Provenance refers to any information describing the production process of an end product, which can be anything from a piece of digital data to a physical object. While this survey focuses on the former type of end product, this definition still leaves room for many different interpretations of and approaches to provenance. These are typically motivated by different application domains for provenance (e.g., accountability, reproducibility, process debugging) and varying technical requirements such as runtime, scalability, or privacy. As a result, we observe a wide variety of provenance types and provenance-generating methods. This survey provides an overview of the research field of provenance, focusing on what provenance is used for (what for?), what types of provenance have been defined and captured for the different applications (what form?), and which resources and system requirements impact the choice of deploying a particular provenance solution (what from?). For each of these three key questions, we provide a classification and review the state of the art for each class. We conclude with a summary and possible future research challenges.
Reproducible Science Promoting Open Science
As computational pipelines become a bigger part of science, it is important to ensure that the results are reproducible, a concern which has come to the fore in recent years. All developed software should be able to be run automatically without any user intervention. In addition to being valuable to the wider community, which may wish to reproduce or extend a published analysis, reproducible research practices allow for better control over the project by the original authors themselves. For example, keeping a non-executable record of parameters and command line arguments leads to error-prone analysis and opens up the possibility that, when the results are to be written up for publication, the researcher will no longer be able to even completely describe the process that led to them. For large projects, the use of multiple computational cores (either in a multi-core machine or distributed across a compute cluster) is necessary to obtain results in a useful time frame. Furthermore, it is often the case that, as the project evolves, it becomes necessary to save intermediate results while down-stream analyses are designed (or redesigned) and implemented. Under many frameworks, this causes having a single point of entry for the computation becomes increasingly difficult. Jug is a software framework which addresses these issues by caching intermediate results and distributing the computational work as tasks across a network. Jug is written in Python without the use of compiled modules, is completely crossplatform, and available as free software under the liberal MIT license.
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
This report describes perspectives from the Workshop on the Future of Research Curation and Research Reproducibility that was collaboratively sponsored by the U.S. National Science Foundation (NSF) and IEEE (Institute of Electrical and Electronics Engineers) in November 2016. The workshop brought together stakeholders including researchers, funders, and notably, leading science, technology, engineering, and mathematics (STEM) publishers. The overarching objective was a deep dive into new kinds of research products and how the costs of creation and curation of these products can be sustainably borne by the agencies, publishers, and researcher communities that were represented by workshop participants. The purpose of this document is to describe the ideas that participants exchanged on approaches to increasing the value of all research by encouraging the archiving of reusable data sets, curating reusable software, and encouraging broader dialogue within and across disciplinary boundaries. How should the review and publication processes change to promote reproducibility? What kinds of objects should the curatorial process expand to embrace? What infrastructure is required to preserve the necessary range of objects associated with an experiment? Who will undertake this work? And who will pay for it? These are the questions the workshop was convened to address in presentations, panels, small working groups, and general discussion.
Summary: This manuscript introduces and describes Dugong, a Docker image based on Ubuntu 16.04, which automates installation of more than 3500 bioinformatics tools (along with their respective libraries and dependencies), in alternative computational environments. The software operates through a user-friendly XFCE4 graphic interface that allows software management and installation by users not fully familiarized with the Linux command line and provides the Jupyter Notebook to assist in the delivery and exchange of consistent and reproducible protocols and results across laboratories, assisting in the development of open science projects.
Fairly high-level entry slides presented to the members of intercollegiate graduate program in Bioinformatics and Genomics at The Pennsylvania State University.
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.
To address this critical need, the Laura and John Arnold Foundation has awarded a grant to a coalition of groups representing the international Earth and space science community, convened by the American Geophysical Union (AGU), to develop standards that will connect researchers, publishers, and data repositories in the Earth and space sciences to enable FAIR (findable, accessible, interoperable, and reusable) data – a concept first developed by Force11.org – on a large scale. This will accelerate scientific discovery and enhance the integrity, transparency, and reproducibility of this data. The resulting set of best practices will include: metadata and identifier standards; data services; common taxonomies; landing pages at repositories to expose the metadata and standard repository information; standard data citation; and standard integration into editorial peer review workflows.
This book contains a collection of 31 case studies of reproducible research workflows, written by academic researchers in the data-intensive sciences. Each case study describes how the author combined specific tools, ideas, and practices in order to complete a real-world research project. Emphasis is placed on the practical aspects of how the author organized his or her research to make it as reproducible as possible.
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.