Skip to content
This repository has been archived by the owner on Jan 21, 2022. It is now read-only.
/ data Public archive

DEPRECATED: Datasets were moved to https://github.com/fatiando-data | Curated sample geoscience data for documentation and tutorials. This repository contains code for downloading and formatting the data for redistribution.

License

Notifications You must be signed in to change notification settings

fatiando/data

Repository files navigation

THIS REPOSITORY IS DEPRECATED

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.


Curated collection of open geophysics data for tutorials and documentation

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).

Downloading

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.

The Ensaio package

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.

Contributing

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

Versions

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

Version Digital Object Identifier (DOI)
v1.0.0 10.5281/zenodo.5167357

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.

Datasets

File name Size Hashes
alps-gps-velocity.csv.xz 0.005 Mb md5:195ee3d88783ce01b6190c2af89f2b14 sha256:77f2907c2a019366e5f85de5aafcab2d0e90cc2c378171468a7705cab9938584
britain-magnetic.csv.xz 2.7 Mb md5:8dbbda02c7e74f63adc461909358f056 sha256:4e00894c2e0fa5b9c547719c8ac08adb6e788a7074c0dae9fb1b2767cf494b38
british-columbia-lidar.csv.xz 4.4 Mb md5:354c725a95036bd8340bc14e043ece5a sha256:03c6f1b99374b8c00c424c788cb6956bc00ab477244bb69835d4171312714fe1
caribbean-bathymetry.csv.xz 7.8 Mb md5:a7332aa6e69c77d49d7fb54b764caa82 sha256:9adaa2ead1cd354206235105489b511c4c46833b2e137a3eadc917243d16f09e
earth-gravity-10arcmin.nc 2.5 Mb md5:56df20e0e67e28ebe4739a2f0357c4a6 sha256:d55134501da0d984f318c0f92e1a15a8472176ec7babde5edfdb58855190273e
earth-geoid-10arcmin.nc 1.3 Mb md5:39b97344e704eb68fa381df2eb47da0f sha256:e98dd544c8b4b8e5f11d1a316684dfbc2612e2860af07b946df46ed9f782a0f6
earth-topography-10arcmin.nc 2.7 Mb md5:c43b61322e03669c4313ba3d9a58028d sha256:e45628a3f559ec600a4003587a2b575402d22986651ee48806930aa909af4cf6
southern-africa-gravity.csv.xz 0.14 Mb md5:1dee324a14e647855366d6eb01a1ef35 sha256:f5f8e5eb6cd97f104fbb739cf389113cbf28ca8ee003043fab720a0fa7262cac
osborne-magnetic.csv.xz 2.2 Mb md5:a9e680c9b746065a7aea6dc82e198af5 sha256:243b1f1ed784c8b175db41c546a6d77486fa5e8901def766fef43c04d18ee26a

GPS velocities for the Alpine region

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.

Airborne magnetic survey of Britain

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.

LiDAR point cloud of the Trail Islands in British Columbia, Canada

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

Single-beam bathymetry of the Caribbean

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.

Global gravity, geoid height, and topography grids

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").

Ground gravity of Southern Africa

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).

Airborne magnetic data of the Osborne Mine and Lightning Creek sill complex, Australia

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.

License

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.

About

DEPRECATED: Datasets were moved to https://github.com/fatiando-data | Curated sample geoscience data for documentation and tutorials. This repository contains code for downloading and formatting the data for redistribution.

Topics

Resources

License

Stars

Watchers

Forks