Session Description: This workshop will provide a hands-on introduction to conducting large-scale bioacoustics research from start to finish: planning a study, deploying automated recording units, managing and analyzing data, and using the results to answer ecological questions. Acoustic monitoring has become a powerful and affordable method for collecting species-presence observations at large spatial and temporal scales, thanks to the development of (1) low-cost acoustic recording units such as the AudioMoth, and (2) open-source software for automated species detection in audio. Acoustic monitoring vastly increases the amount of surveying possible in a study and can allow us to monitor otherwise hard to detect species. However, there are significant barriers to entry for using autonomous recording, including the challenges of managing large-scale data collection and extracting useful information from terabytes of audio data. Our lab has extensive experience collecting and analyzing large-scale acoustic datasets. In this workshop, participants will get hands-on experience with the AudioMoth open-source acoustic recorder: programming the devices with a recording schedule, collecting field recordings, and downloading and inspecting the data. Participants will then use the Python bioacoustics package OpenSoundscape to identify sounds from a target species in the recorded audio data using machine learning models. We will discuss how to interpret the results of machine learning analysis, and how to best leverage the automated methods to generate useful scientific information such as estimates of occupancy, detections of a rare species, or phenological patterns.