Suchergebnisse

The Turing Way is a lightly opinionated guide to reproducible data science and research. It is an open source community-driven guide to reproducible, ethical, inclusive and collaborative data science. Our goal is to provide all the information that data scientists in academia, industry, government and in the third sector need at the start of their projects to ensure that they are easy to reproduce and reuse at the end. The Turing Way is participating in JupyterCon 2020 Sprint Event, which is a 24-hour digital event following the conference. Everyone attending the JupyterCon sprint interested in reproducible, ethical, and inclusive data science and research are welcome to join the full or any part of The Turing Way project, community, and/or this sprint. This sprint is built upon our biweekly Collaboration Cafe that aims to engage with researchers interested in learning and sharing more about data science and related topics in The Turing Way online book: https://the-turing-way.netlify.com.

AbstractAnalogical reasoning, or the ability to find correspondences between entities based on shared relationships, supports knowledge acquisition. As such, the development of this ability during childhood is thought to promote learning. Here, we sought to better understand the mechanisms by which analogical reasoning about semantic relations improves over childhood and adolescence (e.g. chalk is to chalkboard as pen is to…?). We hypothesized that age‐related differences would manifest as differences in the brain regions associated with one or more of the following cognitive functions: (1) controlled semantic retrieval, or the ability to retrieve task‐relevant semantic associations; (2) response control, or the ability to override the tendency to respond to a salient distractor; and/or (3) relational integration, or the ability to consider jointly two mental relations. In order to test these hypotheses, we analyzed patterns of fMRI activation during performance of a pictorial propositional analogy task across 95 typically developing children between the ages of 6 and 18 years old. Despite large age‐related differences in task performance, particularly over ages 6–10 but through to around age 14, participants across the whole age range recruited a common network of frontal, parietal and temporal regions. However, activation in a brain region that has been implicated in controlled semantic retrieval – left anterior prefrontal cortex (BA 47/45) – was positively correlated with age, and also with performance after controlling for age. This finding indicates that improved performance over middle childhood and early adolescence on this analogical reasoning task is driven largely by improvements in the ability to selectively retrieve task‐relevant semantic relationships.

Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired 'inference at a glance' nature of barplots and other similar visualization devices. These "raincloud plots" can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter. ; MA is supported by a Lundbeckfonden Fellowship (R272-2017-4345), the AIAS-COFUND II fellowship programme that is supported by the Marie Skłodowska-Curie actions under the European Union's Horizon 2020 (Grant agreement no 754513), and the Aarhus University Research Foundation, and thanks Lincoln Colling for insightful statistical discussions. KW is funded by the Alan Turing Institute under the EPSRC grant EP/N510129/1. RAK is supported by the Wellcome Trust (grant number 107392/Z/15/Z).

AbstractThe structure of the human brain changes in several ways throughout childhood and adolescence. Perhaps the most salient of these changes is the strengthening of white matter tracts that enable distal brain regions to communicate with one another more quickly and efficiently. Here, we sought to understand whether and how white matter changes contribute to improved reasoning ability over development. In particular, we sought to understand whether previously reported relationships between white matter microstructure and reasoning are mediated by processing speed. To this end, we analyzed diffusion tensor imaging data as well as data from standard psychometric tests of cognitive abilities from 103 individuals between the ages of 6 and 18. We used structural equation modeling to investigate the network of relationships between brain and behavior variables. Our analyses provide support for the hypothesis that white matter maturation (as indexed either by microstructural organization or volume) supports improved processing speed, which, in turn, supports improved reasoning ability.

Presentation of the Turing Way at the Tools, practices and systems for data science and artificial intelligence: scoping workshop. As announced last year, The Alan Turing Institute has received significant new funding from the UKRI Strategic Priorities Fund for a research programme in AI for Science, Engineering, Health, and Government. A key theme of this will include a cross-cutting effort to develop tools, practices, and systems to support all areas of the programme and, more broadly, to wider research in applied data science and AI. A user-centred approach will be taken when creating these tools to ensure they are of practical use to researchers with a clear potential user-community among the Institute's challenge domains. This scoping workshop will bring together data science and AI researchers to collaboratively propose and define short, practical projects that will be supported under this theme. Following the workshop, attendees will develop these into more concrete project outlines which will then considered by the management board for the programme.

Abstract
The moderation of user-generated content on online platforms remains a key solution to protecting people online, but also remains a perpetual challenge as the appropriateness of content moderation guidelines depends on the online community that they aim to govern. This challenge affects marginalized groups in particular, as they more frequently experience online abuse but also end up falsely being the target of content-moderation guidelines. While there have been calls for democratic, community-moderation, there has so far been little research into how to implement such approaches. Here, we present the co-creation of content moderation strategies with the users of an online platform to address some of these challenges. Within the context of AutSPACEs—an online citizen science platform that aims to allow autistic people to share their own sensory processing experiences publicly—we used a community-based and participatory approach to co-design a content moderation solution that would fit the preferences, priorities, and needs of its autistic user community. We outline how this approach helped us discover context-specific moderation dilemmas around participant safety and well-being and how we addressed those. These trade-offs have resulted in a moderation design that differs from more general social networks in aspects such as how to contribute, when to moderate, and what to moderate. While these dilemmas, processes, and solutions are specific to the context of AutSPACEs, we highlight how the co-design approach itself could be applied and useful for other communities to uncover challenges and help other online spaces to embed safety and empowerment.