Unified Resource Browser: An Interactive and Reconfigurable Web Framework for Biomedical Metadata Exploration, Visualization, and Management

Abstract

We present Unified Resource Browser, a web-based framework designed to optimize the configuration, exploration, visualization, and dissemination of complex biomedical metadata. As a core component of Neuroanatomy-Anchored Information Management Platform (NIMP), Unified Resource Browser enables researchers to efficiently access donor information, brain tissue samples, and sequencing data through structured resource tables and advanced search and filtering capabilities. The platform allows users to customize data views, share configurations via direct links, and seamlessly export data for further analysis. Integrated visualization tools offer immediate insights through customizable charts, enhancing data interpretation. By improving the accessibility and usability of biomedical data resources, Unified Resource Browser fosters collaborative research and advances discoveries in brain structure and function.

Introduction

Discovery in life science today is being enabled through computational- and data-intensive research that exploits the enormous amounts of biomedical and health data. Data FAIRness¹, rigor, and reproducibility in a cost-effective manner play a critical role in broadly advancing data science. A vast amount of genomic, clinical, imaging, and other biological data is being generated at an unprecedented rate². As these datasets become more intricate and multifaceted, there is a clear need for more intuitive, dynamic, and customizable tools that allow researchers to seamlessly interact with complex data and foster deeper scientific collaboration³. Data exploration tools for biomedical research aim to provide researchers with the ability to explore vast datasets, find hidden patterns, and generate meaningful insights that can drive scientific discovery and improve clinical outcomes⁴. Effectively navigating, analyzing, and extracting valuable findings from diverse datasets is crucial in managing the complexity, heterogeneity, and scale⁵.

Several tools have been developed to support the integration and exploration of rapidly growing research datasets⁶, including HARVEST⁷ (RRID: SCR_016132), RiskScape⁸, EWAS Data Hub⁹, i2b2t2¹⁰, and VisualSphere¹¹, each focusing on distinct aspects of clinical data representation. These tools differ in their emphasis on interactivity, statistical analysis, and user interface design, with some tailored for data scientists and statisticians, while others optimized for non-technical researchers or clinicians. The effectiveness of these tools is ultimately determined by the specific needs of the research team, whether for advanced data analysis, interactive exploration, or ease of use. As the complexity of data continues to increase, these tools are evolving to address the demands of an expanding and dynamic research environment.

Despite advancements in data exploration tools, several challenges remain to better support usability, flexibility, and collaborative efficiency in research workflows¹². First, the facet options and filtering categories in many systems are static or rigidly defined, limiting users to a single filtering criterion or a fixed set of data fields. Expanding support for multiple filtering categories and implementing more dynamic faceted search functionalities will significantly improve data exploration, minimizing redundant or competing queries. Second, real-time query responsiveness presents a significant opportunity to enhance workflow efficiency. Current systems require users to submit filters and wait for extended data retrieval, which may cause delays that prolong the research analysis cycle. A system that dynamically updates in response to filter adjustments, column reconfigurations, or modified search criteria will enable seamless, intuitive, and efficient interactions. Third, advanced querying functionalities often depend on complex, code-based query syntax, creating a gap between the system’s analytical capabilities and user accessibility. An ideal system will offer a user-friendly interface for advanced queries, eliminating the need for programming expertise or complex syntax. Finally, addressing performance challenges, such as the computational demands of real-time queries on diverse datasets, is essential for improving system responsiveness. Instead of restricting users to predefined subsets or limited data views, optimizing querying approaches or implementing adaptive data-loading mechanisms could facilitate more efficient exploration without requiring offline analyses.

To enhance the FAIR (Findable, Accessible, Interoperable, Reproducible) principles in biomedical data applications, we introduce Unified Resource Browser: an interactive web-based framework designed for exploring biomedical data within Neuroanatomy-Anchored Information Management Platform (NIMP)¹³. Developed under the NIH BRAIN Initiative Cell Atlas Network (BICAN) program (RRID: SCR_022794), NIMP manages diverse biomedical datasets, but as data grows, navigation becomes increasingly complex.

As shown in Figure 1, Unified Resource Browser streamlines data exploration by offering a single, unified interface for all resource types, eliminating the need to follow multiple hyperlinks or navigate deep data layers. It enables researchers to customize resource tables, perform targeted searches, export curated datasets, and leverage integrated visualization tools for deeper insights. Advanced configuration management allows users to save customized views and share them via direct links, promoting collaboration and research reproducibility. By improving data accessibility and usability, Unified Resource Browser facilitates discoveries in brain structure and function.

Figure 1.

Comparison of data exploration with and without Unified Resource Browser. (a) The routine navigation flow of NIMP data page requires users to switch pages. (b) The data navigation using Unified Resource Browser centralizes access to all resource types through a single interface.

Background

The BRAIN Initiative Cell Atlas Network (BICAN)

The NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative (RRID: SCR_006770) is a collaborative effort between federal and non-federal partners, established in April 2013, to revolutionize our understanding of the human brain¹⁴. By accelerating the development and application of innovative neurotechnologies, the initiative seeks to create a dynamic picture of the brain that illustrates how individual cells and complex neural circuits interact in both time and space. NIH plays a central role in this endeavor, with the NIH BRAIN Initiative managed by ten Institutes and Centers whose missions align with the initiative’s goals¹⁵.

The BRAIN Initiative Cell Atlas Network (BICAN) is a collaborative research effort aiming to create comprehensive reference atlases of brain cell types across multiple species, with a strong emphasis on humans¹⁶. Building upon the foundational work of the BRAIN Initiative Cell Census Network, BICAN seeks to map the vast array of neurons and non-neuronal cells in the human brain, providing a molecular and anatomical framework essential for understanding brain function and disorders¹⁷. Launched by the NIH, BICAN supports several grants, collectively projected to total $100 million annually over five years. The primary objective is to generate detailed brain cell atlases in humans, non-human primates, and mice across various stages of life. These atlases are intended to be shared widely within the research community to facilitate studies on brain function and to explore cellular interactions underlying various brain disorders. BICAN is one of three transformative projects outlined in “The BRAIN Initiative® 2.0: From Cells to Circuits, Toward Cures” report. Alongside the BRAIN Initiative Connectivity Across Scales (BRAIN CONNECTS) Network and the Armamentarium for Precision Brain Cell Access, BICAN aims to revolutionize neuroscience research by illuminating the circuit basis of behavior and informing new approaches to treating human brain disorders^{18, 19}.

Neuroanatomy-Anchored Information Management Platform

The University of Texas Health Science Center at Houston (UTHealth Houston) leads the development of the Neuroanatomy-Anchored Information Management Platform for Collaborative BICAN Data Generation, a pivotal infrastructure designed to systematically integrate and manage BICAN tissue resources (RRID: SCR_024684)13. NIMP comprises two integrated portals facilitating BICAN’s collaborative data generation: the Specimen Portal and the Sequence Library (SeqLib) Portal. The Specimen Portal manages tissue resources, tracking the process from donor specimens to brain slabs and annotated brain samples, while the SeqLib Portal oversees workflows from tissue processing through data deposition into assay-specific archives. These portals function in tandem to produce multimodal genomic data, ensuring traceability to anatomical origins via the Allen Brain Atlas²⁰ (RRID: SCR_017001). Underpinned by an agile development strategy, NIMP integrates NHash resource identifiers, metadata standardization, combinatorial dashboarding, resource provenance visualization, and dedicated interfaces with key partners such as the NIH NeuroBioBank (RRID: SCR_003131), sequencing centers, Neuroscience Multi-omic Archive (NeMO)²¹ (RRID: SCR_016152), and the broader BICAN data ecosystem.

Existing Tools for Data Query and Exploration

The exploration of biomedical data has evolved significantly, driven by the increasing complexity and scale of the datasets. Several domain-specific applications have emerged to address the challenges of integrating and analyzing rapidly expanding datasets. Applications such as HARVEST⁷, RiskScape⁸, and EWAS Data Hub⁹ are designed with specific problem-solving orientations. HARVEST⁷ specializes in temporal visualization of longitudinal patient records and extraction of data from clinical notes, and RiskScape⁸ enables visualization and aggregation of near-real-time electronic health record data for chronic disease investigation. EWAS Data Hub⁹ is a comprehensive repository that collects, normalizes, and archives DNA methylation array data and associated metadata. Beyond individual applications, integrative visualization platforms have gained prominence in biomedical research. i2b2t2¹⁰ exemplifies this approach by leveraging Tableau²² to enhance data visualization from the Informatics for Integrating Biology and the Bedside (i2b2) clinical data warehouse. Moreover, VisualSphere11 offers a web-based interactive visualization platform that simplifies data exploration by establishing direct connections to clinical research data repositories and generating interactive visualization dashboards for researchers without requiring programming expertise. The development of these tools reflects a shift toward making biomedical data analytics more accessible, allowing researchers and clinicians from various backgrounds to efficiently derive meaningful insights from large and complex biomedical data.

Methods

Unified Resource Browser is a comprehensive framework designed for biomedical data retrieval, customization, exploration, visualization, sharing, and exporting. It is built on a three-tier architecture comprising the Data Resource, Resource Browser Engine, and Resource Browser Interface. The Data Resource serves as the foundational storage component, managing both resource data collections and user-defined configuration preferences. The Resource Browser Engine acts as the system’s processing core, translating user inputs into optimized database queries and restructuring data for efficient presentation. The Resource Browser Interface provides an interactive and intuitive data exploration environment, enhancing usability and facilitating seamless research workflows.

System Architecture Design

Unified Resource Browser architecture is shown in Figure 2. The Data Resource stores resource data collections and user configurations. The Resource Browser Engine serves as the operational core, including three modules. The configuration management module stores and retrieves user customizations. The query engine transforms user customizations into database queries, processes the configurations, formulates optimized database queries, reorganizes data, and passes the query results to the interface. The Resource Browser Interface provides users with various configuration controls, including resource selection, project filtering, and column-based customizations. The interface features a biomedical data table, which supports real-time data sharing, exporting, and visualization. This design enables efficient data flow from storage to presentation while maintaining optimal system performance and user experience. Unified Resource Browser is developed using Ruby on Rails²³ (RRID: SCR_022129), MySQL²⁴ (RRID: SCR_025972), and HighCharts²⁵ (RRID: SCR_016095). Ruby on Rails²³ forms the foundational web framework, handling server-side operations and processing logic. MySQL²⁴ serves as the backend database, storing and retrieving resource data and user configurations. Highcharts²⁵ provides advanced interactive visualization capabilities, enabling dynamic chart generation.

Figure 2.

System architecture of Unified Resource Browser.

Data Resource Construction

The Data Resource repository comprises biomedical data collections and a user-defined configuration table, with an access control mechanism to manage data access privileges based on user roles. The biomedical data are organized in structured relational tables for efficient access and management. In this paper, the Data Resource is constructed from BICAN projects, currently housing ten preliminary collections: Donor, Slab, Tissue, Dissociated Cell Sample, Enriched Cell Sample, Barcoded Cell Sample, Amplified cDNA, Library, Library Aliquot, and Library Pool. For each collection, we extract relevant metadata to build the resource. User configurations are stored as customizable records that guide individualized data retrieval and presentation. The User Configuration table captures user-specific preferences, including filtering parameters and column-based customizations. Each configuration is identified by a universally unique identifier (UUID), ensuring traceability and facilitating seamless updates. These configurations are linked to individual users through foreign key relationships to maintain data integrity. Table indexing on UUID enhances query performance, enabling rapid access and application of filter settings.

The access control interface is designed to manage resource data access based on the user’s privileges, as shown in Figure 3. This module defines role-based permissions through column permission tables that specify which resource columns are accessible to designated roles, providing secure access to data subsets tailored to specific user roles. When users interact with the system, their assigned roles determine their data visibility and customization capabilities. The module creates a bridge between user preferences and Configuration Management, ensuring that retrieved or modified settings adhere to defined permissions. This architecture allows administrators to dynamically manage permission sets while maintaining data integrity, security, and regulatory compliance across various research scenarios.

Figure 3.

Design of the access control interface.

Resource Browser Interface Design

The Resource Browser Interface presents an interactive and user-centric front end. It comprises three modules: user configuration, resource data table rendering, and resource data sharing, exporting, and visualization. The configuration process starts with customizable resource selection, which guides users to identify and select from available resources (Figure 4.1). The project filtering (Figure 4.2) allows users to refine the displayed data dynamically according to the selected resource’s project. Moreover, column customization (Figure 4.3) features provide extensive flexibility, allowing users to include or exclude specific data columns and tailor the data view to individual analytical needs. Column-based filtering, sorting, and matching (Figure 4.4-6) will further enable granular refinement and targeted data exploration. The resource data table rendering module (Figure 4.7) functions as the interactive viewing core of Unified Resource Browser, enabling real-time, responsive, and user-friendly data interactions. It integrates user-defined configurations with efficiently processed resource data to deliver tailored presentations in tabular format. Upon user interactions, such as filtering, sorting, or pagination, JSON-formatted parameters are transmitted to the backend, which processes these requests to generate customized JSON responses. The front end then parses and displays these responses, dynamically updating the dataset without requiring full page reloads. The rendering process initiates by identifying the specific resource type and its associated database table and then configuring the data columns accordingly. Filtered, sorted, and paginated data is retrieved directly from the query engine, responding to user input parameters. This dynamic loading strategy ensures immediate data updates in response to user actions, such as modifying filter states, adjusting column visibility, or selecting different resource datasets, thereby enhancing the efficiency of research workflows.

Figure 4.

Unified Resource Browser interface main components.

In addition, session-based caching is used to optimize search performance. It distinguishes between exact and partial match logic based on defined parameters, ensuring precise yet flexible data retrieval and significantly improving query efficiency. Data cells are dynamically formatted using SQL-driven field population methods. The module implements server-side pagination, splitting large datasets into manageable segments to enhance loading speed and browser responsiveness. Users can smoothly navigate data subsets using intuitive pagination controls, streamlining the data exploration experience. The module provides immediate visual feedback for user interactions, including active sorting highlights, keyword-based search results, and real-time updates reflecting configuration changes. Configuration adjustments, including project-based, column-based, and general table settings, trigger instant rendering responses. Additionally, responsive design principles ensure the interface adapts seamlessly to various screen sizes, improving overall usability.

To facilitate collaborative research, we designed a URL-based data-sharing capability (Figure 4.8) that enables researchers to disseminate precise snapshots of their personalized data among collaborators. This feature captures the exact states of user selections, filtering conditions, sorting orders, and customized column arrangements, encapsulating them within unique and secure URLs. Recipients of these shared URLs can instantly retrieve and replicate identical interactive data views, eliminating the need to manually reproduce complicated customizations. We also implement data exporting (Figure 4.9) to extend analytical flexibility beyond the browser interface. Users can choose to export selected subsets of data or complete resource datasets directly into CSV format. The export process preserves all user-applied configurations, thus maintaining consistency between the interactive data table and exported files.

Furthermore, the visualization module (Figure 4.10) generates interactive charts to facilitate exploring biomedical data. This module integrates and extends VisualSphere¹¹, a web-based interactive visualization platform streamlining clinical research data analysis. Based on user configurations, the module automatically generates appropriate interactive charts utilizing Highcharts²⁵. Unified Resource Browser supports visualization of two primary data types: continuous and categorical. For continuous variables, users can examine data distributions through histograms or explore relationships via scatter plots. For categorical variables, the system dynamically adjusts the visualization based on the number of distinct categories: pie charts are used when there are five or fewer categories, and bar charts are displayed when the number of categories exceeds five.

Resource Browser Engine

The Resource Browser Engine is a key processing unit for retrieving, reorganizing, and filtering data. The Configuration Management retrieves, passes, and stores the user preferences. The preferences will be stored in the User Configuration Table and transmitted to the Query Engine, which tailors the query statement for the Data Resource. Subsequently, the Data Reorganization module systematically retrieves the reorganized data for rendering the data table and further data sharing, exporting, and visualization.

Configuration Management manages user-specific preferences and configurations, interacting with the User Configuration Table in the Data Resource and the Interface. When the user initiates interactions, such as selecting specific resource types, applying any filtering, or customizing visible columns, the Configuration Management module instantly retrieves or stores these customizations in real-time. Configurations are securely associated with users via UUIDs, ensuring rapid retrieval and precise reflection of users’ settings. The Configuration Management module ensures that each user interaction precisely updates session-specific parameters, such as selected resource properties, filters, sorting states, and selected projects, creating a personalized data-exploration environment.

Once the Query Engine receives user-defined configurations, it translates these dynamic preferences into optimized database queries. The Query Engine employs performance optimization strategies to ensure rapid query execution, including session-based caching to avoid redundant queries, and incremental data retrieval to handle complex, multi-criteria searches. The Query Engine dynamically constructs queries based on parameters such as user roles, resource types, project numbers, and active column permissions, rigorously enforcing role-based access controls. These optimized queries facilitate responsive data retrieval, enabling real-time data interaction and seamless exploratory analyses even on datasets of significant scale and complexity.

After the Query Engine retrieves data, the Data Reorganization module restructures these results to conform to the user’s real-time configuration requirements. It systematically applies user-specified sorting, filtering, and column visibility settings to the query results. This process leverages precise definitions of permissible columns, drawn from the role-based column permissions and user-specific selections stored within the Configuration Management module. Additionally, the Data Reorganization module organizes data for presentation, grouping columns by resource categories or joined data sets as needed to structure data logically. The final reorganized datasets are structured to facilitate immediate rendering by the Resource Browser Interface, ensuring rapid visual feedback and maintaining seamless user experience throughout dynamic interactions.

Results

Data Resources Constructed from NIMP

Unified Resource Browser has been deployed within NIMP system at UTHealth Houston, adhering to UTHealth Houston’s data use agreement. Unified Resource Browser streamlines the comprehensive exploration and visualization of ten distinct types of biomedical resource data, specifically from BICAN projects. To enhance usability and data integrity, NIMP systematically stores metadata corresponding to each resource type. This metadata provides critical contextual information, including details on data provenance, methodologies, and relevance to each resource type. By incorporating structured metadata, the system ensures accurate presentation, seamless integration, and meaningful interpretation of the biomedical resources available within NIMP.

Currently, the Data Resource stores ten distinct resource types from NIMP, illustrating the experimental pipeline for generating single-cell genomics data. Donors are entered into NIMP with a vast array of clinical pathology metadata by Brain Banks. Postmortem brain specimens are divided into thick (typically coronal) Brain Slabs. Brain Slab images are uploaded through a UI with tools to rotate and crop to individual slabs. Tissue can be requested from a Brain Bank by drawing a Region of Interest on the slab images. Multiple Tissues can be combined and processed by a Library Lab to create a Dissociated Cell Sample. Optionally, the cell sample can go through an enrichment step to select specific cell types, yielding an Enriched Cell Sample. The cell barcoding step adds a molecular barcode to each individual cell of the input cell sample. Portions of the Barcoded Cell Sample are then utilized to create one or more Libraries for sequencing. For gene expression analysis, amplification of the input material before Library generation is needed to have accurate and reliable sequencing of Amplified cDNA. Finally, a portion of a Library (Library Aliquot) is combined with other Library Aliquots to form a Library Pool. Each library aliquot in a library pool will have a unique index to allow them to be sequenced together and then demultiplexed, generating a set of FASTQ files ready for alignment for each library aliquot. Table 1 provides an overview of the stored resource data across 19 projects from BICAN community. For example, the Donor resource contains the most extensive metadata with 129 fields from 1,230 donors. Additionally, Library Aliquot contains 61 metadata fields with 13,200 records.

Table 1.

Summary statistics for the stored resource data in NIMP from BICAN projects.

Resource Type	Donor	Slab	Tissue	DCS*	ECS*	BCS*	Amplified cDNA	Library	Library Aliquot	Library Pool
Count of Metadata Fields	129	6	10	7	7	9	9	15	61	12
Total Count	1,230	8,593	12,196	5,426	4,859	10,168	7,835	13,191	13,200	778

^**Abbreviations: DCS, Dissociated Cell Sample; ECS, Enriched Cell Sample; BCS, Barcoded Cell Sample.

Interactive Data Exploration

The exploration process begins with selecting a resource from the dropdown menu (Figure 5A), allowing users to switch between ten available resources and view the related data. In this example, we chose “Donor” to illustrate the functions of interactive data exploration. Once a resource is selected, users can check for existing configurations (Figure 5B) and select from any previously saved ones if available. Upon selection, the system automatically loads the associated filter settings, reproducing previous data views without manual reconfiguration. Users may then proceed with the existing configuration or modify it to meet research needs.

Figure 5.

Configured Unified Resource Browser.

If no configurations exist, users can create new ones. The interface presents a set of configurable parameters specific to the selected resource. As configurations are adjusted, the data table dynamically updates in real time to reflect users’ selections. The Project filtering (Figure 5C) allows users to refine data based on associated projects. In this example, we selected “BICAN-NBB” for further exploration. The interface enables customization through selectable resource-specific columns (Figure 5D). By selecting checkboxes from the column list, users determine which columns to display. In this example, the Donor metadata includes 129 columns for selection, of which seven were chosen for illustration. Based on the selected columns, users can refine their data through filtering, sorting, and matching functions (Figure 5E-G). The filtering function (Figure 5F) supports text-based searches for text variables. The matching function (Figure 5G) assists users in selecting specific categories for categorical variables and range specifications for continuous variables. In this example, the system generates a list of categories for the variable “Autopsy Report”. Following the implementation of the specified configurations, the generated a data table displaying seven selected columns across 175 donors from the “BICAN-NBB” project who had autopsy reports, sorted by the “Repository” column.

Configuration Management for Data Exploration

Once the configuration process is complete, users can save their settings for future retrieval and analysis. In this example, we saved the configuration as shown in part (i) in Figure 5B. To create a new configuration, users can click the “Plus” button, which opens a pop-up window allowing them to name the new configuration (Figure 5B, part ii). Clicking the “Save” button enables users to update and save modifications to the current configuration, with a pop-up reminder (Figure 5B, part iii) appearing before finalizing the update.

Data Sharing, Exporting, and Visualization

After saving the configuration, users can share the configured data with collaborators through a URL link. By clicking the “Copy Link” button (Figure 5H), a pop-up window provides users with a URL link that encapsulates the current data table configuration. Collaborators can access the identical pre-configured data table directly via the provided link. Users also have the option to export or download filtered data in CSV format (Figure 5I), with choices to include either the current page view or the complete filtered dataset. Moreover, the interface offers visualization capabilities through the “Chart” button, facilitating the exploration of categorical and continuous data for selected columns. Figure 5J illustrates the visualization capabilities through examples generated from the selected donor data columns, where categorical variables are automatically rendered as bar charts when containing more than five unique categories and as pie charts when containing five or fewer distinct categories.

Discussion

Unified Resource Browser is an interactive web-based framework for biomedical data exploration, seamlessly integrating data retrieval, customization, visualization, sharing, and exporting. The system improves data accessibility and usability for researchers by leveraging a three-tier architecture: Data Resource, Resource Browser Engine, and Resource Browser Interface. The Data Resource organizes biomedical data in structured relational tables, manages user-defined configurations, and controls access based on user roles. The Resource Browser Engine enables fast, accurate querying and data processing using optimized algorithms. The Resource Browser Interface simplifies interaction with complex datasets through an intuitive design, facilitating efficient data exploration and insight extraction. With interactive components and configuration management, Unified Resource Browser streamlines dataset navigation and customization. The adaptable three-tier design enables seamless integration of new datasets beyond NIMP data, such as electronic health records, fostering innovation and accelerating research without requiring custom tool development.

In comparison to existing biomedical data exploration tools, Unified Resource Browser introduces several distinctive features that enhance research capabilities. While many applications focus on specific domains or individual datasets, Resource Browser provides integrated access to multiple related biomedical data collections from BICAN projects. Researchers can seamlessly navigate various datasets without switching between platforms, while the unified framework ensures flexibility in handling various data types with resource-specific customization. Another notable feature is the use of UUID-based configuration management, which allows users to save, retrieve, and share specific data views. This feature improves reproducibility and eliminates the need to manually reconstruct complex analyses. Furthermore, the system can automatically generate and export visualizations based on the selected data types, allowing users to interact with complex datasets in real-time. Beyond data exploration, the system aids in monitoring data integrity through an intuitive interface that reduces the learning curve for users. These integrated capabilities promote reproducibility, transparency, and efficiency in collaborative workflows, positioning Unified Resource Browser as a comprehensive system that supports biomedical data exploration and accelerates biomedical discovery.

Limitations and future work. The current Data Resource effectively manages NIMP data but may face challenges in scaling to larger datasets. To optimize performance, we plan to explore alternative strategies, including implementing an inverted index. Enhancements to Unified Resource Browser will include range queries for numerical data, allowing users to refine searches efficiently, and provenance tracking visualizations to facilitate seamless exploration of related data. Additionally, we plan to conduct a comprehensive usability evaluation interviewing with users from diverse backgrounds to capture varied perspectives on the system’s utility and effectiveness. We will employ the System Usability Scale (SUS)²⁶ to obtain standardized usability metrics, supplemented by questionnaires to collect user feedback on system functionality and user experience. This formal evaluation will help identify areas for refinement in interface design, performance optimization, and overall usability.

Conclusion

We have introduced the architecture of Unified Resource Browser, a meticulously designed framework that enhances data FAIRness, streamlines analysis, and fosters collaboration. Comprising the Data Resource, Resource Browser Engine, and Interface, this system ensures a seamless workflow from data exploration and retrieval, to processing and visualization. By eliminating the need for complex, code-based queries, the intuitive interface allows researchers to focus on deriving meaningful insights rather than overcoming technical barriers. The integration of dynamic user configurations, real-time data interaction, and advanced visualization tools transforms how researchers engage with complex datasets. Additionally, collaborative features such as URL-based sharing and configuration management promote transparency, reproducibility, and efficiency in data-driven research, ultimately advancing scientific discovery.