Before revision:  But, classic chaos-detection tools are highly sensitive to measurement noise , which has made it difficult to distinguish between stochastic, periodic, and chaotic biological processes. To aid in the experimental study of chaotic systems, we here combine several new mathematical tools into a fully automated processing pipeline, which can accurately assess the presence and degree of chaos in a diverse range of measurements, biological or otherwise, even when those measurements are noisy , and even for difficult edge cases.
After revision:  But, classic chaos-detection tools are highly sensitive to measurement noise , which has made it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, which can accurately assess the presence and degree of chaos in a diverse range of measurements, biological or otherwise, even when those measurements are noisy , and even for difficult edge cases.