Dashboard Viewer Description
This visualization is designed as a flexible, narrative web-based format for presenting the nutritional and compositional profile of a food item through multiple coordinated charts. In this case, chickpea is used as an example of the kind of food that can be explored in this format. The layout moves from broad composition to more detailed nutrient classes, then to whole-food signature and comparison views, allowing the audience to understand both the internal structure of a food and its position relative to similar foods. Features such as section navigation, summary cards, enlarged figure views, and short interpretive text make the visualization readable, interactive, and suitable for both detailed study and quick exploration. In the chickpea example, the format highlights overall composition, amino-acid pattern, carbohydrate structure, lipid classes, mineral profile, and comparison with other legumes.
The target audience includes food science students, nutrition researchers, educators, and general audiences interested in food composition. Because the format is modular and easy to navigate, it can be reused for other foods beyond chickpea, such as lentils, beans, grains, or other nutrient-dense ingredients. This makes it useful not only for academic analysis but also for teaching, science communication, and comparative food studies.
The intended impact is to make complex nutritional data easier to interpret and more engaging to explore. Rather than presenting food composition as a long list of values, this format helps users see patterns, relationships, and distinctive signatures visually. Using chickpea as an example demonstrates how a single food can be communicated as a complete profile, while also showing that the same visualization structure could be applied to many other foods in a consistent and informative way.
Network Viewer Description
This visualization is designed as an interactive network viewer for exploring relationships between food samples and their defining compositional features. Each food is represented as a sample node, while connected nodes represent categories such as primary group, secondary group, processing type, and selected nutrient or compound families including lipids, carbohydrates, minerals, amino acids, and macronutrients. The design emphasizes user control: viewers can paste tab-separated data, choose which feature families to include, set thresholds for minimum values and top features, search for nodes by name, and inspect details through direct interaction. The network layout, zoom and pan functions, hover and click behavior, legend, and node detail panel make the structure readable while still allowing the user to explore a complex multivariable dataset. Because the graph is rendered locally without external libraries, it is also lightweight and easy to distribute or use offline.
The target audience includes food science students, nutrition researchers, data visualization learners, and educators who want to study how foods relate to one another through shared compositional traits. It is especially useful for audiences who may find large nutrient tables difficult to interpret, since the network format turns tabular data into visible patterns of connection. The interface also supports exploratory learning, making it appropriate for classroom demonstrations, research prototypes, and independent investigation of food datasets.
The intended impact is to make food composition data more intuitive, interactive, and relational. Instead of viewing each food as an isolated row in a spreadsheet, users can see how foods connect through categories and dominant chemical or nutritional features. This supports pattern recognition, comparison, and discovery of shared or distinctive characteristics across samples. In an educational or research context, the visualization encourages users to think of food data as a network of relationships, helping them better understand classification, processing effects, and compositional similarity in a more engaging and interpretable form.
UMAP Viewer Description
This visualization is designed as an interactive UMAP dashboard that helps users explore relationships among food compounds in a clear, visually organized way. The main scatter plot maps compounds by their UMAP coordinates so that chemically or nutritionally similar items appear close together, making clustering patterns easy to spot. The design supports interpretation through multiple visual encodings: points can be colored by variables such as subcategory or food, shaped by another category, and resized by concentration. A left-hand control panel lets users upload or paste TSV data, filter by food, subcategory, status, text search, and concentration range, while summary statistics and a preview table provide context and help connect the abstract plot to the underlying records. Hover labels reveal detailed metadata, including compound name and structure, allowing close inspection without overcrowding the screen. Overall, the layout balances overview and detail, giving users both a high-level map of the dataset and tools for targeted analysis.
The primary audience is food science students, nutrition researchers, data visualization learners, and instructors who want to examine large biochemical datasets without needing advanced programming skills. Because the interface is interactive and relatively intuitive, it can also support communication with interdisciplinary audiences, such as public health researchers or policy professionals interested in nutrient and compound distributions across foods.
The intended impact is to make complex food composition data more accessible, interpretable, and engaging. Instead of reading long tables of compounds and concentrations, users can quickly identify clusters, outliers, and similarities among foods and compound classes. This encourages exploratory analysis, supports hypothesis generation, and helps users better understand how nutritional and biochemical profiles relate across foods. In an educational setting, the dashboard can strengthen data literacy by showing how dimensionality reduction and interactive filtering turn dense scientific data into interpretable visual evidence.
Geographical Distribution Description
This interactive web application presents a visually rich and data-driven exploration of global food systems through an interactive map interface. Built using modern web technologies, including HTML, CSS, JavaScript, and the Leaflet mapping library, the project visualizes the geographic origins, production patterns, and nutritional composition of a set of foods. By combining spatial data, production statistics, and nutritional insights, the tool offers users a multidimensional understanding of how food is distributed, cultivated, and consumed across the world.
At its core, the application allows users to toggle between two primary perspectives: the native origin of foods and their collection locations. This dual-view approach highlights the journey of food from its historical and biological roots to its modern globalized presence. Users can interact with map markers representing individual foods, explore trade routes, and examine a heatmap that visualizes country-level production volumes based on data from the Food and Agriculture Organization (FAO). The heatmap dynamically adjusts based on selected food categories, allowing for both aggregate and item-specific analysis.
A key feature of the application is its filtering system, which enables users to categorize foods by groups such as grains, legumes, fruits, vegetables, and animal products. This enhances usability and allows for focused exploration. Additionally, the interface includes a detailed information panel that displays scientific classification, origin data, processing methods, and macronutrient composition for each selected food. The macronutrient breakdown is presented visually with proportional bars, making complex nutritional data intuitive and accessible.
The target audience for this tool is broad but primarily includes students, educators, researchers, and individuals interested in food systems, nutrition, geography, and sustainability. It is particularly valuable in educational settings, where it can serve as an engaging teaching aid for topics such as agricultural geography, global trade, and dietary science. Policy analysts and sustainability advocates may also find the visualization useful for understanding food production disparities and supply chain dynamics.
The intended impact of this project is to deepen awareness of the interconnectedness of global food systems. By illustrating how foods originate in specific regions but are cultivated and consumed worldwide, the application emphasizes the role of globalization in shaping diets and agricultural practices. It also encourages users to think critically about food sourcing, sustainability, and nutritional diversity. Ultimately, the project aims to bridge the gap between data and understanding by transforming complex datasets into an interactive, visually compelling experience that fosters curiosity and informed decision-making about food.