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Abstract Surface performance is critically influenced by topography in virtually all real-world applications. The current standard practice is to describe topography using one of a few industry-standard parameters. The most commonly reported number is$$R$$ $R$a, the average absolute deviation of the height from the mean line (at some, not necessarily known or specified, lateral length scale). However, other parameters, particularly those that are scale-dependent, influence surface and interfacial properties; for example the local surface slope is critical for visual appearance, friction, and wear. The present Surface-Topography Challenge was launched to raise awareness for the need of a multi-scale description, but also to assess the reliability of different metrology techniques. In the resulting international collaborative effort, 153 scientists and engineers from 64 research groups and companies across 20 countries characterized statistically equivalent samples from two different surfaces: a “rough” and a “smooth” surface. The results of the 2088 measurements constitute the most comprehensive surface description ever compiled. We find wide disagreement across measurements and techniques when the lateral scale of the measurement is ignored. Consensus is established through scale-dependent parameters while removing data that violates an established resolution criterion and deviates from the majority measurements at each length scale. Our findings suggest best practices for characterizing and specifying topography. The public release of the accumulated data and presented analyses enables global reuse for further scientific investigation and benchmarking. 

Thousands of students around the country have participated in activities using the Data Jam model, creating poetry, songs, videos, or sculpture to improve their data literacy, gain knowledge of local science research, and creatively express their findings. This article introduces the Data Jam model and how teachers can use it in classroom or after-school settings, supported by vignettes of student projects and feedback from teachers and students. 

ABSTRACT We report on the first observation of a radio-quiet active galactic nucleus (AGN) in polarized X-rays: the Seyfert 1.9 galaxy MCG-05-23-16. This source was pointed at with the Imaging X-ray Polarimetry Explorer (IXPE) starting on 2022 May 14 for a net observing time of 486 ks, simultaneously with XMM-Newton (58 ks) and NuSTAR (83 ks). A polarization degree Π smaller than 4.7 per cent (at the 99 per cent confidence level) is derived in the 2–8 keV energy range, where emission is dominated by the primary component ascribed to the hot corona. The broad-band spectrum, inferred from a simultaneous fit to the IXPE, NuSTAR, and XMM-Newton data, is well reproduced by a power law with photon index Γ = 1.85 ± 0.01 and a high-energy cutoff EC = 120 ± 15 keV. A comparison with Monte Carlo simulations shows that a lamp-post and a conical geometry of the corona are consistent with the observed upper limit, a slab geometry is allowed only if the inclination angle of the system is less than 50°.