Sci-Ops

Collaborating on reproducible manuscripts for dummies: an introduction using git, R Markdown and Zotero

This document is an introduction to a workflow that facilitates collaborating with other researchers if your aim is to apply Open Science principles. Background This workflow is consistent with the following Open Science principles: Being as transparent as possible. In scientific publications of empirical endeavours, it is important that people can inspect how the results derive from the collected data. One way to achieve this is to include deparate analysis scripts and let the readers (and the future ‘you’) sort things out for themselves.

A poor person's guide to Open Sciencing GDPR compliant data management

In this brief post I’ll outline a simple data management strategy that is consistent with both GDPR and Open Science principles. For readers unfamiliar with these: the EU’s General Data Protection Regulation (GDPR) somewhat sternly encourages treating personal data decently, and Open Science principles promote sharing data as much as possible. When are data personal? We deal with this in more detail in Crutzen, Peters & Mondschein (2019), but basically, personal data are data about a person.