
# Dataset Card for LFID Magnetic Field Data

You will need the package
https://chaosmagpy.readthedocs.io/en/master/


## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
  - [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
  - [Source Data](#source-data)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [LIFD DataSets homepage](https://cemac.github.io/LIFD_ML_Datasets/)
- **Repository:** [LIFD GitHub Repo](https://github.com/cemac/LIFD_ML_Datasets/)
- **Point of Contact:** [*coming soon*]()

### Dataset Summary

A description of the dataset:

The gufm1 model is a global geomagnetic model based on spherical harmonics, covering the period 1590 - 1990, and is described in the publication:
[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)


### Supported Tasks and Leaderboards


### Data Fields

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.
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.


## Dataset Creation

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/)

### Source Data

## Additional Information

### Dataset Curators


### Licensing Information
MIT Licence 


### Citation Information


### Contributions


task_categories:feature-extraction task_categories:image-to-image task_categories:time-series-forecasting task_categories:object-detection task_categories:unconditional-image-generation task_ids:multivariate-time-series-forecasting annotations_creators:no-annotation language_creators:other multilinguality:monolingual source_datasets:gufm1 model language:en license:mit region:us
{"annotations_creators": ["no-annotation"], "language_creators": ["other"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": ["gufm1 model"], "task_categories": ["feature-extraction", "image-to-image", "time-series-forecasting", "object-detection", "unconditional-image-generation"], "task_ids": ["multivariate-time-series-forecasting"], "pretty_name": "LIFD Magnetic Fields", "tags": []}
TAGS
#task_categories-feature-extraction #task_categories-image-to-image #task_categories-time-series-forecasting #task_categories-object-detection #task_categories-unconditional-image-generation #task_ids-multivariate-time-series-forecasting #annotations_creators-no-annotation #language_creators-other #multilinguality-monolingual #source_datasets-gufm1 model #language-English #license-mit #region-us 


# Dataset Card for LFID Magnetic Field Data

You will need the package
URL


## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
- Dataset Structure
  - Data Fields
- Dataset Creation
  - Source Data
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: LIFD DataSets homepage
- Repository: LIFD GitHub Repo
- Point of Contact: [*coming soon*]()

### Dataset Summary

A description of the dataset:

The gufm1 model is a global geomagnetic model based on spherical harmonics, covering the period 1590 - 1990, and is described in the publication:
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, URL


### Supported Tasks and Leaderboards


### Data Fields

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.
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.


## Dataset Creation

The native model representation is converted into a discrete dataset in physical space and time, using the Python package Chaosmagpy

### Source Data

## Additional Information

### Dataset Curators


### Licensing Information
MIT Licence 





### Contributions


# Dataset Card for LFID Magnetic Field Data

You will need the package
URL ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
- Dataset Structure
  - Data Fields
- Dataset Creation
  - Source Data
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: LIFD DataSets homepage
- Repository: LIFD GitHub Repo
- Point of Contact: [*coming soon*]() ### Dataset Summary

A description of the dataset:

The gufm1 model is a global geomagnetic model based on spherical harmonics, covering the period 1590 - 1990, and is described in the publication:
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, URL ### Supported Tasks and Leaderboards ### Data Fields

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.
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. ## Dataset Creation

The native model representation is converted into a discrete dataset in physical space and time, using the Python package Chaosmagpy ### Source Data ## Additional Information ### Dataset Curators ### Licensing Information
MIT Licence ### Contributions
TAGS
#task_categories-feature-extraction #task_categories-image-to-image #task_categories-time-series-forecasting #task_categories-object-detection #task_categories-unconditional-image-generation #task_ids-multivariate-time-series-forecasting #annotations_creators-no-annotation #language_creators-other #multilinguality-monolingual #source_datasets-gufm1 model #language-English #license-mit #region-us 
 # Dataset Card for LFID Magnetic Field Data

You will need the package
URL ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
- Dataset Structure
  - Data Fields
- Dataset Creation
  - Source Data
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: LIFD DataSets homepage
- Repository: LIFD GitHub Repo
- Point of Contact: [*coming soon*]() ### Dataset Summary

A description of the dataset:

The gufm1 model is a global geomagnetic model based on spherical harmonics, covering the period 1590 - 1990, and is described in the publication:
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, URL ### Supported Tasks and Leaderboards ### Data Fields

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.
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. ## Dataset Creation

The native model representation is converted into a discrete dataset in physical space and time, using the Python package Chaosmagpy ### Source Data ## Additional Information ### Dataset Curators ### Licensing Information
MIT Licence ### Contributions