Abstract-Binder is an open source web service that lets users create sharable, interactive, reproducible environments in the cloud. It is powered by other core projects in the open source ecosystem, including JupyterHub and Kubernetes for managing cloud resources. Binder works with pre-existing workflows in the analytics community, aiming to create interactive versions of repositories that exist on sites like GitHub with minimal extra effort needed. This paper details several of the design decisions and goals that went into the development of the current generation of Binder.Index Terms-cloud computing, reproducibility, binder, mybinder.org, shared computing, accessibility, kubernetes, dev ops, jupyter, jupyterhub, jupyter notebooks, github, publishing, interactivityBinder is a free, open source, and massively publicly available tool for easily creating sharable, interactive, reproducible environments in the cloud.The scientific community is increasingly unified around reproducibility. A survey in 2016 of 1,576 researchers reported that 90% of respondents believed there exists a reproducibility crisis in the scientific community. A majority of respondents also reported difficulty reproducing the work of colleagues [Bak16]. Similar results have been reported in the cell biology community [The] and the machine learning community [Pin17]. Making research reproducible requires pursuing two sub-goals, both of which are difficult to achieve: as well as the "data heavy" approach many fields are adopting, these problems become more complex yet more tractable than ever before.Fortunately, as the problem has grown more complex, the open source community has risen to meet the challenge. Tools for packaging analytics environments into "containers" allow others to re-create the computational environments needed to run analyses and evaluate results. Online communities make it easier to share and discover scientific results. A myriad of open source tools are freely available for doing analytics in open and transparent ways. New paradigms for writing code and displaying results in rich, engaging formats allow results to live next to the prose that explains their purpose.However, manual implementation of this processes is complex, and reproducing the full stack of another person's work is too labor intensive and error-prone for day-to-day use. A recent study of scientific repositories found that citation of "both visualization tools as well as common software packages (such as MATLAB) was a widespread failure" [SSM18]. As a result, the technical barriers limit practical reproducibility. To lower the technical barriers of sharing computational work, we introduce Binder 2.0, a tool that we believe makes reproducibility more practically possible.

LicenseAuthors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY).

No abstract

The classical one-dimensional (1D) Child–Langmuir law was previously extended to two dimensions by numerical calculation in planar geometries. By considering an axisymmetric cylindrical system with axial emission from a circular cathode of radius r, outer drift tube radius R>r, and gap length L, we further examine the space charge limit in two dimensions. Simulations were done with no applied magnetic field as well as with a large (100 T) longitudinal magnetic field to restrict motion of particles to 1D. The ratio of the observed current density limit JCL2 to the theoretical 1D value JCL1 is found to be a monotonically decreasing function of the ratio of emission radius to gap separation r/L. This result is in agreement with the planar results, where the emission area is proportional to the cathode width W. The drift tube in axisymmetric systems is shown to have a small but measurable effect on the space charge limit. Strong beam edge effects are observed with J(r)/J(0) approaching 3.5. Two-dimensional axisymmetric electrostatic particle-in-cell simulations were used to produce these results.

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.