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BACKGROUND: Bio-logging and animal tracking datasets continuously grow in volume and complexity, documenting animal behaviour and ecology in unprecedented extent and detail, but greatly increasing the challenge of extracting knowledge from the data obtained. A large variety of analysis methods are being developed, many of which in effect are inaccessible to potential users, because they remain unpublished, depend on proprietary software or require significant coding skills. RESULTS: We developed MoveApps, an open analysis platform for animal tracking data, to make sophisticated analytical tools accessible to a global community of movement ecologists and wildlife managers. As part of the Movebank ecosystem, MoveApps allows users to design and share workflows composed of analysis modules (Apps) that access and analyse tracking data. Users browse Apps, build workflows, customise parameters, execute analyses and access results through an intuitive web-based interface. Apps, coded in R or other programming languages, have been developed by the MoveApps team and can be contributed by anyone developing analysis code. They become available to all user of the platform. To allow long-term and cross-system reproducibility, Apps have public source code and are compiled and run in Docker containers that form the basis of a serverless cloud computing system. To support reproducible science and help contributors document and benefit from their efforts, workflows of Apps can be shared, published and archived with DOIs in the Movebank Data Repository. The platform was beta launched in spring 2021 and currently contains 49 Apps that are used by 316 registered users. We illustrate its use through two workflows that (1) provide a daily report on active tag deployments and (2) segment and map migratory movements. CONCLUSIONS: The MoveApps platform is meant to empower the community to supply, exchange and use analysis code in an intuitive environment that allows fast and traceable results and feedback. By bringing together analytical experts developing movement analysis methods and code with those in need of tools to explore, answer questions and inform decisions based on data they collect, we intend to increase the pace of knowledge generation and integration to match the huge growth rate in bio-logging data acquisition.
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The presence of the head in an MRI scanner leads to inhomogeneities in the magnetic field. These cause the 'susceptibility artifacts' of image distortion and signal dropout. In this paper, we evaluate a technique called passive shimming, which has the potential to reduce field inhomogeneities and the resultant artifacts. A piece of a magnetically active material (pyrolytic graphite) is held on the roof of the participant's mouth by a plastic mouth mould. We evaluate the effects in several different ways. We show that the presence of a shim reduces field inhomogeneity across much of the brain. From field maps, we generate simulations of EPI image intensity and BOLD sensitivity. Both of these are mainly improved by the presence of shim, although there were small reductions in some regions. Measured EPI image intensity also mostly increased. Finally, we ran a reward-punishment task in our subjects, and found that the presence of a shim increased functional sensitivity in the orbitofrontal cortex. Using the BOLD sensitivity measure, we provide estimates of the improvement to be expected in functional studies for a range of neural structures. Passive shims are quick to make and reasonably comfortable to wear, and have substantial potential for researchers investigating inferior frontal brain regions using MRI.