Beta Version
DULS Guide to
🔬 Open Science & Research Data Management
Comprehensive Guide to Open Science, Data Life Cycle, Data Management Plans & FAIR Principles
🎯 What is Open Science?
Open Science is a movement aimed at making scientific research and its dissemination accessible to all levels of an inquiring society. It encompasses practices that make research outputs, methods, and processes transparent, reproducible, and accessible to the global scientific community and beyond.
🔬 Core Principles of Open Science
- Open Access: Free availability of research publications and outputs
- Open Data: Making research data freely available for reuse and verification
- Open Source: Sharing software, code, and computational methods
- Open Peer Review: Transparent and inclusive review processes
- Open Educational Resources: Freely accessible teaching materials
- Citizen Science: Public participation in scientific research
📈 Benefits of Open Science
- Enhanced Reproducibility: Enables verification and replication of research
- Accelerated Discovery: Faster scientific progress through knowledge sharing
- Increased Impact: Higher citation rates and broader reach
- Collaboration: Facilitates global research partnerships
- Public Trust: Increases transparency and accountability
📊 FAIR Data Principles
F - Findable
Purpose: Make data discoverable through proper metadata and identifiers
- Persistent identifiers (DOIs, URIs)
- Rich, descriptive metadata
- Searchable data catalogs
- Clear data documentation
A - Accessible
Purpose: Ensure data can be retrieved and accessed by users and machines
- Open protocols (HTTP, FTP)
- Clear access procedures
- Authentication when needed
- Metadata remains accessible
I - Interoperable
Purpose: Enable data integration with other datasets and systems
- Standard file formats
- Controlled vocabularies
- Common data models
- Linked data principles
R - Reusable
Purpose: Support data reuse for future research and applications
- Clear usage licenses
- Detailed provenance information
- Quality documentation
- Community standards
♻️ Data Life Cycle Models
The data lifecycle represents the stages that research data goes through from initial planning to final preservation and reuse. Understanding this cycle is crucial for effective data management throughout a research project.
1. Plan
Design data collection strategy, determine formats, estimate volumes, and establish quality requirements.
2. Collect
Gather or create data following standardized procedures with consistent documentation.
3. Process
Clean, organize, and transform data while maintaining detailed processing records.
4. Analyze
Apply analytical methods and create visualizations with reproducible workflows.
5. Preserve
Archive data in appropriate repositories with comprehensive metadata and documentation.
6. Share
Publish and disseminate data with proper licenses and access controls.
📋 Data Management Plans (DMP)
A Data Management Plan (DMP) is a formal document that describes how research data will be handled during and after a research project.
📝 Why Create a DMP?
- Funder Requirements: Most funding agencies now require DMPs
- Research Efficiency: Improves project organization and workflow
- Risk Mitigation: Prevents data loss and ensures backup strategies
- Collaboration: Facilitates team coordination and data sharing
- Impact: Increases research visibility and citation potential
1. Data Collection
- What data will be generated?
- What file formats will be used?
- What will be the dataset size?
- How will quality be ensured?
2. Documentation
- How will data be documented?
- What metadata standards?
- How will data be organized?
- What documentation format?
3. Storage & Backup
- Where will data be stored?
- How will data be backed up?
- Who will have access?
- What security measures?
4. Data Sharing
- How will data be shared?
- When will it be available?
- What usage restrictions?
- Which repository to use?
🛠️ DMP Tools and Platforms
🌍 DMPonline
Provider: Digital Curation Centre (DCC)
- Cost: Free
- Features: Templates from major funders
- Coverage: UK, Europe, International
- Languages: Multiple languages supported
🇺🇸 DMPTool
Provider: University of California
- Cost: Free
- Features: US funder templates
- Coverage: NSF, NIH, DOE agencies
- Integration: ORCID, institutional systems
🏛️ Data Repositories
- Zenodo: General repository, free DOIs
- Figshare: Academic publisher supported
- Dryad: Focus on publications
- OSF: Project-based workflows
💾 Storage Solutions
- Institutional: University systems
- Cloud: Google Drive, OneDrive, Dropbox
- Research: OSF, Globus, iRODS
- Secure: TREs, encrypted storage
🌟 Real-World Examples
🧬 Genomics: All of Us Program
Precision medicine initiative collecting health data from 1+ million participants
- Multi-petabyte genomic dataset
- FAIR implementation with controlled vocabularies
- Privacy protection through de-identification
- Cloud-based research workbench
🌍 Climate: ESGF
Global infrastructure for climate model data distribution
- Petabytes of climate model outputs
- Federated architecture worldwide
- NetCDF/HDF5 standard formats
- Enables IPCC assessment reports
📊 Social Science: European Social Survey
Cross-national survey across Europe since 2002
- Biennial surveys, 20+ years of data
- Standardized questionnaires
- Free access after registration
- Multiple file formats available
⚛️ Physics: CERN Open Data
High-energy physics data from LHC experiments
- Collision events and simulated data
- Educational use and research validation
- ROOT framework and analysis tools
- 10,000+ students using real data annually