skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: A convergence research perspective on graduate education for sustainable urban systems science
Abstract Sustainable urban systems (SUS) science is a new science integrating work across established and emerging disciplines, using diverse methods, and addressing issues at local, regional, national, and global scales. Advancing SUS requires the next generation of scholars and practitioners to excel at synthesis across disciplines and possess the skills to innovate in the realms of research, policy, and stakeholder engagement. We outline key tenets of graduate education in SUS, informed by historical and global perspectives. The sketch is an invitation to discuss how graduates in SUS should be trained to engage with the challenges and opportunities presented by continuing urbanization.  more » « less
Award ID(s):
1929943
PAR ID:
10441317
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
npj Urban Sustainability
Volume:
1
Issue:
1
ISSN:
2661-8001
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract. Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR–NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR–NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR–NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change. 
    more » « less
  2. null (Ed.)
    As technology advances, data-driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research-practice partnership that brings together STEM+C instructors and researchers from three universities and education research and consulting groups. We aim to use high-frequency monitoring data collected from real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings have improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses. 
    more » « less
  3. With increasing recognition of the importance of reproducibility in computer science research, a wide range of efforts to promote reproducible research have been implemented across various sub-disciplines of computer science. These include artifact review and badging processes, and dedicated reproducibility tracks at conferences. However, these initiatives primarily engage active researchers and students already involved in research in their respective areas. In this paper, we present an argument for expanding the scope of these efforts to include a much larger audience, by introducing more reproducibility content into computer science courses. We describe various ways to integrate reproducibility content into the curriculum, drawing on our own experiences, as well as published experience reports from several sub-disciplines of computer science and computational science. 
    more » « less
  4. Abstract The current United Nations Decade of Ocean Science for Sustainable Development (2021–2030; hereafter, the Decade) offers a unique opportunity and framework to globally advance ocean science and policy. Achieving meaningful progress within the Decade requires collaboration and coordination across Decade Actions (Programs, Projects, and Centres). This coordination is particularly important for the deep ocean, which remains critically under‐sampled compared to other ecosystems. Despite the limited sampling, the deep ocean accounts for over 95% of Earth's habitable space, plays a crucial role in regulating the carbon cycle and global temperatures, and supports diverse ecosystems. To collectively advance deep‐ocean science, we gathered representatives from 20 Decade Actions that focus at least partially on the deep ocean. We identified five broad themes that aim to advance deep‐ocean science in alignment with the Decade's overarching 10 Challenges: natural capital and the blue economy, biodiversity, deep‐ocean observing, best practices in data sharing, and capacity building. Within each theme, we propose concrete objectives (termed Cohesive Asks) and milestones (Targets) for the deep‐ocean community. Developing these Cohesive Asks and Targets reflects a commitment to better coordination across deep‐ocean Decade Actions. We aim to build bridges across deep‐ocean disciplines, which encompass natural science, ocean observing, policy, and capacity development. 
    more » « less
  5. null (Ed.)
    As technology advances, data driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high frequency monitoring data collected from real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings has improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses. 
    more » « less