Shortening the cycle from data collection to research publications is a competitive advantage for researchers. Existing technologies for inventory systems such as UPC barcoding systems can be coupled with flexible mobile or handheld devices to advance efficiency, productivity, automation, and integrity in data flows, from data collection to sample processing to database management and analysis, and finally publication. At the University of Texas, the Data Flow Infrastructure Initiative (DFII) has introduced handheld devices with integrated barcode scanners as a mechanism to enhance research productivity and information access. These devices are established technology and provide a flexible but consistent platform for research data collection and data management. They are not in widespread use yet in the research community. Additional application benefits will accrue by using handheld devices to deliver data on demand in teaching applications. Introducing research scientists, graduate students, and the UT community to the merits and flexibility of these data collection technologies will provide avenues for innovation as well as improving efficiency. The objective of this project is to bring the technology and expertise with handheld systems to a diverse set of pilot projects and establish proficiency here at The University of Texas at Austin necessary for widespread application.
At the University of Texas, we have implemented a pilot project in three research labs covering the fields of microbial ecology, water resources decision support, and biogeochemistry to introduce these technologies. DFII will use the NautizX5™ handheld device that includes barcode scanning, bluetooth, stylus, and keypad data inputs coupled with Pendragon Forms Software™, a program that allows users to create custom data collection forms structured into an SQL or Access platform thus allowing concurrent data management, data collection and analysis in field and lab settings.
Results include the elimination of most manual data entry, reducing data entry error, tracking effectiveness at data collection, and increased sampling efficiency and consistency over multi-year experiments.