Reproducibility in experiments is necessary to verify claims and to reuse prior work in experiments that advance research. However,the traditional model of publication validates research claims through peer-review without taking reproducibility into account. Workflows encapsulate experiment descriptions and components and are suitable for representing reproducibility. Additionally, they can be published alongside traditional patterns as a form of documentation for the experiment which can be combined with linked open data. For reproducibility utilising published datasets, it is necessary to declare the conditions or restrictions for permissible reuse. In this paper, we take a look at the state of workflow reproducibility through a browser based tool and a corresponding study to identify how workflows might be combined with traditional forms of documentation and publication. We also discuss the licensing aspects for data in workflows and how it can be annotated using linked open data ontologies
Reproducibility, a cornerstone of research, requires defined data formats, which include the set-up and output of experiments. The Real-time PCR Data Markup Language (RDML) is a recommended standard of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines. Despite the popularity of the RDML format for analysis of qPCR data, handling of RDML files is not yet widely supported in all PCR curve analysis softwares. Results: This study describes the open source RDML package for the statistical computing language R.RDML is compatible with RDML versions ≤ 1.2 and provides functionality to (i) import RDML data; (ii) extract sample information (e.g., targets, concentration); (iii) transform data to various formats of the R environment; (iv) generate human readable run summaries; and (v) to create RDML files from user data. In addition, RDML offers a graphical user interface to read, edit and create RDML files.
We present a toolchain for computational research consisting of Sacred and two supporting tools. Sacred is an open source Python framework which aims to provide basic infrastructure for running computational experiments independent of the methods and libraries used. Instead, it focuses on solving universal everyday problems, such as managing configurations, reproducing results, and bookkeeping. Moreover, it provides an extensible basis for other tools, two of which we present here: Labwatch helps with tuning hyperparameters, and Sacredboard provides a web-dashboard for organizing and analyzing runs and results.
Replication of scientific experiments is critical to the advance of science. Unfortunately, the discipline of Computer Science has never treated replication seriously, even though computers are very good at doing the same thing over and over again. Not only are experiments rarely replicated, they are rarely even replicable in a meaningful way. Scientists are being encouraged to make their source code available, but this is only a small step. Even in the happy event that source code can be built and run successfully, running code is a long way away from being able to replicate the experiment that code was used for. I propose that the discipline of Computer Science must embrace replication of experiments as standard practice. I propose that the only credible technique to make experiments truly replicable is to provide copies of virtual machines in which the experiments are validated to run. I propose that tools and repositories should be made available to make this happen. I propose to be one of those who makes it happen.
We present noWorkflow, an open-source tool that systematically and transparently collects provenance from Python scripts, including data about the script execution and how the script evolves over time. During the demo, we will show how noWorkflow collects and manages provenance, as well as how it supports the analysis of computational experiments.We will also encourage attendees to use noWorkflow for their own scripts.
Achieving research reproducibility is challenging in many ways: there are social and cultural obstacles as well as a constantly changing technical landscape that makes replicating and reproducing research difficult. Users face challenges in reproducing research across different operating systems, in using different versions of software across long projects and among collaborations, and in using publicly available work. The dependencies required to reproduce the computational environments in which research happens can be exceptionally hard to track – in many cases, these dependencies are hidden or nested too deeply to discover, and thus impossible to install on a new machine, which means adoption remains low. In this paper, we present ReproZip, an open source tool to help overcome the technical difficulties involved in preserving and replicating research, applications, databases, software, and more. We examine the current use cases of ReproZip, ranging from digital humanities to machine learning. We also explore potential library use cases for ReproZip, particularly in digital libraries and archives, liaison librarianship, and other library services. We believe that libraries and archives can leverage ReproZip to deliver more robust reproducibility services, repository services, as well as enhanced discoverability and preservation of research materials, applications, software, and computational environments.