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This paper explores a comprehensive framework to develop students’ data literacy by guiding them in making sense of complex data visualizations. With the growing complexity and prevalence of data visualizations in media, it’s crucial to equip students with the skills to critically analyze and engage with these visual forms of data. This toolkit emphasizes the importance of fostering data habits of mind, rather than mere computational proficiency, and encourages students to consider what a visualization is conveying, how it was created, and why it was created. 

Birds have a diverse community of “permanent” arthropods that complete their entire life cycle on the body of the host. Because some of these arthropods are parasites that reduce host fitness, birds control them by grooming, which consists of preening with the beak and scratching with the feet. Although preening is the primary component of grooming, scratching is essential for controlling arthropods on the head and neck, which cannot be preened. Several unrelated groups of birds have evolved comb-like pectinate claws on the middle toenail of each foot. We tested the role of these claws in the control of arthropods by experimentally removing teeth from the claws of captive western cattle egrets (Bubulcus ibis) infested with chewing lice (Insecta: Phthiraptera), feather mites (Acari: Sarcoptiformes), and nasal mites (Acari: Mesostigmata). After a period of 4 mo, we compared the abundance of arthropods on experimental birds to that of control birds with intact teeth. We used video to quantify the grooming rates of the captive birds, which groomed twice as much as wild birds. Experimental and control birds did not differ significantly in grooming time. Both groups virtually eradicated the chewing lice, but not feather mites or nasal mites. We found no support for the hypothesis that pectinate claws increase the efficiency of arthropod control by grooming. Experiments with wild birds are needed to test the hypothesis further under conditions in which birds devote less time to grooming. 

In this study, 82 middle and high school teachers engaged with the InSTEP online professional learning platform to develop their expertise in teaching data science and statistics. We investigated teachers’ engagement within the platform, aspects of the platform that were most and least effective in building teachers’ expertise, and the extent to which teachers’ self-efficacy changed. Using mixed methods, we collected, analyzed and integrated multiple data sources. 

In this study, 82 middle and high school teachers engaged with the InSTEP online professional learning platform to develop their expertise in teaching data science and statistics. We investigated teachers’ engagement within the platform, aspects of the platform that were most and least effective in building teachers’ expertise, and the extent to which teachers’ self-efficacy changed. Using mixed methods, we collected, analyzed and integrated multiple data sources. 

This paper describes how we have sustained the Montage image mosaic engine (http://montage.ipac.caltech.edu) first released in 2002, to support the ever-growing scale and complexity of modern data sets. The key to its longevity has been its design as a toolkit written in ANSI-C, with each tool performing one distinct task, for easy integration into scripts, pipelines and workflows. The same code base now supports Windows, JavaScript and Python by taking advantage of recent advances in compilers. The design has led to applicability of Montage far beyond what was anticipated when Montage was first built, such as supporting observation planning for the JWST. Moreover, Montage is highly scalable and is in wide use within the IT community to develop advanced, fault-tolerant cyber-infrastructure, such as job schedulers for grids, workflow orchestration, and restructuring techniques for processing complex workflows and pipelines.