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Add Open Science 101 modules to curriculum and update table of contents
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‎.github/workflows/book.yml.bak‎

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‎.github/workflows/deploy.yml‎

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concurrency:
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group: pages
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cancel-in-progress: true
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runs-on: ubuntu-latest

‎_toc.yml‎

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- file: docs/Area1_Engagement/Newsletters/readme.md
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- file: docs/Area1_Engagement/Open-Science-Stories/readme.md
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- file: docs/Area1_Engagement/Outreach/readme.md
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- file: docs/Area2_Capacity_Sharing/readme.md
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- file: docs/Area2_Capacity_Sharing/Open-Science-101/readme.md
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title: Area 2 - Capacity Sharing
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sections:
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- file: docs/Area2_Capacity_Sharing/Open-Science-101/readme.md
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- file: docs/Area2_Capacity_Sharing/Open-Science-101/modules/open_science_ethos_module.md
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title: Module 1 - Open Science Ethos
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- file: docs/Area2_Capacity_Sharing/Open-Science-101/modules/open_data_module.md
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title: Module 2 - Open Data
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- file: docs/Area2_Capacity_Sharing/Open-Science-101/modules/open_software_module.md
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title: Module 3 - Open Software
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- file: docs/Area2_Capacity_Sharing/Open-Science-101/modules/open_results_module.md
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title: Module 4 - Open Results
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- file: docs/Area2_Capacity_Sharing/Open-Science-101/modules/open_science_tools_module.md
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title: Module 5 - Open Science Tools
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- file: docs/Area2_Capacity_Sharing/Activity_Templates/readme.md
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- file: docs/Area2_Capacity_Sharing/ScienceCore/readme.md
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- file: docs/Area3_Incentives/readme.md

‎book-requirements-Linux-macOS.txt‎

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‎docs/Area2_Capacity_Sharing/Open-Science-101/modules/open_science_tools_module.md‎

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# Open Science Tools
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## Tools of Open Science
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Open science tools are any resources or instruments that support your research through:
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- Data storage and discovery
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- Code collaboration and version control
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- Persistent identification
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- Documentation and communication
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- Community engagement
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## Repositories: Storing Data and Code
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### What is a Repository?
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A repository is a digital storage system that:
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- Stores research data or software
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- Provides access and discoverability
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- Ensures long-term preservation
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- Often includes version control features
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### Repository vs. Archive
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**Repository**
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- Often uses version control systems (Git, Mercurial, Subversion)
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- Enables collaborative development
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- Tracks changes and versions
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- Examples: GitHub, GitLab, institutional repositories
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**Archive**
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- Permanent storage of final versions
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- No ongoing development
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- Ensures long-term preservation
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- Examples: Zenodo, institutional archives
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## Example Repositories for Data and Software
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### Data Repositories
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- **Zenodo** - Multidisciplinary data and software archiving
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- **Figshare** - Shareable datasets, presentations, and papers
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- **DataVerse** - Institutional data repositories
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- **Open Science Framework (OSF)** - Project management and data sharing
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- **Harvard Dataverse** - Institutional repository for research data
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### Discipline-Specific Repositories
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- **GenBank** - Genetic sequences
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- **Protein Data Bank** - 3D protein structures
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- **NOAA** - Climate and environmental data
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- **Dryad** - Data associated with publications
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### Software Repositories
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- **GitHub** - Version control and collaboration
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- **GitLab** - Alternative to GitHub with institutional options
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- **PyPI** - Python packages
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- **CRAN** - R packages
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- **Zenodo** - Long-term preservation of research software
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## Version Control: Tracking Changes
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### What is Version Control?
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Version control is the practice of tracking and managing changes to:
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- Code and software
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- Documentation
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- Configuration files
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- Research outputs
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### Benefits of Version Control
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- **Track history** - See who made what changes and when
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- **Collaboration** - Multiple people can work on same project
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- **Revert changes** - Go back to previous versions
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- **Branching** - Explore alternative approaches
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- **Integration** - Combine work from multiple developers
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## Types of Software Version Control
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### Centralized Version Control
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- Single central repository
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- All changes go to one location
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- Examples: Subversion (SVN), Perforce
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- Advantages: Simple, clear authority
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- Disadvantages: Single point of failure
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### Distributed Version Control
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- Every developer has a complete copy
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- Changes can be tracked locally
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- Examples: Git, Mercurial, Bazaar
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- Advantages: Redundancy, offline capability
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- Disadvantages: More complex
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## Version Control Platforms
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Version control platforms expand the usefulness of version control by:
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- Providing centralized storage
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- Enabling collaboration features
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- Hosting documentation
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- Managing access controls
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- Facilitating issue tracking
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### Popular Platforms
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**GitHub**
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- Built on Git
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- Large community of developers
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- Extensive CI/CD integration
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- Free public repositories
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- Wide adoption in open science
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**GitLab**
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- Similar to GitHub
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- Strong institutional support
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- Self-hosting options
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- More enterprise features
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**Bitbucket**
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- Supports Git and Mercurial
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- Good for private repositories
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- Jira integration
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## Persistent Identifiers (PIDs)
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### What is a Persistent Identifier?
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A persistent identifier (PID) is a long-lasting reference to a digital resource that:
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- Remains valid over time
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- Points to the same resource across locations
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- Enables reliable citation
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- Supports data discovery
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### Types of Persistent Identifiers
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**DOI (Digital Object Identifier)**
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- Standard for academic research
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- Managed by CrossRef, DataCite
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- Example: 10.5281/zenodo.1234567
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- Supports: Articles, datasets, software
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**ORCID (Open Researcher and Contributor ID)**
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- Unique identifier for researchers
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- Distinguishes between similar names
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- Links all your publications and contributions
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- Free to obtain
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**ARK (Archival Resource Key)**
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- Provides persistent access
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- Often used for institutional repositories
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- Less commercial than DOI
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**Handle System**
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- Protocol and system for PID resolution
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- Foundation for DOI system
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- Distributed infrastructure
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### Benefits of Using PIDs
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- **Discoverability** - Others can find your work reliably
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- **Citation** - Enables accurate attribution
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- **Tracking** - Monitor impact and reuse
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- **Accessibility** - Persistent links remain valid
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- **Integration** - Works across platforms and services
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## Additional Open Science Tools
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### Documentation and Collaboration
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- **Overleaf** - Collaborative LaTeX writing
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- **Google Docs** - Cloud-based document collaboration
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- **Jupyter Notebooks** - Executable research documents
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- **OSF** - Project management and collaboration
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### Communication and Sharing
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- **Preprint servers** - arXiv, bioRxiv, medRxiv
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- **Open Science Framework** - Workflows and transparency
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- **Slack/Discord** - Research team communication
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### Data Management
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- **Data Stewardship Wizard** - Data management planning
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- **DMPTool** - Institutional DMP support
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## Key Takeaways
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- Use repositories to store and preserve data and code
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- Implement version control for tracking changes and collaboration
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- Assign persistent identifiers (DOI, ORCID) to enable reliable discovery and citation
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- Choose tools appropriate for your research community
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- Proper tool selection supports reproducibility and research impact

‎docs/Area2_Capacity_Sharing/Open-Science-101/modules/open_software_module.md‎

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# Open Software and Code
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## From Code to Software
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### The Software Development Process
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1. **Source Code** - Scientists write code to:
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- Produce software for data analysis
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- Model observations
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- Control instruments
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- Process experimental data
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2. **Software Products** - Code is refined into:
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- Standalone applications
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- Libraries and packages
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- Tools and utilities
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- Web services
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## Types of Software
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### General Purpose Software
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- Software produced for widespread use
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- Not specialized for particular scientific domains
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- Examples: Python, R, Jupyter, Git
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### Domain-Specific Software
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- Specialized for particular scientific fields
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- Examples: GROMACS (molecular dynamics), yt (astrophysics), Astropy (astronomy)
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### Research Software
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- Created specifically for a research project
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- May have limited applicability beyond that project
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- Often undocumented and difficult to reuse
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## Benefits of Open Software
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- **Reproducibility** - Others can run your exact code
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- **Verification** - Community can check for errors
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- **Reusability** - Code can be adapted for new purposes
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- **Acceleration** - Avoids duplication of effort
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- **Quality** - Open review improves code quality
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## Considerations for Assessing Open Software
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When evaluating software for use:
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### Functionality
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- Will it solve your scientific problem?
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- Can it reproduce expected results?
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- Does it handle edge cases?
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### Usability
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- Is documentation clear and complete?
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- Are there working examples?
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- Is the user interface intuitive?
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### Reliability
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- How actively maintained is it?
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- Are bugs reported and fixed?
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- What's the track record?
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### Performance
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- What are computational requirements?
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- Can it scale to your data size?
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- Are there benchmarks available?
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## Documentation
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### Why Documentation Matters
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Documentation is the written description of any product or service that:
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- Explains what the code does
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- Shows how to use it
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- Documents assumptions and limitations
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- Provides examples
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### Types of Documentation
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- **README** - Project overview, installation, basic usage
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- **Tutorials** - Step-by-step guides for common tasks
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- **API docs** - Complete reference of functions and classes
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- **Comments** - Inline explanation of code logic
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- **Examples** - Working code samples
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### Making Code Reproducible
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Good documentation includes:
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- System requirements and dependencies
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- Installation instructions
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- Data format specifications
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- Parameter explanations
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- Expected outputs and examples
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## Licensing Open Software
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### Why License Your Software?
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A license:
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- Clarifies how others can use your software
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- Protects your intellectual property
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- Enables collaboration
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- Ensures attribution
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### Common Open Source Licenses
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- **MIT License** - Permissive, minimal restrictions
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- **Apache 2.0** - Permissive, includes patent protection
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- **GPL (GNU General Public License)** - Copyleft, requires derivatives to be open
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- **BSD License** - Permissive, similar to MIT
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## Sharing Software
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### Where to Share Code
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- **GitHub** - Version control, collaboration, discoverability
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- **GitLab** - Similar to GitHub, often with institutional support
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- **Bitbucket** - Version control, private and public repositories
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- **Software repositories** - Zenodo, Figshare for research software
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- **Package repositories** - PyPI (Python), CRAN (R), etc.
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## Key Takeaways
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- Open software enables reproducibility and accelerates scientific discovery
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- Thorough documentation is essential for reusability
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- Choose an appropriate open license to clarify usage rights
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- Share code in accessible repositories with version control
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- Quality code and documentation benefit both users and future maintainers

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