<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are an expert at identifying data availability statements in scientific text. A data availability statement describes where research data can be found or how it can be accessed.<|eot_id|><|start_header_id|>user<|end_header_id|>

Determine if the following text contains a data availability statement. Answer with 'yes' or 'no'.

Text: In all experiments , we obtain this estimatorh Ttr , λ , γ by learning it on a dataset Z tr of T tr training tasks , each comprising a dataset Z n of n input - output pairs ( x , y ) ∈ X × Y. We perform this meta - training for different values of λ ∈ { λ 1 , . . . , λ p } and γ ∈ { γ 1 , . . . , γ r } and we select the best estimator based on the prediction error measured on a separate set Z va of T va validation tasks . Once such optimal λ and γ values have been selected , we report the average risk of the corresponding estimator on a set Z te of T te test tasks . In particular , for the synthetic data we considered 10 ( 30 for the real data ) candidates values for both λ and γ in the range [ 10 −6 , 10 3 ] ( [ 10 −3 , 10 3 ] for the real data ) with logarithmic spacing . Note that the tasks in the test and validation sets Z te and Z va are all provided with both a training and test dataset both sampled from the same distribution . Since we are interested in measuring the performance of the algorithm trained with n points , the training datasets have all the same sample size n as those in the meta - training datasets in Z tr , while the test datasets contain n points each , for some positive integer n . Indeed , in order to evaluate the performance of a bias h , we need to first train the corresponding algorithmw h on the training dataset Z n , and then test its performance on the test set Z n , by computing the empirical risk R Z n ( w h ( Z n ) ) .<|eot_id|><|start_header_id|>assistant<|end_header_id|>

no<|eot_id|>