Make sure to utilize resources that are available to assist you in helping to write a good DMP.However, the same should apply to research that is being undertaken outside of a specific funded proposal. Given DMPs are a requirement of funding agencies, it is nearly always neccesary to plan from the start. Make sure to plan from the start to avoid confusion, data loss, and increase efficiency.Therefore, plan in collaboration with these individuals. Collaborators involved in the data collection and processing of your research data bring diverse expertise. 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.

Zoom 5.15.6 instal the new for windows