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Securing the future of research computing in the biosciences
Joanna Leng1, Massa Shoura2, Tom C B McLeish3
1School of Computing, University of Leeds, Leeds, United Kingdom.
Plos Computational Biology
|May 17, 2019
Summary
Scientific discovery relies on technological advancements, necessitating investment in equipment and researcher training. As biosciences generate more data, their computational needs are rising, requiring collaborative solutions and institutional support.
Area of Science:
- Computational biology
- Bioinformatics
- Biophysics
Background:
- Technological progress is a key driver of scientific discovery, requiring continuous investment in research infrastructure and personnel training.
- Predicting future infrastructure needs for research is challenging.
- Historically, high computational demands were concentrated in fields like particle physics and astronomy.
Discussion:
- Modern biosciences, particularly quantitative biology and imaging, are generating unprecedented data volumes.
- Computational requirements in biosciences are rapidly increasing, potentially surpassing those in physical sciences.
- There is a critical need for advanced modeling and simulation tools to interpret complex biophysical experimental data.
Key Insights:
- Bioscience communities must collaborate to develop necessary software and provide essential skills training.
- Research institutions must acknowledge computation's expanding role across scientific disciplines.
- Public awareness regarding the capabilities and limitations of computing, especially concerning health data, is essential.
Outlook:
- Sustained investment in computational infrastructure and training is vital for future scientific breakthroughs.
- Interdisciplinary collaboration and institutional support are key to meeting evolving research demands.
- Enhanced public understanding of computing's role in science and data privacy is necessary.
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