Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Opportunities and challenges in long-read sequencing data analysis

Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.

Certification or Advanced Degrees

The value of training for a data sciences professional is in the eye of the beholder. And dependent on the scope and breadth of that training and the cost and time frame of that training. Value for the employee may differ from value for the employer. The lens is different and value may depend on what lens you look through. Training can be online or on-site, short term with specific focus or longer term with greater breadth and less depth. Career goals should also be considered when determining value. Certification in Spark is not valuable if you do not want to work with Spark. A PhD in management psychology is not as valuable if you do not want to manage people. The fact that training (both certification and degree programs) is valuable is not debatable. Maximizing that value for both employee and employer is always a preferable option. But is it realistic?

A Bioinformatics Primer to Data Science, with Examples for Metabolomics

With the increasing importance of big data in biomedicine, skills in data science are a foundation for the individual career development and for the progress of science. This chapter is a practical guide to working with high-throughput biomedical data. It covers how to understand and set up the computing environment, to start a research project with proper and effective data management, and to perform common bioinformatics tasks such as data wrangling, quality control, statistical analysis, and visualization, with examples on metabolomics data. Concepts and tools related to coding and scripting are discussed. Version control, knitr and Jupyter notebooks are important to project management, collaboration, and research reproducibility. Overall, this chapter describes a core set of skills to work in bioinformatics, and can serve as a reference text at the level of a graduate course and interfacing with data science.

Biocuration: Distilling data into knowledge

Data, including information generated from them by processing and analysis, are an asset with measurable value. The assets that biological research funding produces are the data generated, the information derived from these data, and, ultimately, the discoveries and knowledge these lead to. From the time when Henry Oldenburg published the first scientific journal in 1665 (Proceedings of the Royal Society) to the founding of the United States National Library of Medicine in 1879 to the present, there has been a sustained drive to improve how researchers can record and discover what is known. Researchers’ experimental work builds upon years and (collectively) billions of dollars’ worth of earlier work. Today, researchers are generating data at ever-faster rates because of advances in instrumentation and technology, coupled with decreases in production costs. Unfortunately, the ability of researchers to manage and disseminate their results has not kept pace, so their work cannot achieve its maximal impact. Strides have recently been made, but more awareness is needed of the essential role that biological data resources, including biocuration, play in maintaining and linking this ever-growing flood of data and information. The aim of this paper is to describe the nature of data as an asset, the role biocurators play in increasing its value, and consistent, practical means to measure effectiveness that can guide planning and justify costs in biological research information resources’ development and management.

Data Science in Radiology: A Path Forward

Artificial intelligence (AI), especially deep learning, has the potential to fundamentally alter clinical radiology. AI algorithms, which excel in quantifying complex patterns in data, have shown remarkable progress in applications ranging from self-driving cars to speech recognition. The AI application within radiology, known as radiomics, can provide detailed quantifications of the radiographic characteristics of underlying tissues. This information can be used throughout the clinical care path to improve diagnosis and treatment planning, as well as assess treatment response. This tremendous potential for clinical translation has led to a vast increase in the number of research studies being conducted in the field, a number that is expected to rise sharply in the future. Many studies have reported robust and meaningful findings; however, a growing number also suffer from flawed experimental or analytic designs. Such errors could not only result in invalid discoveries, but also may lead others to perpetuate similar flaws in their own work. This perspective article aims to increase awareness of the issue, identify potential reasons why this is happening, and provide a path forward. Clin Cancer Res; 24(3); 532-4. ©2017 AACR.