Schneider, M. V. | Search bioRxiv

bioRxiv2017

Best Practice Data Life Cycle Approaches for the Life Sciences

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a life cycle view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain.\n\nHere we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on omics datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

bioinformatics↗

bioRxiv2017

A global perspective on bioinformatics training needs

In the last decade, life-science research has become increasingly data-intensive and computational. Nevertheless, basic bioinformatics and data stewardship are still only rarely taught in life-science degree programmes, creating a widening skills gap that spans educational levels and career roles. To better understand this situation, we ran surveys to determine how the skills dearth is affecting the need for bioinformatics training worldwide. Perhaps unsurprisingly, we found that respondents wanted more short courses to help boost their expertise and confidence in data analysis and interpretation. However, it was evident that most respondents appreciated their need for training only after designing their experiments and collecting their data. This is clearly rather late in the research workflow, and suboptimal from a training perspective, as skills acquired to address a specific need at a particular time are seldom retained, engendering a cycle of low confidence in trainees. To ensure that such skill gaps do not continue to create barriers to the progress of research, we argue that universities should strive to bring their life-science curricula into the digital-data era. Meanwhile, the demand for point-of-need training in bioinformatics and data stewardship will grow. While this situation persists, international groups like GOBLET are increasing their efforts to enlarge the community of trainers and quench the global thirst for bioinformatics training.

scientific communication and education↗