Scientific instruments are at the heart of the scientific process, from 17th‐century telescopes and microscopes, to modern particle colliders and DNA sequencing machines. Nowadays, most scientific instruments in biomedical research come from commercial suppliers [1], [2], and yet, compared to the biopharmaceutical and medical devices industries, little is known about the interactions between scientific instrument makers and academic researchers. Our research suggests that this knowledge gap is a cause for concern.

The lack of standard terminology means that we do not clearly distinguish between situations in which there is not enough information to attempt repetition, and those in which attempts do not yield substantially the same outcome. To reduce confusion, I propose an intuitive, unambiguous neologism: ‘preproducibility’. An experiment or analysis is preproducible if it has been described in adequate detail for others to undertake it. Preproducibility is a prerequisite for reproducibility, and the idea makes sense across disciplines.

A fundamental challenge for open science is how best to create and share documents containing computational results. Traditional methods involve maintaining the code, generated tables and figures, and text as separate files and manually assembling them into a finished document. As projects grow in complexity, this approach can lead to procedures which are error prone and hard to replicate. Fortunately, new tools are emerging to address this problem and librarians who provide data services are ideally positioned to provide training. In the workshop we’ll use RStudio to demonstrate how to create a "compilable" document containing all the text elements (including bibliography), as well as the code required to create embedded graphs and tables. We’ll demonstrate how the process facilitates making revisions when, for example, a reviewer has suggested a revision or when there has been a change in the underlying data. We’ll also demonstrate the convenience of integrating version control into the workflow using RStudio’s built-in support for git.

Top climate scientists say their field can improve its transparency. A group of researchers presented their findings on reproducibility in climate science to the National Academies of Sciences, Engineering and Medicine yesterday as part of a monthslong examination of scientific transparency.

For my Honors Senior Project, I read five research papers in the field of computational biology and attempted to reproduce the results. However, for the most part, this proved a challenge, as many details vital to utilizing relevant software and data had been excluded. Using Geir Kjetil Sandve's paper "Ten Simple Rules for Reproducible Computational Research" as a guide, I discuss how authors of these five papers did and did not obey these rules of reproducibility and how this affected my ability to reproduce their results.

When scientists are able to recreate earlier research results, published by other scientists, the research is considered reproducible. But what happens when the results don’t match? It means that the initial research is non-reproducible. Reproducibility, or non-reproducibility, of scientific experiments seems straightforward; it implies that an experimental result is either valid or invalid. In fact, researchers affiliated with Stanford University, Tufts University, and University of Ioannina in Greece concluded in 2005 that a majority of all research findings are false. How do those invalid results end up in scientific papers? A group of Stanford researchers concluded that, in many cases, bias is to blame.