Real-World Examples

Case studies and examples from research teams using bead

Economics Research Pipeline

Team: 4 researchers at Central European University
Project: Firm-level analysis using confidential administrative data

The Challenge

  • Multiple confidential datasets requiring different access levels
  • Complex data cleaning with manual verification steps
  • Analysis needed to be reproducible for journal submission
  • Results shared with policy makers who needed different data views

The Solution

Stage 1: Data Ingestion

# Data engineer creates source beads for each dataset
$ bead new tax-records-2023
$ cd tax-records-2023
$ python src/import_tax_data.py  # Connects to secure database
$ bead save confidential-data

$ cd ..
$ bead new trade-statistics
$ cd trade-statistics
$ curl -o output/trade.csv https://api.statistics.gov/trade
$ bead save public-data

Stage 2: Data Validation and Cleaning

$ bead new data-validation
$ cd data-validation
$ bead input add tax-records-2023
$ bead input add trade-statistics

# Manual verification step documented in README
$ python src/flag_outliers.py
$ python src/manual_review.py  # Creates report for human review
$ bead save validated-data

Stage 3: Analysis

$ bead new firm-analysis
$ cd firm-analysis
$ bead input add validated-data
$ stata -b do src/regression_analysis.do
$ bead save analysis-results

Stage 4: Public Reporting

$ cd ..
$ bead new public-report
$ cd public-report
$ bead input add analysis-results
$ python src/anonymize_results.py  # Removes sensitive details
$ R -f src/create_charts.R
$ bead save public-outputs

Outcomes

  • 100% reproducible analysis for journal review
  • Clear data lineage for policy discussions
  • Automated compliance with data protection requirements
  • Team coordination across different access levels

Biomedical Meta-Analysis

Team: International collaboration, 8 institutions
Project: COVID-19 treatment effectiveness across multiple studies

The Challenge

  • Data from different countries with varying formats
  • Statistical methods needed validation across institutions
  • Results needed frequent updates as new studies became available
  • Regulatory submission required complete reproducibility trail

The Solution

Distributed Data Collection:

# Each institution creates standardized data beads
# Institution A (Germany)
$ bead new covid-patients-germany
$ cd covid-patients-germany
$ python src/extract_hospital_data.py
$ python src/standardize_format.py
$ bead save germany-data

# Institution B (USA)
$ cd ..
$ bead new covid-patients-usa
$ cd covid-patients-usa
$ SAS src/extract_ehr_data.sas
$ python src/convert_to_standard.py
$ bead save usa-data

Central Analysis Hub:

$ bead new meta-analysis-v1
$ cd meta-analysis-v1
$ bead input add germany-data
$ bead input add usa-data
$ bead input add italy-data
# ... add all institutions

$ R -f src/meta_analysis.R
$ python src/sensitivity_analysis.py
$ bead save meta-results-v1

Regulatory Submission:

$ cd ..
$ bead new fda-submission
$ cd fda-submission
$ bead input add meta-results-v1
# Create exact copies of all analysis for regulatory review
$ python src/prepare_submission.py
$ bead save fda-package

Key Features

  • Version control for iterating analysis methods
  • Standardized interfaces between institutions
  • Audit trail for regulatory compliance
  • Rapid updates when new data becomes available

Climate Science Modeling

Team: 12 researchers across 3 universities
Project: Regional climate projections using ensemble modeling

The Challenge

  • Massive datasets (500GB+ per model run)
  • Computationally expensive models requiring HPC resources
  • Results needed for policy briefs with tight deadlines
  • Multiple model versions being tested simultaneously

The Solution

Data Management Strategy:

# Large climate datasets stored externally, referenced in beads
$ bead new climate-data-2024
$ cd climate-data-2024
$ echo "Data location: /hpc/climate/global_2024.nc" > data_location.txt
$ python src/create_subset.py  # Creates manageable sample
$ bead save climate-subset

# Model configurations as lightweight beads
$ cd ..
$ bead new model-config-v2
$ cd model-config-v2
$ cp config/physics_params.json output/
$ cp config/grid_definition.nc output/
$ bead save model-configs

Ensemble Modeling:

# Multiple model runs with different parameters
$ for param in temperature precipitation wind; do
    bead new "climate-model-${param}"
    cd "climate-model-${param}"
    bead input add climate-subset
    bead input add model-config-v2
    sbatch --job-name="$param" src/run_model.sh
    bead save "model-results-${param}"
    cd ..
  done

Analysis and Reporting:

$ bead new ensemble-analysis
$ cd ensemble-analysis
$ bead input add model-results-temperature
$ bead input add model-results-precipitation
$ bead input add model-results-wind

$ python src/ensemble_statistics.py
$ python src/uncertainty_analysis.py
$ bead save ensemble-results

# Policy brief generation
$ cd ..
$ bead new policy-brief
$ cd policy-brief
$ bead input add ensemble-results
$ python src/create_summary.py
$ latex policy_brief.tex
$ bead save policy-outputs

Innovations

  • External data references for massive datasets
  • HPC integration with job scheduling
  • Ensemble management across parameter sweeps
  • Automated reporting for policy stakeholders

Digital Humanities Project

Team: 6 researchers, mix of humanists and computer scientists
Project: Analysis of historical newspaper archives for social movements

The Challenge

  • Unstructured text data requiring NLP processing
  • Iterative methodology development with humanities scholars
  • Need for reproducible qualitative coding
  • Integration of quantitative and qualitative methods

The Solution

Text Processing Pipeline:

# Historical newspaper digitization
$ bead new newspaper-archive
$ cd newspaper-archive
$ python src/scan_to_text.py archives/
$ python src/clean_ocr_errors.py
$ bead save digitized-texts

# NLP preprocessing
$ cd ..
$ bead new text-preprocessing
$ cd text-preprocessing
$ bead input add digitized-texts
$ python src/tokenize.py
$ python src/named_entity_recognition.py
$ bead save processed-texts

Human-in-the-Loop Analysis:

# Qualitative coding with validation
$ cd ..
$ bead new qualitative-coding
$ cd qualitative-coding
$ bead input add processed-texts
$ python src/extract_samples.py  # Creates samples for human coding
# Manual coding step documented with inter-rater reliability
$ python src/validate_coding.py
$ bead save coded-samples

# Machine learning on coded data
$ cd ..
$ bead new classification-model
$ cd classification-model
$ bead input add coded-samples
$ python src/train_classifier.py
$ python src/apply_to_corpus.py
$ bead save classified-texts

Mixed-Methods Analysis:

$ cd ..
$ bead new historical-analysis
$ cd historical-analysis
$ bead input add classified-texts
$ python src/quantitative_trends.py
$ python src/case_study_selection.py
$ bead save analysis-results

# Humanities interpretation
$ cd ..
$ bead new interpretation
$ cd interpretation
$ bead input add analysis-results
# Qualitative analysis documented in notebooks
$ jupyter nbconvert --execute interpretation.ipynb
$ bead save final-analysis

Unique Aspects

  • Human coding validation within reproducible framework
  • Mixed quantitative/qualitative methods
  • Iterative development with domain experts
  • Documentation of subjective decisions

Survey Research Operations

Team: Survey organization with 20+ ongoing projects
Project: Standardized survey data processing across multiple studies

The Challenge

  • Multiple survey waves with evolving questionnaires
  • Different sampling methods requiring different weights
  • Quality control across multiple data collectors
  • Rapid turnaround for client deliverables

The Solution

Template-Based Approach:

# Master template for survey processing
$ bead new survey-template
$ cd survey-template
$ cp templates/* src/
$ bead save survey-framework

# Project-specific instances
$ cd ..
$ bead develop survey-framework client-a-wave1/
$ cd client-a-wave1
$ python src/configure_survey.py --config client_a.json
$ bead save client-a-processing

Quality Control Pipeline:

# Automated quality checks
$ cd ..
$ bead new qc-wave1
$ cd qc-wave1
$ bead input add raw-survey-data
$ python src/duplicate_detection.py
$ python src/response_quality.py
$ python src/generate_qc_report.py
$ bead save qc-results

# Data cleaning decisions tracked
$ cd ..
$ bead new cleaning-wave1
$ cd cleaning-wave1
$ bead input add qc-results
$ python src/apply_cleaning_rules.py
$ bead save clean-data

Client Deliverables:

$ cd ..
$ bead new client-deliverable
$ cd client-deliverable
$ bead input add clean-data
$ python src/create_weights.py
$ SPSS -f src/create_spss_file.sps
$ python src/generate_codebook.py
$ bead save client-package

Operational Benefits

  • Standardized workflows across projects
  • Quality control automation
  • Client deliverable consistency
  • Audit trails for survey methodology

Key Lessons from Real-World Usage

What Works Well

  1. Clear Role Separation: Teams succeed when different roles (data engineers, analysts, visualization) have clear responsibilities

  2. Standardized Naming: Consistent naming conventions prevent confusion in large projects

  3. Documentation Culture: Teams that document everything in README files have better collaboration

  4. Version Discipline: Explicit version management prevents “which data did we use?” problems

Common Pitfalls

  1. Over-Beading: Creating too many small beads can create complexity rather than reducing it

  2. Under-Documentation: Assuming team members will understand bead contents without documentation

  3. Access Control Afterthoughts: Not planning for different data sensitivity levels from the start

  4. Storage Planning: Not anticipating storage growth as projects accumulate bead versions

Success Factors

  • Team Training: Invest time in training all team members on bead workflows
  • Tool Integration: Integrate bead with existing tools (HPC, databases, analysis software)
  • Policy Alignment: Ensure bead usage aligns with institutional data policies
  • Workflow Design: Design bead workflows that match team communication patterns

These examples demonstrate that bead scales from small research teams to large international collaborations, adapting to different disciplines and computational requirements while maintaining reproducibility guarantees.