The Scholarly Data Share (SDS) is a lightweight web interface that facilitates access to large, curated research datasets in long-term storage. The first version, SDS 1.0, facilitated sharing public datasets without access restrictions. The new version, SDS 2.0, provides controlled access to datasets at various stages in the research data life cycle. This update enables granular and customizable access control for a variety of research domains and use cases. In this paper, we discuss the features, implementation, and use cases of SDS 2.0, as well as outlining our plans for future enhancements to the service.

This paper describes a specific project and generalizable workflow for supplementing informational outreach tools with effective and sustainable eXtended Reality (XR) experiences. The goal is to enhance the communicative power and impact of the objective data presented by these online tools by coupling them with engaging, immersive experiences. This paper focuses on the specific challenge of helping users to better understand and appreciate the causes and impacts of climate change by enhancing an existing collection of table- and map-based information through the addition of climate-focused XR experiences. The ultimate goal is to inspire viewers to change their behaviors and to take action at a personal, family, or community scale. While this project focuses on climate change, the methods presented are applicable to a wide variety of informational outreach tools and web sites, such as those promoting healthy habits, personal safety, or disease prevention.

The Scholarly Data Share (SDS) is a lightweight web interface that facilitates access to large, curated research datasets stored in a tape archive. SDS addresses the common needs of research teams working with and managing large and complex datasets, and the associated storage. The service adds several key features to the standard tape storage offerings that are of particular value to the research community: (1) the ability to capture and manage metadata, (2) metadata-driven browsing and retrieval over a web interface, (3) reliable and scalable asynchronous data transfers, and (4) an interface that hides the complexity of the underlying storage and access infrastructure. SDS is designed to be easy to implement and sustain over time by building on existing tool chains and proven open-source software and by minimizing bespoke code and domain-specific customization. In this paper, we describe the development of the SDS and the implementation of an instance to provide access to a large collection of geospatial datasets.

Over the past decade, cyberinfrastructure community like XSEDE has substantially fostered and enriched knowledge discovery of scholars, researchers, and engineers from a variety of domains through enabling access to advanced computing systems, where continuing support for classic packages and parallel computing frameworks (e.g., MPI and OpenMP) has been well established. However, with the rise of "Big Data" era, an ever increasing demand from user community is the desire to run sophisticated, state-of-the-art distributed frameworks that handle various data related tasks. Examples include Hadoop and Spark for data processing and analytics, Cassandra and Redis for scalable on-disk and in-memory data stores, Apache Airflow for distributed workflow engine, just to name a few. Though by design such frameworks provision high-level, user friendly programming APIs for business logic composition, their deployment process oftentimes is both complex and complicated, requiring expertise well beyond what the majority of cyberinfrastructure users may have. To bridge the gap, in this paper we propose the concept of provisioning such frameworks through "cyberinfrastructure managed", system wide services, especially through leveraging "high level, complexity hiding interfaces" principle when designing interfaces that are exposed to users for framework setup and shutdown. In particular, we use Sparkas-a-Service at Indiana University as a concrete case study to illustrate how we applied the design principles. Furthermore, to demonstrate the generality of the design, we showcase Cassandraas-a-Service, a work-in-progress prototype.

Computers accelerate our ability to achieve scientific breakthroughs. As technology evolves and new research needs come to light, the role for cyberinfrastructure as “knowledge” infrastructure continues to expand. In essence, cyberinfrastructure can be thought of as the integration of supercomputers, data resources, visualization, and people that extends the impact and utility of information technology. This chapter discusses cyberinfrastructure, the related topics of science gateways and campus bridging, and identifies future challenges and opportunities in cyberinfrastructure.

Large-scale text analysis algorithms are important to many fields as they interrogate reams of textual data to extract evidence, correlations, and trends not readily discoverable by a human reader. Unfortunately, there is often an expertise mismatch between computational researchers who have the technical and programming skills necessary to develop workflows at scale and domain scholars who have knowledge of the literary, historical, scientific, or social factors that can affect data as it is manipulated. Our work focuses on the use of scalable computational notebooks as a model to bridge the accessibility gap for domain scholars, putting the power of HPC resources directly in the hands of the researchers who have scholarly questions. The computational notebook approach offers many benefits, including: fine-grained control through modularized functions, interactive analysis that puts the "human in the loop", scalable analysis that leverages Spark-as-a-Service, and complexity hiding interfaces that minimize the need for HPC expertise. In addition, the notebook approach makes it easy to share, reproduce, and sustain research workflows. We illustrate the applicability of our approach with usage scenarios on HPC systems as well as within a restricted computing environment to access sensitive, in-copyright data, and demonstrate the usefulness of the notebook approach with three examples from three different domains and data sources. These sources include historical topic trends in ten thousand scientific articles, sentiment analysis of tweets, and literary analysis of the copyrighted works of Kurt Vonnegut using non-consumptive techniques.

The Stampede 1 supercomputer was a tremendous success as an XSEDE resource, providing more than eight million successful computational simulations and data analysis jobs to more than ten thousand users. In addition, Stampede 1 introduced new technology that began to move users towards many core processors. As Stampede 1 reaches the end of its production life, it is being replaced in phases by a new supercomputer, Stampede 2, that will not only take up much of the original system's workload, but continue the bridge to technologies on the path to exascale computing. This paper provides a brief summary of the experiences of Stampede 1, and details the design and architecture of Stampede 2. Early results are presented from a subset of Intel Knights Landing nodes that are bridging between the two systems.

Please direct any questions regarding the data, data collection, methods used in the administration of this survey and/or the summarization of responses provided in this document (or the corresponding report) to Julie Wernert at Indiana University, jwernert@iu.edu.

This material is based upon work supported by the National Science Foundation under grants 0504075, 0451237, 0723054, 1062432, 0116050, 0521433, 0503697, and 1053575, and several IBM Shared University Research grants and support provided by Lilly Endowment, Inc. for the Indiana University Pervasive Technology Institute. Any opinions, findings and conclusions or recommendations expressed herein are those of the authors and do not necessarily reflect the views of the supporting agencies.

As digital data sources grow in number and size, they pose an opportunity for computational investigation by means of text mining, NLP, and other text analysis techniques. R is a popular and powerful text analytics tool; however, it needs to run in parallel and requires special handling to protect copyrighted content against full access (consumption). The HathiTrust Research Center (HTRC) currently has 11 million volumes (books) where 7 million volumes are copyrighted. In this paper we propose HTRC TextRWeb, an interactive R software environment which employs complexity hiding interfaces and automatic code generation to allow large-scale text analytics in a non-consumptive means. For our principal test case of copyrighted data in HathiTrust Digital Library, TextRWeb permits us to code, edit, and submit text analytics methods empowered by a family of interactive web user interfaces. All these methods combine to reveal a new interactive paradigm for large-scale text analytics on the web.

Recent advances in acquiring high throughput neuroimaging and genomics data provide exciting new opportunities to study the influence of genetic variation on brain structure and function. Research in this emergent field, known as imaging genetics, aims to identify the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs). Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. However, the scale and complexity of the imaging genetic data have presented critical computational bottlenecks requiring new concepts and enabling tools. In this paper, we present our initial efforts on developing a set of massively parallel strategies to accelerate a widely used SCCA implementation provided by the Penalized Multivariate Analysis (PMA) software package. In particular, we exploit parallel packages of R, optimized mathematical libraries, and the automatic offload model for Intel Many Integrated Core (MIC) architecture to accelerate SCCA. We create several simulated imaging genetics data sets of different sizes and use these synthetic data to perform comparative study. Our performance evaluation demonstrates that a 2-fold speedup can be achieved by the proposed acceleration. The preliminary results show that by combining data parallel strategy and the offload model for MIC we can significantly reduce the knowledge discovery timelines involving applying SCCA on large brain imaging genetics data.

Over the years, R has been adopted as a major data analysis and mining tool in many domain fields. As Big Data overwhelms those fields, the computational needs and workload of existing R solutions increases significantly. With recent hardware and software developments, it is possible to enable massive parallelism with existing R solutions with little to no modification. In this paper, we evaluated approaches to speed up R computations with the utilization of the Intel Math Kernel Library and automatic offloading to Intel Xeon Phi SE10P Co-processor. The testing workload includes a popular R benchmark and a practical application in health informatics. There are up to five times speedup gains from using MKL with a 16 cores without modification to the existing code for certain computing tasks. Offloading to Phi co-processor further improves the performance. The performance gains through parallelization increases as the data size increases, a promising result for adopting R for big data problem in the future.

Development of cost-effective groundwater remediation and containment systems is an essential component of protecting the nation's water resources.Safeguarding these resources is vital to human health and to the nation's food supplies, yet financial resources for such projects is becoming increasingly scarce.Our research directly addresses this social conflict by casting it as a constrained optimization problem that is solved and visualized using cluster resources at the University at Buffalo Center for Computational Research (UB CCR).

Stewart, C.A., M.R. Link, E. Wernert, W.K. Barnett, T.M. Miller. 2012. Information technology in support of research, scholarship, and creative activities: A strategic plan for Research Technologies – a division of UITS and a PTI Service and Cyberinfrastructure Center. Indiana University, Bloomington, IN. PTI Technical Report PTI-TR12-007.

Immersive visualization makes use of the medium of virtual reality (VR) - it is a subset of virtual reality focused on the application of VR technologies to scientific and information visualization. As the name implies, there is a particular focus on the physically immersive aspect of VR that more fully engages the perceptual and kinesthetic capabilities of the scientist with the goal of producing greater insight. The immersive visualization community is uniquely positioned to address the analysis needs of the wide spectrum of domain scientists who are becoming increasingly overwhelmed by data. The outputs of computational science simulations and high-resolution sensors are creating a data deluge. Data is coming in faster than it can be analyzed, and there are countless opportunities for discovery that are missed as the data speeds by. By more fully utilizing the scientists visual and other sensory systems, and by offering a more natural user interface with which to interact with computer-generated representations, immersive visualization offers great promise in taming this data torrent. However, increasing the adoption of immersive visualization in scientific research communities can only happen by simultaneously lowering the engagement threshold while raising the measurable benefits of adoption. Scientists time spent immersed with theirmore » data will thus be rewarded with higher productivity, deeper insight, and improved creativity. Immersive visualization ties together technologies and methodologies from a variety of related but frequently disjoint areas, including hardware, software and human-computer interaction (HCI) disciplines. In many ways, hardware is a solved problem. There are well established technologies including large walk-in systems such as the CAVE{trademark} and head-based systems such as the Wide-5{trademark}. The advent of new consumer-level technologies now enable an entirely new generation of immersive displays, with smaller footprints and costs, widening the potential consumer base. While one would be hard-pressed to call software a solved problem, we now understand considerably more about best practices for designing and developing sustainable, scalable software systems, and we have useful software examples that illuminate the way to even better implementations. As with any research endeavour, HCI will always be exploring new topics in interface design, but we now have a sizable knowledge base of the strengths and weaknesses of the human perceptual systems and we know how to design effective interfaces for immersive systems. So, in a research landscape with a clear need for better visualization and analysis tools, a methodology in immersive visualization that has been shown to effectively address some of those needs, and vastly improved supporting technologies and knowledge of hardware, software, and HCI, why hasn't immersive visualization 'caught on' more with scientists? What can we do as a community of immersive visualization researchers and practitioners to facilitate greater adoption by scientific communities so as to make the transition from 'the promise of virtual reality' to 'the reality of virtual reality'.« less