💡New ideas or suggestions for the notebook are welcome!

Find how to contribute in our publication guidelines:
🔗 github.com/eds-book/eds...

The executable notebook is citable and the latest version is archived in Zenodo:
🔗 doi.org/10.5281/zeno...

Notebook metadata and link to the original paper:
🔗 w3id.org/ro-id/d14c54...

Appreciations

🙏 Author of the notebook, @lucky-j-yang.bsky.social for his effort to share a great notebook.

🙌 Zehao Liu (Hangzhou Dianzi University) & Rizzieri Pedruzzi (Rio de Janeiro State University) for their revision to improve the notebook content.

✨@alejo-coca for editing the submission

👏The author explores field observations (with changed latitutde/longitude information) and run simulations experiments to verify whether the signal comes from a possible source, or to separate the regional baseline from the signal.

Highlights:
✅ Explore vehicle-based observation data
✅ Analyse the synchronisation of measurement signals
✅ Simulate the source of measurement signals
✅ Use simulation experiments to break down measurement signals and obtain regional baseline values

💡Are you interested in reading further or providing feedback on any of the new version?: github.com/eds-book/eds...

🙏 We thank all notebook authors who have approved changes of their original contributions. Special thanks to the Executable Books team who have been working closely with multiple open-source communities towards a stable release of JB2.

The upgrade has major improvements for a better user experience, including:

🔹Dedicated landing page highlighting core aspects of our community
🔹Improved navigation to the main sections for ease of development & contribution
🔹More powerful metadata and in-browser execution across published notebooks

🙏 Thank you to authors @alejo-coca.bsky.social , @annefouilloux.bsky.social, @rblourenco.bsky.social ,
@ampersandmcd.bsky.social, @douglas-rao.bsky.social & @scotthosking.bsky.social and @ukri.org funding.

💡New ideas or suggestions for the notebook are welcome!

Find how to contribute in our publication guidelines:

🔗 github.com/alan-turing-...

The executable notebook is citable and the latest version is archived in Zenodo:
🔗 doi.org/10.5281/zeno...

Notebook metadata and link to the original paper:
🔗 w3id.org/ro-id/7ad44b...

#appreciations

🙏 Cam Appel for authoring the notebook idea and implementation.

🙌 @ethan.weecology.org & Louisa Van Zeeland (The Alan Turing Institute) for their revision and collaboration to improve the notebook content.

✨ @alejo-coca.bsky.social for editing the submission.

👏The author shows how DeepForest @weecology.bsky.social, an open-source Python library providing tools for object detection to the biological monitoring community, could be powerful to learn and improve detection of livestock in aerial imagery.

💡Learn about DeepForest: deepforest.readthedocs.io

Highlights:
✅ Detect livestock in airborne imagery using the pre-built livestock detection model
✅ Fine-tune the model using an open dataset
✅ Evaluate the model’s performance before and after fine-tuning
✅ Save and share model checkpoints via @hf.co allowing for reproducibility and collaboration