Open Science needs Open Data: Automated semantic description of biological data through Machine Learning

生物研究正在经历一场数据革命，每天都会产生大量数据。与此同时，资助者和出版商越来越多地要求提供这些数据。使数据可查找、可访问、可互操作和可重用（FAIR）需要研究人员付出大量努力，以确保他们的数据被描述得足够好，以便其他人可以搜索和重用它。尽管越来越多的元数据标准正在产生，以帮助研究人员描述他们的数据，但这些标准的使用并不广泛或容易。生物数据需要解放。该项目将为申请人提供一个令人兴奋的机会，通过开发新的机器学习算法来自动标注具有本体术语的生命科学数据，从而解决描述不佳的生命科学数据集问题。该项目旨在开发现代计算方法，以帮助生物学家更好地描述他们的数据。这将有望提高数据的质量，允许其他研究人员更容易地访问FAIR数据，并提供更多的元数据，可以为有意义的数据集成提供动力。将学习机器学习，数据管理，本体和社区软件开发的各种科学方法和方法。

Biological research is undergoing a data revolution, where huge amounts of data are being generated every day. This is happening alongside increasing demands from funders and publishers to make these data available. Making data Findable, Accessible, Interoperable, and Reusable (FAIR) requires a great deal of a researcher's effort to ensure that their data is described well enough so that others can search for and reuse it. Although more and more metadata standards are being produced to help researchers describe their data, the use of these standards is not widespread or easy. Biological data needs to be liberated. This project will give the applicant an exciting opportunity to address the problem of badlydescribed life science datasets by developing new machine learning algorithms to automate the annotation of life science data with ontology terms. This project aims to develop modern computational methods to help biologists to better describe their data. This will hopefully improve the quality of data, allowing other researchers to access FAIR data more easily, and with a greater amount of metadata that can power meaningful data integration. A wide variety of scientific approaches and methodologies to Machine Learning, data management, ontologies, and community software development will be learned.