Interactive Visualization Tool Research Articles

Interactive Visualization Tool to Understand and Monitor Health Disparities in Diabetes Care and Outcomes

Interactive Visualization Tool to Understand and Monitor Health Disparities in Diabetes Care and Outcomes

The NCBI Comparative Genome Viewer (CGV) is an interactive visualization tool for the analysis of whole-genome eukaryotic alignments.

We report a new visualization tool for analysis of whole-genome assembly-assembly alignments, the Comparative Genome Viewer (CGV) (https://ncbi.nlm.nih.gov/genome/cgv/). CGV visualizes pairwise same-species and cross-species alignments provided by National Center for Biotechnology Information (NCBI) using assembly alignment algorithms developed by us and others. Researchers can examine large structural differences spanning chromosomes, such as inversions or translocations. Users can also navigate to regions of interest, where they can detect and analyze smaller-scale deletions and rearrangements within specific chromosome or gene regions. RefSeq or user-provided gene annotation is displayed where available. CGV currently provides approximately 800 alignments from over 350 animal, plant, and fungal species. CGV and related NCBI viewers are undergoing active development to further meet needs of the research community in comparative genome visualization.

Graph Sampling for Map Comparison

Comparing two road maps is a basic operation that arises in a variety of situations. A map comparison method that is commonly used, mainly in the context of comparing reconstructed maps to ground truth maps, is based on graph sampling . The essential idea is to first compute a set of point samples on each map, and then to match pairs of samples—one from each map—in a one-to-one fashion. For deciding whether two samples can be matched, different criteria, e.g., based on distance or orientation, can be used. The total number of matched pairs gives a measure of how similar the maps are. Since the work of Biagioni and Eriksson [11, 12], graph sampling methods have become widely used. However, there are different ways to implement each of the steps, which can lead to significant differences in the results. This means that conclusions drawn from different studies that seemingly use the same comparison method, cannot necessarily be compared. In this work we present a unified approach to graph sampling for map comparison. We present the method in full generality, discussing the main decisions involved in its implementation. In particular, we point out the importance of the sampling method (GEO vs. TOPO) and that of the matching definition, discussing the main options used, and proposing alternatives for both key steps. We experimentally evaluate the different sampling and matching options considered on map datasets and reconstructed maps. Furthermore, we provide a code base and an interactive visualization tool to set a standard for future evaluations in the field of map construction and map comparison.

PROTOCOL: Methods used in the development, production and updating of evidence and gap maps: A scoping review.

Evidence and gap maps (EGMs) are an increasinly popular approach used in evidence synthesis. As an approach they address broad research questions, describing the existing evidence base, highlighting evidence gaps and providing an interactive visual tool for knowledge users. The purpose of this methodological study is to explore the the processes used in the development of EGM's and how they are reported. The aim is to better understand current practice and identify where clearer guidance is needed to support their production.

Magic NeRF lens: interactive fusion of neural radiance fields for virtual facility inspection

Virtual reality (VR) has become an important interactive visualization tool for various industrial processes including facility inspection and maintenance. The capability of a VR application to present users with realistic simulations of complex systems and immerse them in, for example, inaccessible remote environments is often essential for using VR in real-world industrial domains. While many VR solutions have already been developed to support virtual facility inspection, previous systems provide immersive visualizations only with limited realism, because the real-world conditions of facilities are often difficult to reconstruct with accurate meshes and point clouds or typically too time-consuming to be consistently updated in computer-aided design (CAD) software toolkits. In this work, we present Magic NeRF Lens, a VR framework that supports immersive photorealistic visualizations of complex industrial facilities leveraging the recent advancement of neural radiance fields (NeRF). We introduce a data fusion technique to merge a NeRF model with the polygonal representation of it’s corresponding CAD model, which optimizes VR NeRF rendering through magic-lens-style interactions while introducing a novel industrial visualization design that can support practical tasks such as facility maintenance planning and redesign. We systematically benchmarked the performance of our framework, investigated users’ perceptions of the magic-lens-style visualization design through a visual search experiment to derive design insights, and performed an empirical evaluation of our system through expert reviews. To support further research and development of customized VR NeRF applications, the source code of the toolkit was made openly available.

Abstract LB252: iCellSight: An interactive tool for analyzing and visualizing in-situ high-plex cellular protein data

Abstract Introduction The increased use of tissue-based multiplex immunohistochemistry (mIHC) in cancer research has significantly enhanced the generation of spatial hyper-plex images, crucial for understanding complex tumor-immune microenvironments. This advancement has led to a growing demand for spatial data analysis tools that enable quick and insightful evaluation. While existing spatial analysis software like Steinbock and SPIAT offer foundational support, effectively managing the high-dimensional and voluminous cellular data to derive meaningful biological insights remains challenging. This is especially true for researchers lacking bioinformatics expertise who seek to conduct quick preliminary evaluations. To address this gap, we introduce iCellSight: a user-friendly, code-free interactive analysis and visualization tool tailored to empower researchers with rapid and intuitive interpretation of in-situ cellular data. Material and methods A formalin-fixed, paraffin-embedded colorectal carcinoma (CRC) patient tissue section was subjected to staining using COMET (Lunaphore, Switzerland), which automates sequential immunofluorescence staining and imaging of up to 40 protein markers. The stacked image file produced was analyzed with HALO (Indica Labs, USA), which generated cell-level data like cell coordinates, stain intensity values and user-defined cell types. We developed a Shiny App tool, using the Shiny R package, with key features for cell data interpretation: (1) user-input cell number subsampling; (2) dimensional reduction plots with PhenoGraph for unsupervised clustering, allowing a choice of UMAP or t-SNE, and neighbor number selection of 30, 50 or 150; (3) 2D tissue space mapping of PhenoGraph clusters; and (4) heatmap visualizations displaying cluster-wise biomarker expression (with customizable color schemes and scaling), biomarker co-expression, and cluster pair-wise distance. Results Using iCellSight, 297,000 cells were detected from the CRC sample, where 27 clusters were identified. We observed 3 distinct clusters from the t-SNE plot that consist of macrophages, granulocytes, and NK cells and monocytes respectively. From the cluster projection in 2D tissue space, the cluster of NK cells and monocytes was found to be densely populated in a particular area of the tumor nest. Conclusion We presented iCellSight, a code-free visualization and analysis tool which streamlines the semi-automated analysis of spatial hyper-plex protein data. Designed with an intuitive user interface, this app enables researchers to efficiently navigate its features without requiring any prior coding expertise. We anticipate that the visualizations and analysis provided by this tool will significantly assist researchers in their data analyses and facilitate the validation of experimental hypotheses. Citation Format: Yancun Zhu, Felicia Wee, Menaka Priyadharsani Rajapakse, Esther Gek Teo, Nicholas Ang, Solomonraj Wilson, Zheng Yi Ho, Willa Wen-You Yim, Li Yen Chong, Craig Ryan Joseph, Jeffrey Chun Lim, Zhen Wei Neo, Chwee Ming Lim, Bernett Lee, Olaf Rotzschke, Joe Yeong, Mai Chan Lau. iCellSight: An interactive tool for analyzing and visualizing in-situ high-plex cellular protein data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB252.

InVADo: Interactive Visual Analysis of Molecular Docking Data.

Molecular docking is a key technique in various fields like structural biology, medicinal chemistry, and biotechnology. It is widely used for virtual screening during drug discovery, computer-assisted drug design, and protein engineering. A general molecular docking process consists of the target and ligand selection, their preparation, and the docking process itself, followed by the evaluation of the results. However, the most commonly used docking software provides no or very basic evaluation possibilities. Scripting and external molecular viewers are often used, which are not designed for an efficient analysis of docking results. Therefore, we developed InVADo, a comprehensive interactive visual analysis tool for large docking data. It consists of multiple linked 2D and 3D views. It filters and spatially clusters the data, and enriches it with post-docking analysis results of protein-ligand interactions and functional groups, to enable well-founded decision-making. In an exemplary case study, domain experts confirmed that InVADo facilitates and accelerates the analysis workflow. They rated it as a convenient, comprehensive, and feature-rich tool, especially useful for virtual screening.

Abstract 4921: A user-friendly R Shiny web app for predicting cancer genetic dependencies using deep learning

Abstract Understanding the genetic vulnerabilities (i.e., genetic dependencies) of cancer cells is crucial for developing novel anti-cancer treatments. The Cancer Dependency Map (DepMap) projects, conducted by the Broad Institute and Wellcome Sanger Institute, have performed comprehensive genome-wide CRISPR-Cas9 knockout screens across diverse cancer cell lines. These data resources have enabled computational biologists to glean insights into cancer genetic dependencies using sophisticated computational models. As different genomic mechanisms govern tumorigenesis and genetic vulnerability, the relationship between cancer genomics and genetic dependencies is nonlinear, making the prediction of genetic dependencies challenging. Addressing this challenge, we previously developed a deep learning model, namely DeepDEP, to predict cancer genetic dependencies using multi-omic profiles (Chiu et al. DOI:10.1126/sciadv.abh1275). DeepDEP has a transfer learning scheme that integrates genomic representations captured from unlabeled tumor samples of the Cancer Genome Atlas (TCGA) and genomic features that are predictive of genetic dependencies in cell lines. Such a design enables the application of DeepDEP to both cell lines and tumors. The model demonstrated superior performance compared to various conventional machine learning methods. Prediction results were validated using independent cell-line datasets and patient clinical data. Here, we developed an intuitive web application for DeepDEP which allows users to easily utilize the model on their in-house data. Users can simply indicate whether the query sample is derived from a cancer cell line or a tumor, upload any combination of mutation, gene expression, DNA methylation, and copy number alteration datasets, and view the predicted dependencies of 1,298 cancer-relevant genes. Along with the genetic dependency predictions, users are provided with model performance metrics, detailed gene annotations, and links to useful resources such as Ensembl and COSMIC. Our web application features a variety of interactive visualizations to guide users to interpret the results at the levels of individual genes and pathways. The tool has two supplemental modules to enhance its functionality. One allows users to search for similar cell lines to the query sample within the DepMap project or for similar tumors within the TCGA project. The other supplemental module provides data exploration tools for pre-calculated predictions across all TCGA tumors. In summary, this user-friendly web application provides researchers with access to deep learning predictions of cancer genetic dependencies with various interactive visualization and analysis tools. It is anticipated that this application will facilitate the study of cancer genetic dependencies and enhance the efficiency of anti-cancer drug discovery. Citation Format: Michael J. Kasper, Li-Ju Wang, Michael Ning, Yufei Huang, Yu-Chiao Chiu. A user-friendly R Shiny web app for predicting cancer genetic dependencies using deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4921.

An interactive visualization and data portal tool (PALTIDE) for relative sea level and palaeotidal simulations of the northwest European shelf seas since the Last Glacial Maximum

ABSTRACTRelative sea level (RSL) predictions based on glacial isostatic adjustment (GIA) simulations and palaeotidal predictions generated by hydrodynamic models using GIA‐generated palaeotopographies are available in the published literature, and datasets are available via data repositories. However, these data are often difficult to extract for specific locations or timeslices, requiring users to request datasets from corresponding authors. To overcome the intractability of these data and to enable users to interrogate datasets themselves without requiring offline requests, we have developed PALTIDE, an online visualization tool with intuitive user interface accessible at https://shiny.bangor.ac.uk/paleotidal/. The model domain for this interactive visualization tool is the northwest European continental shelf, covering the period from the Last Glacial Maximum (LGM) to the present day, and is based on previous GIA simulations by Bradley and colleagues and hydrodynamic simulations using Regional Ocean Modelling System (ROMS) published by Ward and colleagues. The tool is developed in R and utilizes a number of packages including shiny and bslib for the frontend, and arrow, raster and the tidyverse for backend data processing. The tool enables visualizations and data downloads for RSL, tidal amplitude and tide‐dependent parameters for any location within the model domain over 1000‐year timesteps from the LGM to the present.

An interactive visualization tool for educational outreach in protein contact map overlap analysis.

Recent advancements in contact map-based protein three-dimensional (3D) structure prediction have been driven by the evolution of deep learning algorithms. However, the gap in accessible software tools for novices in this domain remains a significant challenge. This study introduces GoFold, a novel, standalone graphical user interface (GUI) designed for beginners to perform contact map overlap (CMO) problems for better template selection. Unlike existing tools that cater more to research needs or assume foundational knowledge, GoFold offers an intuitive, user-friendly platform with comprehensive tutorials. It stands out in its ability to visually represent the CMO problem, allowing users to input proteins in various formats and explore the CMO problem. The educational value of GoFold is demonstrated through benchmarking against the state-of-the-art contact map overlap method, map_align, using two datasets: PSICOV and CAMEO. GoFold exhibits superior performance in terms of TM-score and Z-score metrics across diverse qualities of contact maps and target difficulties. Notably, GoFold runs efficiently on personal computers without any third-party dependencies, thereby making it accessible to the general public for promoting citizen science. The tool is freely available for download for macOS, Linux, and Windows.

Multiwavelength Catalog of 10,000 4XMM-DR13 Sources with Known Classifications

We present a collection of ∼10,000 X-ray sources from the 4th XMM-Newton Serendipitous Source Catalog (4XMM-DR13) with literature-verified classifications and multi-wavelength (MW) counterparts. We describe the process by which MW properties are obtained and an interactive online visualization tool we developed.

Abstract B029: Combinations of metformin and BH3-mimetic drugs synergistically induce immunogenic cell death in breast cancer cells

Abstract There is a compelling medical need for novel and safe treatment options for patients with advanced and difficult to treat breast cancer. Our previous findings have demonstrated that a combination of metformin and navitoclax (BCL-2/BCL-XL inhibitor) successfully prevents tumor growth in patient derived xenograft and syngrafted mouse models of aggressive MYC-driven breast cancer. This outcome is due to the synthetic lethal action of these drugs in tumors with elevated MYC expression. Here, we explored the combinations of metformin with all clinically applicable anti-apoptotic BCL-2 family protein inhibitors (BH3-mimetics) in a panel of 15 triple-negative breast cancer cell lines with distinct endogenous MYC levels, as well as inducible MYC models. We used cytotoxicity assays and analyzed the data with SynergyFinder, an interactive analysis and visualization tool, to calculate the degree of combination synergy utilizing Zero interaction potency reference model. Through these cytotoxicity assays, we defined which metformin and BH3-mimetic drug combination induces most synergistic cell death in a MYC-synthetic lethal manner. Additionally, we used DAMP reporter cell lines and peripheral blood mononuclear cell-based dendritic cell activation assays to evaluate the immunogenic potential of combination-induced cell death. We show that targeting MCL-1 or BCL-2/XL with BH3-mimetics MIK-665 and navitoclax, respectively, and combining them with metformin, induces synergistic cell death in MYC-high cancer cells. We also show that cell death caused by these drug combinations promotes maturation of dendritic cells. In conclusion, targeting MYC-high tumors with synthetic lethal strategies, including BH3-mimetics and metformin, may offer new opportunities to combine with immunotherapies for the treatment of breast cancer. Citation Format: Mariel Savelius, Linda Id, Johanna Anttila, Ruixian Liu, Ilida Suleymanova, Jani Saarela, Juha Klefström. Combinations of metformin and BH3-mimetic drugs synergistically induce immunogenic cell death in breast cancer cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B029.

CyanoCyc cyanobacterial web portal.

CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists' work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research.

MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes.

The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.

Cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data

BackgroundHighly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R.ResultsHere, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples.ConclusionsThe cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis.It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html. The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer.

Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.

To compare how clinical researchers generate data-driven hypotheses with a visual interactive analytic tool (VIADS, a visual interactive analysis tool for filtering and summarizing large datasets coded with hierarchical terminologies) or other tools. We recruited clinical researchers and separated them into "experienced" and "inexperienced" groups. Participants were randomly assigned to a VIADS or control group within the groups. Each participant conducted a remote 2-hour study session for hypothesis generation with the same study facilitator on the same datasets by following a think-aloud protocol. Screen activities and audio were recorded, transcribed, coded, and analyzed. Hypotheses were evaluated by seven experts on their validity, significance, and feasibility. We conducted multilevel random effect modeling for statistical tests. Eighteen participants generated 227 hypotheses, of which 147 (65%) were valid. The VIADS and control groups generated a similar number of hypotheses. The VIADS group took a significantly shorter time to generate one hypothesis (e.g., among inexperienced clinical researchers, 258 s versus 379 s, p = 0.046, power = 0.437, ICC = 0.15). The VIADS group received significantly lower ratings than the control group on feasibility and the combination rating of validity, significance, and feasibility. The role of VIADS in hypothesis generation seems inconclusive. The VIADS group took a significantly shorter time to generate each hypothesis. However, the combined validity, significance, and feasibility ratings of their hypotheses were significantly lower. Further characterization of hypotheses, including specifics on how they might be improved, could guide future tool development.

2022 IEEE Scientific Visualization Contest Winner: Multifield Analysis of Vorticity-Driven Lateral Spread in Wildfire Ensembles.

We present an interactive visual analysis tool for analyzing the spread of wildfires and what influences their evolution. Multiple time-varying 3-D scalar and vector fields are investigated and related to each other to identify causes of atypical fire spread. We present a visual analysis approach that allows for a comparative analysis of multiple runs of a simulation ensemble on different levels of detail. Overview visualizations combined with volume renderings and flow visualizations provide an intuitive understanding of the fire spread.

Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?

Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. Because it is not yet a well-understood cognitive process, the need to improve the process goes unrecognized. Without an impactful hypothesis, the significance of any research project can be questionable, regardless of the rigor or diligence applied in other steps of the study, e.g., study design, data collection, and result analysis. In this perspective article, the authors provide a literature review on the following topics first: scientific thinking, reasoning, medical reasoning, literature-based discovery, and a field study to explore scientific thinking and discovery. Over the years, scientific thinking has shown excellent progress in cognitive science and its applied areas: education, medicine, and biomedical research. However, a review of the literature reveals the lack of original studies on hypothesis generation in clinical research. The authors then summarize their first human participant study exploring data-driven hypothesis generation by clinical researchers in a simulated setting. The results indicate that a secondary data analytical tool, VIADS—a visual interactive analytic tool for filtering, summarizing, and visualizing large health data sets coded with hierarchical terminologies, can shorten the time participants need, on average, to generate a hypothesis and also requires fewer cognitive events to generate each hypothesis. As a counterpoint, this exploration also indicates that the quality ratings of the hypotheses thus generated carry significantly lower ratings for feasibility when applying VIADS. Despite its small scale, the study confirmed the feasibility of conducting a human participant study directly to explore the hypothesis generation process in clinical research. This study provides supporting evidence to conduct a larger-scale study with a specifically designed tool to facilitate the hypothesis-generation process among inexperienced clinical researchers. A larger study could provide generalizable evidence, which in turn can potentially improve clinical research productivity and overall clinical research enterprise.

IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality.

With the increasing pervasiveness of Artificial Intelligence (AI), many visual analytics tools have been proposed to examine fairness, but they mostly focus on data scientist users. Instead, tackling fairness must be inclusive and involve domain experts with specialized tools and workflows. Thus, domain-specific visualizations are needed for algorithmic fairness. Furthermore, while much work on AI fairness has focused on predictive decisions, less has been done for fair allocation and planning, which require human expertise and iterative design to integrate myriad constraints. We propose the Intelligible Fair Allocation (IF-Alloc) Framework that leverages explanations of causal attribution (Why), contrastive (Why Not) and counterfactual reasoning (What If, How To) to aid domain experts to assess and alleviate unfairness in allocation problems. We apply the framework to fair urban planning for designing cities that provide equal access to amenities and benefits for diverse resident types. Specifically, we propose an interactive visual tool, Intelligible Fair City Planner (IF-City), to help urban planners to perceive inequality across groups, identify and attribute sources of inequality, and mitigate inequality with automatic allocation simulations and constraint-satisfying recommendations (IF-Plan). We demonstrate and evaluate the usage and usefulness of IF-City on a real neighborhood in New York City, US, with practicing urban planners from multiple countries, and discuss generalizing our findings, application, and framework to other use cases and applications of fair allocation.

Medals and likes: A methodology for big data image dataset analysis of Olympic athletic beauty on Instagram

This article seeks to understand how the Cultural Analytics’ methodological approach and computational tools help interpret large image datasets. A set of 87 730 images of 389 Olympic athletes was collected from Instagram and analyzed, featuring a timespan from September 2011 to November 2020. The image set was structured and organized using computer vision processing combined with interactive visualization tools (Google Vision, PixPlot, Image Network Plotter). The analysis, mixing quantitative and qualitative methods, identified patterns represented as image clusters. Regular personal computers served as the hardware platform. Approximately 60 % of the athletes’ posts were related to non-sports topics, highlighting common characteristics of the visual culture disseminated on Instagram, such as selfies, lifestyle, leisure, travel, and food. Images of sports content, considered a central aspect of the research, had a lower frequency of publications featuring topics such as competitions, training, exercises, and sports practices in general. Beyond this result, the study offers a possible technical framework for similar researchers using large image datasets.