Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility.

SIGMOD Reproducibility has three goals: Highlight the impact of database research papers; Enable easy dissemination of research results; Enable easy sharing of code and experimentation set-ups. In short, the goal is to assist in building culture where sharing results, code, and scripts of database research is the norm rather than the exception. The challenge is to do this efficiently, which means building technical expertise on how to do better research via creating repeatable and shareable research. The SIGMOD Reproducibility Committee is here to help you with this.

Transparency, open sharing, and reproducibility are core features of science, but not always part of daily practice. Journals can increase transparency and reproducibility of research by adopting the TOP Guidelines. TOP includes eight modular standards, each with three levels of increasing stringency. Journals select which of the eight transparency standards they wish to adopt for their journal, and select a level of implementation for the selected standards. These features provide flexibility for adoption depending on disciplinary variation, but simultaneously establish community standards.

Our working definition for reproducible research is that a research result can be replicated by another investigator. Our focus is data science and the reproducibility of computational studies and/or analysis of digital data. This note summarizes best practices to facilitate reproducible research in data science (and computational science more generally). It is expected that all research conducted with funding from the DSE will be performed in accordance with these guidelines to the extent possible.

Recent years have seen an increase in alarming signals about the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in our field and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility.

A presentation by Philip B. Stark of University of California at Berkeley that gives a great 101-style look into what the everyday researcher can do to make their science more reproducible.