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Data literacy is increasingly relevant to everyday life and is a priority for educators across disciplinary boundaries. This study introduces a framework for characterizing data literacy instrucFon along five key dimensions. It then applies this framework to examine instances of data literacy instrucFon like explanaFons of data-related concepts and tasks/quesFons that invite learners to acFvely engage in data-related pracFces in a sample of lessons from a science and social studies high school textbook. By juxtaposing findings from science and social studies contexts, it examines how these disciplinary approaches compare with each other and idenFfies areas where these approaches could expand and build on each other to support more effecFve and holisFc data literacy development. 

This document summarizes the efforts of the EMMI Rapid Reaction Task Force on “Suppression and (re)generation of quarkonium in heavy-ion collisions at the LHC”, centered around their 2019 and 2022 meetings. It provides a review of existing experimental results and theoretical approaches, including lattice QCD calculations and semiclassical and quantum approaches for the dynamical evolution of quarkonia in the quark-gluon plasma as probed in high-energy heavy-ion collisions. The key ingredients of the transport models are itemized to facilitate comparisons of calculated quantities such as reaction rates, binding energies, and nuclear modification factors. A diagnostic assessment of the various results is attempted and coupled with an outlook for the future. 

Data science is increasingly relevant to daily life and has garnered significant attention in education. While data science education has been traditionally focused on technical training, justice considerations are increasingly brought up given growing concerns over fairness and justice in data science. This paper introduces a framework for justice-oriented data science education that comprises five areas grounded in a broad range of literature. To explore and refine the framework in authentic contexts, we applied it to discourse data from one participatory design workshop with teachers. Analysis demonstrated the presence of this framework’s areas and their rich connections in teachers’ thinking. The framework offers educators a tool to integrate data science, justice issues, and disciplinary content in K-12 classrooms. 

We present high-resolution maps of the dust reddening in the Magellanic Clouds (MCs). The maps cover the Large and Small Magellanic Cloud (LMC and SMC) area and have a spatial angular resolution between ∼26 arcsec and 55 arcmin. Based on the data from the optical and near-infrared (IR) photometric surveys, including the Gaia Survey, the SkyMapper Southern Survey (SMSS), the Survey of the Magellanic Stellar History (SMASH), the Two Micron All Sky Survey (2MASS), and the near-IR YJKS VISTA survey of the Magellanic Clouds system (VMC), we have obtained multiband photometric stellar samples containing over 6 million stars in the LMC and SMC area. Based on the measurements of the proper motions and parallaxes of the individual stars from Gaia Early Data Release 3 (Gaia EDR3), we have built clean samples that contain stars from the LMC, SMC, and Milky Way (MW), respectively. We apply the spectral energy distribution (SED) fitting to the individual sample stars to estimate their reddening values. As a result, we have derived the best-fitting reddening values of ∼1.9 million stars in the LMC, 1.5 million stars in the SMC, and 0.6 million stars in the MW, which are used to construct dust reddening maps in the MCs. Our maps are consistent with those from the literature. The resultant high-resolution dust maps in the MCs are not only important tools for reddening correction of sources in the MCs, but also fundamental for the studies of the distribution and properties of dust in the two galaxies.