The dataset entitled “imputedsalt_VerBa” holds data structures to be relayed to statistical modeling of salinity in near-coastal environments of the Northern Gulf of Mexico. The content therein is created by the ‘covardr2formodel.R’ script that represents terminal decisions on variable setup and transformations). The design ideal is that the content of this directory would be copied to some path of “input” within a model building repository. The table in this directory would be especially suitable for both (1) formal data releases supporting as needed interpretive publications and (2) simultaneous long-term storage as copies within a model building repository in some specific terminal decisions (variable transforms or even dropping of whole data classes by switches in ‘covardr2formodel.R) are reflected in these data and they are a step removed as it were from the “raw” data in the ‘covardr/’ directory.

The dataset folder entitled “LagMa” holds data structures consisting of statistical predictions of daily salinity time series for the Laguna Madre (LagMa) group, generated from the makESTUSAL software repository described by Asquith and others (2023b). The statistical methods included multiple methods of machine learning, which produced the daily salinity prediction and attendant credible uncertainties included in the data release. The geographic scope of the LagMa group includes the predictions for three locations defined using agency code and salinity site abbreviations.

The dataset folder entitled “SabLa” holds data structures consisting of statistical predictions of daily salinity time series for the Sabine Lake (SabLa) group, generated from the makESTUSAL software repository described by Asquith and others (2023b). The statistical methods included multiple methods of machine learning, which produced the daily salinity prediction and attendant credible uncertainties included in the data release. The geographic scope of the SabLa group includes the predictions for two locations defined using agency code and salinity site abbreviations.

The dataset folder entitled “SanAn” holds data structures consisting of statistical predictions of daily salinity time series for the San Antonio Bay (SanAn) group, generated from the makESTUSAL software repository described by Asquith and others (2023b). The statistical methods included multiple methods of machine learning, which produced the daily salinity prediction and attendant credible uncertainties included in the data release. The geographic scope of the SanAn group includes the predictions for five locations defined using agency code and salinity site abbreviations.

The dataset entitled “imputedsalt_LagMa” holds data structures to be relayed to statistical modeling of salinity in near-coastal environments of the Northern Gulf of Mexico. The content therein is created by the ‘covardr2formodel.R’ script that represents terminal decisions on variable setup and transformations). The design ideal is that the content of this directory would be copied to some path of “input” within a model building repository. The table in this directory would be especially suitable for both (1) formal data releases supporting as needed interpretive publications and (2) simultaneous long-term storage as copies within a model building repository in some specific terminal decisions (variable transforms or even dropping of whole data classes by switches in ‘covardr2formodel.R) are reflected in these data and they are a step removed as it were from the “raw” data in the ‘covardr/’ directory.

The dataset folder entitled “MatLa” holds data structures consisting of statistical predictions of daily salinity time series for the Matagorda-Lavaca Bay (MatLa) group, generated from the makESTUSAL software repository described by Asquith and others (2023b). The statistical methods included multiple methods of machine learning, which produced the daily salinity prediction and attendant credible uncertainties included in the data release. The geographic scope of the MatLa group includes the predictions for seven locations defined using agency code and salinity site abbreviations.

The dataset folder entitled “MatLa” holds data structures consisting of statistical predictions of daily salinity time series for the Matagorda-Lavaca Bay (MatLa) group, generated from the makESTUSAL software repository described by Asquith and others (2023b). The statistical methods included multiple methods of machine learning, which produced the daily salinity prediction and attendant credible uncertainties included in the data release. The geographic scope of the MatLa group includes the predictions for seven locations defined using agency code and salinity site abbreviations.

The dataset entitled “imputedsalt_MatLa” holds data structures to be relayed to statistical modeling of salinity in near-coastal environments of the Northern Gulf of Mexico. The content therein is created by the ‘covardr2formodel.R’ script that represents terminal decisions on variable setup and transformations). The design ideal is that the content of this directory would be copied to some path of “input” within a model building repository. The table in this directory would be especially suitable for both (1) formal data releases supporting as needed interpretive publications and (2) simultaneous long-term storage as copies within a model building repository in some specific terminal decisions (variable transforms or even dropping of whole data classes by switches in ‘covardr2formodel.R) are reflected in these data and they are a step removed as it were from the “raw” data in the ‘covardr/’ directory.

The dataset entitled “imputedsalt_MatLa” holds data structures to be relayed to statistical modeling of salinity in near-coastal environments of the Northern Gulf of Mexico. The content therein is created by the ‘covardr2formodel.R’ script that represents terminal decisions on variable setup and transformations). The design ideal is that the content of this directory would be copied to some path of “input” within a model building repository. The table in this directory would be especially suitable for both (1) formal data releases supporting as needed interpretive publications and (2) simultaneous long-term storage as copies within a model building repository in some specific terminal decisions (variable transforms or even dropping of whole data classes by switches in ‘covardr2formodel.R) are reflected in these data and they are a step removed as it were from the “raw” data in the ‘covardr/’ directory.

The dataset entitled “imputedsalt_CalLa” holds data structures to be relayed to statistical modeling of salinity in near-coastal environments of the Northern Gulf of Mexico. The content therein is created by the ‘covardr2formodel.R’ script that represents terminal decisions on variable setup and transformations). The design ideal is that the content of this directory would be copied to some path of “input” within a model building repository. The table in this directory would be especially suitable for both (1) formal data releases supporting as needed interpretive publications and (2) simultaneous long-term storage as copies within a model building repository in some specific terminal decisions (variable transforms or even dropping of whole data classes by switches in ‘covardr2formodel.R) are reflected in these data and they are a step removed as it were from the “raw” data in the ‘covardr/’ directory.