Wildlife monitoring at scale is one of ecology’s most data-intensive challenges. Camera traps deployed across remote landscapes can accumulate thousands of images in a single survey season — far more than any team can manually review in a reasonable time. Automatically identifying the species in each photograph would free researchers to focus on analysis rather than image sorting, but doing this accurately requires a model trained on a large and diverse set of wildlife images. This notebook shows how to combine two open tools to tackle this problem: the Atlas of Living Australia (ALA) — Australia’s national biodiversity data platform — and SpeciesNet, a deep learning model developed by Google specifically for wildlife image classification. We query ALA for camera-trap images of target species, download a sample, run SpeciesNet to automatically identify animals in each image, and then visualise the results.
While machine learning models achieve promising results in diabetes prediction, clinical adoption remains limited due to black-box nature and lack of stakeholder-specific communication. This study proposes a novel multi-level explanation framework that translates a single XGBoost…
This deposit contains the field data, synthetic training datasets, trained network weights and analysis code supporting the article "Physics-informed neural network inversion of electrical resistivity tomography data: amortized optimization with field validation in the Moroccan M…
Physics-informed neural networks (PINNs) are promoted as a general differential-equation solver, but the accuracy actually achieved varies by orders of magnitude across problem types, and that variation is rarely laid out in one place. This report is a method atlas: a runnable ca…
This dataset release represents Part 4 of the comprehensive young trefoil crop agricultural analysis project. While Part 1, Part 2 and Part 3 provided the raw image captures and camera parameters and machine-learning-ready image tiles. Part 4 delivers fully processed, georeferenc…
This record contains field-deployment datasets for two seabird species, streaked shearwaters and black-tailed gulls, used in the paper “Automated Ethogram Elaboration: A Cross-species Deep-learning Model Deployed On-board Enables Acceleration-triggered Capture of Diverse Behaviou…
INR-QSM — a subject-specific UNSUPERVISED deep-learning dipole inversion using an implicit neural representation. No pretrained weights: a sine-activated coordinate MLP (SIREN) is OPTIMIZED per-subject so that the susceptibility it represents, pushed through the QSM dipole forwar…