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 files you saved, together with the code and references to upstream inputs. 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 edit data-cleaning
$ cd data-cleaning
# revise the data cleaning script
$ python clean_data.py
# save updated bead
$ 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 past versions in long-term archive (this can be automated with scripts)

2. 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 edit /shared/dropbox/analysis_latest.zip

Network Storage

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

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 new sensitive-analysis-public
$ cd sensitive-analysis-public
$ bead input add sensitive-analysis
$ python sanitize.py  # Remove PII, aggregate data
$ bead save public-data

Communication Patterns

Some useful patterns, though not enforced by bead itself, help teams work together smoothly.

Documentation Standards

Every shared bead should include a README file to help future users. See the (Social Science Template README)[https://social-science-data-editors.github.io/template_README/] for a complete documentation example.

# Analysis README

## Purpose
What this bead does and why it exists

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

## Usage
python analyze.py

## Dependencies
- Python 3.11+
- pandas, scikit-learn
- R for some statistics

## Contact
Alice Smith (alice@university.edu)

Take special care when documenting your data dependencies. bead explicitly declares and manages these for you. You don’t want to have outdated or incorrect information in your README, which can be very confusing. No documentation is better than wrong documentation.

How to Run Your Code

bead does not actually run your code, so you should communicate with your users about how to do that. The best approach is to have a single entry point script, like run_analysis.sh or Makefile, that handles all the steps. This way, users only need to know one command to get started.

# Example Makefile
all: output/report.pdf
output/report.pdf: src/analyze.py input/data.csv
   python src/analyze.py input/data.csv output/report.pdf

Change Communication

Remember, your output is someone else’s input. If you change something significant, let your team know.

# Describe major changes
$ echo "Updated model with new features" >> output/CHANGELOG.md
$ bead save my-beads  # Creates timestamped version

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

Workflow

# 1. Data engineers update source data
$ bead save shared-storage

# 2. Analysts update
$ bead input update raw-data
$ python analysis.py
$ bead save my-beads

Best Practices Summary

  1. Establish clear naming conventions for beads
  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.