Boilerplate Language Examples for REDCap at USA
Please cite the publication below in study manuscripts using REDCap for data collection and management. We recommend the following boilerplate language:
Study data were collected and managed using REDCap electronic data capture tools hosted at the University of South Alabama.1 REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources.
1Paul A. Harris, Robert Taylor, Robert Thielke, Jonathon Payne, Nathaniel Gonzalez, Jose G. Conde, Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42(2):377-81.
Link to article: http://www.sciencedirect.com/science/article/pii/S1532046408001226
General Boilerplate Language
REDCap (Research Electronic Data Capture) data collection projects rely on a thorough study-specific data dictionary defined in an iterative self-documenting process by all members of the research. The iterative development and testing process results in a well-planned data collection strategy for individual studies. REDCap also contains a survey tool for building and managing online surveys. The research team can create and design surveys in a web browser and engage potential respondents using a variety of notification methods. Both REDCap and REDCap Survey systems provide secure, web-based applications that are flexible enough to be used for a variety of types of research, provide an intuitive interface for users to enter data and have real time validation rules (with automated data type and range checks) at the time of entry. These systems offer easy data manipulation with audit trails and reporting and an automated export mechanism to common statistical packages (SPSS, SAS, Stata, R/S-Plus).
REDCap (Research Electronic Data Capture) data collection projects rely on a thorough study-specific data dictionary defined in an iterative self-documenting process by all members of the research team. The iterative development and testing process results in a well-planned data collection strategy for individual studies. REDCap servers are securely housed in an on-site limited access data center managed by the Computer Center at the University of South Alabama. All web-based information transmission is encrypted. The data is all stored on a private, firewall protected network. All users are given individual user ids and passwords and their access is restricted on a role-specific basis. REDCap was developed specifically around HIPAA-Security guidelines and is implemented and maintained according to University of South Alabama guidelines. REDCap currently supports > 500 academic/non-profit consortium partners on six continents and 38,800 research end-users.
P.A. Harris, R. Thielke, R. Taylor, J. Payne, N. Gonzalez, J.G. Conde. Research Electronic Data Capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 2008 (doi:10.1016/j.jbi.2008.08.010).
P.A. Harris, R. Taylor, R. Thielke, J. Lee, R. Sanders, M. Isozaki, H. Howard, S. Hemphill, C. McGraw, B. Nieves, A. Peshansky, A. Nida, M. Lin. The REDCap Consortium Project: A Case Study in Collaborative Software Development for Clinical Research Informatics. (Panel Presentation – AMIA Spring Conference, 2008)
P. Harris, R. Thielke, R. Schuff, J. Obeid, M. Oium. The REDCap consortium – A case study in translational research informatics resource sharing among academic institutions. (AMIA Spring Conference, 2007) P.A. Harris. REDCap (Research Electronic Data CAPture) project progress report for informatics resource sharing / collaboration at ten academic institutions. (Clinical Research, 2007)
P.A. Harris, N. Gonzalez, M Silva-Ramos, J.G. Conde. Web-based data collection – collaborative development of metadata collection and export modules. (Clinical Research, 2006) P.A. Harris, J.D. Payne. Creating custom web-based data collection systems. (Clinical Research, 2005) (Abstract)