Correction: Open science challenges, benefits and tips in early career and beyond

Bridging the gap between scientific research of rare diseases and the affected patients and families

In 2015, my father was diagnosed with primary progressive aphasia (PPA), a type of frontotemporal dementia that my family and I knew nothing about. Medical professionals told us that there was no research on the disease, and I believed this until very recently. I took a class in neurobiology, leading me to attempt to document this lack of research and create a call to action for the research and treatment of rare disorders. However, I was met with an overwhelming amount of information regarding PPA that I was not expecting to find. I was frustrated that I was not given this information; moreover, I did not understand why it was all being 'hidden' from me. After discussion with my mother, I realized that my science education allowed me to find and interpret this information, but more importantly, that not everyone has this same privilege. My call to action pivoted into a call for better communication and for open access to biomedical information. Regardless of the existence and quality of literature about rare diseases, most of the information is out of reach of the public.The public often does not have the scientific literacy to understand the complexities of the genre that is required for comprehension. I recognize that not every patient and family may wish to access the information generated by biomedical research. I argue that they have a right to examine these findings because they are the ones that are being the most deeply affected by these disorders. While the translation of information may seem cumbersome , the impact it could have on patients, caregivers, and providers is worth the effort.

Ten simple rules for training yourself in an emerging field

The opportunity to participate in and contribute to emerging fields is increasingly prevalent in science. However, simply thinking about stepping outside of your academic silo can leave many students reeling from the uncertainty. Here, we describe 10 simple rules to successfully train yourself in an emerging field, based on our experience as students in the emerging field of ecological forecasting. Our advice begins with setting and revisiting specific goals to achieve your academic and career objectives and includes several useful rules for engaging with and contributing to an emerging field.

Machine learning for condensed matter physics

Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter at the quantum and atomistic levels, and describes how these interactions result in both mesoscopic and macroscopic properties. CMP overlaps with many other important branches of science, such as chemistry, materials science, statistical physics, and high-performance computing. With the advancements in modern machine learning (ML) technology, a keen interest in applying these algorithms to further CMP research has created a compelling new area of research at the intersection of both fields. In this review, we aim to explore the main areas within CMP, which have successfully applied ML techniques to further research, such as the description and use of ML schemes for potential energy surfaces, the characterization of topological phases of matter in lattice systems, the prediction of phase transitions in off-lattice and atomistic simulations, the interpretation of ML theories with physics-inspired frameworks and the enhancement of simulation methods with ML algorithms. We also discuss in detail the main challenges and drawbacks of using ML methods on CMP problems, as well as some perspectives for future developments.