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linking some papers

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@@ -54,12 +54,7 @@ https://chaosmagpy.readthedocs.io/en/master/
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  A description of the dataset:
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  The gufm1 model is a global geomagnetic model based on spherical harmonics, covering the period 1590 - 1990, and is described in the publication:
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- Andrew Jackson, Art R. T. Jonkers and Matthew R. Walker (2000), “Four centuries of geomagnetic secular variation from historical records”, Phil. Trans. R. Soc. A.358957–990, http://doi.org/10.1098/rsta.2000.0569
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- The native model representation is converted into a discrete dataset in physical space and time, using the Python package https://chaosmagpy.readthedocs.io/en/master/
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- The dataset has dimension (181, 361, 401) whose axes represent co-latitude, longitude, time, and whose values are the radial magnetic field at the core-mantle boundary (radius 3485km) in nT.
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- The colatitude takes values (in degrees): 0,1,2,3,…180; longitude (degrees) takes values -180,-179,….180; and time is yearly 1590, 1591, …1990.
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  ### Supported Tasks and Leaderboards
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  ### Data Fields
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  ## Dataset Creation
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  ### Source Data
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  ## Additional Information
 
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  A description of the dataset:
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  The gufm1 model is a global geomagnetic model based on spherical harmonics, covering the period 1590 - 1990, and is described in the publication:
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+ [Andrew Jackson, Art R. T. Jonkers and Matthew R. Walker (2000), “Four centuries of geomagnetic secular variation from historical records”, Phil. Trans. R. Soc. A.358957–990, http://doi.org/10.1098/rsta.2000.0569](https://royalsocietypublishing.org/doi/10.1098/rsta.2000.0569)
 
 
 
 
 
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  ### Supported Tasks and Leaderboards
 
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  ### Data Fields
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+ The dataset has dimension (181, 361, 401) whose axes represent co-latitude, longitude, time, and whose values are the radial magnetic field at the core-mantle boundary (radius 3485km) in nT.
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+ The colatitude takes values (in degrees): 0,1,2,3,…180; longitude (degrees) takes values -180,-179,….180; and time is yearly 1590, 1591, …1990.
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+
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  ## Dataset Creation
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+ The native model representation is converted into a discrete dataset in physical space and time, using the Python package [Chaosmagpy](https://chaosmagpy.readthedocs.io/en/master/)
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+
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  ### Source Data
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  ## Additional Information