Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
About this guide Camera traps have become one of the most widely used tools in wildlife research, conservation, and biodiversity monitoring. Their applications range from species inventories and long-term ecological monitoring to behavioural studies, population estimation, citizen science, and the development of artificial intelligence for wildlife conservation. As the popularity and accessibility of camera trapping continue to grow, so too does the range of resources available to support researchers and practitioners, including books, field manuals, standardised protocols, best-practice guidelines, software platforms, coding packages for data management and analysis, online training materials, and a growing number of open-access AI and citizen science tools. This guide is intended to complement, rather than replace, these existing resources. Instead of providing a comprehensive review of camera trap methodology, it brings together practical advice, field-tested recommendations, and lessons learned from camera trap projects conducted across a range of ecosystems and study objectives. The guide draws on the collective experience of researchers and conservation practitioners involved in long-term camera trap programmes, combining insights from both the scientific literature and practical field applications.
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StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
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