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Research software infrastructure is critical for accelerating science, and yet, these digital public goods are often unsustainably funded. Solving this problem requires an appreciation of the intrinsic value of research software outputs, and greater investment of time and effort into effectively funding maintenance of software at scale.
To best learn from data about large-scale complex systems, physics-based models representing the laws of nature must be integrated into the learning process. Inverse theory provides a crucial perspective for addressing the challenges of ill-posedness, uncertainty, nonlinearity and under-sampling.
Emerging exascale architectures and systems will provide a sizable increase in raw computing power for science. To ensure the full potential of these new and diverse architectures, as well as the longevity and sustainability of science applications, we need to embrace software ecosystems as first-class citizens.
Software is essential to computational science research, and yet it hasn’t achieved first-class status when it comes to citations. It’s time for all of us in the research community to change this behavior.
Jenea Adams, a second year PhD student, found it hard to connect with other Black women in her field of study. She then decided to create the Black Women in Computational Biology Network, which has attracted the support of many researchers. We spoke to her about the Network, as well as gender and racial inequality.
Over the past decade, the materials science community has fostered the development of materials databases from high-performance computation. While these databases have achieved great success, there are still several challenges to be addressed for the community to realize the full potential of the materials-by-design era.
COVID-19 models have been extensively used to inform public health officials about potential interventions. Nevertheless, careful attention must be taken when extrapolating projections and parameters across different regions, as there is no one-size-fits-all model for the pandemic.
