Enabling Interactive Analytics of Secure Data using Cloud Kotta

Research, especially in the social sciences and humanities, is increasingly reliant on the application of data science methods to analyze large amounts of (often private) data. Secure data enclaves provide a solution for managing and analyzing private data. However, such enclaves do not readily support discovery science---a form of exploratory or interactive analysis by which researchers execute a range of (sometimes large) analyses in an iterative and collaborative manner. The batch computing model offered by many data enclaves is well suited to executing large compute tasks; however it is far from ideal for day-to-day discovery science. As researchers must submit jobs to queues and wait for results, the high latencies inherent in queue-based, batch computing systems hinder interactive analysis. In this paper we describe how we have augmented the Cloud Kotta secure data enclave to support collaborative and interactive analysis of sensitive data. Our model uses Jupyter notebooks as a flexible analysis environment and Python language constructs to support the execution of arbitrary functions on private data within this secure framework.Comment: To appear in Proceedings of Workshop on Scientific Cloud Computing, Washington, DC USA, June 2017 (ScienceCloud 2017), 7 page

Health Care Evolves From Reactive to Proactive.

Decoding health and disease pathways drives healthcare evolution. Historically, therapeutic paradigms have relied on interventions that mitigate symptoms of established diseases. Increasingly, molecular insights into pathophysiology now provide unprecedented opportunities to offer curative solutions or even prevent disease and thereby secure longitudinal wellness. These opportunities extend past individual patients to entire populations and geographies. Moreover, they optimize prospective healthspan across lifespan. Linking discovery science and its translatable innovations beyond reactive disease intervention to proactive prevention will maximize society’s returns creating the greatest benefit for the greatest number of people globally

Fear and Foxes: An Educational Primer for Use with "Anterior Pituitary Transcriptome Suggests Differences in ACTH Release in Tame and Aggressive Foxes".

The way genes contribute to behavior is complicated. Although there are some single genes with large contributions, most behavioral differences are due to small effects from many interacting genes. This makes it hard to identify the genes that cause behavioral differences. Mutagenesis screens in model organisms, selective breeding experiments in animals, comparisons between related populations with different behaviors, and genome-wide association studies in humans are promising and complementary approaches to understanding the heritable aspects of complex behaviors. To connect genes to behaviors requires measuring behavioral differences, locating correlated genetic changes, determining when, where, and how these candidate genes act, and designing causative confirmatory experiments. This area of research has implications from basic discovery science to human mental health

The Joint Center for Energy Storage Research: A New Paradigm for Battery Research and Development

The Joint Center for Energy Storage Research (JCESR) seeks transformational change in transportation and the electricity grid driven by next generation high performance, low cost electricity storage. To pursue this transformative vision JCESR introduces a new paradigm for battery research: integrating discovery science, battery design, research prototyping and manufacturing collaboration in a single highly interactive organization. This new paradigm will accelerate the pace of discovery and innovation and reduce the time from conceptualization to commercialization. JCESR applies its new paradigm exclusively to beyond-lithium-ion batteries, a vast, rich and largely unexplored frontier. This review presents JCESR's motivation, vision, mission, intended outcomes or legacies and first year accomplishments.Comment: 17 pages, 14 figures, 96 reference

Generalized Shortest Path Kernel on Graphs

We consider the problem of classifying graphs using graph kernels. We define a new graph kernel, called the generalized shortest path kernel, based on the number and length of shortest paths between nodes. For our example classification problem, we consider the task of classifying random graphs from two well-known families, by the number of clusters they contain. We verify empirically that the generalized shortest path kernel outperforms the original shortest path kernel on a number of datasets. We give a theoretical analysis for explaining our experimental results. In particular, we estimate distributions of the expected feature vectors for the shortest path kernel and the generalized shortest path kernel, and we show some evidence explaining why our graph kernel outperforms the shortest path kernel for our graph classification problem.Comment: Short version presented at Discovery Science 2015 in Banf

Spectrum, Volume 46, Issue 10

Highlights include: Reopening of Sacred Heart University’s Discovery Science Center and Planetarium -- FedEx Shooter and Gun Control -- Lifting COVID-19 Restrictions -- Registering for Fall 2021 -- Not in Kansas Anymore Colloquium -- Best Buddies Friendship Wal

Advancing discovery science with fair data stewardship:Findable, accessible, interoperable, reusable

This report summarizes a presentation by Dr. Michel Dumontier. It reviews innovative scientific research methods created by data science, and the need to develop infrastructure, methodologies, and user communities to advance data science. Stakeholders have proposed a set of principles to make digital resources findable, accessible, interoperable, and reusable—FAIR. FAIR principles provide guidelines, do not require specific technologies, and allow communities of stakeholders to define specific FAIR standards and develop metrics to quantify them. Libraries can be part of the new data ecosystemby providing education, data stewardship, and infrastructure

Genomics, “Discovery Science,” Systems Biology, and Causal Explanation: What Really Works?

Diverse and non-coherent sets of epistemological principles currently inform research in the general area of functional genomics. Here, from the personal point of view of a scientist with over half a century of immersion in hypothesis driven scientific discovery, I compare and deconstruct the ideological bases of prominent recent alternatives, such as “discovery science,” some productions of the ENCODE project, and aspects of large data set systems biology. The outputs of these types of scientific enterprise qualitatively reflect their radical definitions of scientific knowledge, and of its logical requirements. Their properties emerge in high relief when contrasted (as an example) to a recent, system-wide, predictive analysis of a developmental regulatory apparatus that was instead based directly on hypothesis-driven experimental tests of mechanism