Citation:
Citation_Information:
Originator: Marco Grillo, Stefano Schiaparelli and Falk Huettmann
Publication_Date: 20240501
Title:
Antarctic copepod interaction data mining manuscript (Grillo et al.): Data Section 3 of 3: Machine Learning Ensemble Models for predicted distribution models
Edition: 1
Geospatial_Data_Presentation_Form: raster digital data
Other_Citation_Details: Compiled data set
Online_Linkage: xxx
Larger_Work_Citation:
Citation_Information:
Originator: Grillo et al. from compiled data sources, e.g. Macroscope
Publication_Date: 20240501
Title:
Machine learning applied to species occurence and interactions: the missing link in open access biodiversity assessment and modelling of Antarctic plankton distribution
Edition: 1
Geospatial_Data_Presentation_Form: raster digital data
Description:
Abstract:
This data set is 1 of 3 parts of a wider manuscript by Grillo et al. Here we present and describe the ensemble models used which are provided in a Salford Predictive Modeler format files. They are zipped and ready to be used for a model run using data described in Grillo et al.
It includes the following ensemble model algorithms: CARTs, TreeNet (boosting) and RandomForest (bagging)
This is one data section out of 3 other data set sections (GIS predictors, presence only locations, and .machine learning ensemble model predictions).
Purpose: Foundation for the manuscript by Grillo et al.
Supplemental_Information: See details in Grillo et al.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date:Ending_Date:
Currentness_Reference: ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Description_of_Geographic_Extent: World
Bounding_Coordinates:
West_Bounding_Coordinate: -29.5313
East_Bounding_Coordinate: -11.6016
North_Bounding_Coordinate: 68.1389
South_Bounding_Coordinate: 60.2398
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: biota
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword:
Place:
Place_Keyword_Thesaurus: None
Place_Keyword:
High resolution 250m pixel predictors, geographic information system (GIS), data, data mining, machine learning, open access
Access_Constraints: None, as described in the manuscript.
Use_Constraints: None, as described in the manuscript.
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Marco Grillo
Contact_Organization:
Department of Physical Sciences, Earth and Environment (DSFTA), University of Siena,
Contact_Address:
Address_Type: mailing and physical
Address:City: Reykjavik,
State_or_Province:Postal_Code:Country: Iceland
Contact_Voice_Telephone:Contact_Electronic_Mail_Address: grillomarco94@gmail.com
Data_Set_Credit:
Salford Predictive Modeler SPM is used, details provided in the manuscript.
Native_Data_Set_Environment:
Data come from Salford Predictive Modeler SPM details provided in the manuscript.