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Agile (data) science: a (draft) manifesto

Science has a data management as well as a project management problem. While industrial grade data science teams have embraced the *agile* mindset, and adopted or created all kind of tools to manage reproducible workflows, academia-based science is still (mostly) mired in a mindset that's focused on a single final product (a paper), without focusing on incremental improvement and, over all, reproducibility. In this report we argue towards the adoption of the agile mindset and agile data science tools in academia, to make a more responsible, sustainable, and above all, reproducible science.


A survey study of success factors in data science projects

In recent years, the data science community has pursued excellence and m...

How do Data Science Workers Collaborate? Roles, Workflows, and Tools

Today, the prominence of data science within organizations has given ris...

Tools and Recommendations for Reproducible Teaching

It is recommended that teacher-scholars of data science adopt reproducib...

How Do Data Science Workers Communicate Intermediate Results?

Data science workers increasingly collaborate on large-scale projects be...

Continuously Updated Data Analysis Systems

When doing data science, it's important to know what you're building. Th...

Toward a System Building Agenda for Data Integration

In this paper we argue that the data management community should devote ...