Group Sunspot Numbers: A New Reconstruction of Sunspot Activity Variations from Historical Sunspot Recordings and with Algorithms from Machine Learning
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Recording of historical sunspots and construction of a comprehensive database are among the most sought after research activities in solar physics. Here, we revisit the issues and remaining questions on the reconstruction of the so-called Group Sunspot Numbers (GSN) that was pioneered by D. Hoyt and colleagues. We used the modern tools of Artificial Intelligence (AI) by applying various algorithms based on Machine Learning (ML) to GSN records. The goal is to offer a new vision in the reconstruction of sunspot activity variations,i.e., a Bayesian reconstruction in order to obtain a complete probabilistic GSN record from 1610 to 2020. This new GSN reconstruction is consistent with the historical GSN records. In addition, we performed a comparison between our new probabilistic GSN record and the most recent GSN reconstructions produced by ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Group Sunspot Numbers"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Recording of historical sunspots and construction of a comprehensive database are among the most sought after research activities in solar physics. Here, we revisit the issues and remaining questions on the reconstruction of the so-called Group Sunspot Numbers (GSN) that was pioneered by D. Hoyt and colleagues. We used the modern tools of Artificial Intelligence (AI) by applying various algorithms based on Machine Learning (ML) to GSN records. The goal is to offer a new vision in the reconstruction of sunspot activity variations,i.e., a Bayesian reconstruction in order to obtain a complete probabilistic GSN record from 1610 to 2020. This new GSN reconstruction is consistent with the historical GSN records. In addition, we performed a comparison between our new probabilistic GSN record and the most recent GSN reconstructions produced by ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Recording of historical sunspots and construction of a comprehensive database are among the most sought after research activities in solar physics. Here, we revisit the issues and remaining questions on the reconstruction of the so-called Group Sunspot Numbers (GSN) that was pioneered by D. Hoyt and colleagues. We used the modern tools of Artificial Intelligence (AI) by applying various algorithms based on Machine Learning (ML) to GSN records. The goal is to offer a new vision in the reconstruction of sunspot activity variations,i.e., a Bayesian reconstruction in order to obtain a complete probabilistic GSN record from 1610 to 2020. This new GSN reconstruction is consistent with the historical GSN records. In addition, we performed a comparison between our new probabilistic GSN record and the most recent GSN reconstructions produced by 
Group Sunspot Numbers