As indicated above, engaging your team is a benefit of data management planning.5.15.6 Visualize sf objects with leaflet.5.14 Hands On: Clean and Integrate Datasets.5.13.9 Sharing and releasing your package.5.13.8 Checking and installing your package.5.12.4 Examples: Minimizing work with functions.5.8.6 Workflows to avoid merge conflicts.5.8.3 Collaborating with a trusted colleague without conflicts.5.8 Git Collaboration and Conflict Management.5.7.5 Setting up git on an existing project.5.7.4 Working locally with Git via RStudio.5.7.3 Create a remote repository on GitHub.5.7 Version Control with git and GitHub.5.4.3 Data repositories: built for data (and code).5.3.5 Provanance & Preserving Computational Workflows.5.2.3 Setting up the R environment on your local computer.5.2.2 Introduction to reproducible research.5.2 Reproducible Research, RStudio and Git/GitHub Setup.5.1.8 NSF (and other) Template Support for DMPs.5.1.7 Tools in Support of Creating a DMP.5.1.2 When to Plan: The Data Life Cycle.5 Reproducible Research Techniques - Data Training.3.5.6 Considerations for scaling and audience sizes.3.4.4 Scientific programming for reproducible research.3.4.1 Why collaborating in a reproducible manner.3.4 Skills and Tools for Reproducible and Team Science.3.3.2 Designing and facilitating effective meetings. 3.1.3 Culture, structure, and leadership.
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