Data Hubs

The goal of the HydroFrame project is to provide a community framework for sophisticated high resolution hydrologic simulation across the entire continental US. To accomplish this we are building an integrated software framework for continental scale hydrologic simulation and data analysis built with multi-scale configurable components. The multi-scale requirements of this domain drive the design of the proposed framework.

We present the initial progress on the HydroFrame community platform using an automated Kepler workflow that performs end-to-end hydrology simulations involving data ingestion, preprocessing, analysis, modeling, and visualization. We will demonstrate how different modules of workflow can be reused and repurposed for the three target user groups. Moreover, the Kepler workflow ensures complete reproducibility through a built-in provenance framework that collects workflow specific parameters, software versions and hardware system configuration.

The HydroFrame project is a community platform designed to facilitate integrated hydrologic modeling across the US. As a part of HydroFrame, we seek to design innovative workflow solutions that create pathways to enable hydrologic analysis for three target user groups: the modeler, the analyzer, and the domain science educator. We present the initial progress on the HydroFrame community platform using an automated Kepler workflow. This workflow performs end-to-end hydrology simulations involving data ingestion, preprocessing, analysis, modeling, and visualization.

In previous work, we provided static domain and parameter datasets for the National Water Model (NWM) and Parflow (PF-CONUS) on demand, at regional watershed scales. We extend this functionality by connecting existing cloud applications and tools into a virtual ecosystem that supports extraction of domain and parameter datasets, execution of NWM and PF-CONUS models, and collaboration.

A key takeaway from the COVID-19 crisis is the need for scalable methods and systems for ingestion of big data related to the disease, such as models of the virus, health surveys, and social data, and the ability to integrate and analyze the ingested data rapidly. One specific example is the use of the Internet of Things and wearables (i.e., the Oura ring) to collect large-scale individualized data (e.g., temperature and heart rate) continuously and to create personalized baselines for detection of disease symptoms.

Quantum materials research is a rapidly growing domain of materials research, seeking novel compounds whose electronic properties are born from the uniquely quantum aspects of their constituent electrons. The data from this rapidly evolving area of quantum materials requires a new community-driven approach for collaboration and sharing the data from the end-to-end quantum material process.

The Kepler-driven provenance framework provides an Autonomous Provenance Collection capability for Hydrologic research. The framework scales to capture model parameters, user actions, hardware specifications and facilitates quick retrieval for actionable insights, whether the scientist is handling a small watershed simulation or a large continental-scale problem.

In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real-world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT).

The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented.

There is significant variability in neutralizing antibody responses (which correlate with immune protection) after COVID-19 vaccination, but only limited information is available about predictors of these responses. We investigated whether device-generated summaries of physiological metrics collected by a wearable device correlated with post-vaccination levels of antibodies to the SARS-CoV-2 receptor-binding domain (RBD), the target of neutralizing antibodies generated by existing COVID-19 vaccines.

The HydroFrame project combines cutting-edge environmental modeling approaches with modern software principals to build an end-to-end workflow for regional and continental scale scientific applications, by enabling modelers to extract static datasets from continental datasets and execute them using high performance computing hardware hosted at Princeton University. In prior work we have provided the capability for users to extract domain data for the ParFlow model at local scales and execute them using freely accessible cloud computing services (i.e. MyBinder.org).

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease.

The HydroFrame is a community platform that facilitates integrated hydrologic modeling across the United States. We design innovative workflow solutions that create pathways to enable hydrologic analysis for three target uses: modeling, analysis, and domain science. As part of our contribution to HydroFrame, we run HydroFrame workflows in the Kepler system, utilizing its automated workflow capabilities to perform end-to-end hydrology simulations involving data ingestion, preprocessing, analysis, modeling, and visualization.

The Quantum Foundry is a collaborative research center headquartered at the University of California, Santa Barbara (UCSB), focused on advancing the field of quantum science and engineering through the development of new materials and devices for use in quantum technologies. The foundational infrastructure of the center's various initiatives is one that enables smart, national-scale materials science and manufacturing.