Two weeks ago (1st-4th of August 2016) we hosted a coding sprint at Stanford aimed at making neuroimaging data processing and analysis tools more portable and accessible. You might have heard about BIDS – it is a new standard for organizing and describing neuroimaging datasets that we have recently proposed. Containers (also known as “operating-system-level virtualization”) are very lightweight virtual machines that can encapsulate any piece of code along with all of the libraries necessary to run it. Docker and Singularity are two examples of container technologies. The reason we are so excited about containers for reproducible data analysis is that they provide a way to package a piece of software which can run in the same way across many different computing platforms, from a laptop to a supercomputer. Creating containerized and BIDS-aware versions of all of the major neuroimaging analysis packages is critical to our center’s mission: providing data analysis as an free and open service to incentivize researchers to share data.
If you are an R user it has probably happened to you that you upgraded some R package in your R installation, and then suddenly your R script or application stopped working. One strategy is that you create a new package library for a new project. A package library is just a directory that holds all installed R packages. (In addition to the ones that are installed with R itself.) This is why we created the pkgsnap tool. This is a very simple package with two exported functions: 1) snap takes a snapshot of your project library. It writes out the names and versions of the currently installed packages into a text file. You can put this text file into the version control repository of the project, to make sure it is not lost, and 2) restore uses the snapshot file to recreate the package project library from scratch. It installs the recorded versions of the recorded packages, in the right order.
Reproducibility is receiving increased attention across many domains of science and genomics is no exception. Efforts to identify copy number variations (CNVs) from exome sequence (ES) data have been increasing. Many algorithms have been published to discover CNVs from exomes and a major challenge is the reproducibility in other datasets. Here we test exome CNV calling reproducibility under three conditions: data generated by different sequencing centers; varying sample sizes; and varying capture methodology.
Standardized, reproducible, and feasible quantification ofb-cell function (BCF) is necessary for the evaluation of interventions to improve insulin secretion and important for comparison across studies. We therefore characterized the re-sponses to, and reproducibility of, standardized methods of in vivo BCF across different glucose tolerance states. Reproducibility for the AST was very good, with ICC values >0.8 across all variables and populations.
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.
In what European science chief Carlos Moedas calls a "life-changing" move, E.U. member states today agreed on an ambitious new open-access (OA) target. All scientific papers should be freely available by 2020, the Competitiveness Council—a gathering of ministers of science, innovation, trade, and industry—concluded after a 2-day meeting in Brussels. But some observers are warning that the goal will be difficult to achieve.