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Groundwater depletion threatens global freshwater resources, necessitating urgent water management and policies to meet current and future needs. However, existing data-intensive approaches to assessments do not fully account for the complex human, climate, and water interactions within transboundary groundwater systems. Here, we present the design of and findings from a pilot participatory modeling workshop aiming to advance understanding of the hydrologic–human–climate feedback loops underpinning groundwater systems. Using participatory modeling tools and methods from the system dynamics tradition, we captured the mental models of researchers from water, social, data, and systems sciences. A total of 54 feedback loops were identified, demonstrating the potential of this methodology to adequately capture the complexity of groundwater systems. Based on the workshop outcomes, as an illustrative example, we discuss the value of participatory system modeling as a conceptualization tool, bridging perspectives across disciplinary silos. We further discuss how outcomes may inform future research on existing knowledge gaps around groundwater issues, and in doing so, advance interdisciplinary, use-inspired research for water decision-making more broadly. 

Neural methods are state-of-the-art for urban prediction problems such as transportation resource demand, accident risk, crowd mobility, and public safety. Model performance can be improved by integrating exogenous features from open data repositories (e.g., weather, housing prices, traffic, etc.), but these uncurated sources are often too noisy, incomplete, and biased to use directly. We propose to learn integrated representations, called EquiTensors, from heterogeneous datasets that can be reused across a variety of tasks. We align datasets to a consistent spatio-temporal domain, then describe an unsupervised model based on convolutional denoising autoencoders to learn shared representations. We extend this core integrative model with adaptive weighting to prevent certain datasets from dominating the signal. To combat discriminatory bias, we use adversarial learning to remove correlations with a sensitive attribute (e.g., race or income). Experiments with 23 input datasets and 4 real applications show that EquiTensors could help mitigate the effects of the sensitive information embodied in the biased data. Meanwhile, applications using EquiTensors outperform models that ignore exogenous features and are competitive with "oracle" models that use hand-selected datasets. 

Publishers are increasingly using graphical abstracts to facilitate scientific search, especially across disciplinary boundaries. They are presented on various media, easily shared and information rich. However, very small amount of scientific publications are equipped with graphical abstracts. What can we do with the vast majority of papers with no selected graphical abstract? In this paper, we first hypothesize that scientific papers actually include a "central figure" that serve as a graphical abstract. These figures convey the key results and provide a visual identity for the paper. Using survey data collected from 6,263 authors regarding 8,353 papers over 15 years, we find that over 87% of papers are considered to contain a central figure, and that these central figures are primarily used to summarize important results, explain the key methods, or provide additional discussion. We then train a model to automatically recognize the central figure, achieving top-3 accuracy of 78% and exact match accuracy of 34%. We find that the primary boost in accuracy comes from figure captions that resemble the abstract. We make all our data and results publicly available at https://github.com/viziometrics/centraul_figure. Our goal is to automate central figure identification to improve search engine performance and to help scientists connect ideas across the literature.