Life Cycle Phases (rows) Being part of a military command and having familiarity with the field of intelligence, the authors reviewed the U.S. Intelligence Cycle for applicability. Following the review, the authors decided to adopt this model for use within their framework. The life cycle consists of the following phases: • Planning and Direction – Establish the consumer’s intelligence requirements and plan intelligence activities accordingly. • Collection – Gather the raw data required to produce the finished product. • Processing and Exploitation – Convert the raw data into a comprehensible format that is usable for production of the finished product. • Analysis and Production – Integrate, evaluate, analyze, and prepare the processed information for inclusion in the finished product. • Dissemination – Deliver the finished product to the consumer that requested it and to others as applicable. • Evaluation – Continually acquire feedback during the Intelligence Cycle and evaluate that feedback to refine each individual step and the cycle as a whole. Project Background The Readiness Integration Center (RIC) is a Department of Defense (DoD) science gateway purpose built to provide data analysis and decision support capabilities to the Naval Aviation community with a primary emphasis on aircraft readiness and safety. The RIC ingests data from a variety of DoD authoritative sources (i.e., the systems of record charged with being the official data store), including: aircraft sensor data related to heat, vibration, built-in-test results, and fault identification; flight logs; aircraft status; maintenance records; repair manuals; supply status; and engineering data. RIC users come from several communities such as maintenance, supply, training, and engineering. These users have access to dozens of custom-built, automated and manual analysis tools as well as source data, processed data, and high-performance computing resources. Problem & Solution Recognizing that “garbage in” leads to “garbage out,” many organizations focus their efforts on pursuing a zero- defect data ingestion process. While these efforts are critical for reducing quality-related risks, the authors wanted to ensure their science gateway not only had procedures for managing quality-related risks at ingestion but also for managing such risks during subsequent analysis and reporting. By having published procedures in place before an incident, teams could better mitigate the cascade of risks that could occur if poor quality data, despite best efforts, made its way into the science gateway. For example, erroneous data could inadvertently be used during modeling and simulation activities. Then, the results of the modeling and simulation activities that were based on erroneous data could be used as evidence for a written report. That written report, based on the results of modeling and simulation activities that were based on erroneous data, could then be used as a basis for critical decisions. To ensure the science gateway had procedures for managing quality-related risks such as those in the example above, the authors developed a quality risk management framework covering 36 unique areas where quality risks could arise. This framework is intended to be used, as it was by the authors, to evaluate a science gateway’s readiness to manage quality issues arising at data ingestion and subsequent phases such as analysis and dissemination. Results In the case of the authors’ science gateway, use of the framework provided critical insights. Specifically, quality risk management procedures were written and published for only 15 of the 36 areas identified by the matrix, quality risk management procedures were written but not published for 14 of the 36 areas, and 7 of the 36 areas had no associated quality risk management procedures. Based on these findings, the authors worked with other team members to ensure the missing quality risk management procedures were written and/or publishe