The a mount of data produced in support of scientific research continues to grow rapidly. Despite the accumulation and demand for scientific data, relatively little data are actually made available for the broader scientific community. We surmise that one root of this problem is the perceived difficulty of electronically publishing scientific data and associated metadata in a way that makes it discoverable. We propose exploiting Semantic Web technologies and best practices to make metadata both discoverable and easy to publish. We share experiences in curating metadata to illustrate the cumbersome nature of data reuse in the current research environment. We also make recommendations with a real-world example of how data publishers can provide their metadata by adding limited additional markup to HTML pages on the Web. With little additional effort from data publishers, the difficulty of data discovery, access, and sharing can be greatly reduced and the impact of research data greatly enhanced.

Linking named mentions detected in a source document to an existing knowledge base provides disambiguated entity referents for the mentions.This allows better document analysis, knowledge extraction and knowledge base population.Most of the previous research extensively exploited the linguistic features of the source documents in a supervised or semi-supervised way.These systems therefore cannot be easily applied to a new language or domain.In this paper, we present a novel unsupervised algorithm named Quantified Collective Validation that avoids excessive linguistic analysis on the source documents and fully leverages the knowledge base structure for the entity linking task.We show our approach achieves stateof-the-art English entity linking performance and demonstrate successful deployment in a new language (Chinese) and two new domains (Biomedical and Earth Science).Experiment datasets and system demonstration are available at http://tw.rpi.edu/web/doc/hanwang_emnlp_2015 for research purpose.

For some time, the term cyberinfrastructure has been used to describe the collected set of computer-based software and services utilized to support and advance science, engineering, and education. Over the past decade there has been an evolution of that cyberinfrastructure aimed at enabling more robust and virtual, distributed scientific research set in a context of an interdisciplinary virtual observatory, i.e. using cyberinfrastructure to enhance data stewardship. Early efforts, in for example, the Virtual Solar-Terrestrial Observatory (VSTO) applied the then emergent semantic web data frameworks to provide production access to observational datasets from solar-terrestrial physics. That observatory provides virtual access to highly distributed and heterogeneous datasets in a common way, i.e. they appear to be organized, stored, accessed and used as if they were local or in other words, not exposing the underlying organization of the data holdings. More recently, an even larger science community, oriented around carbon in Earth has implemented a Deep Carbon Virtual Observatory (DCVO). The DCVO integrates (rather than develops) an existing set of application cyberinfrastructure, i.e. data and information stores, catalogues, collaboration and network tools, and application services. In this paper we indicate the development of the embedded semantic technology within the DCVO and indicate future directions.

The important topic of global climate change builds on a huge collection of scientific research. It is common for agencies releasing climate change information to be served with requests for all supporting materials resulting in a particular conclusion. Capturing and presenting global change provenance, linking to the research papers, data sets, models, analyses, observations, satellites, etc., that support the key research findings in this domain can increase understanding and aid in reproducibility of results and conclusions. The U.S. Global Change Research Program is now coordinating the production of a national climate assessment (NCA) that presents our best understanding of global change. We are now developing a global change information system that will present the content of that report and its provenance, including the scientific support for the findings of the assessment. We are using an approach that will present this information both through a human accessible Web site as well as a machine-readable interface for automated mining of the provenance graph. We plan to use the developing World Wide Web Consortium (W3C) PROV data model and ontology for this system. This paper will describe an overview of the process of developing the NCA and how the provenance trail of the report and each of the technical inputs can be captured and represented using the W3C PROV ontology. This will improve the visibility into the assessment process, increase understanding and possibility of reproducibility, and ultimately increase the credibility and trust of the resulting report.

NASA provides a wide variety of Earth-observing satellite data products to a diverse community. These data are annotated with quality information in a variety of ways, with the result that many users struggle to understand how to properly account for quality when dealing with satellite data. To address this issue, a Data Quality Screening Service (DQSS) is being implemented for a number of datasets. The DQSS will enable users to obtain data files in which low-quality pixels have been filtered out, based either on quality criteria recommended by the science team or on the user's particular quality criteria. The objective is to increase proper utilization of this critical quality data in science data analysis of satellite data products.

Understanding the Earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array of distributed supercomputers. The resulting data archive, spread over several sites, currently contains upwards of 100 TB of simulation data and is growing rapidly. Looking toward mid-decade and beyond, we must anticipate and prepare for distributed climate research data holdings of many petabytes. The Earth System Grid (ESG) is a collaborative interdisciplinary project aimed at addressing the challenge of enabling management, discovery, access, and analysis of these critically important datasets in a distributed and heterogeneous computational environment. The problem is fundamentally a Grid problem. Building upon the Globus toolkit and a variety of other technologies, ESG is developing an environment that addresses authentication, authorization for data access, large-scale data transport and management, services and abstractions for high-performance remote data access, mechanisms for scalable data replication, cataloging with rich semantic and syntactic information, data discovery, distributed monitoring, and Web-based portals for using the system.

In the emerging world of Grid Computing, shared computational, data, other distributed resources are becoming available to enable scientific advancement through collaborative research and collaboratories. This paper describes the increasing role of ontologies in the context of Grid Computing for obtaining, comparing and analyzing data. We present ontology entities and a declarative model that provide the outline for an ontology of scientific information. Relationships between concepts are also given. The implementation of some concepts described in this ontology is discussed within the context of the Earth System Grid II (ESG)[1].