Posts about reproducible paper (old posts, page 18)

Using the Nextflow framework for reproducible in-silico omics analyses across clusters and clouds

Reproducibility has become one of biology’s most pressing issues. This impasse has been fueled by the combined reliance on increasingly complex data analysis methods and the exponential growth of biological datasets. When considering the installation, deployment and maintenance of bioinformatic pipelines, an even more challenging picture emerges due to the lack of community standards. The effect of limited standards on reproducibility is amplified by the very diverse range of computational platforms and configurations on which these applications are expected to be applied (workstations, clusters, HPC, clouds, etc.). With no established standard at any level, diversity cannot be taken for granted.

BIDS Apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the richness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Cancer reproducibility project releases first results

The Reproducibility Project: Cancer Biology launched in 2013 as an ambitious effort to scrutinize key findings in 50 cancer papers published in Nature, Science, Cell and other high-impact journals. It aims to determine what fraction of influential cancer biology studies are probably sound — a pressing question for the field. In 2012, researchers at the biotechnology firm Amgen in Thousand Oaks, California, announced that they had failed to replicate 47 of 53 landmark cancer papers2. That was widely reported, but Amgen has not identified the studies involved.

Enabling Reproducibility for Small and Large Scale Research Data Sets

A large portion of scientific results is based on analysing and processing research data. In order for an eScience experiment to be reproducible, we need to able to identify precisely the data set which was used in a study. Considering evolving data sources this can be a challenge, as studies often use subsets which have been extracted from a potentially large parent data set. Exporting and storing subsets in multiple versions does not scale with large amounts of data sets. For tackling this challenge, the RDA Working Group on Data Citation has developed a framework and provides a set of recommendations, which allow identifying precise subsets of evolving data sources based on versioned data and timestamped queries. In this work, we describe how this method can be applied in small scale research data scenarios and how it can be implemented in large scale data facilities having access to sophisticated data infrastructure. We describe how the RDA approach improves the reproducibility of eScience experiments and we provide an overview of existing pilots and use cases in small and large scale settings.

Opening the Publication Process with Executable Research Compendia

A strong movement towards openness has seized science. Open data and methods, open source software, Open Access, open reviews, and open research platforms provide the legal and technical solutions to new forms of research and publishing. However, publishing reproducible research is still not common practice. Reasons include a lack of incentives and a missing standardized infrastructure for providing research material such as data sets and source code together with a scientific paper. Therefore we first study fundamentals and existing approaches. On that basis, our key contributions are the identification of core requirements of authors, readers, publishers, curators, as well as preservationists and the subsequent description of an executable research compendium (ERC). It is the main component of a publication process providing a new way to publish and access computational research. ERCs provide a new standardisable packaging mechanism which combines data, software, text, and a user interface description. We discuss the potential of ERCs and their challenges in the context of user requirements and the established publication processes. We conclude that ERCs provide a novel potential to find, explore, reuse, and archive computer-based research.

Supporting Data Reproducibility at NCI Using the Provenance Capture System

Scientific research is published in journals so that the research community is able to share knowledge and results, verify hypotheses, contribute evidence-based opinions and promote discussion. However, it is hard to fully understand, let alone reproduce, the results if the complex data manipulation that was undertaken to obtain the results are not clearly explained and/or the final data used is not available. Furthermore, the scale of research data assets has now exponentially increased to the point that even when available, it can be difficult to store and use these data assets. In this paper, we describe the solution we have implemented at the National Computational Infrastructure (NCI) whereby researchers can capture workflows, using a standards-based provenance representation. This provenance information, combined with access to the original dataset and other related information systems, allow datasets to be regenerated as needed which simultaneously addresses both result reproducibility and storage issues.