An Intelligent Concept Agent for Assisting with the Application of Metadata

项目摘要 生物医学研究人员正在产生越来越多的复杂和多样化的数据。这些数据各不相同 从基因组序列到表型测量和成像数据，这是巨大的。如果研究人员 数据科学家可以有效地利用这些数据，然后我们可以深入了解疾病机制和 如何解决这些问题。然而，主要的绊脚石是越来越难找到并整合 相关数据集，因为缺乏足够的元数据。研究克罗恩病的研究人员可能会错过 由于缺乏描述性标签，某些微生物群落如何影响肠道组织学的关键数据集 数据。目前，由于海量的元数据，应用元数据是困难、耗时和容易出错的 每种数据类型的标准混淆和重叠。通常，专门的“数据辩手”被雇佣来 应用元数据，但即使是这些专家也因为缺乏好的工具而受阻。在这里，我们建议开发一种 研究人员和数据讨论者可以用来帮助他们应用元数据的智能代理。代理是 基于元数据元素的个性化仪表板，这些元数据元素可以从多个专门的 门户网站，以及维基百科等网站。这些元素可以与分类器结合，这些分类器可用于 自我识别可能与之相关的数据集，使选择合适的词汇表变得更容易 研究人员。我们将针对许多目标用例部署该系统，包括注释 国家生物医学信息中心生物样本库，以及 图共享存储库。

PROJECT ABSTRACT Biomedical investigators are generating increasing amounts of complex and diverse data. This data varies tremendously, from genome sequences through phenotypic measurements and imaging data. If researchers and data scientists can tap into this data effectively, then we can gain insights into disease mechanisms and how to tackle them. However, the main stumbling block is that it is increasingly hard to find and integrate the relevant datasets due to the lack of sufficient metadata. A researcher studying Crohn's disease may miss a crucial dataset on how certain microbial communities affect gut histology due to the lack of descriptive tags on the data. Currently, applying metadata is difficult, time-consuming and error prone due to the vast sea of confusing and overlapping standards for each datatype. Often specialized `data wranglers' are employed to apply metadata, but even these experts are hindered by lack of good tools. Here we propose to develop an intelligent agent that researchers and data wranglers can use to assist them apply metadata. The agent is based around a personalized dashboard of metadata elements that can be collected from multiple specialized portals, as well as sites such as Wikipedia. These elements can be coupled with classifiers that can be used to self-identify datasets to which they may be relevant, making the selection of appropriate vocabularies easier for researchers. We will deploy the system for a number of targeted use cases, including annotation of the National Center for Biomedical Information Bio-Samples repository, and annotation of images within the Figshare repository.