against Prolific's $76, so the larger majorities it needs barely register in the price of a finished label:

Schema

One row per question.

column	description
prompt	The instruction shown to annotators, e.g. "Select the image that shows exactly five yellow rubber ducks."
image1, image2	The two images of the pair, embedded (display order was randomized per annotator on both platforms)
correct_image	The ground truth: which image matches the prompt (image1/image2)
category	object-recognition or one of attention-counting, attention-absence, attention-search, attention-spatial, attention-state, attention-binding
difficulty	Measured difficulty class (easy / medium)
task_id	Task identifier (t001-t300)
votes_image1_rapidata, votes_image2_rapidata	Rapidata vote counts per image (~50 per question)
weighted_results_image1_rapidata, weighted_results_image2_rapidata	Rapidata vote shares weighted by each annotator's chance-corrected score (sum to 1)
detailed_results_rapidata	JSON list of every Rapidata response: votedFor (image1/image2), annotatorId, userScore, country, language, sessionIndex, createdAt
votes_image1_prolific, votes_image2_prolific	Prolific vote counts per image (10 per question)
detailed_results_prolific	JSON list of every Prolific response: votedFor, annotatorId, responseTimeMs, createdAt

from datasets import load_dataset

import json

ds = load_dataset("Rapidata/rapidata-vs-prolific", split="train")

# overall single-answer accuracy, per platform
for platform in ["rapidata", "prolific"]:
    votes = [
        v["votedFor"] == r["correct_image"]
        for r in ds
        for v in json.loads(r[f"detailed_results_{platform}"])
    ]
    print(platform, sum(votes) / len(votes))  # rapidata 0.916, prolific 0.980

Notes on the data

Annotator identifiers are anonymized sequential ids (r0001…, p0001…). They preserve which answers came from the same person but cannot be linked back to platform accounts.

Prolific participants were paid a fixed reward sized from the estimated completion time at the platform-recommended £19.40/hour. Rapidata responses were collected with a single API order at standard pricing, no audience filtering. The attention-skill images were generated with gpt-image-2.