OpenWorm is an international collaboration with the aim of understanding how the behavior of Caenorhabditis elegans (C. elegans) emerges from its underlying physiological processes. The project has developed a modular simulation engine to create computational models of the worm. The modularity of the engine makes it possible to easily modify the model, incorporate new experimental data and test hypotheses. The modeling framework incorporates both biophysical neuronal simulations and a novel fluid-dynamics-based soft-tissue simulation for physical environment-body interactions. The project's open-science approach is aimed at overcoming the difficulties of integrative modeling within a traditional academic environment. In this article the rationale is presented for creating the OpenWorm collaboration, the tools and resources developed thus far are outlined and the unique challenges associated with the project are discussed.

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The adoption of powerful software tools and computational methods from the software industry by the scientific research community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm Foundation is an independent research organization working towards an integrative simulation of the nematode Caenorhabditis elegans, with the aim of providing a powerful new tool to understand how the organism's behaviour arises from its fundamental biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future directions.This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

Animal behavior is increasingly being recorded in systematic imaging studies that generate large data sets. To maximize the usefulness of these data there is a need for improved resources for analyzing and sharing behavior data that will encourage re-analysis and method development by computational scientists 1 . However, unlike genomic or protein structural data, there are no widely used standards for behavior data. It is therefore desirable to make the data available in a relatively raw form so that different investigators can use their own representations and derive their own features. For computational ethology to approach the level of maturity of other areas of bioinformatics, we need to address at least three challenges: storing and accessing video files, defining flexible data formats to facilitate data sharing, and making software to read, write, browse, and analyze the data. We have developed an open resource to begin addressing these challenges using worm tracking as a model.To store video files and the associated feature and metadata, we use a Zenodo.org community (an open-access repository for data) that provides durable storage, citability, and supports contributions from other groups. We have also developed a web interface that enables filtering based on feature histograms that can return, for example, fast and curved worms in addition to more standard searches for particular strains or genotypes ( Fig. 1 and http://movement.openworm.org/). The database consists of 14,874 single-worm tracking experiments representing 386 genotypes (building on 9,203 experiments and 305 genotypes in a previous publication 2 ) and includes data from several larval stages as well as ageing data consisting of over 2,700 videos of animals tracked daily from the L4 stage to death. Full resolution videos are available in HDF5 containers that include gzip-compressed video frames, timestamps, worm outline and midline, feature data, and experiment metadata. HDF5 files are compatible with multiple languages including MATLAB, R, Python, and C. We have also developed an HDF5 video reader that allows video playback with adjustable speed and zoom (important when reviewing high-resolution, multi-worm tracking data), as well as toggling of worm segmentation over the original video to verify segmentation accuracy during playback.Secondly, we have defined an interchange format named Worm tracker Commons Object Notation (WCON), to facilitate data sharing and software reuse among groups working on worm behavior. WCON uses the widely supported JSON format to store tracking data as text that is both human and machine readable. It is compatible with single and multi-worm 3 data, at any resolution: from a single point representing worm position over time 4 , to many points representing the high-resolution skeleton of a moving worm 2 . Importantly, it also supports custom feature additions so that individual labs can store their own specific data sets alongside the universal set of basic worm data. WCON readers are available for Python, MATL...

This study evaluates efficacy and effectiveness of ‘Doing Anger Differently’ (DAD), a group treatment for reactively aggressive 12–15 year old males. DAD uses percussion exercises to aid treatment. Study 1 compared a ten‐week treatment with a waitlist control at pre, post and 6 month (treatment group only) follow‐up. Study 2 replicated Study 1, but also followed up controls at 6 months. In study 1 (N = 54) the treatment resulted in lowered trait anger (Cohen’s d = −1.3), aggression‐reports (d = −1.0) and depression (d = −0.6), and increased self‐esteem (d = 0.6), all maintained at six months. In study 2 (N = 65), aggression‐reports fell to one fifth of pre‐treatment levels at nine months follow‐up (d = −1.2), with lowered trait anger (d = −0.4) and anger expression (d = −0.3) post‐treatment.