Managing and Downloading Data

Understanding Storage Quotas

Each subscription tier has a storage limit:

Checking Storage Usage

1. Go to Account Settings or Dashboard
2. View Storage Used meter
3. Shows: Used / Total (e.g., "2.3 GB / 10 GB")

Managing Storage

If approaching limit:

1. Download and delete old studies:

• Download data for completed studies
• Archive ZIP files on your computer/server
• Delete study from platform to free space

1. Clean up abandoned sessions:

• Remove data from participants who never completed
• Use "Delete participant" function carefully

1. Compress data (automatic):

• Platform automatically compresses old data
• Recent data: uncompressed for fast access
• Old data: compressed to save space

1. Upgrade tier:

• Contact administrator about upgrading
• Higher tiers have more storage

Important: Always download data before deleting studies!

Study Analytics

Accessing Analytics

1. Go to My Research Studies
2. Click Analytics next to your study

1. In Browse Data, select your study
2. Analytics shown automatically

Key Metrics

Recruitment:

• Total participants started
• Completion rate
• Average time to complete
• Dropout points (where participants stopped)

Progress (for chains):

• Items completed distribution
• Current item for in-progress participants
• Sticking points (items with high abandonment)

Data Quality:

• Missing data counts
• Error rates
• Upload failures

Using Analytics

Monitor recruitment:

• Track progress toward sample size goal
• Identify if recruitment is too slow
• Plan when to close study

Identify problems:

• High abandonment at specific test → too difficult or buggy
• Many upload failures → technical issue
• Long average times → tests too long or confusing

Optimize:

• Adjust parameters if tests too hard/easy
• Shorten battery if completion rate low
• Fix technical issues causing abandonment

Common Tasks

Export for Publication

1. Download all data
2. Create analysis scripts (R, Python, SPSS)
3. Generate summary statistics
4. Document:

• Parameter values used
• Data collection dates
• Any exclusions/preprocessing
• Sample characteristics

Share Data with Collaborators

Option 1: Download and share files

1. Download ZIP
2. Share via secure file transfer (email, cloud storage)
3. Include README explaining file structure

1. Go to Study Settings
2. Add collaborator username
3. They can access data directly through platform

Merge Data from Multiple Studies

If running same test across multiple studies:

<h1>R example</h1>
study1 <- read</em>csv("study1/corsi<em>summary.csv") %>% mutate(study = "study1")

study2 <- read</em>csv("study2/corsi<em>summary.csv") %>% mutate(study = "study2")

combined <- bind</em>rows(study1, study2)

Include study variable to track data source.

Identify and Handle Missing Data

<h1>Python example</h1>
import pandas as pd

data = pd.read<em>csv("corsi</em>summary.csv")

<h1>Check for missing</h1> print(data.isnull().sum())

<h1>Remove participants with missing critical data</h1> data<em>clean = data.dropna(subset=['score', 'reaction</em>time<em>mean'])

<h1>Or fill with mean/median</h1>
data</em>filled = data.fillna(data.mean())

Best Practices

1. Download Data Regularly

Don't wait until study ends:

• Download weekly or monthly during data collection
• Acts as backup if technical issues occur
• Allows ongoing analysis and quality checks

2. Version Control

Keep organized archives:

MyStudy/
├── data<em>2025-10-15</em>pilot/ ├── data<em>2025-10-31</em>main<em>n50/ ├── data</em>2025-11-15<em>main</em>n100/
└── data<em>2025-11-30</em>final/

Document what each version contains.

3. Check Data Quality Early

After first 5-10 participants:

• Verify data files look correct
• Check for obvious errors
• Test your analysis scripts
• Catch issues before collecting full sample

4. Document Parameters

Save study metadata:

• Which parameter values were used
• Any changes made mid-study
• Reasons for changes

This is crucial for methods sections and replication.

5. Exclude Systematically

Document exclusion criteria:

• How you define incomplete data
• RT outlier criteria
• Performance-based exclusions
• Apply consistently

6. Protect Participant Privacy

• Keep data files secure (encrypted storage)
• Remove identifying information if needed
• Follow IRB/ethics requirements
• Don't share raw data publicly without permission

Troubleshooting

Data Not Appearing

Causes:

• Participant hasn't completed test yet
• Upload failed (network issue)
• Wrong study token
• Browser blocked upload

Solutions:

1. Wait for participant to complete
2. Check participant's browser console for errors
3. Verify study token correct
4. Ask participant to retry with stable internet

Downloaded File Won't Open

Causes:

• ZIP file corrupted during download
• No program to open CSV files
• File encoding issue

Solutions:

1. Re-download the file
2. Use Excel, R, Python, or text editor for CSV
3. Check file encoding (should be UTF-8)

Missing Participant Data

Causes:

• Participant started but didn't finish
• Uploaded to wrong study token
• Data filtered by date range

Solutions:

1. Check if participant status is "In Progress"
2. Verify they completed all tests in chain
3. Check correct study selected
4. Look in other studies (if using multiple)

Data Doesn't Match Expectations

Causes:

• Wrong parameter configuration
• Test version mismatch
• Participant misunderstood instructions

Solutions:

1. Check study_metadata.json for parameter values
2. Review test version used
3. Check first few participants' data carefully
4. Consider pilot testing before main study

Related Topics

• Getting Started - Creating studies and collecting data
• Study Analytics - Detailed progress monitoring
• Configuring Test Parameters - Adjusting test behavior
• Troubleshooting - Solving common problems

Need more help? Contact your platform administrator or refer to test-specific documentation for details about particular data file formats.

Related Topics

• Getting Started
• Study Analytics
• Troubleshooting