Posts about reproducibility guidelines (old posts, page 2)

ACM SIGMOD 2016 Reproducibility Guidelines

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 and Openness Promotion (TOP) Guidelines

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.

University of Washington's eScience Institute Guidelines for Reproducible & Open Science

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.

A Practical Guide for Improving transparency and Reproducibility in Neuroimaging Research

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.

Science is "show me," not "trust me"

A blog post from Philip B. Stark, Associate Dean of the Division of Mathematical and Physical Sciences, UC Berkeley Professor of Statistics, and winner of one of BITSS’ Leamer-Rosenthal Prizes for Open Social Science. This post discuss the core elements of reproducibility; its principles and practices.