EarthCube Building Blocks: Collaborative Proposal: A Geo-Semantic Framework for Integrating Long-Tail Data and Models

该项目提供了一种独特的变革性方法来整合现有的和新兴的长尾模型和数据资源。许多挑战阻碍了模型与数据的无缝集成。这些挑战迫使科学家手动执行集成过程。主要的挑战是模型和数据资源之间的知识延迟的结果，而其他挑战则源于信息技术的不充分采用和利用。当用户打算集成长尾数据（单个研究人员或小型研究小组收集的数据）和长尾模型（由个人或小型建模社区开发的模型）时，知识延迟挑战呈指数级增长。本研究的目标是开发一个基于语义技术和方法的框架，以支持长尾？模型和数据集成。愿景是开发一个分散的基于知识的平台，可以很容易地在由个人和小团体研究人员组成的地球科学社区中采用。该项目提供了一种独特的变革性方法来集成现有的和新兴的长尾模型和数据资源。该项目将开发一个知识框架，以关闭模型的循环？通过首先调查整合地球科学中两个领先的长尾资源示例所需的概念架构来查询数据源：社区表面动态建模系统（CSDMS）和可持续环境可操作数据（SEAD）。该项目还将开发一个基于上下文的数据模型，该模型提供对元数据属性的显式解释。研究人员将从各种地理信息学系统中获取元数据概念和语义，并提供确保资源之间概念集成的工具。接下来，该项目将开发一个知识发现工具，它允许模型和来自不同贡献者的数据的自动耦合。最后，该项目将提供CSDMS建模框架中知识框架的原型物理实现，以演示其如何推进模型与数据之间的无缝发现、选择和集成，以及如何实现多个地球科学长尾资源之间的资源动态可重用性。

The project offers a unique and transformative approach to integrate existing and emerging long-tail model and data resources. Many challenges hinder the seamless integration of models with data. These challenges compel scientists to perform the integration process manually. The primary challenges are a consequence of the knowledge latency between model and data resources and others are derived from inadequate adoption and exploitation of information technologies. Knowledge latency challenges increase exponentially when a user aims to integrate long-tail data (data collected by individual researchers or small research groups) and long-tail models (models developed by individuals or small modeling communities).The goal of this research is to develop a framework rooted in semantic techniques and approaches to support ?long-tail? models and data integration. The vision is to develop a decentralized knowledge-based platform that can be easily adopted across geoscience communities comprising of individual and small group researchers. This project offers a unique and transformative approach to integrate existing and emerging long-tail model and data resources. The project will develop a knowledge framework to close the loop from models? queries back to data sources by first investigating the required concepts architecture for integrating two leading examples of long-tail resources in geoscience: Community Surface Dynamic Modeling System (CSDMS) and Sustainable Environment Actionable Data (SEAD). The project will also develop a context-based data model that provides an explicit interpretation of a metadata attribute. The researchers will capture the metadata concepts and semantic from various geo-informatics systems and provide tools for ensuring conceptual integration between the resources. Next, the project will develop a knowledge discovery tool that allows automated coupling of a model and data coming from different contributors. Finally, the project will provide a prototype physical implementation of the knowledge framework in CSDMS modeling framework to demonstrate how it can advance the seamless discovery, selection, and integration between models and data, and how to achieve dynamic reusability of resources across multiple Earth Science long-tail resources.