Computational Analysis of Lifespan Experiment Reproducibility

Independent reproducibility is essential to the generation of scientific knowledge. Optimizing experimental protocols to ensure reproducibility is an important aspect of scientific work. Genetic or pharmacological lifespan extensions are generally small compared to the inherent variability in mean lifespan even in isogenic populations housed under identical conditions. This variability makes reproducible detection of small but real effects experimentally challenging. In this study, we aimed to determine the reproducibility of C. elegans lifespan measurements under ideal conditions, in the absence of methodological errors or environmental or genetic background influences. To accomplish this, we generated a parametric model of C. elegans lifespan based on data collected from 5,026 wild-type N2 animals. We use this model to predict how different experimental practices, effect sizes, number of animals, and how different ‘shapes’ of survival curves affect the ability to reproduce real longevity effects. We find that the chances of reproducing real but small effects are exceedingly low and would require substantially more animals than are commonly used. Our results indicate that many lifespan studies are underpowered to detect reported changes and that, as a consequence, stochastic variation alone can account for many failures to reproduce longevity results. As a remedy, we provide power of detection tables that can be used as guidelines to plan experiments with statistical power to reliably detect real changes in lifespan and limit spurious false positive results. These considerations will improve best-practices in designing lifespan experiment to increase reproducibility.

Reproducibility analysis of the scientific workflows

In this dissertation I deal with the requirements and the analysis of the reproducibility. I set out methods based on provenance data to handle or eliminate the unavailable or changing descriptors in order to be able reproduce an – in other way – non-reproducible scientific workflow. In this way I intend to support the scientist’s community in designing and creating reproducible scientific workflows.

Home Healthcare Transparent Toxicology: Towards improved reproducibility and data reusability

The concept of reproducibility is one of the foundations of scientific practice and the bedrock by which scientific validity can be established. However, the extent to which reproducibility is being achieved in the sciences is currently under question. Several studies have shown that much peer-reviewed scientific literature is not reproducible. One crucial contributor to the obstruction of reproducibility is the lack of transparency of original data and methods. Reproducibility, the ability of scientific results and conclusions to be independently replicated by independent parties, potentially using different tools and approaches, can only be achieved if data and methods are fully disclosed.

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  • 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.

    Video: Singularity – Containers for Science, Reproducibility, and HPC

    Explore how Singularity liberates non-privileged users and host resources (such as interconnects, resource managers, file systems, accelerators …) allowing users to take full control to set-up and run in their native environments. This talk explores Singularity how it combines software packaging models with minimalistic containers to create very lightweight application bundles which can be simply executed and contained completely within their environment or be used to interact directly with the host file systems at native speeds. A Singularity application bundle can be as simple as containing a single binary application or as complicated as containing an entire workflow and is as flexible as you will need.

    Data Science Environments partners publish reproducibility book

    Researchers from the UW’s eScience Institute, New York University Center for Data Science and Berkeley Institute for Data Science (BIDS) have authored a new book titled The Practice of Reproducible Research. Representatives from the three universities, all Moore-Sloan Data Science Environments partners, joined on January 27, 2017, at a symposium hosted by BIDS. There, speakers discussed the book’s content, including case studies, lessons learned and the potential future of reproducible research practices.

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  • 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.

    JoVE Builds on Ten Years of Making Science Clearer, More Reproducible

    JoVE, the leading creator and publisher of video solutions that increase productivity in scientific research and education, today announced 2017 plans to mark the Company’s 10th anniversary. This year-long initiative will include the introduction of new Engineering and the Physical Sciences Collections within JoVE Science Education. JoVE will launch ten major initiatives, including a new JoVE Unlimited pricing formula, enhanced web experience, and establish a number of grants to advance scientific research and education.

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