Establishing a distributed national research infrastructure providing bioinformatics support to life science researchers in Australia

Application of a bioinformatics training delivery method for reaching dispersed and distant trainees

Many initiatives have addressed the global need to upskill biologists in bioinformatics tools and techniques. Australia is not unique in its requirement for such training, but due to its large size and relatively small and geographically dispersed population, Australia faces specific challenges. A combined training approach was implemented by the authors to overcome these challenges. The “hybrid” method combines guidance from experienced trainers with the benefits of both webinar-style delivery and concurrent face-to-face hands-on practical exercises in classrooms. Since 2017, the hybrid method has been used to conduct 9 hands-on bioinformatics training sessions at international scale in which over 800 researchers have been trained in diverse topics on a range of software platforms. The method has become a key tool to ensure scalable and more equitable delivery of short-course bioinformatics training across Australia and can be easily adapted to other locations, topics, or settings.

Bioinformatics in Mexico: A diagnostic from the academic perspective and recommendations for a public policy

In this work, we present a diagnostic analysis of strengths, weaknesses, opportunities and threats (SWOT) of the current state of Bioinformatics in Mexico. We conducted semi-structured interviews among researchers and academics with key expertise in this field, identified by bibliometric analyses and qualitative sampling techniques. Additionally, an online survey was conducted reaching a higher number of respondents. Among the relevant findings of our study, the lack of specialized human resources and technological infrastructure stood out, along with deficiencies in the number and quality of academic programs, scarce public investment and a weak relationship between public and private institutions. However, there are great opportunities for developing a national Bioinformatics to support different economic sectors. In our opinion, this work could be useful to favor a comprehensive network among Mexican researchers, in order to lay the foundations of a national strategy towards a well designed public policy.

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. Here 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.

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. Here 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.