Chloe : Flexible, Efficient Data Provenance and Management

1 Abstract Reproducible and sharable research requires robust data provenance during and after the experimental process. Each laboratory and experiment has its own goals and methods, and these change frequently. Planning, managing, and collecting data from research crops are particularly labor-intensive tasks, given the tightly compressed time schedule and the operating environments. Moving from a lab’s present record-keeping approach to an electronic ecosystem that improves provenance is an additional burden for groups without dedicated, consistent computational support to make that transition and then to adapt the system as needed. This high barrier to entry and the press of field work makes it easy to postpone “computerizing”. I have developed Chloe to reduce manual effort during experiments and maintain data provenance. A flexible, modular system, Chloe integrates simple equipment, data collection strategies, and software into workflows. The design lets one use parts without deploying the whole. This reduces the barriers to entry while still improving workflow efficiency and making Chloe accessible to a wide range of users. I offer guidance on ways to adapt Chloe to one’s own experimental situation. Chloe has been tested and refined with many changes of students, hardware, and experimental goals over the last fourteen years. Though originally designed for maize genetics and computational experiments, Chloe can accommodate other types of experiments, wetbench work, and other crops.