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:
- en
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: LIFD Magnetic Fields
size_categories: []
source_datasets: [gufm1 model]
tags: []
task_categories:
- feature-extraction
- image-to-image
- time-series-forecasting
- object-detection
- unconditional-image-generation
task_ids:
- multivariate-time-series-forecasting
---

# 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

*coming soon - Kaggle links?* 

### 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

The dataset was initially created by Angela Fan, Ethan Perez, Yacine Jernite, Jason Weston, Michael Auli, and David Grangier, during work done at Facebook AI Research (FAIR).

### Licensing Information

The licensing status of the dataset hinges on the legal status of the [Pushshift.io](https://files.pushshift.io/reddit/) data which is unclear.

### Citation Information


### Contributions

