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Participation in technical/research internships may improve undergraduate graduation rates and persistence in science, technology, engineering, and mathematics (STEM), yet little is known about the benefits of these activities a) for community college students, b) when hosted by national laboratories, and c) beyond the first few years after the internship. We applied Social Cognitive Career Theory (SCCT) to investigate alumni perspectives about how CCI at Lawrence Berkeley National Laboratory (LBNL) impacted their academic/career activities. We learned that alumni had low confidence and expectations of success in STEM as community college students. Participation in CCI increased their professional networks, expectations of success, and STEM skills, identity, and self-efficacy/confidence. Hispanic/Latinx alumni recalled the positive impact of mentors who prioritized personal connections, and women valued "warm" social environments. We propose several additions to the SCCT model, to better reflect the supports and barriers to STEM persistence for community college students.

A student-led mathematics bootcamp has been designed and implemented to help foster community building, improve confidence in mathematical skills, and provide mathematical resources for incoming physical chemistry doctoral students. The bootcamp is held immediately before the start of the first semester of graduate school and uses an active learning approach to review and practice undergraduate-level mathematics problems over 5 days in small student groups. This work includes the development and presentation of a new, publicly available mathematics curriculum for the bootcamp on select mathematics topics, including calculus, linear algebra, functions, differential equations, statistics, and coding in Python, aiming at improving students' confidence and learning experiences in graduate quantum mechanics and statistical physics courses. Surveys before and after the bootcamp showed an increase in students' confidence in problem-solving in key mathematical areas and social aspects of peer-led group learning. Qualitative and quantitative analyses demonstrate that the bootcamp reduced prior inequities in students' confidence metrics based on gender and mathematical background.

Problem solving is a key component of authentic scientific research and practice in organic chemistry. One factor that has been shown to have a major role in successful problem solving in a variety of disciplines is metacognitive regulation, defined as the control of one's thought processes through the use of planning, monitoring, and evaluation strategies. Despite the growing interest in assessing and promoting metacognition in the field of chemical education, few studies have investigated this topic in the context of organic chemistry students. To gain a deeper understanding of how and why students make use of strategies related to metacognitive regulation in their approaches to solving problems, we conducted interviews with Organic Chemistry I, Organic Chemistry II, and graduate organic chemistry students and used multiple measures to examine students' metacognition. As a part of these interviews, students verbalized their thoughts as they worked on complex predict-the-product problems and completed a self-report instrument indicating which planning, monitoring, and evaluation strategies they had used while completing each problem. Think-aloud protocols were analyzed for the presence of each of the behaviors included on the self-report instrument, and students' use of metacognitive strategies was compared to identify differences between students with different levels of experience and between students who generated more and less successful solutions to the problems. Students who generated more successful solutions to the problems tended to report using a greater number of metacognitive strategies. When asked why they did or did not use certain metacognitive strategies, students indicated a number of factors, such as not feeling able to use these strategies effectively or believing that using these strategies was unnecessary. The results of this study support the importance of teaching metacognitive problem-solving strategies in organic chemistry courses and suggest several methods for the assessment and instruction of metacognition.

Ultrafast laser pump-probe spectroscopy is an important and growing field of physical chemistry that allows the measurement of chemical dynamics on their natural time scales, but undergraduate laboratory courses lack examples of such spectroscopy and the interpretation of the dynamics that occur. Here we develop and implement an ultrafast pump-probe spectroscopy experiment for the undergraduate physical chemistry laboratory course at the University of California Berkeley. The goal of the experiment is to expose students to concepts in solid-state chemistry and ultrafast spectroscopy via classic coherent phonon dynamics principles developed by researchers over multiple decades. The experiment utilizes a modern high-repetition-rate 800 nm femtosecond Ti:sapphire laser, split pulses with a variable time delay, and sensitive detection of transient reflectivity signals using the lock-in technique. The experiment involves minimal intervention from students and is therefore easy and safe to implement in the laboratory. Students first perform an intensity autocorrelation measurement on the femtosecond laser pulses to obtain their temporal duration. Then, students measure the pump-probe reflectivity of a single-crystal antimony sample to determine the period of coherent phonon oscillations initiated by an ultrafast pulse excitation, which is analyzed by fitting to a sine wave. Students who completed the experiment in-person obtained good experimental results, and students who took the course remotely due to the COVID-19 pandemic were provided with the data they would have obtained during the experiment to analyze. Evaluation of student written and oral reports reveals that the learning goals were met, and that students gained an appreciation for the field of ultrafast laser-induced chemistry.

Understanding the impact of undergraduate research experiences (UREs) and course-based undergraduate research experiences (CUREs) is crucial as universities debate the value of allocating scarce resources to these activities. We report on the Berkeley Undergraduate Research Evaluation Tools (BURET), designed to assess the learning outcomes of UREs and CUREs in chemistry and other sciences. To validate the tools, we administered BURET to 70 undergraduate students in the College of Chemistry and 19 students from other STEM fields, comparing the performance of students who had less than one year of undergraduate research to those with more than one year of research experience. Students wrote reflections and responded to interviews during poster presentations of their research project. BURET asks students to communicate the significance of their project, analyze their experimental design, interpret their data, and propose future research. Scoring rubrics reward students for integrating disciplinary evidence into their narratives. We found that the instruments yielded reliable scores, and the results clarified the impacts of undergraduate research, specifically characterizing the strengths and weaknesses of undergraduate researchers in chemistry at our institution. Students with at least a year of research experience were able to use disciplinary evidence more effectively than those with less than one year of experience. First-year students excelled at explaining the societal relevance of their work, but they incorporated only minimal discussion of prior research into their reflections and presentations. Students at all levels struggled to critique their own experimental design. These results have important implications for undergraduate learning, suggesting areas for faculty members, graduate student research mentors, and CURE or URE programs to improve undergraduate research experiences.

A central practice in the discipline of organic chemistry is the ability to solve certain fundamental problems, including predicting reactivity, proposing mechanisms, and designing syntheses. These problems are encountered frequently by both students and practitioners, who need to utilize vast amounts of content knowledge in specific ways to generate reasonable solutions. To gain insight into how one of these major problem types can be solved, we have investigated student approaches to complex predict-the-product problems through the detailed analysis of think-aloud interviews. This work led to the creation of a general workflow model that describes the reasoning pathways of students with varying levels of expertise when attempting to predict organic reactivity. The problems used in this study were designed to be non-trivial and potentially ambiguous to elicit ''true'' problem solving and discourage a purely memorization-based approach, even from more experienced organic chemists. Rich descriptions of undergraduate and graduate student interviews are provided, and student thought processes are characterized in terms of common problem-solving actions. These actions were developed into the workflow model using an iterative method that combined results from our analysis with the experiences of instructors and feedback from both undergraduate focus groups and graduate students. The workflow serves as both a potential instructional tool and a model for student thinking. This model is general enough to be applied to both successful and unsuccessful solution pathways by both novice undergraduates and more expert-like graduate students. Characteristics of more successful and more experienced problem solvers are investigated, and concrete strategies that can be recommended to students are discussed. The results of this study complement existing work on other fundamental problem types in organic chemistry and suggest a variety of teaching interventions to develop students into more successful organic problem solvers.

Historically, efforts to improve academic climate have been siloed-many efforts involve the collection of data to understand issues affecting diversity at an institutional level, while others prioritize recruitment and retention of historically marginalized groups. Few initiatives, however, effectively combine the two in order to create concrete action plans to eliminate structural barriers that hinder the retention of minorities in STEM. In this Editorial, we present the history and details of a collaborative effort to improve the academic climate of the Department of Chemistry at University of California, Berkeley. This initiative began in 2016 as a graduate student-led, grassroots movement to develop a method to assess the department's academic climate. Over the past several years-and with support from stakeholders at all levels-it has grown into a department-wide effort to systematically collect data, exchange ideas, and implement goal-oriented interventions to make our academic community more inclusive. With the recent development of a five-year strategic plan and funding increase to provide financial support for student-led programs, we have institutionalized a method to maintain the initiative's momentum. Here, we share our approaches, insights, and perspectives from community members who have shaped this movement. We also provide advice to help other academic communities determine a practical path toward affecting positive cultural change.

Ongoing efforts to improve diversity in science, technology, engineering, and mathematics (STEM) primarily manifest as attempts to recruit more women and individuals from historically marginalized groups. Yet, these efforts fail to repair the specific, systemic issues within academic communities that hinder diverse individuals from persisting and thriving in STEM. Here, we present the results of a quantitative, multiyear effort to make the academic climate of an R1 STEM department more inclusive. We use a student-led, department-specific, faculty-supported initiative to assess and improve the climate of the Department of Chemistry at the University of California, Berkeley, as a case study. Our results provide quantitative evidence that community discussions grounded in our own data, alongside cooperative community efforts to address the issues present in those data, are effective methods for driving positive change. Longitudinal assessment of our academic climate from 2018 to 2020 via annual department-wide surveys indicates that these interventions have succeeded in shifting the perception of our academic climate. This study confirms the positive outcomes of having a practical, sustainable, and data-driven framework for affecting change within a graduate community.

Increasing access to undergraduate research is critical in efforts to retain students pursuing careers in STEM. Alternatives to traditional research positions, such as course based undergraduate research experiences (CUREs), have played important roles in engaging more undergraduates in research. However, these opportunities are only a subset of possible nontraditional research experiences which can enrich the undergraduate experience. In this study, we critically examined and improved the research-group-led undergraduate research program (GURP) to better meet the needs of undergraduates seeking research. Specifically, we investigated if the program was successful with a greater diversity of data sets, if assessments of learning were consistent across different data sets and teaching environments, and if the program promoted student engagement in research. We have found that this model is scalable, robust, and adaptable to different implementations while producing consistent and positive learning outcomes for students. Especially remarkable are students' increased self-identification as scientists and statistically significant gains in self-perceived competency across multiple domains of knowledge. This program model has shown promising results as a partially and fully online research experience for undergraduates and has benefited program alumni in their research careers. To assist research groups starting similar programs, we have created public data sets and instructional resources. We believe that GURP programs can work in a variety of situations and hope that they can become a tool to increase interest and build communities for young researchers.

Many cultural and institutional barriers have prevented chemistry from realizing greater calls for diversity in academia. Though recent work has elucidated how the measures of success used in academia can disadvantage students from underrepresented groups at the undergraduate level, thorough understanding of how success metrics are valued by minoritized students at the graduate level is lacking. Here, we use data generated from the UC Berkeley Department of Chemistry's student-led climate survey to investigate both how graduate students prioritize and how faculty employ common metrics for graduate student success. Results revealed that faculty undervalued metrics preferred by students from underrepresented groups (URGs) in STEM such as underrepresented people of color, women, LGBTQ+ students, and first-generation students. Priorities of students that do not identify as underrepresented displayed no statistically significant differences compared to faculty values. Questions regarding publication record, one of the often-used measures of success in STEM academia, suggest that graduate students, particularly those belonging to URGs, challenge the use of publication record as the primary metric of success in graduate school. These findings highlight some of the ways that definitions of academic success can be exclusionary for graduate students from underrepresented backgrounds and encourage re-envisioning graduate school success in ways that reflect the values of diverse student populations.

It is well-documented that the representation of women and racial/ethnic minorities diminishes at higher levels of academia, particularly in science, technology, engineering, and math (STEM). Sense of belonging-the extent to which an individual believes they are accepted, valued, and included in a community-is often emphasized as an important predictor of retention throughout academia. While literature addressing undergraduate sense of belonging is abundant, there has been little investigation of sense of belonging in graduate communities. Because graduate training is required to generate new scientific leaders, it is important to understand and address sense of belonging at the graduate level-paying explicit attention to devising strategies that can be used to increase representation at higher levels of academia. Here, a visual narrative survey and item response modeling are used to quantify sense of belonging among graduate students, postdoctoral researchers, and faculty within the Department of Chemistry at the University of California, Berkeley. Results suggest that graduate students, postdoctoral researchers, and faculty all experience impostor phenomenon. Respondents struggle most with maintaining positive self-perceptions of their productivity, capabilities as a scientist, and success-particularly in comparison to their peers. Communicating about science with peers, talking about teaching hurdles, and engaging in mentoring relationships also contribute significantly to sense of belonging. Faculty members have the highest sense of belonging, while senior graduate students and postdoctoral researchers are least likely to feel a sense of belonging. Additionally, graduate students and postdoctoral researchers who identify as underrepresented, as well as those who submit manuscripts for publication less than their peers, are less likely to feel a sense of belonging. This is the first study to generate a quantitative, nuanced understanding of sense of belonging within the entire graduate academic community of an R1 STEM department. We envision that these methods can be implemented within any research-focused academic unit to better understand the challenges facing community members and identify factors to address in promoting positive culture change. Furthermore, these methods and results should provide a foundation for devising interventions that academic stakeholders can use to directly improve graduate education.

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Participating in undergraduate research yields positive outcomes for undergraduate students, and universities are seeking ways to engage more students in undergraduate research earlier in their academic careers. Typically, undergraduate students perform research either as part of an apprenticeship where a student receives individual mentorship in a research lab setting from an experienced researcher in the field of interest or in a course-based undergraduate research experience where students work in a classroom or teaching laboratory to investigate open-ended research questions. In this work, we implement a model of undergraduate research that combines aspects of those two methods to provide benefits to undergraduate students and research groups. A cohort of 20 first-year undergraduate students at the University of California-Berkeley were recruited to work on a project investigating data previously collected by the Alivisatos research group. Over a semester, these students learned about nanomaterials and the research process, pursued curiosity-driven research in teams, and presented their results at a formal poster session. Students from this program showed quantifiable gains in their self-identification as researchers and scientists. This program was developed to be a model for other research groups, departments, and universities to combine the benefits of traditional apprenticeship research and course-based undergraduate research to provide a research experience for large numbers of undergraduate students early in their college education.

Species distribution models are key to evaluate how climate change threatens European forests and tree species distributions. However, current models struggle to integrate ecophysiological processes. Mechanistic models are complex and have high parameter requirements. Some correlative species distribution models have tried to include traits but so far have struggled to directly connect to ecophysiological processes. Here, we propose a new strategy in which species distributions are based on safety margins which represent species' proximity to their physiological thresholds. We derived frost and drought safety margins for 38 European tree species as the difference between physiological tolerance traits and local maximum stress. We used LT50 and Ψ50 as tolerance traits for frost and drought, respectively, and local minimum temperature and minimum soil water potential as maximum stress. We integrated these safety margins into a species distribution model, which tests if the probability of species presence declines rapidly when the safety margin reaches zero, when physiological stress exceeds the species' tolerance traits. Our results showed thaet 35 of the 38 studied species had their distribution explained by one or both safety margins. We demonstrated that safety‐margins‐based model can be efficiently transferred to species for which occurrence data are not available. The probability of presence dropped dramatically when the frost safety margin reached zero, whereas it was less sensitive to the drought safety margin. This differential sensitivity may be due to the more complex regulation of drought stress, especially as water is a shared resource, whereas frost is not. Our analysis provides a new approach to link species distributions to their physiological limits and shows that, in Europe, frost and drought safety margins are important determinants of species distributions. [ABSTRACT FROM AUTHOR]

Peer learning programs have been developed to support many introductory science courses by providing additional instruction and engagement. These programs are beneficial to both learners and instructors, providing content learning and confidence and attitudinal gains. There is evidence that the benefits of peer learning are higher when students are taught by a near-peer instructor who is close to the learner in age and experience. Building on this existing evidence, a peer learning program, the Undergraduate Teacher-Scholar (UGTS) Program, was created, involving both graduate student and near-peer instructors teaching in the same discussion or laboratory section. This format creates a vertical learning community within the course structure. The Teacher-Scholar program provides the opportunity to compare the roles of graduate student and near-peer instructors teaching the same students the same instructional material. We report differences and similarities in the perceived roles of the graduate student and near-peer instructors by all of the stakeholders involved in the program. Both graduate student and near-peer instructors are valued by students; however, the near-peer instructors provide mentorship and role model qualities that are not replaced by a graduate student instructor.

Over the past several decades, green chemistry has gained prominence in chemistry education. However, the development of green chemistry curricula has not reached all levels of education equally, focusing mainly on elective and upper division courses. We deliberately focused our green chemistry curriculum redesign on a high enrollment introductory general chemistry laboratory course at the University of California, Berkeley. We developed over 30 new experiments that introduced students to green chemistry concepts and applications, while maintaining canonical general chemistry learning goals. The context of the curriculum and required course assignments encouraged students to use green chemistry principles to explore and solve real-world problems. After completing this redesigned course, we hypothesized that students would value green chemistry and feel more confident in their green chemistry knowledge. We developed new methods to measure students' attitudes toward and understanding of green chemistry as a system instead of isolated reactions or processes. These assessments allowed us to better understand both the progression and limitations in student green chemistry and systems-thinking. Since over 2000 students complete the laboratory course each year, we used a combination of fixed response items and free response items from online surveys and in-class assignments and exams. This approach allowed efficient assessment of thousands of students, while still gaining valuable and nuanced views of student understanding and attitudes. These assessments indicated that the new general chemistry laboratory curriculum succeeded in providing an environment in which students learned green chemistry concepts and realized that chemistry has connections to their future courses and professions.

The RNA recognition motif (RRM), one of the most common RNA binding domains, contains three highly conserved aromatic amino acids that participate in stacking interactions with RNA bases. The contribution of these highly conserved aromatic amino acids to the affinity of the complex formed between the N-terminal RRM of the U1A protein and stem loop of U1 snRNA is investigated.

Protein-RNA interactions were investigated in terms of mutation of a conserved aromatic residue.