R, Spatial Analysis, Spatial Ecology, Data Workflows

• MSU Graduate Spatial Ecology Lab: https://space-lab-msu.github.io/MSUGradSpatialEcology/
  • resources for using R, RStudio, RMarkdown, and many spatial analyses packages in R!
• For a slightly older collection of resources, click here to navigate to some great resources for using R for ecology and spatial analysis! 
• Favorite overall R tutorial
• The SpaCE Lab R Guide for Spatial Ecology & collaborative research: https://space-lab-msu.github.io/r_guide/
• Data should align with FAIR principles (be Findable, Accessible, Interoperable, and Reusable): https://www.go-fair.org/fair-principles/ and with CARE principles (Collective Benefit, Authority to Control, Responsibility, Ethics): https://www.gida-global.org/care, to ensure that data sharing respects data sovereignty and the use of Indigenous data and Indigenous Knowledge.

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• Environmental Data Initiative Guidelines for Thematic Standardization of Data to ensure reproducible workflows with harmonized data so that they are interoperable and reusable: https://edirepository.org/resources/thematic-standardization.

Environmental Data Initiative Guidelines for Thematic Standardization of Data to ensure reproducible workflows with harmonized data so that they are interoperable and reusable: https://edirepository.org/resources/thematic-standardization.