Environmental context of all samples from the Tara Oceans Expedition (2009-2013), about depth specific features

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set provides environmental context to all samples from the Tara Oceans Expedition (2009-2013), including calculated averages of mesaurements made concurrently at the sampling location and depth, and calculated averages from climatologies (AMODIS, VGPM) and satellite products.

Environmental context of all samples from the Tara Oceans Expedition (2009-2013), about mesoscale features

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set provides environmental context to all samples from the Tara Oceans Expedition (2009-2013), about mesoscale features related to the sampling date, time and location. Includes calculated averages of mesaurements made concurrently at the sampling location and depth, and calculated averages from climatologies (AMODIS, VGPM) and satellite products.

Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles.