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和变革性的方法来整合现有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整合过程。主要挑战是模型和数据资源之间的知识延迟造成的，其他挑战是由于对信息技术的采用和利用不足造成的。当用户试图集成长尾数据(由个人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加。本研究的目标是开发一个植根于语义技术和方法的框架，以支持？长尾？模型和数据集成。其愿景是开发一个分散的、以知识为基础的平台，该平台可以很容易地在由个人和小组研究人员组成的地球科学界采用。该项目提供了一种独特且具有变革性的方法来整合现有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？通过首先研究集成两个地学长尾资源的领先实例：社区表面动态建模系统(CSDMS)和可持续环境可行动数据(SEAD)所需的概念体系结构，查询回数据源。该项目还将开发一个基于上下文的数据模型，提供对元数据属性的明确解释。研究人员将从各种地理信息学系统获取元数据概念和语义，并提供工具确保资源之间的概念集成。接下来，该项目将开发一个知识发现工具，允许自动耦合来自不同贡献者的模型和数据。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，以演示它如何推进模型和数据之间的无缝发现、选择和集成，以及如何实现跨多个地球科学长尾资源的资源动态重用。

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.