This LibGuide from the University of Utah outlines some first steps, tutorials, and toolkits related to making research reproducible, with a strong focus on quantitative and computational research.
The workshop summarized in this report was designed not to address the social and experimental challenges but instead to focus on the latter issues of improper data management and analysis, inadequate statistical expertise, incomplete data, and difficulties applying sound statistical inference to the available data.
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.
PDBF documents are a hybrid format. They are a valid PDF and a valid HTML page at the same time. You can now optionally add an VirtualBox OVA file with a complete operating system to the PDBF document. Yes, this means that the resulting file is a valid PDF, HTML, and OVA file at the same time. If you change the file extension to PDF and open it with an PDF viewer, you can see the static part of the document.
This year, also SMI will introduce an Award for Reproducibility to be granted to authors of accepted papers who are willing to provide a complete open-source implementation of their algorithm. The reproducibility stamp does not affect the reviewing process or the requirements for your submission to be accepted. The awarded papers will receive an additional 5 to 10 minutes in their presentation to give a live demo and will be recognized during the SMI closing ceremony. More information on the web site soon.