The adoption and availability of diverse application design and support platforms are making generic scientific application orchestration increasingly difficult. In such an evolving environment, higher level abstractions of design primitives are critically important using which end users have a chance to craft their own applications without a complete technical grasp of the lower level details. In this research, we introduce a novel scientific workflow design platform that supports high level tools for data integration, process description and analytics based on a visual language for naive users and advanced options for computing savvy programmers in one single platform, called VisFlow. We describe its salient features and advantages using a complex scientific application in natural resources and ecology. Video: https://youtu.be/ 2YSYVyOuuk.

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.

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.