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The rapid growth of artificial intelligence (AI) and machine learning (ML) has led to significant innovations but also raised ethical concerns. Researchers and students designed an ethical online game in this study to spread awareness about making informed decisions when using AI and ML. Conducted within a directed research group (DRG) curricular method, the study engages students as co‐researchers to develop a game, from developing ideas to playtesting the game in a class setting. The study employs a quantitative methodology to analyze a survey that 32 students, each with diverse backgrounds and knowledge in game development, conducted after each class session over three semesters. Findings indicate that self‐reported engagement changes depending on the activities done in each session, with students feeling capable of contributing to research and game design.

 

Historically, the African American female population has been underrepresented within the STEM workforce, creating non-inclusive environments. The metaphor of getting a seat at the table reflects the idea of inclusivity where Black women’s opinions are valued, and they can make decisions and create change within the workplace. Intersectionality poses several unique challenges for Black women pursuing careers in the video games industry. We motivate further research on this group through existing literature and insights from running a research group on the group design of human-centered data science games in a collaborative university setting. We take an autoethnographic perspective on the topic, with the first and second authors grounding their findings in their own experiences as Black women in STEM and higher education and with video game development studios. We outline tangible actions toward the recruitment, development, and retention of Black women in the video games industry in the future. 

From our smartphones to our social media, artificial intelligence (AI) algorithms are becoming ubiquitous in our everyday lives. However, the conveniences that they bring come alongside many potential social and political harms. It is imperative that members of the public develop data ethics literacy to interpret AI’s harms and benefits daily. The immersive and transformative nature of games may enable a wide range of people to explore complex ethical concepts in AI and data science through the lens of speculative design. In this project, we focus on the learning process of a diverse group of students from two universities as they embark upon a process of game design to teach ethical thinking in data science/AI. Through qualitative analysis of semi-structured interviews, we apply a speculative game design framework to identify aspects that aid student learning. 

Soil microbiomes are heterogeneous, complex microbial communities. Metagenomic analysis is generating vast amounts of data, creating immense challenges in sequence assembly and analysis. Although advances in technology have resulted in the ability to easily collect large amounts of sequence data, soil samples containing thousands of unique taxa are often poorly characterized. These challenges reduce the usefulness of genome-resolved metagenomic (GRM) analysis seen in other fields of microbiology, such as the creation of high quality metagenomic assembled genomes and the adoption of genome scale modeling approaches. The absence of these resources restricts the scale of future research, limiting hypothesis generation and the predictive modeling of microbial communities. Creating publicly available databases of soil MAGs, similar to databases produced for other microbiomes, has the potential to transform scientific insights about soil microbiomes without requiring the computational resources and domain expertise for assembly and binning. 

Abstract. Rock fractures are a key contributor to a broad array of Earth surface processes due to their direct control on rock strength as well as rock porosity and permeability. However, to date, there has been no standardization for the quantification of rock fractures in surface process research. In this work, the case is made for standardization within fracture-focused research, and prior work is reviewed to identify various key datasets and methodologies. Then, a suite of standardized methods is presented as a starting “baseline” for fracture-based research in surface process studies. These methods have been shown in pre-existing work from structural geology, geotechnical engineering, and surface process disciplines to comprise best practices for the characterization of fractures in clasts and outcrops. This practical, accessible, and detailed guide can be readily employed across all fracture-focused weathering and geomorphology applications. The wide adoption of a baseline of data collected using the same methods will enable comparison and compilation of datasets among studies globally and will ultimately lead to a better understanding of the links and feedbacks between rock fracture and landscape evolution.

 

Switchgrass (Panicum virgatum L.) remains the preeminent American perennial (C4) bioenergy crop for cellulosic ethanol, that could help displace over a quarter of the US current petroleum consumption. Intriguingly, there is often little response to nitrogen fertilizer once stands are established. The rhizosphere microbiome plays a critical role in nitrogen cycling and overall plant nutrient uptake. We used high-throughput metagenomic sequencing to characterize the switchgrass rhizosphere microbial community before and after a nitrogen fertilization event for established stands on marginal land. We examined community structure and bulk metabolic potential, and resolved 29 individual bacteria genomes via metagenomic de novo assembly. Community structure and diversity were not significantly different before and after fertilization; however, the bulk metabolic potential of carbohydrate-active enzymes was depleted after fertilization. We resolved 29 metagenomic assembled genomes, including some from the ‘most wanted’ soil taxa such as Verrucomicrobia, Candidate phyla UBA10199, Acidobacteria (rare subgroup 23), Dormibacterota, and the very rare Candidatus Eisenbacteria. The Dormibacterota (formally candidate division AD3) we identified have the potential for autotrophic CO utilization, which may impact carbon partitioning and storage. Our study also suggests that the rhizosphere microbiome may be involved in providing associative nitrogen fixation (ANF) via the novel diazotroph Janthinobacterium to switchgrass.