Posts about reproducibility guidelines (old posts, page 1)

Leveraging Statistical Methods to Improve Validity and Reproducibility of Research Findings

Scientific discoveries have the profound opportunity to impact the lives of patients. They can lead to advances in medical decision making when the findings are correct, or mislead when not. We owe it to our peers, funding sources, and patients to take every precaution against false conclusions, and to communicate our discoveries with accuracy, precision, and clarity. With the National Institutes of Health’s new focus on rigor and reproducibility, scientists are returning attention to the ideas of validity and reliability. At JAMA Psychiatry, we seek to publish science that leverages the power of statistics and contributes discoveries that are reproducible and valid. Toward that end, I provide guidelines for using statistical methods: the essentials, good practices, and advanced methods.

NIH Request for Information on Strategies for NIH Data Management, Sharing, and Citation

This Request for Information (RFI) seeks public comments on data management and sharing strategies and priorities in order to consider: (1) how digital scientific data generated from NIH-funded research should be managed, and to the fullest extent possible, made publicly available; and, (2) how to set standards for citing shared data and software. Response to this RFI is voluntary. Responders are free to address any or all of the items in Sections I and II, delineated below, or any other relevant topics respondents recognize as important for NIH to consider. Respondents should not feel compelled to address all items. Instructions on how to respond to this RFI are provided in "Concluding Comments."

A statistical definition for reproducibility and replicability

Everyone agrees that reproducibility and replicability are fundamental characteristics of scientific studies. These topics are attracting increasing attention, scrutiny, and debate both in the popular press and the scientific literature. But there are no formal statistical definitions for these concepts, which leads to confusion since the same words are used for different concepts by different people in different fields. We provide formal and informal definitions of scientific studies, reproducibility, and replicability that can be used to clarify discussions around these concepts in the scientific and popular press.

ACM | The TOMS Initiative and Policies for Replicated Computational Results (RCR)

TOMS accepts manuscripts for an additional, and presently optional, review of computational results. This Replicated Computational Results (RCR) review is focused solely on replicating any computational results that are included in a manuscript. If the results are successfully replicated, the manuscript receives a special RCR designation when published. This page outlines the TOMS policies for determining the RCR designation.

A practical guide for improving transparency and reproducibility in neuroimaging research

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.