Artificial intelligence (AI) can rapidly process large ecological datasets, uncover patterns, and inform conservation decisions, but responsible adoption depends as much on human-centric skills as on technical methods. Ecologists face steep learning curves, an overwhelming and fast-evolving model landscape, uneven access to data and computing, and a growing transparency deficit. These challenges require human-centric skills like time management, critical thinking, collaboration, communication, creativity, and project management to select, implement, interpret, and responsibly translate AI outputs into ecological insight and action. We led a workshop, EcoViz+AI: Visualization and AI for Ecology, that brought together 35 experts to synthesize practical guidance for navigating these challenges across the AI pipeline. Using workshop discussions and experiences as a foundation, this position paper proposes practical solutions and complementary human-centric skill development to address these challenges: (1) educational resources that support opportunity-cost reasoning (time management) and methodological judgment (critical thinking), (2) communities of practice that build inclusive shared expertise (collaboration and mentorship), (3) effective visualizations that improve interpretability and strengthen transparency of model behavior and uncertainty (creativity and communication), and (4) computational resources that reduce implementation burden through shared data, extensible code, and accessible infrastructure (project management and problem-solving). Our workshop compiled resources, including science communication videos for five AI use cases and repositories for ecology-related AI models and communities of practice. Emphasizing human-centric skills and working in tandem with efforts to promote open science and computational literacy can make AI in ecology more rigorous, equitable, and ecologically relevant, advancing research and conservation.

When the row and column variables consist of the same category in a two-way contingency table, it is called a square contingency table. Since square contingency tables have an association structure due to the concentration of observed values near the main diagonal, a primary objective is to examine symmetric relationships and transitions between variables. Various models and measures have been proposed to analyze these structures to understand the changes between two variables’ behavior at two-time points or cohorts. This is necessary for a detailed investigation of individual categories and their interrelationships, such as shifts in brand preferences. We propose a novel approach to correspondence analysis (CA) for evaluating departures from symmetry in square contingency tables with nominal categories, using a modified divergence statistic. This approach ensures that well-known divergence statistics can also be visualized and regardless of the divergence statistics used, the CA plot consists of two principal axes with equal contribution rates. Notably, the scaling of the departures from symmetry provided by the modified divergence statistic is independent of sample size, allowing for meaningful comparisons and unification of results across different tables. Confidence regions are also constructed to enhance the accuracy of the CA plot.

We present ASP Chef Mustache, an extension of ASP Chef that enhances template-based rendering of answer set programming (ASP) solutions using a logic-less templating system inspired by Mustache. Our approach integrates data visualization frameworks such as Tabulator, Chart.js, and vis.js, enabling interactive representations of ASP interpretations as tables, charts, and graphs. Mustache queries in templates support advanced constructs for formatting, sorting, and multi-stage expansion, facilitating the generation of rich, structured outputs. We demonstrate the power of this framework through a series of use cases, including data analysis for the Italian VQR, visualization of blocking sets in graphs, and scheduling problems. The result is a versatile tool for bridging declarative problem solving and modern web-based visual analytics.

In epidemiological investigations, pathogen genomics can provide insights and test epidemiological hypotheses that would not have been possible through traditional epidemiology. Tools to synthesize genomic analysis with other types of data are a key requirement of genomic epidemiology. We propose a new ‘phylepic’ visualization that combines a phylogenomic tree with an epidemic curve. The combination visually links the molecular time represented in the tree to the calendar time in the epidemic curve, a correspondence that is not easily represented by existing tools. Using an example derived from a foodborne bacterial outbreak, we demonstrated that the phylepic chart communicates that what appeared to be a point-source outbreak was in fact composed of cases associated with two genetically distinct clades of bacteria. We provide an R package implementing the chart. We expect that visualizations that place genomic analyses within the epidemiological context will become increasingly important for outbreak investigations and public health surveillance of infectious diseases.

Despite admonitions to address attrition in experiments – missingness on Y – alongside best practices designed to encourage transparency, most political science researchers all but ignore it. A quantitative literature search of this journal – where we would expect to find the most conscientious reporting of attrition – shows low rates of discussion of the issue. We suspect that there is confusion on the link between when attrition occurs and the type of validity it threatens when present, and limited connection to and guidance on which estimands are threatened by different attrition patterns. This is all exacerbated by limited tools to identify, investigate, and report patterns attrition. We offer the R package – attritevis – to visualize attrition over time, by intervention, and include a step-by-step guide to identifying and addressing attrition that balances post hoc analytical tools with guidance for revising designs to ameliorate problematic attrition.

The Gaia mission DR3 provides accurate data of around two billion stars in the Galaxy, including a classification based on astronomical classes of objects. In this work we present a web visualization tool to analyze one of the products published in the DR3, the Outlier Analysis Self-Organizing Map†.

The article is devoted to computer modeling, visualization, and synthesis of a digital antenna array used for transmitting and receiving signals in industrial applications. The paper proposes an iterative method for the amplitude-phase synthesis of an antenna array according to the requirements for the envelope of the side lobes. The proposed method allows to determine the complex amplitudes of the elements of a digital antenna array for any given weight function, based on the theorems of matrix theory. The difference of the method lies in the iterative procedure for choosing the weight function, taking into account the excess level of the side lobes of the digital antenna array. In this regard, in the course of solving the synthesis problem, a weight function was found that leads to the fulfilment of the requirements for radiation patterns and does not lead to a decrease in the directivity coefficient. The signal-to-noise ratio was used as a criterion. For the first time, an analytical expression is given for the formation of a weight function in the course of an iterative process that takes into account the requirements for the envelope of the side lobes. The operability and convergence of the proposed method was confirmed in the course of numerical studies on the example of a digital antenna array. The performed numerical analysis confirmed the effectiveness of the proposed synthesis method.

Large-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in, for example, tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users’ trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.