This paper takes a technical services perspective on user experience (UX) research into student searching behaviors. In this observational study, students were free to search as they normally would ...while conducting research for an upcoming essay or assignment. Researchers took careful note of the search process, including how searches were composed and which metadata fields students looked at in their results lists. The findings of the study, and how local technical services staff responded to them, are discussed in this paper. The project was a useful way to prioritize the work of technical services based on insights from user searching behavior and to help ensure library resources are discoverable in the most effective manner.
The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has ...lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function.
Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various ...reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss their problems. This paper addresses the early detection of depression using machine learning models based on messages on a social platform. In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata. An ensemble of both approaches is shown to achieve state-of-the-art results in a current early detection task. Furthermore, the currently popular <inline-formula><tex-math notation="LaTeX">ERDE</tex-math> <mml:math><mml:mrow><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>D</mml:mi><mml:mi>E</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="friedrich-ieq1-2885515.gif"/> </inline-formula> score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated. A slightly modified metric is proposed and compared to the original score. Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.
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