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Expediting the Production of High Value, Standardized, and Transparent Data

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R44TR000030-01A1
Agency Tracking Number: R44TR000030
Amount: $132,789.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NCATS
Solicitation Number: PA11-096
Solicitation Year: 2012
Award Year: 2012
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
7 Thurber Boulevard
Smithfield, RI 02917-
United States
DUNS: 11787640
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: Yes
Principal Investigator
 (401) 331-1500
Business Contact
Phone: (401) 232-3282
Research Institution

DESCRIPTION (provided by applicant): Researchers and contractors are under increasing pressure to produce rapid turnaround times for data releases and publications. Inadequate data management is a common threat to good research and impedes the quick release of high value data. Despite its importance, many organizations do not have standardized data management practices that allow them to prepare high quality datasets quickly, effectively, and with transparency. As a result, production delays, inefficiencies, inconsistencies, and data errors are common. This proposal describes the development of a practical, user-friendly electronic tool, the Data ExpediterTM (DE) Syntax Generator, which uses step-by-step data management procedures combined with cutting edge,innovative algorithms to standardize and automate processes involved in creating high quality analytic files. Designed with researchers in health, social, and educational sciences in mind, the product walks the user through a best practice model for datamanagement, specifically for tasks involved in creating clean and accurate data analytic files. Readily available metadata, syntax, and reports make monitoring studies, creating new databases, controlling data file releases, implementing changes, analyzingdata, and training new project staff faster and more cost effective than ever. Phase I aims are to: (1) develop and test a working prototype; (2) conduct focus group research to assess the acceptance of the prototype; and (3) develop a full set of functional specifications. Upon demonstration of Phase I feasibility, Phase II aims are to: (1) build the commercialized DE Syntax Generator; (2) conduct usability testing to demonstrate ease of use; (3) conduct field tests in actual research settings at Harvardand Brown Universities; and (4) develop educational and product support materials. As an outcomes-driven innovation, the product will occupy a unique place in the market with its guided data management process and large array of automation tools. The products provides a unique and affordable solution to a common problem by using a standardized, uniform framework and automated syntax generation while allowing data users (e.g., researchers, administrators, those working with electronic patient records) to house data in their existing statistical software. The DE Syntax Generator replaces the chaotic, slow, and error-ridden, data management process with an organized, streamlined, and complete sequence of steps with interwoven quality control and automation to ensure rapid production of quality data. The DE Syntax Generator will improve the efficiency of studies, enhance data sharing, and decrease data and reporting errors. In sum, the product addresses an unmet need by helping researchers and data managers to follow state-of-the art practice guidelines for data management (transparency and proper documentation of all procedures) while getting the job done better, faster, and less expensively. PUBLIC HEALTH RELEVANCE: The Data Expediter Syntax Generatorsoftware application offers health, social, and education researchers a practical, cost-effective solution for managing, rapidly processing and documenting study data utilizing technology that makes these high-value activities readily transparent. The product has the potential to decrease data and reporting errors, increase data quality, improve research study efficiency, and enhance data sharing.

* Information listed above is at the time of submission. *

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