Appreciations
🙏 Authors, Abner Bogan (CUAHSI) & Lindsay Platt (CUAHSI).
🙌 Reviewers, Heather Kropp (Hamilton College), @nquarderer.bsky.social (Earth Lab, University of Colorado – Boulder) & Kimberlee Wong (CUAHSI)
✨Editor, @alejo-coca (The Alan Turing Institute)

👏The authors show how targets, an open-source R package, could be a powerful tool to build a reproducible data pipeline for a complex environmental analysis.

💡Learn about targets: https://docs.ropensci.org/targets

💡New ideas or suggestions for the notebook are welcome!

Find how to contribute in our publication guidelines:
🔗 https://github.com/eds-book/eds-book/blob/main/book/contribute.md#notebooks

The executable notebook is citable and the latest version is archived in Zenodo:
🔗 https://doi.org/10.5281/zenodo.17171136

Notebook metadata and link to the original paper:
🔗 https://w3id.org/ro-id/d14c540e-0a98-4c7f-a028-d535535369ac

Appreciations

🙏 Author of the notebook, Lucky J. Yang from for his effort to share a great notebook.

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

✨@alejo-coca for editing […]

👏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

💡New ideas or suggestions for the notebook are welcome!

Find how to contribute in our publication guidelines:
🔗 https://github.com/alan-turing-institute/environmental-ds-book/blob/master/book/publishing/guidelines.md

The executable notebook is citable and the latest version is archived in Zenodo:
🔗 https://doi.org/10.5281/zenodo.14279235

Notebook metadata and link to the original paper:
🔗 https://w3id.org/ro-id/7ad44bec-6784-437f-b5f3-2199b43a5303

#appreciations

🙏 Author of the notebook, Cam Appel for his efforts in validating the notebook idea and implementation with the EDS book and DeepForest communities.

🙌 @ethanwhite & Louisa Van Zeeland (The Alan Turing Institute) for their revision and collaboration to improve the notebook […]

👏The author shows how DeepForest @weecology, 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: https://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 @huggingface allowing for reproducibility and […]