TRANSFORMATIVE TECHNOLOGIES - Mining for the best side of bioscience data, with machine learning

变革性技术 - 通过机器学习挖掘生物科学数据的最佳一面

• 批准号: 2890716
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890716
• 关键词: TRANSFORMATIVE; TECHNOLOGIES; Mining; best; side

Biology is being transformed by large-scale data sets, but how do you find the right one? The data needs a 'dating profile': sounds easy but it turns out to be a bottleneck that is holding back research progress and research culture. This project tackles that metadata bottleneck. We aim to help researchers to show the best side of their data.Data-intensive bioscience depends upon online repositories that share the "Big Data". There's little value in sharing data, if you can't tell which organism, sample, or conditions it came from, so the databases also need descriptions of the data, termed metadata. Top-tier repositories pay professional data curators to deal with their metadata but many other repositories cannot do so. Even curators can't invent metadata, the original researchers have to describe their research for the curators. This project first aims to understand current data descriptions in research data repositories, using text mining and machine learning, in particular named entity recognition in free-text descriptions. Based on this evidence, you will research the simplest ways to improve the descriptions in future. The project will test real-time feedback that encourages researchers to provide better descriptions, for example using controlled vocabularies. You will work with software developers to test and evaluate simple feedback processes, in practice, for biological data repositories. By then, you will also be an expert data steward.Improving data descriptions will accelerate data-intensive bioscience across many research fields, as this bottleneck applies to many repositories and even electronic lab notebooks. Making the data easier to re-use will also reward the researchers who share their data, supporting the "Open Science" aspect of the new research culture.The team: Andrew Millar (Edinburgh) and Jason Swedlow (Dundee), are biologists who also develop and run data repositories, and help researchers to manage their data. We have access to internationally-adopted repositories (e.g. https://idr.openmicroscopy.org), their metadata, and to their software developers, who can help to implement feedback processes.Ian Simpson (Edinburgh Informatics) applies natural language processing (text mining) software tools to analyse bioscience literature, and has worked on several "Big Data" bioscience projects, including with Andrew Millar. The project is based with the BioRDM team in the C.H. Waddington building, at the focal point of SynthSys, the interdisciplinary biology research centre at the University of Edinburgh, where many labs generate, analyse and model large-scale biological data. More information at https://www.ed.ac.uk/biology/synthsys

大规模数据集正在改变生物学，但如何找到合适的数据集？数据需要“约会概况”：听起来很简单，但事实证明它是阻碍研究进展和研究文化的瓶颈。该项目解决了元数据瓶颈。我们的目标是帮助研究人员展示其数据最好的一面。数据密集型生物科学依赖于共享“大数据”的在线存储库。如果您无法分辨数据来自哪个生物体、样本或条件，那么共享数据就没有什么价值，因此数据库还需要数据的描述，称为元数据。顶级存储库支付专业数据管理员来处理其元数据，但许多其他存储库无法这样做。即使策展人也无法发明元数据，原始研究人员必须为策展人描述他们的研究。该项目首先旨在使用文本挖掘和机器学习，特别是自由文本描述中的命名实体识别，了解研究数据存储库中的当前数据描述。基于这些证据，您将研究未来改进描述的最简单方法。该项目将测试实时反馈，鼓励研究人员提供更好的描述，例如使用受控词汇。您将与软件开发人员合作，在实践中测试和评估生物数据存储库的简单反馈流程。到那时，您还将成为一名专家数据管理员。改进数据描述将加速许多研究领域的数据密集型生物科学，因为这个瓶颈适用于许多存储库甚至电子实验室笔记本。使数据更易于重复使用也将奖励共享数据的研究人员，支持新研究文化的“开放科学”方面。该团队：Andrew Millar（爱丁堡）和 Jason Swedlow（邓迪）是生物学家，他们也开发和运行数据存储库，并帮助研究人员管理他们的数据。我们可以访问国际采用的存储库（例如 https://idr.openmicroscopy.org）、其元数据以及可以帮助实施反馈流程的软件开发人员。Ian Simpson（爱丁堡信息学）应用自然语言处理（文本）挖掘）软件工具来分析生物科学文献，并参与了多个“大数据”生物科学项目，包括与安德鲁·米勒（Andrew Millar）合作。该项目基于 C.H. 的 BioRDM 团队。沃丁顿大楼位于爱丁堡大学跨学科生物学研究中心 SynthSys 的中心，许多实验室在这里生成、分析和建模大规模生物数据。更多信息请访问 https://www.ed.ac.uk/biology/synthsys