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

生物学正在被大规模的数据集所改变，但你如何找到合适的呢？这些数据需要一个“约会档案”：听起来很简单，但事实证明，这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他们数据的最好的一面。数据密集型生物科学依赖于共享“大数据”的在线存储库。共享数据没有什么价值，如果你不能分辨出它来自哪个生物体、样本或条件，所以数据库还需要数据的描述，称为元数据。顶级存储库付钱给专业的数据管理员来处理他们的元数据，但许多其他存储库无法做到这一点。即使策展人也不能发明元数据，最初的研究人员必须向策展人描述他们的研究。该项目首先旨在利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，来理解研究数据存储库中的当前数据描述。基于这些证据，你将研究最简单的方法来改进将来的描述。该项目将测试实时反馈，鼓励研究人员提供更好的描述，例如使用受控词汇表。您将与软件开发人员合作，在实践中测试和评估生物数据库的简单反馈过程。到那时，你也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，因为这一瓶颈适用于许多存储库，甚至是电子实验室笔记本。使数据更易于重复使用也将奖励分享数据的研究人员，支持新的研究文化的“开放科学”方面。团队：安德鲁·米勒(爱丁堡)和杰森·斯威德洛(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。Ian Simpson(爱丁堡信息学)应用自然语言处理(文本挖掘)软件工具分析生物科学文献，并参与了几个“大数据”生物科学项目，包括与安德鲁·米勒的合作。该项目的基础是位于爱丁堡大学跨学科生物学研究中心SynthSys的C.H.Waddington大楼的BioRDM团队，许多实验室在那里生成、分析和模拟大规模生物数据。欲了解更多信息，请访问https://www.ed.ac.uk/biology/synthsys。