2017 — 2020 |
Guo, Philip Klemmer, Scott (co-PI) [⬀] Voytek, Bradley Hollan, James |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Nrt-Ige: Augmenting, Piloting, and Scaling Computational Notebooks to Train New Graduate Researchers in Data-Centric Programming @ University of California-San Diego
Every research area is confronting torrents of data arriving with increasing velocity, volume, and variety. The unprecedented scales of data, the accelerating pace of its accumulation, and disappearing disciplinary boundaries bring exciting research and development opportunities but also challenge traditional graduate education. A central challenge for virtually every discipline is ensuring students have the computational skills needed for increasingly data-intensive research and necessary for a competitive and rapidly evolving job market. Across the biological, physical, and social sciences, one common approach to addressing this challenge is to create bootcamps, which are introductory short courses for new graduate students. This National Science Foundation Research Traineeship (NRT) award in the Innovations in Graduate Education (IGE) Track to the University of California - San Diego will expand on the bootcamp approach and simultaneously exploit the growing movement for the creation of computational notebooks by augmenting Jupyter Notebook, a web-based notebook with novel online facilities for data-centric programming training for graduate students in a wide range of disciplines. This has the potential to improve the efficacy of training graduate students in data-centric programming and expand its impact by making new instructional facilities widely available via the web.
Building on open-source Jupyter Notebook software and widely deployed educational tools the investigators have developed to support tutoring (Python Tutor), feedback (PeerStudio), discussion (Talkabout), and activity capture (ChronoViz), the project will augment Jupyter Notebook to assist in training new graduate students in data-centric programming. This project will iteratively design, pilot, and evaluate the augmented notebooks and associated new companion curricula in bootcamps and ongoing classes to validate the approach, and make the resulting system, tailored to specific discipline requirements, available online to be widely shared, evolved, and extended in bootcamps, graduate classes, and online courses. In addition, project members will participate in the UC San Diego STARS (Summer Training for Academic Research Success) outreach program and involve high school students from the innovative Preuss School on campus and other high schools in the area. To extend impact beyond publication, education, and local outreach, project members will work closely with the Project Jupyter team to form a community to further develop and evolve this approach for training students in data-centric programming and help fulfill the promise that increased sharing of data and analyses holds for advancing open scientific collaboration and reproducible science.
The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, potentially transformative models for STEM graduate education training. The Innovations in Graduate Education Track is dedicated solely to piloting, testing, and evaluating novel, innovative, and potentially transformative approaches to graduate education.
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1 |
2017 — 2020 |
Voytek, Bradley |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Oscillatory Phase Dynamics Coordinate Cognitive Neural Networks @ University of California-San Diego
One of the most fundamental questions in neuroscience is how the 86 billion neurons in the human brain can coordinate their activity to give rise to our amazing cognitive abilities. With support from the National Science Foundation, Dr. Voytek and his research team at UC San Diego will examine the role that rhythmic fluctuations in the brain's electrical activity, namely neural oscillations or 'brain waves', play in shaping how the brain processes information and how groups of neurons communicate with one another. Using a sophisticated blend of human electrophysiology and innovative brain-state-dependent stimulus presentation, these projects will test how neural oscillations route the flow of information influence the brain's activity and, therefore, impact learning and memory. The results of these projects have broad implications ranging from age-related cognitive decline to cognitive enhancement. Additionally, this award will provide the resources for building open source methods and tutorials for analyzing complex, non-linear neural data, as well as support Dr. Voytek's extensive work in science communication and outreach. These open science, science communication, and diversity outreach initiatives will be distributed freely online with code and other materials and, ultimately, may be expanded into a Summer School focused on training students how to analyze oscillatory neural data and how to communicate their research to the public.
These projects will extend our understanding of oscillatory phase as a neural communication mechanism. This is critical, as oscillations have been implicated in healthy cognitive functioning by allowing for efficient information transmission, top-down attentional control, cognitive control, and working memory maintenance, among many other functions. By extension, dysfunctional phase dynamics may underlie a variety of cognitive disruptions such as those seen in normal aging, as well as in psychiatric and neurological disease. These projects will significantly advance our understanding of network phase dynamics in human cognition specifically, and as a general a computational mechanism in general. This award will also support the development of a suite of computational tools, open source methods, and code for analyzing oscillatory phase networks and their computational capacity. Additionally, it will support continued development of a system for real-time, phase-triggered stimulus presentation, which will open entirely new avenues of cognitive research.
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1 |
2019 — 2021 |
Voytek, Bradley T. |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Tools For Parameterizing and Visualizing Electrophysiological Rhythmic and Arrhythmic Features @ University of California, San Diego
Project Summary Cognition requires the dynamic coordination of neural ensembles across multiple brain regions. This is one of the biggest neuroscientific questions: how do neural populations form transient communication networks in the service of cognition? One exciting candidate mechanism by which this occurs is through the coupling of neural oscillations between brain regions. These oscillations are a ubiquitous feature of electrophysiology, occurring across species. Despite their wide study, recent work has highlighted many pitfalls in analyzing oscillations, largely centered around three major issues: 1) Oscillations should be measured relative to the aperiodic (1/f) background because, strictly speaking, oscillations are defined as any regions of the power spectrum that rise above the 1/f background, which has itself been shown to be dynamic in relation to both cognition and disease; 2) Most tools for extracting and quantifying oscillations assume that they are sinusoidal despite the fact that they rarely ever are. Further, those non-sinusoidal features may carry critical physiological information; 3) Traditional methods can conflate bursting and non-bursting oscillations, despite the rapidly mounting evidence that the two oscillatory modes are distinct, and may even play different functional roles. In this project we will significantly expand upon analytic software and platforms, developed by my lab, to test the validity of our tools against real and simulated data. These tools are designed specifically to address the three major oscillation analysis issues outlined above. After testing, these analytic toolboxes will then be moved online, to permit cloud-based, large-scale analysis of oscillations, the 1/f background, non-sinusoidal waveform features, and oscillatory bursts. We will then leverage new, dynamic, interactive in-brower visualization tools for data processing and exploration. All of these will be done using open-source tools, built to industry standards of software development, in a transparent manner. 1
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0.958 |