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.
A ReproZip demo has been accepted at SIGMOD 2016: "ReproZip: Computational Reproducibility With Ease." F. Chirigati, R. Rampin, D. Shasha, and J. Freire.
We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors—a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis—for a large subset (N = 72) of the original papers and their corresponding replication attempts.
Researchers on social media ask at what point replication efforts go from useful to wasteful. The problem of irreproducibility in science has gained widespread attention, but one aspect that is discussed less often is how to find the right balance between replicating findings and moving a field forward from well-established ones.
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.