Clinical and Research Data Warehouse (CRDW)

Fee* schedule for Rutgers CRDW services

*Prices subject to change

Fees associated with consultation, data analytics services, data extraction (ETL), data tenancy, and other CRDW services are charged according to affiliation (Rutgers users, non-Rutgers academic, and/or industry/external non-academic) as is standard for core facilities in compliance with federal Uniform Guidance. Please contact us at crdw_requests@oarc.rutgers.edu for further information and project quotes.

SKUDescriptionRutgers rateExternal academicPrivate sectorUnit
User Support and Consulting
CRDW-4.0 Data Extraction Services$90$141.30$282.60Per hour
CRDW-5.0Supervised Data Transfer Services (in/out)$75$117.75$235.50Per hour
CRDW-5.1Unsupervised Data Transfer Services (in/out)$25$39.25$78.50Per transfer
CRDW-6.0Consultation and Data Analytic Services$90$141.30$282.60Per hour
Instrument usage
GPC-1.0Standard High Performance GPU unit (HPC cluster)$0.31$0.49$0.971 GPU card per hour
HPC-1.0Standard High Performance CPU node (HPC cluster)$0.01$0.02$0.031 CPU/8GB RAM/2 hours
HPC-2.0High Memory High Performance CPU node (HPC cluster)0.020.030.061 CPU/32GB RAM/2 hours
VM-1.0Standard Virtual Machine (Linux OS or Windows)$318$499.26$998.524vCPU/16GB RAM/100GB system storage/year
Data Storage
CRDW 8.0Isilon Storage – Standard encrypted production storage for unstructured data$0.24$0.38$0.75Per GB/Year
CRDW 10.0Block Storage – Standard encrypted production storage for structured data$1.96$3.08$6.15Per GB/Year
CRDW-14.0Object Storage – Encrypted Object data storage for unstructured data$0.08$0.13$0.25Per GB/Year

Description for CRDW services

CRDW 1: Pre-consultation (Counts)

CRDW 1, pre-consultation (Counts), is an initial assessment of the clinical data available, generating counts and statistics to outline the volume and types of stored data. It helps in understanding the data scope before delving into deeper analysis.

CRDW 8: Encrypted object storage (Isilon)

Used for storing unstructured data at low cost. For example, Medical Images would typically use this type of storage. All data is encrypted at rest and protected by Erasure Encoding.

CRDW 4: Data extraction (ETL)

ETL involves gathering, transforming, and loading data from different sources into the clinical data warehouse. It ensures collected information is cleaned and organized for easy analysis and retrieval.

CRDW 10: Encrypted block storage

Used by both CRDW Databases and Virtual Hosting Compute, this high-performance storage is used where high IO performance is required. All data is encrypted at rest, and is protected using NetBackup, Database Tools, and Data Domain appliances.

CRDW 5: Data transfer (ingestion and egress)

Data Transfer manages moving data into (Ingestion) and out of (Egress) the warehouse. Ingestion brings data in, ensuring proper storage, while Egress securely exports data for analysis, reporting, or sharing with external systems.

BM1: Bare metal node

BM1 offers full access to a dedicated node in the clinical data warehouse, with size and configuration subject to availability. Users manage software installation and maintenance, receiving limited support from warehouse sysadmins for assistance.

CRDW 6: Data analytic services

A comprehensive examination of clinical data to identify trends, patterns, and correlations. It includes data mining, predictive modeling, and statistical analysis, aimed at supporting clinical decisions, improving patient outcomes, and advancing research.

VM 1: Standard virtual machine

VM 1 is a standard virtual machine setup in the clinical data warehouse. It includes 4 virtual CPUs, 16 GB of RAM, and 40 GB of disk space. This ready-to-use configuration comes with user-selected software for data analysis and processing tasks, enabling smooth computing operations within the warehouse.

CRDW 7: Consultation

The consultation service offers expertise in interpreting data analysis results. CRDW staff members work closely with researchers for better addressing their requests and research needs, guide them through clinical data complexities, and provide actionable insights for clinical practice and research.