6  Module 4: Data Sharing and Resource Management (35 minutes)

6.1 Content Block: Collaborative Data Practices (15 minutes)

6.1.1 Opening Reality Check (2 minutes)

Ask: “How many of you have been part of a collaboration where data sharing was seamless and easy?” [Few hands usually go up]

Say: “Data sharing is often the biggest practical barrier to effective collaboration. Let’s look at frameworks that make it work.”

6.1.2 The FAIR+ Framework (4 minutes)

Present Framework: “The FAIR principles were designed for open science, but we need to extend them for collaborative team science.”

FAIR Principles:

  • Findable: Team members can locate relevant data and resources
  • Accessible: Appropriate permissions and access protocols exist
  • Interoperable: Data works across different systems and analyses
  • Reusable: Clear documentation enables future use

The ‘+’ Addition:

  • Secure: Privacy, confidentiality, and compliance protections

6.1.3 Findable: Shared Repositories and Metadata (2 minutes)

Common Problem: “The data exists somewhere, but no one can find it when they need it.”

Solutions:

  • Central registry of all project datasets with descriptions
  • Consistent naming conventions for files and versions
  • Metadata templates that everyone uses
  • Search functionality within shared repositories

Quick Example: “Instead of ‘Analysis_final_v3_JMS.xlsx’, use ‘2024-03-15_participant-survey_cleaned_smith.xlsx’”

6.1.4 Accessible: Permission Systems (3 minutes)

Key Principle: “Default to open within the team, closed to the outside, with explicit exceptions.”

Access Levels:

  • Full Access: Core team members, can read/write/modify
  • Analysis Access: Can download and analyze, cannot modify originals
  • Metadata Access: Can see what exists, request specific datasets
  • No Access: Sensitive data with special restrictions

Implementation Tools:

  • Cloud platforms with granular permissions (Google Drive, Box, institutional systems)
  • Version control systems (Git for code, specialized tools for data)
  • Access logging for sensitive data compliance

6.1.5 Interoperable: Compatible Formats (2 minutes)

Common Failure: “Everyone saves data in their preferred format, nothing works together.”

Best Practices:

  • Agreed-upon file formats for different data types
  • Standard variable naming across datasets
  • Common coding schemes for categorical variables
  • Documentation templates that everyone uses

6.1.6 Reusable: Documentation and Licensing (2 minutes)

The Documentation Imperative: “If you can’t understand the data 6 months from now, no one else will either.”

Essential Documentation:

  • Data collection protocols and any changes over time
  • Variable definitions and coding schemes
  • Quality control procedures and known limitations
  • Analysis scripts with comments explaining logic

6.2 Activity 4: Data Sharing Agreement Simulation (20 minutes)

6.2.1 Role Assignment and Scenario Setup (3 minutes)

Scenario: Multi-site study examining social media use and mental health outcomes among adolescents. Site A (major university) has collected data from 500 participants. Site B (smaller college) wants to access this data for secondary analysis.

Roles (5 people per group):

  1. Site A Principal Investigator: Collected the data, protective of participants
  2. Site B Researcher: Wants access for legitimate secondary research
  3. Site A Compliance Officer: Responsible for legal/ethical compliance
  4. Site B IRB Representative: Must ensure ethical standards
  5. Data Manager: Technical expert on security and systems

Key Constraints:

  • Data includes sensitive mental health information
  • Participants consented to “research by the study team and approved collaborators”
  • Site A IRB approval required for data sharing
  • Site B has different data security infrastructure

6.2.2 Negotiation Phase (15 minutes)

Instructions to Groups: “You have 15 minutes to negotiate a data sharing agreement. You must address these issues:”

Required Agreement Elements:

  1. What data can be shared? (raw data, processed data, aggregate data only?)
  2. Access controls: How will Site B access and store the data?
  3. Permitted analyses: What research questions can Site B pursue?
  4. Publication rights: How are publications handled? Authorship?
  5. Security requirements: What technical safeguards are needed?
  6. Compliance verification: How is adherence to agreement monitored?

Your Facilitation Strategy:

  • Let tensions emerge naturally - don’t smooth over disagreements too quickly
  • Intervene only if discussion becomes personal or completely stuck
  • Note common sticking points for debrief discussion
  • Watch for creative solutions that balance competing interests

Common Sticking Points You’ll Observe:

  • Site A wants extensive oversight, Site B wants autonomy
  • Publication timelines and approval processes
  • Technical security requirements vs. practical constraints
  • What happens if Site B violates the agreement

6.2.3 Debrief Discussion (7 minutes)

Debrief Questions:

  1. “What was hardest to negotiate? Why?”
  2. “What solutions did you find for balancing protection with access?”
  3. “How did the different perspectives (PI vs. compliance vs. IRB) create tension?”
  4. “What would make this process easier in real life?”

Key Learning Points to Draw Out:

  • Start data sharing conversations early in collaboration planning
  • Different stakeholders have legitimate but competing concerns
  • Technical solutions can resolve some trust issues
  • Clear agreements prevent bigger conflicts later
  • **Templates and institutional support make negotiations faster”

Transition: “Data sharing is often where issues of fairness and inclusion become most visible. Let’s talk about building teams where everyone can contribute effectively.”}