具有计算流行病学数据集的数字图书馆中的数据映射框架

S. Hasan, Sandeep Gupta, E. Fox, K. Bisset, M. Marathe
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引用次数: 6

摘要

计算流行病学运用计算机模型和信息学工具对疾病的时空传播进行推理。收集、使用和修改的模型、数据源、数据表示和模式的多样性推动了数字图书馆(DL)框架的发展,以支持计算流行病学。异构内容包括元数据、文本、表格、电子表格、实验描述和大型结果文件。目前还没有公认的框架允许对这些内容进行统一访问。我们提出了一个为数字图书馆系统量身定制的框架,以支持计算网络流行病学数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data mapping framework in a digital library with computational epidemiology datasets
Computational epidemiology employs computer models and informatics tools to reason about the spatio-temporal spread of diseases. The diversity of models, data sources, data representations, and modalities that are collected, used, and modified motivate the development of a digital library (DL) framework to support computational epidemiology. The heterogeneous content includes metadata, text, tables, spreadsheets, experimental descriptions, and large result files. There is no accepted framework that allows unified access to such content. We propose a framework for a digital library system tailored to such datasets to support computational network epidemiology.
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