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We present a solution to image-based cell counting with dot annotations for both 2D and 3D cases. Current approaches have two major limitations: 1) inability to provide precise locations when cells overlap; and 2) reliance on costly labeled data. To address these two issues, we first adopt the inverse distance kernel, which yields separable density maps for better localization. Second, we take advantage of unlabeled data by self-supervised learning with focal consistency loss, which we propose for our pixel-wise task. These two contributions complement each other. Together, our framework compares favorably against stateof- the-art methods, including methods using full annotations on 2D and 3D benchmarks, while significantly reducing the amount of labeled data needed for training. In addition, we provide a tool to expedite the labeling process for dot annotations. Finally, we make the source code and labeling tool publicly available. 

Exploratory data analysis of high-dimensional datasets is a crucial task for which visual analytics can be especially useful. However, the ad hoc nature of exploratory analysis can also lead users to draw incorrect causal inferences. Previous studies have demonstrated this risk and shown that integrating counterfactual concepts within visual analytics systems can improve users’ understanding of visualized data. However, effectively leveraging counterfactual concepts can be challenging, with only bespoke implementations found in prior work. Moreover, it can require expertise in both counterfactual subset analysis and visualization to implement the functionalities practically. This paper aims to help address these challenges in two ways. First, we propose an operator-based conceptual model for the use of counterfactuals that is informed by prior work in visualization research. Second, we contribute the Co-op library, an open and extensible reference implementation of this model that can support the integration of counterfactual-based subset computation with visualization systems. To evaluate the effectiveness and generalizability of Co-op, the library was used to construct two different visual analytics systems each supporting a distinct user workflow. In addition, expert interviews were conducted with professional visual analytics researchers and engineers to gain more insights regarding how Co-op could be leveraged. Finally, informed in part by these evaluation results, we distil a set of key design implications for effectively leveraging counterfactuals in future visualization systems. 

Counterfactuals – expressing what might have been true under different circumstances – have been widely applied in statistics and machine learning to help understand causal relationships. More recently, counterfactuals have begun to emerge as a technique being applied within visualization research. However, it remains unclear to what extent counterfactuals can aid with visual data communication. In this paper, we primarily focus on assessing the quality of users’ understanding of data when provided with counterfactual visualizations. We propose a preliminary model of causality comprehension by connecting theories from causal inference and visual data communication. Leveraging this model, we conducted an empirical study to explore how counterfactuals can improve users’ understanding of data in static visualizations. Our results indicate that visualizing counterfactuals had a positive impact on participants’ interpretations of causal relations within datasets. These results motivate a discussion of how to more effectively incorporate counterfactuals into data visualizations. 

The Ordway-Swisher Biological Station (OSBS) is a 38-km2 reserve owned by the University of Florida and is part of the National Ecological Observatory Network (NEON). The reserve contains several iconic Florida habitats, such as sandhill, mesic hammock, and scrubby flatwoods. While plants and animals have been extensively studied at OSBS, the fungi remain poorly known. Fungal inventories are critical to increase knowledge of both fungal diversity and species ranges, and thus to provide foundational data for a wide array of applications in ecology and resource management. Here, we present the results of a nine-year effort to collect, preserve, and DNA barcode the macrofungi at OSBS. This effort generated >1200 vouchered specimens and 984 ITS rDNA sequences, representing more than 546 species. Our sampling was dominated by Basidiomycota and revealed a high diversity of symbiotic ectomycorrhizal fungi, particularly species of Amanita, Cortinarius, and Russula. Sampling curves and both Chao1 and Jacknife1 richness estimators suggest that our DNA barcoding efforts captured only about half of the macrofungi species and that a more complete inventory would detect 897–1177 macrofungi species at OSBS. Our sampling found more species of macrofungi at OSBS than the known number of vertebrate animal species at the reserve and our estimates also suggest that there are likely more macrofungi species than plant species at OSBS. This study is the first comprehensive macrofungi inventory within a NEON site and highlights the importance of long-term monitoring to provide novel data on fungal diversity, community structure, conservation, biogeography, and taxonomy.