Research Web Based Data Services

The Vendor is required to provide for a research web based data services.
- A web based solution for research data services.
- The system must fully support the following capabilities:
•    High-performance computing: Must provide access to powerful servers with significant processing capacity (minimum up to 24 CPU cores and 256 GB RAM per compute node) to support large-scale statistical analysis, simulations, and data-intensive research workloads.
•    Scalable research platform: Must enable execution of custom programs, batch jobs, and parallel processing, allowing simultaneous analysis across multiple large datasets without performance degradation.
•    Direct high-speed data access: Must offer low-latency, high-throughput access to hosted datasets, enabling efficient querying, data extraction, and integration for big-data research.
•    Programming language support: Must support a broad range of analytical and statistical programming languages, including but not limited to SAS, Python, R, and MATLAB, with support for user-installed packages and libraries.
•    Dedicated user storage: Must provide a secure, dedicated user directory for storing programs and output data, along with large temporary/scratch disk space for intermediate processing and computation results.
•    Secure remote accessibility: Must be accessible via SSH (Secure Shell) and support secure web-based environments such as RStudio Server, Jupyter Notebooks, and browser-based data analysis tools.
•    Multi-user and concurrent processing support: Platform must support simultaneous access by multiple users and allow concurrent execution of multiple jobs without significant performance loss.
•    Data security and compliance: Must meet institutional and industry-standard security requirements, including user authentication, access controls, encrypted connections, and secure data storage.
•    Reliability and system uptime: Must ensure high availability, regular system maintenance, backup, and minimal downtime for uninterrupted research access.
•    Large-scale data handling: Must be capable of handling very large structured and unstructured datasets, including high-volume tables, time-series data, and panel data.
•    Interoperability and data integration: Must allow cross-dataset linking, merging, and integration across multiple data sources and formats.
•    Performance optimization: Must support efficient job scheduling, queuing, and workload management to maximize system utilization and user productivity.
•    User-friendly analytical environment: Must provide both command-line and graphical user interfaces, suitable for beginner and advanced users.
•    Export and results management: Must allow users to download, export, and archive processed results securely for offline analysis and reporting.
•    Version control and reproducibility support: Platform should support reproducible research workflows, including version control compatibility and environment consistency.
•    Technical documentation and user support: Must provide comprehensive technical documentation, tutorials, and responsive technical support to assist users with platform usage.
•    Educational and research use readiness: Must be suitable for both academic research and instructional use, allowing integration into coursework and research projects.
•    Scalability and future expansion: Must support scaling of compute, storage, and user capacity based on institutional growth and evolving research demands.
- The main document for this RFP are not accessible on our website. Kindly reach out to the contact person listed in this description for more details.