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In high energy physics (HEP), analysis metadata comes in many forms—from theoretical cross-sections, to calibration corrections, to details about file processing. Correctly applying metadata is a crucial and often time-consuming step in an analysis, but designing analysis metadata systems has historically received little direct attention. Among other considerations, an ideal metadata tool should be easy to use by new analysers, should scale to large data volumes and diverse processing paradigms, and should enable future analysis reinterpretation. This document, which is the product of community discussions organised by the HEP Software Foundation, categorises types of metadata by scope and format and gives examples of current metadata solutions. Important design considerations for metadata systems, including sociological factors, analysis preservation efforts, and technical factors, are discussed. A list of best practices and technical requirements for future analysis metadata systems is presented. These best practices could guide the development of a future cross-experimental effort for analysis metadata tools.

Relative brain size has long been considered a reflection of cognitive capacities and has played a fundamental role in developing core theories in the life sciences. Yet, the notion that relative brain size validly represents selection on brain size relies on the untested assumptions that brain-body allometry is restrained to a stable scaling relationship across species and that any deviation from this slope is due to selection on brain size. Using the largest fossil and extant dataset yet assembled, we find that shifts in allometric slope underpin major transitions in mammalian evolution and are often primarily characterized by marked changes in body size. Our results reveal that the largest-brained mammals achieved large relative brain sizes by highly divergent paths. These findings prompt a reevaluation of the traditional paradigm of relative brain size and open new opportunities to improve our understanding of the genetic and developmental mechanisms that influence brain size.

Foundational engineering courses are critical to student success in engineering programs. The conceptually challenging content of these courses establishes the requisite knowledge for future classes. Thus, it is no surprise that such courses can serve as barriers or gatekeepers to successful student progress through the undergraduate curriculum. Although the difficulty of the courses may be necessary, often other features of the course delivery such as large class environments or a few very high-stakes assessments can further exacerbate these challenges. And especially problematic, past studies have shown that grade penalties associated with these courses and environments may disproportionately impact women. On the faculty side, institutions often turn to non-tenure track instructional faculty to teach multiple sections of foundational courses each semester. Although having faculty whose sole role is dedicated to quality teaching is an asset, benefits would likely be maximized when such faculty have clear metrics for paths to promotion, some autonomy and ownership regarding the curriculum, and overall job satisfaction. However, literature suggests that faculty, like students, note ill effects from large classes, such as challenges connecting and building rapport with students and having time to offer individualized feedback to students. Our NSF IUSE project focuses on instructors of largemore »foundational engineering students with the belief that by better understanding the educational environment from their perspective we can improve the quality of the teaching and learning environment for all engineering students. Our project regularly convenes faculty teaching an array of core courses (e.g,. Mathematics, Chemistry, Mechanics, Physics) and uses insights from these meetings and individual interviews to identify possible leverage points where our project or the institution more broadly might affect change. Parallel to this effort, we have been working with data stewards on campus to gain access to institutional data (e.g., student course and grade histories, student evaluations of faculty teaching) to link and provide aggregate deidentified results to faculty to feed more information in to their decision-making. We are demonstrating that regular engagement between faculty and institutional leaders around analyzed and curated data is essential to continuous and systematic improvement. Efforts to date have included building an institutional data explorer dashboard (e.g., influences of pre-requisite courses on future courses) and drafting reports to be sent to department heads and associate deans which gather priorities identified in the first year of our research. For example, participating instructors identified that clarity of promotion paths across non-tenure track teaching faculty from different departments varied greatly, and the institution as a whole could benefit from clarified university-wide guidance. While some findings may be institution-specific (NSF IUSE Institutional Transformation track), as a large public research institution, peer-institutions with high engineering enrollments often face similar challenges and so findings from our change efforts potentially have broad applicability.« less

Engineering students develop competencies in fundamental engineering courses (FECs) that are critical for success later in advanced courses and engineering practice. Literature on the student learning experience, however, associate these courses with challenging educational environments (e.g., large class sizes) and low student success rates. Challenging educational environments are particularly prevalent in large, research-intensive institutions. To address concerns associated with FECs, it is important to understand prevailing educational environments in these courses and identify critical points where improvement and change is needed. The Academic Plan Model provides a systematic way to critically examine the factors that shape the educational environment. It includes paths for evaluation and adjustment, allowing educational environments to continuously improve. The Model may be applied to various levels in an institution (e.g., course, program, college), implying that a student’s entire undergraduate learning experience is the result of several enacted academic plans that are interacting with each other. Thus, understanding context-specific factors in a specific educational environment will yield valuable information affecting the undergraduate experience, including concerns related to attrition and persistence. In order to better understand why students are not succeeding in large foundational engineering courses, we developed a form to collect data on why students withdraw frommore »certain courses. The form was included as a requirement during the withdrawal process. In this paper, we analyzed course withdrawal data from several academic departments in charge of teaching large foundational engineering courses, and institutional transcript data for the Spring 2018 semester. The withdrawal dataset includes the final grades that students expected to receive in the course and the factors that influenced their decision to withdraw. Institutional transcript data includes demographic information (e.g., gender, major), admissions data (e.g., SAT scores, high school GPA), and institutional academic information (e.g., course grades, cumulative GPA). Results provide a better understanding of the main reasons students decide to withdraw from a course, including having unsatisfactory grades, not understanding the professor, and being overwhelmed with work. We also analyzed locus of control for the responses, finding that the majority of students withdrawing courses consider that the problem is outside of their control and comes from an external source. We provide analysis by different departments and different specific courses. Implications for administrators, practitioners, and researchers are provided.« less