Michael J. Stealey

HydroShare: Sharing Diverse Environmental Data Types and Models as Social Objects with Application to the Hydrology Domain

The types of data and models used within the hydrologic science community are diverse. New repositories have succeeded in making data and models more accessible, but are, in most cases, limited to particular types or classes of data or models and also lack the type of collaborative and iterative functionality needed to enable shared data collection and modeling workflows. File sharing systems currently used within many scientific communities for private sharing of preliminary and intermediate data and modeling products do not support collaborative data capture, description, visualization, and annotation. In this article, we cast hydrologic datasets and models as “social objects” that can be published, collaborated around, annotated, discovered, and accessed. This article describes the generic data model and content packaging scheme for diverse hydrologic datasets and models used by a new hydrologic collaborative environment called HydroShare to enable storage, management, sharing, publication, and annotation of the diverse types of data and models used by hydrologic scientists. The flexibility of HydroShares data model and packaging scheme is demonstrated using multiple hydrologic data and model use cases that highlight its features.

An Architecture for Mining and Visualization of U.S. Higher Educational Data

Higher education has undergone considerable change in the past decades. As a result, the higher education community is collecting and disseminating a great deal of data that is typically used to benchmark performance or satisfy reporting requirements. This data is a rich source for scholarly inquiry, and particularly interesting for questions related to investment strategies within the academy. However, the real value of these data sets can often only realized when the data is viewed and studied across the aggregate collection of data sources. This is a complex task that requires gathering, cleaning, and applying consistent metadata standards to data sets. This paper presents a Unified Data Framework that allows the aggregation of high demand data sources into a single useful research resource that is relevant to research in higher education. The Unified Data Framework guides the aggregation of existing and new data sets, and provides the option of connecting and automatically, or semi-automatically, updating data from the original sources. The Unified Data Framework presents to researchers of higher education a robust suite of analytic tools for data mining and visualization of combined and complex data sources.

Cyberinfrastructure and Web Apps for Managing and Disseminating the National Water Model

Hydrologic modeling can be used to provide warnings before, and to support operations during and after floods. Recent technological advances have increased our ability to create hydrologic models over large areas. In the United States (U.S.), a new National Water Model (NWM) that generates hydrologic variables at a national scale was released in August 2016. This model represents a substantial step forward in our ability to predict hydrologic events in a consistent fashion across the entire U.S. Nevertheless, for these hydrologic results to be effectively communicated, they need to be put in context and be presented in a way that is straightforward and facilitates management-related decisions. The large amounts of data produced by the NWM present one of the major challenges to fulfill this goal. We created a cyberinfrastructure to store NWM results, “accessibility” web applications to retrieve NWM results, and a REST API to access NWM results programmatically. To demonstrate the utility of this cyberinfrastructure, we created additional web apps that illustrate how to use our REST API and communicate hydrologic forecasts with the aid of dynamic flood maps. This work offers a starting point for the development of a more comprehensive toolset to validate the NWM while also improving the ability to access and visualize NWM forecasts, and develop additional national-scale-derived products such as flood maps. (KEY TERMS: data management; cyberinfrastructure; hydrologic modeling; data visualization; flooding; decision support systems.) Souffront Alcantara, Michael A., Christian Kesler, Michael J. Stealey, E. James Nelson, Daniel P. Ames, and Norm L. Jones, 2018. Cyberinfrastructure and Web Apps for Managing and Disseminating the National Water Model. Journal of the American Water Resources Association (JAWRA) 54 (4): 859–871. https://doi.org/10.1111/ 1752-1688.12608

Advancing distributed data management for the HydroShare hydrologic information system

Abstract HydroShare ( https://www.hydroshare.org ) is an online collaborative system to support the open sharing of hydrologic data, analytical tools, and computer models. Hydrologic data and models are often large, extending to multi-gigabyte or terabyte scale, and as a result, the scalability of centralized data management poses challenges for a system such as HydroShare. A distributed data management framework that enables distributed physical data storage and management in multiple locations thus becomes a necessity. We use the iRODS (Integrated Rule-Oriented Data System) data grid middleware as the distributed data storage and management back end in HydroShare. iRODS provides a unified virtual file system for distributed physical storages in multiple locations and enables data federation across geographically dispersed institutions around the world. In this paper, we describe the iRODS-based distributed data management approaches implemented in HydroShare to provide a practical demonstration of a production system for supporting big data in the environmental sciences.

xDCI, a data science cyberinfrastructure for interdisciplinary research

This paper introduces xDCI, a Data Science Cyber-infrastructure to support research in a number of scientific domains including genomics, environmental science, biomedical and health science, and social science. xDCI leverages open-source software packages such as the integrated Rule Oriented Data System and the CyVerse Discovery Environment to address significant challenges in data storage, sharing, analysis and visualization. We provide three example applications to evaluate xDCI for different domains: analysis of 3D images of mice brains, videos analysis of neonatal resuscitation, and risk analytics. Finally, we conclude with a discussion of potential improvements to xDCI.