Update README.md

README.md

@@ -23,7 +23,7 @@ Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
 |License|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)|
 |Citations|[1] [2]|
 
-***LenDB*** consists of seismograms recorded from multiple different seismic detection networks from across the globe [1, 2]. The processed dataset consists of 1,244,942 multivariate time series, with 3 channels, each of length 540, with two classes: earthquake and noise. This version of the dataset has been split into cross-validation folds based on seismic detection network (i.e., such that seismograms for a given network do not appear in both a training and validation fold).
+***LenDB*** consists of seismograms recorded from multiple different seismic detection networks from across the globe [1, 2]. The sampling rate is 20 Hz.  The processed dataset consists of 1,244,942 multivariate time series, with 3 channels, each of length 540 (i.e., just under 30 seconds of data per time series at a sampling rate of 20 Hz), with two classes: earthquake and noise. This version of the dataset has been split into cross-validation folds based on seismic detection network (i.e., such that seismograms for a given network do not appear in both a training and validation fold).
 
 [1] Fabrizio Magrini, Dario Jozinovic, Fabio Cammarano, Alberto Michelini, and Lapo Boschi. (2020). Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale. *Artificial Intelligence in Geosciences*, 1:1–10.