Introduction to Geospatial Raster and Vector Data with Python

The Lesson

Check out https://carpentries-incubator.github.io/geospatial-python/ to view the lesson as it currently stands. Episodes 1-7 and Episode 12 are currently complete. Epsiodes 1-7 have been taught previously in workshop settings, read more about that experience and feedback here. Other episodes still need to be fleshed out and we welcome contributions!

Teaching this lesson?

Do you want to Introduction to Geospatial Raster and Vector Data with Python? This material is open-source and freely available. Are you planning on using our material in your teaching? We would love to help you prepare to teach the lesson and receive feedback on how it could be further improved, based on your experience in the workshop.

You can notify us that you plan to teach this lesson by creating an issue in this repository. Also, it would be great if you can update this overview of all workshops taught with this lesson material. This helps us show the impact of developing open-source lessons to our funders.

Contributing

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.

Please see the current list of issues for ideas for contributing to this repository. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon. Look for the tag . This indicates that the mantainers will welcome a pull request fixing this issue.

We are looking for more active developers of this lesson. Edits to existing lesson episodes or additions to new episodes are welcome. If you are interested in contributing a new episode, you can either start by creating a new Github Issue to start discussion or dive in and submit a new Pull Request.

Check out the Contribution Guide and more detailed guidelines on proper formatting, how to preview the lesson locally, and how to write new episodes.

️💚️ Thanks Contributors! 💚

Made with contrib.rocks.

Acknowledgements

Funding

The data and lessons in this workshop were originally developed through a hackathon funded by the National Ecological Observatory Network (NEON) - an NSF funded observatory in Boulder, Colorado - in collaboration with Data Carpentry, SESYNC and CYVERSE. NEON is collecting data for 30 years to help scientists understand how aquatic and terrestrial ecosystems are changing.

Thanks Geospatial R Authors!

This lesson would not be possible without the many contributions from the authors of the Introduction to Geospatial Raster and Vector Data with R and Introduction to Geospatial Concepts lessons. These lessons have served as templates for this geospatial python lesson.

Data Carpentry Introduction to Geospatial Raster and Vector Data with R Leah Wasser; Megan A. Jones; Jemma Stachelek; Lachlan Deer; Zack Brym; Lauren O'Brien; Ana Costa Conrado; Aateka Shashank; Kristina Riemer; Anne Fouilloux; Juan Fung; Marchand; Tracy Teal; Sergio Marconi; James Holmquist; Mike Smorul; Punam Amratia; Erin Becker; Katrin Leinweber Editors: Jemma Stachelek; Lauren O'Brien; Jane Wyngaard https://doi.org/10.5281/zenodo.1404424

Data Carpentry Introduction to Geospatial Concepts Leah Wasser; Megan A. Jones; Lauren O'Brien; Jemma Stachelek; Tom Wright; Tracy Teal; Dev Paudel; Jane Wyngaard; Anne Fouilloux; Bidhyananda Yadav; Chris Prener; Tyson Swetnam; Erin Becker; Katrin Leinweber Editor(s): Tyson Swetnam; Chris Prener https://doi.org/10.5281/zenodo.1404414

Maintainer(s)

The current maintainers of this lesson are:

• Ryan Avery
• Francesco Nattino
• Ou Ku
• Maurice de Kleijn

Citation

To cite this lesson, please consult with CITATION