The collection of FAIR data for Fatiando a Terra has been moved to https://github.com/fatiando-data

This is so that each dataset can be released independently, avoiding duplication of identical data in users' computers. As a result, this repository and the associated data at https://doi.org/10.5281/zenodo.5167357 will no longer be updated.

This is a place to format and prepare public open-licensed datasets for use in our tutorials and documentation.

We include the source code that prepares the datasets for redistribution by filtering, standardizing, converting coordinates, compressing, etc. The goal is to make loading the data as easy as possible (e.g., a single call to pandas.read_csv or xarray.load_dataset). Whenever possible, the code also downloads the original data (otherwise the original data are included in this repository).

The easiest way to download and use the datasets is using Pooch. For example, the following code downloads, caches (stores a local copy), verifies the download integrity, and loads into a pandas.DataFrame our Alpine GPS dataset from the v1.0.0 release:

import pooch
import pandas

file_path = pooch.retrieve(
    url="doi:10.5281/zenodo.5167357/alps-gps-velocity.csv.xz",
    known_hash="md5:195ee3d88783ce01b6190c2af89f2b14",
)
data = pandas.read_csv(file_path)

To load other data from other releases, replace the file name, DOI, and MD5 hash in the code above.

These datasets are also accessible through Ensaio:

import ensaio.v1 as ensaio

file_path = ensaio.fetch_alps_gps()
data = pandas.read_csv(file_path)

Ensaio uses Pooch under the hood but provides a simpler interface, with the DOI, file names, and hashes all stored internally.

See our Contributing Guidelines for information on proposing new datasets and making changes to this repository.

The curated datasets are published through Zenodo. Each release is assigned a unique DOI (see the table below). The entire collection can be reached through https://doi.org/10.5281/zenodo.5167356

NOTE: This collection uses semantic version (i.e., MAJOR.MINOR.BUGFIX format). Major releases mean that backwards incompatible changes were made to the data. Minor releases add new data without changing existing files. Bug fix releases fix errors in a previous release that makes the data unusable. Changes to the current data files will always be published as a major release unless the file(s) in the previous release was unusable/corrupted.

This is a compilation of 3D GPS velocities for the Alps. The horizontal velocities are reference to the Eurasian frame. All velocity components and even the position have error estimates, which is very useful and rare to find in a lot of datasets.

• Original source: Sánchez et al. (2018)
• Original license: CC-BY
• More information: prepare.ipynb

This is a digitized version of an airborne magnetic survey of Britain. Data are sampled where flight lines crossed contours on the archive maps. Contains only the total field magnetic anomaly, not the magnetic field intensity measurements or corrections. Contains British Geological Survey materials © UKRI 2021.

• Original source: British Geological Survey
• Original license: Open Government Licence
• More information: prepare.ipynb

This is a point cloud sliced to the small Trail Islands North of Vancouver to reduce the data size. The islands have some nice looking topography and their isolated nature creates problems for some interpolation methods.

• Original source: LidarBC
• Original license: Open Government Licence - British Columbia
• More information: prepare.ipynb

This dataset is a compilation of several single-beam bathymetry surveys displaying a wide range of tectonic activity, uneven distribution, and even clear systematic errors in some of the survey lines. The original data file was compressed with LZMA to save space and make it possible to upload it to this GitHub repository.

• Original source: NOAA NCEI
• Original license: Public domain
• More information: prepare.ipynb

This dataset includes global 10 arc-minute resolution grids of gravity acceleration (gravitational and centrifugal) at 10 km geometric height, geoid height, and topography/bathymetry (referenced to "sea level").

• Original source: EIGEN-6C4 (gravity and geoid) and ETOPO1 ice surface (topography) generated by the ICGEM calculation service
• Original license: CC-BY (EIGEN-6C4) and public domain (ETOPO1)
• More information: prepare.ipynb

This is a public domain compilation of ground measurements of gravity from Southern Africa. The observations are the absolute gravity values in mGal. The horizontal datum is not specified and heights are referenced to "sea level", which we will interpret as the geoid (which realization is likely not relevant since the uncertainty in the height is probably larger than geoid model differences).

• Original source: NOAA NCEI
• Original license: Public domain
• More information: prepare.ipynb

This is a section of a survey acquired in 1990 by the Queensland Government, Australia. The data are good quality with approximately 80 m terrain clearance and 200 m line spacing. The anomalies are very visible and present interesting processing and modelling challenges, as well as plenty of literature about their geology.

• Original source: Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra
• Original license: CC-BY
• More information: prepare.ipynb

All Python source code is made available under the BSD 3-clause license. You can freely use and modify the code, without warranty, so long as you provide attribution to the authors.

Unless otherwise specified, all data files and figures created by the code are available under the Creative Commons Attribution 4.0 License (CC-BY). The licenses for the original source data are specified in this README.md file and the Jupyter notebooks.