In topological condensed matter physics, where major discoveries could hold big implications for fields like information technology, the reliability of such discoveries could be greatly enhanced by taking several steps, like presenting larger sets of data, say Sergey Frolov and colleagues. Their insights are based in part on four original experiments they did that correspond to either theory predictions or published work. “Overall,” write the authors, “although replication crises are typically perceived to be a problem in fields less quantitative than physics, the overemphasis on smoking- gun claims has the potential to affect the reliability of findings irrespective of field.”  Electrons in solids are restricted by their energies to certain intervals or bands that determine the solid’s electronic properties. These bands can be re-ordered into new topologies in non-traditional or re-structured materials, yielding unique electronic properties that could be useful in applications such as quantum computing and data storage. Guided by theory, condensed matter physicists design experiments to identify when a new topological regime has been achieved in a material, looking for a particular “smoking gun” signal that indicates the new topology. Now, Sergey Frolov and colleagues show in four experiments in topological physics that these smoking gun signals can also arise from trivial phenomena that mimic the true signal of a topological change. By fine-tuning their experimental parameters, Frolov et al. suggest, some researchers may be introducing a confirmation bias that points to the “discovery” of a smoking gun. Releasing comprehensive data sets, disclosing the full volume of their study and exploring alternative scenarios for the signal could help mitigate the risk of misleading smoking guns, Frolov et al. conclude.

Quantum computers could become a prime target for malicious hackers, according to Swaroop Ghosh, professor of computer science and of electrical engineering in the Penn State School of Electrical Engineering and Computer Science, and Suryansh Upadhyay, who recently received his doctorate in electrical engineering from Penn State. They recently authored a paper, set to publish in the Proceedings of the Institute of Electrical and Electronics Engineers, highlighting the need to develop new defense mechanisms covering not just the software and programs running on these systems, but the physical components that power them.

A commentary coauthored by IIASA experts and senior representatives from the UN and the international statistics community discusses the implications of recent changes to the Demographic and Health Surveys (DHS) program and highlights the importance of integrating citizen science more fully into official data systems.

The Mount Sinai Tisch Cancer Center (TCC) has launched a new artificial intelligence (AI) platform to help connect more cancer patients across the Mount Sinai Health System to life-saving clinical trials. The platform, which was created by the AI company Triomics, is called PRISM, and is powered by Triomics’ OncoLLM, a large language model-based pipeline built especially for cancer care. As an early adopter of this new technology, Mount Sinai becomes the first National Cancer Institute (NCI)-designated Comprehensive Cancer Center in New York City to deploy this oncology-specific AI tool for systemwide clinical trial matching.