Open Code is not enough: Towards a replicable future for geographic data science

Open science practices are a large and healthy part of computational geography and the burgeoning field of spatial data science. In many forms, open geospatial cyberinfrastructure adheres to a varying and informal set of practices and codes that empower levels of collaboration that are impossible otherwise. Pathbreaking work in geographical sciences has explicitly brought these concepts into focus for our current model of open science in geography. In practice, however, these blend together into a somewhat ill-advised but easy-to-use working definition of open science: you know open science when you see it (on GitHub). However, open science lags far behind the needs revealed by this level of collaboration. In this paper, we describe the concerns of open geographic data science, in terms of replicability and open science. We discuss the practical techniques that engender community-building in open science communities, and discuss the impacts that these kinds of social changes have on the technological architecture of scientific infrastructure.