Detecting changes in the activity of a transcription factor (TF) in response to a perturbation provides insights into the underlying cellular process. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects positional motif enrichment associated with changes in transcription observed in response to a perturbation. TFEA detects positional motif enrichment within a list of ranked regions of interest (ROIs), typically sites of RNA polymerase initiation inferred from regulatory data such as nascent transcription. Therefore, we also introduce muMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent transcription (eg. PRO-Seq), CAGE, histone ChIP-Seq, and accessibility data (e.g., ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights.

Weaving the visual arts into a science curriculum can both help develop scientific imagination and engage non-scientists.

We investigate students' sense of ownership of multiweek final projects in an upper-division optics lab course. Using a multiple case study approach, we describe three student projects in detail. Within-case analyses focused on identifying key issues in each project, and constructing chronological descriptions of those events. Cross-case analysis focused on identifying emergent themes with respect to five dimensions of project ownership: student agency, instructor mentorship, peer collaboration, interest and value, and affective responses. Our within-and cross-case analyses yielded three major findings. First, coupling division of labor with collective brainstorming can help balance student agency, instructor mentorship, and peer collaboration. Second, students' interest in the project and perceptions of its value can increase over time; initial student interest in the project topic is not a necessary condition for student ownership of the project. Third, student ownership is characterized by a wide range of emotions that fluctuate as students alternate between extended periods of struggle and moments of success while working on their projects. These findings not only extend the literature on student ownership into a new educational domainnamely, upper-division physics labs-they also have concrete implications for the design of experimental physics projects in courses for which student ownership is a desired learning outcome. We describe the course and projects in sufficient detail that others can adapt our results to their particular contexts.

The ability to develop, use, and refine models of experimental systems is a nationally recognized learning outcome for undergraduate physics lab courses. However, no assessments of students' model-based reasoning exist for upper-division labs. This study is the first step toward development of modeling assessments for optics and electronics labs. We interviewed 35 lab instructors about the ways they incorporate modeling in their courses, and we used their self-reported learning goals and activity designs to identify test contexts and objectives that are likely relevant across many institutional settings. The study design was informed by the Modeling Framework for Experimental Physics, which conceptualizes modeling as consisting of multiple subtasks: making measurements, constructing system models, comparing data to predictions, proposing causes for discrepancies, and enacting revisions to models or apparatus. We found that each modeling subtask was identified by multiple instructors as an important learning outcome for their course. Based on these results, we argue that test objectives should include probing students' competence with most modeling subtasks, and test items should be designed to elicit students' justifications for choosing particular modeling pathways. In addition to discussing these and other implications for assessment, we also identify future areas of research related to the role of modeling in optics and electronics labs. *

In experimental physics, lab notebooks play an essential role in the research process. For all of the ubiquity of lab notebooks, little formal attention has been paid to addressing what is considered "best practice" for scientific documentation and how researchers come to learn these practices in experimental physics. Using interviews with practicing researchers, namely, physics graduate students, we explore the different experiences researchers had in learning how to effectively use a notebook for scientific documentation. We find that very few of those interviewed thought that their undergraduate lab classes successfully taught them the benefit of maintaining a lab notebook. Most described training in lab notebook use as either ineffective or outright missing from their undergraduate lab course experience. Furthermore, a large majority of those interviewed explained that they did not receive any formal training in maintaining a lab notebook during their graduate school experience and received little to no feedback from their advisors on these records. Many of the interviewees describe learning the purpose of, and how to maintain, these kinds of lab records only after having a period of trial and error, having already started doing research in their graduate program. Despite the central role of scientific documentation in the research enterprise, these physics graduate students did not gain skills in documentation through formal instruction, but rather through informal hands-on practice.