Case Study Template: Cross-border Collaboration with Large Files

Sharing large files across teams, especially internationally, can be challenging. This case study template helps you document successful file transfer workflows and share best practices with your organization.

Use this as a starting point to demonstrate ROI, improve processes, or request institutional pilots of file transfer tools.

Background

Team: Describe your lab, department, or organization.

Example: "Our genomics research lab at University X collaborates with three international institutions across the US, EU, and Asia."

Data Type: What kind of files are you sharing?

Example: "Whole genome sequencing data (FASTQ files), typically 200-500GB per dataset."

Collaboration Scope: Who are you working with?

Example: "We share data with collaborators in London, Tokyo, and Boston on a weekly basis."

Challenge

Describe the problem you faced before using a specialized file transfer tool.

Common Challenges

• Size Limits: Email bounced at 25MB. Google Drive free tier (15GB) was too small.
• Unreliable Uploads: Cloud storage uploads failed at 90%, requiring restarts.
• No Tracking: We had no idea if collaborators received files until they replied (often days later).
• Security Concerns: PHI/PII data couldn't be shared via generic cloud storage without compliance risk.

Example: "We were using Dropbox, but frequent upload failures and no download tracking meant we spent hours following up via email to confirm delivery."

Solution

Explain how you addressed the challenge using a file transfer tool.

Key Features Used

• Send Transfers: Upload large datasets (up to 1TB) with resumable uploads
• Request Files (Vouchers): Collect data from field sites or collaborators without requiring them to create accounts
• Download Tracking: Know exactly when files were accessed
• Expiry Windows: Automatically delete files after 30 days to reduce storage/security risk

Example: "We started using TeraSender to send sequencing data via email delivery with 14-day expiry windows. Collaborators received immediate notifications and could resume downloads if interrupted."

Results

Quantify the impact. What improved?

Metrics to Track

• Time Saved: How many hours per week did you save?
• Fewer Re-Uploads: How often did uploads fail before vs. after?
• Better Tracking: Did you reduce follow-up emails?
• Compliance: Did you meet security/audit requirements more easily?

Example: "We reduced file transfer follow-up emails by 80%. Resumable uploads eliminated failed transfers, saving ~4 hours/week. Download tracking provided audit trails for IRB compliance."

Lessons Learned

Share what worked well and what you'd do differently.

Best Practices Developed

• Naming Conventions: Use clear, versioned filenames (e.g., dataset_v2.3_2024-05-22.tar.gz)
• Include Checksums: Always include MD5/SHA256 hashes for verification
• Set Short Expiry Windows: 14-day expiry reduced security risk without inconveniencing collaborators
• Use Vouchers for Data Collection: Field sites could upload directly without VPN or institutional accounts

Example: "We now include a README with every transfer, explaining file structure and checksums. This reduced 'how do I open this?' questions by 90%."

Recommended Settings

Share your workflow configuration so others can replicate it.

• Transfer Size: 200GB-500GB typical, up to 1TB for large cohorts
• Delivery Method: Email delivery for specific collaborators
• Expiry: 14 days (balances security and convenience)
• Tracking: Enabled for all transfers (compliance requirement)
• Vouchers: 30-day expiry for data collection from multiple sites

Call to Action

Want to try this workflow at your institution?

We're offering a 30-day Pro trial for labs and departments interested in piloting secure, large file transfers.

Contact us to request an institutional pilot and see if this workflow fits your team's needs.

This case study template is designed to help you document and share file transfer best practices within your organization. Customize it with your specific data, metrics, and workflows.