Team Collaboration

Patterns and practices for using bead in team environments

Working Together with bead

When you save a bead, your teammates can open it and get exactly the same setup you had. No missing files, no version confusion, no “it works on my machine” problems. They can run your analysis, modify it, and build on your work.

Basic Collaboration Pattern

The Handoff Workflow

# Researcher A creates and shares a bead
$ bead new data-cleaning
$ cd data-cleaning
$ bead input add raw-survey
$ python clean_data.py
$ bead save shared-storage

# Researcher B continues the work
$ bead develop data-cleaning -x
$ cd data-cleaning
$ python analyze_cleaned.py
$ bead save shared-storage

Now both researchers have their work saved, and anyone can see exactly what each person contributed.

Team Organization Patterns

1. Distributed Development

How it works: Everyone has their own workspace plus shared storage for finished work.

# Each researcher sets up their environment
$ bead box add alice-dev ~/alice/beads
$ bead box add shared-team /mnt/shared/team-beads
$ bead box add archive /archive/project-beads

What people do:

  • Work on their own computers
  • Save finished work to shared storage
  • Keep important versions in long-term archive

2. Hub-and-Spoke Model

How it works: One person manages the shared data, everyone else connects to it.

# Project lead maintains central boxes
$ bead box add project-data /shared/data
$ bead box add project-results /shared/results

# Team members connect to shared resources
$ bead box add shared-data /shared/data
$ bead box add my-work ~/personal-beads

3. Pipeline Architecture

Different teams handle different steps:

Raw Data → Cleaning → Analysis → Visualization → Paper
   ↓         ↓         ↓           ↓           ↓
Team A   Team B   Team C      Team D      Team E

Team A finishes their work and saves a bead. Team B uses that bead as input for their step. And so on.

Sharing Strategies

File-Based Sharing

# Copy beads to shared storage
$ cp ~/.beads/analysis_*.zip /shared/dropbox/

# Team member loads from shared location
$ bead develop /shared/dropbox/analysis_latest.zip

Version Control Integration

# Store bead metadata in git (not the data)
$ git add .bead-meta/
$ git commit -m "Update analysis with new methodology"

# Others can see what inputs are needed
$ git pull
$ bead input load survey-data
$ bead input load census-data

Network Storage

# Mount network drive as bead box
$ bead box add team-server /mnt/research-server/beads
$ bead save team-server

Handling Conflicts and Versions

Naming Conventions

Use descriptive, timestamped names to avoid confusion:

# Good naming patterns
survey-analysis-v1
customer-segmentation-final
population-model-2024q1

Version Management

# Work with specific versions
$ bead develop analysis_20250730T120000.zip

# Update to latest when ready
$ bead input update survey-data
$ bead save team-results

Conflict Resolution

When team members modify the same analysis:

# Create branches with descriptive names
$ bead develop base-analysis approach-a/
$ bead develop base-analysis approach-b/

# Compare results before merging approaches
$ diff approach-a/output/ approach-b/output/

Access Control and Security

Tiered Access Model

# Public data - everyone can access
$ bead box add public-data /shared/public

# Team data - project members only
$ bead box add team-data /restricted/team-only

# Sensitive data - limited access
$ bead box add sensitive /encrypted/sensitive-data

Data Sanitization

# Create public versions of sensitive analyses
$ bead develop sensitive-analysis public-version/
# Replace sensitive data with synthetic data
# Document what was changed
$ bead save public-archive

Communication Patterns

Documentation Standards

Every shared bead should include:

# Analysis README

## Purpose
What this bead does and why it exists

## Inputs
- survey-data: Customer survey responses (v2024-07)
- demographics: Population statistics for weighting

## Outputs  
- segmentation.csv: Customer segments with probabilities
- report.pdf: Analysis summary

## Usage
```bash
python analyze.py

Dependencies

  • Python 3.9+
  • pandas, scikit-learn
  • R for some statistics

Contact

Alice Smith (alice@university.edu)


### Change Communication

```bash
# Tag major changes
$ bead save results  # Creates timestamped version
$ echo "Updated model with new features" > CHANGELOG.md

Quality Assurance

Peer Review Process

  1. Create review branch: bead develop analysis reviewer-name/
  2. Test reproduction: Can reviewer run the analysis?
  3. Validate outputs: Do results make sense?
  4. Check documentation: Is it clear what the bead does?
  5. Approve for sharing: bead save approved-results

Testing Protocol

# Standard testing workflow
$ bead develop analysis test-env/
$ cd test-env
$ make test  # Run validation tests
$ diff output/ expected/  # Compare to known good results

Large Team Coordination

Role Definitions

Data Engineers: Create and maintain source beads

  • Raw data ingestion
  • Data quality checks
  • Format standardization

Analysts: Transform data into insights

  • Statistical analysis
  • Model development
  • Results interpretation

Visualization Team: Create presentations

  • Chart and graph generation
  • Report compilation
  • Dashboard creation

Workflow Orchestration

# Daily workflow coordination
# 1. Data engineers update source data
$ bead save raw-data-$(date +%Y%m%d)

# 2. Analysts get notified and update
$ bead input update raw-data
$ python daily_analysis.py
$ bead save daily-results

# 3. Viz team creates reports
$ bead input update daily-results
$ R -f generate_dashboard.R
$ bead save daily-dashboard

Common Challenges and Solutions

Challenge: Dependency Confusion

Problem: Team members using different versions of inputs Solution: Use explicit version references and update notifications

Challenge: Storage Management

Problem: Accumulating many bead versions Solution: Implement archival policies and cleanup procedures

Challenge: Onboarding New Team Members

Problem: Complex setup for new researchers Solution: Create onboarding beads with examples and documentation

Challenge: Reproducibility Across Systems

Problem: Analysis works on one system but not another Solution: Document system requirements and use containerization

Best Practices Summary

  1. Establish clear naming conventions for beads and versions
  2. Use shared storage that all team members can access
  3. Document everything - assume others will use your work
  4. Test reproducibility before sharing beads
  5. Communicate changes when updating shared dependencies
  6. Implement access controls based on data sensitivity
  7. Archive old versions to manage storage costs
  8. Train team members on bead workflows and conventions

Working together with bead takes some coordination, but once your team gets the hang of it, everyone can build on each other’s work without the usual headaches.