NASA Earth Exchange (NEX) is a data, supercomputing and knowledge collaboratory that houses NASA satellite, climate and ancillary data where a focused community can come together to address large-scale challenges in Earth sciences. As NEX has been growing into a petabyte-size platform for analysis, experiments and data production, it has been increasingly important to enable users to easily retrace their steps, identify what datasets were produced by which process chains, and give them ability to readily reproduce their results. This can be a tedious and difficult task even for a small project, but is almost impossible on large processing pipelines. We have developed an initial reproducibility and knowledge capture solution for the NEX, however, if users want to move the code to another system, whether it is their home institution cluster, laptop or the cloud, they have to find, build and install all the required dependencies that would run their code. This can be a very tedious and tricky process and is a big impediment to moving code to data and reproducibility outside the original system. The NEX team has tried to assist users who wanted to move their code into OpenNEX on Amazon cloud by creating custom virtual machines with all the software and dependencies installed, but this, while solving some of the issues, creates a new bottleneck that requires the NEX team to be involved with any new request, updates to virtual machines and general maintenance support. In this presentation, we will describe a solution that integrates NEX and Docker to bridge the gap in code-to-data migration. The core of the solution is saemi-automatic conversion of science codes, tools and services that are already tracked and described in the NEX provenance system, to Docker - an open-source Linux container software. Docker is available on most computer platforms, easy to install and capable of seamlessly creating and/or executing any application packaged in the appropriate format. We believe this is an important step towards seamless process deployment in heterogeneous environments that will enhance community access to NASA data and tools in a scalable way, promote software reuse, and improve reproducibility of scientific results.
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.
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.
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.
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.
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.