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Abstract The Allocations Service for the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program is charged with accepting, reviewing, and processing researchers’ requests to use resources that are integrated into the ACCESS ecosystem. We present as a case study the metrics framework used to evaluate the Allocations Service project, a metrics framework that aligns with the project’s goals and identifies key performance indicators (KPIs). Several of our top-level KPIs reflect complex concepts and are composite measures built from suites of metrics compiled from two primary sources: a well-instrumented allocations and accounting system and an annual survey of the ACCESS researcher community. This approach allows us to describe and measure complex concepts such as “democratization” and “ecosystem access time” in a quantitative manner and to target improvements to project activities. The metrics framework is augmented by metrics to measure the performance of the project team, to describe general ecosystem and allocations activity, and to capture publications from the researcher community. We used this framework to gather and present data as part of the ACCESS Allocations Service first annual NSF panel review. The metrics were largely successful at communicating our progress, but we also encountered a few unexpected technical issues with the data and calculations themselves, which we are continuing to refine. Presented here as a case study, this approach to a metrics framework for the Allocations Service has proved valuable in complementing more subjective descriptions of the project, its accomplishments, and progress toward our goals. 

The need to train sustainability scientists and engineers to address the complex problems of our world has never been more apparent. We organized an interdisciplinary team of instructors from universities in the states of Maine, New Hampshire, and Rhode Island who designed, taught, and assessed a multi-university course to develop the core competencies necessary for advancing sustainability solutions. Lessons from the course translate across sustainability contexts, but our specific focus was on the issues and trade-offs associated with dams. Dams provide numerous water, energy, and cultural services to society while exacting an ecological toll that disrupts the flow of water, fish, and sediment in rivers. Like many natural resource management challenges, effective dam decisions require collaboration among diverse stakeholders and disciplines. We linked key sustainability principles and practices related to interdisciplinarity, stakeholder engagement, and problem-solving to student learning outcomes that are generalizable beyond our dam-specific context. Students and instructors co-created class activities to build capacity for interdisciplinary collaboration and encourage student leadership and creativity. Assessment results show that students responded positively to activities related to stakeholder engagement and interdisciplinary collaboration, particularly when practicing nested discussion and intrapersonal reflection. These activities helped broaden students’ perspectives on sustainability problems and built greater capacity for constructive communication and student leadership. 

Aging infrastructure and growing interests in river restoration have led to a substantial rise in dam removals in the United States. However, the decision to remove a dam involves many complex trade-offs. The benefits of dam removal for hazard reduction and ecological restoration are potentially offset by the loss of hydroelectricity production, water supply, and other important services. We use a multiobjective approach to examine a wide array of trade-offs and synergies involved with strategic dam removal at three spatial scales in New England. We find that increasing the scale of decision-making improves the efficiency of trade-offs among ecosystem services, river safety, and economic costs resulting from dam removal, but this may lead to heterogeneous and less equitable local-scale outcomes. Our model may help facilitate multilateral funding, policy, and stakeholder agreements by analyzing the trade-offs of coordinated dam decisions, including net benefit alternatives to dam removal, at scales that satisfy these agreements. 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.