In this session, Bass Connections provided tips and best practices for managing complex, team-based projects, including:

• Project scoping
• Project charters and project planning
• Working on teams
• Communicating within teams

In this session, the Duke Center for Data and Visualization Sciences shared best practices for documenting and organizing research materials for teams, including:

• Shared folders
• File naming conventions
• Data management tools
• Managing data for sharing and reproducibility
• Types of documentation (readme files, research notebooks, and documenting for future use)

In this session, Duke Libraries provided guidance on how to conduct a literature review, including:

• What is a literature review?
• How do you do one?
• What makes a good source?
• General search strategies
• Choosing a citation management software
• Using subject area librarians

In this session, the Duke Social Science Research Institute introduced qualitative research methods and practices, including:

• When should you use qualitative research?
• Developing an interview/focus group instrument
• Conducting focus groups
• Conducting interviews
• Note taking, transcription and coding (in brief; see later session on analysis)

In this session, the Duke Social Science Research Institute introduced best practices in survey design, including:

• When should you use surveys?
• Survey design
• Survey administration
• Avoiding survey bias

In this session, the Duke Social Science Research Institute introduced qualitative coding, with a focus on NVivo (software for coding qualitative research data), including:

• What is qualitative analysis?
• How do you develop and apply coding?
• What is NVivo and how do you get it?
• How to use it

In this session, the Duke Social Science Research Institute shared best practices for community engaged research, including:

• Why to conduct community engaged research
• Developing reciprocal partnerships
• Ethical engagement
• Understanding and respecting local context
• Stakeholder engagement and management

In this session, the Duke Center for Data and Visualization Sciences provided tips for reproducible computational analysis, including:

• Finding and using existing data sources
• Data wrangling: normalizing and cleaning
• Literate coding: combining prose and code to render analytical reports
• Version control (git & GitHub): self-document the story of your code-based project and collaborations
• Computational reproducibility: exemplary tools (e.g. OpenRefine, R, Python)

In this session, the Duke Center for Data and Visualization Sciences introduced tips for communicating using data visualizations, including:

• Why we visualize
• Principles of effective data visualization
• Pitfalls to avoid
• Critique and rework of a published visualization

In this session, the Duke Center for Data and Visualization Sciences introduced tips for creating a great research poster, including:

• Characteristics of a good poster
• How to get started with your layout and storytelling
• Basic graphic design principles
• Pitfalls to avoid
• Critique and revision of a real poster draft