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Download data: Metadata structure

The metadata structure is a format with recruitment and progress information on the participants in a study.

When to use metadata structure

Metadata structure is ideal when you need:

  • Recruitment tracking — Monitor participant completion and dropout rates
  • Session information — View duration, batch, and status per participant
  • Progress reporting — Check which participants have completed which tasks

For actual response data, use Tree, Table, or Annotations structures.

Compatible file types

Metadata structure data is currently available in the CSV file type.

Structure

Each row represents one participation and each column one attribute of that participation:

  • name — Participant's nickname/alias
  • version — Experiment version number
  • batch — Recruitment batch identifier
  • status — Participation status (e.g., started, finished)
  • tasks_finished — Number of tasks completed
  • dur_mins — Duration in minutes
  • started — Start timestamp
  • progressed — Last progress timestamp

Loading metadata

Open the CSV file directly in Microsoft Excel or Google Sheets:

  1. Use File → Open or File → Import
  2. Select the .csv file
  3. Use filters to view specific statuses or batches:
    • In Excel: Data → Filter
    • In Sheets: Data → Create a filter
  4. Calculate statistics using formulas:
    • Average duration: =AVERAGE(F:F) (assuming duration is in column F)
    • Count finished: =COUNTIF(D:D, "finished") (assuming status is in column D)
import pandas as pd

# Load the metadata CSV file
data = pd.read_csv('Meadows_myStudy_metadata.csv')

# Display the first few rows
print(data.head())

# Filter by status
finished = data[data['status'] == 'finished']
print(f"Completed participations: {len(finished)}")

# Calculate average duration
avg_duration = data['dur_mins'].mean()
print(f"Average duration: {avg_duration:.1f} minutes")

# Load the metadata CSV file
data <- read.csv('Meadows_myStudy_metadata.csv')

# Display the first few rows
head(data)

# Filter by status
finished <- subset(data, status == 'finished')
cat("Completed participations:", nrow(finished), "\n")

# Calculate average duration
avg_duration <- mean(data$dur_mins, na.rm = TRUE)
cat("Average duration:", round(avg_duration, 1), "minutes\n")

Example

Metadata for 2 participations:

name version batch status tasks_finished dur_mins started progressed
mature-martin 1 a started 1 13 2021-03-27 17:58:04+00:00 2021-03-27 17:58:19.941
moving-guinea 1 a finished 2 22 2021-03-27 19:02:34+00:00 2021-03-27 19:02:42.485