New technologies have led to vast troves of large and complex data sets across many
scientific domains and industries. People routinely use machine learning techniques not …

We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks
and conducting holistic model comparison, integrated with the Dynabench platform. Our …

Journal editors have a large amount of power to advance open science in their respective
fields by incentivising and mandating open policies and practices at their journals. The Data …

Creating online data repositories that follow Findable, Accessible, Interoperable, and
Reusable (FAIR) principles has been a significant focus in the research community to …

We carry out efforts to reproduce computational results for seven published articles and
identify barriers to computational reproducibility. We then derive three principles to guide the …

In the computational science and engineering (CSE) community, there is a prevailing belief
that adopting better software development practices and investing in software quality will …

In 2016, JASA Applications and Case Studies (ACS) introduced a reproducibility initiative to
address the lack of standardized practices for reproducibility in scientific research. This …

There is growing interest in data science and the challenges that scientists can solve
through its application. The growing interest is in part due to the promise of “extracting value …

This article expands upon my presentation to the panel on “The Radical Prescription for
Change” at the 2017 ASA (American Statistical Association) symposium on A World Beyond …

Computational methods are in full swing in communication science. Part of their promise is
to make communication research more reproducible. However, how this plays out in practice …