Author: Maëlle Salmon
This blog post is the 3rd of a 3-post series about a data-driven overview of rOpenSci onboarding. The 1st post is about data collection, the 2nd is about quantifying work done by authors, reviewers and editors.
Our onboarding process ensures that packages contributed by the community undergo a transparent, constructive, non adversarial and open review process. Before even submitting my first R package to rOpenSci onboarding system in December 2015, I spent a fair amount of time reading through previous issue threads in order to assess whether onboarding was a friendly place for me: a newbie, very motivated to learn more but a newbie nonetheless. I soon got the feeling that yes, onboarding would help me make my package better without ever making me feel inadequate.
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In this blog post, I shall explore how a tidytext analysis of review processes (via their GitHub threads) might help us characterize the social weather of onboarding.
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Read the full post here: rOpenSci | The social weather of rOpenSci onboarding system