conference-talk

Function Follows Form: A Practical Guide to Research Data Curation

Date and Time: 
Tuesday, April 5th, 2016
Location: 
Center Green
Speaker: 
Julia Collins

Whether your data science adventures rely on data you produced or leverage data sources managed by others, the ability to explore, analyze, and visualize data all depend on access to the data themselves. The ability of other data scientists to verify and build upon your findings also depends on their access to the same data sources. This talk will review the role of data curation in the data science workflow. We will review data management best practices applicable to all levels of data generation and use, from small exploratory studies to large satellite data sets

Speaker Description: 

Julia Collins is a software developer at the National Snow and Ice Data Center in Boulder, Colorado. She currently provides software engineering support for tools used to manage and process large Earth science data sets, as well as supporting the development of user interfaces and data storage strategies for community-based monitoring activities and qualitative data sources.

Event Category:

ARTView: A Community Weather Radar Data GUI

Date and Time: 
Monday, April 4th, 2016
Location: 
Center Green
Speaker: 
Nick Guy

With file formats, naming conventions, and aging platforms, working with weather radar data has been a notoriously difficult endeavor until recently. Tools for plotting and analyzing radar data have existed generally at an institutional or proprietary level. Community tools, such as Solo II/3, have been indispensible. However, they do not necessarily take advantage of modern computing technologies and address the shift of academic and government institutions to open source platforms for cost savings, performance, and development flexibility.

Speaker Description: 
Nick Guy is Associate Research Scientist and Project Manager of King Air Research Facility at the Department of Atmospheric Science in the University of Wyoming

Event Category:

Whales On A Plane: Deploying Software To NSF / NCAR Research Aircraft w/ Docker

Date and Time: 
Monday, April 4th, 2016
Location: 
Center Green
Speaker: 
Erik Johnson

Docker is a maturing open-source, Linux-based containerization technology that provides a convenient means to package, distribute and execute software in a fast and isolated environment.

NCAR's Earth Observing Laboratory (EOL) is using Docker to deploy applications and associated services to NSF / NCAR research aircraft. In this talk, I will discuss the benefits provided by Docker, and tools, such as Docker Compose and Docker Hub, and techniques used to facilitate Docker-based deployment of NCAR EOL applications and services, which include:

Speaker Description: 

Erik Johnson is a software engineer at NCAR's Earth Observing Laboratory, responsible for full-stack web development and devops for the Field Catalog and related Catalog tools using Free and Open-Source technologies. Erik has previously worked at start-ups and contracted to NOAA and NASA.

Event Category:

Extending the geographic extent of existing land cover data using active machine learning and covariate shift corrective sampling

Date and Time: 
Monday, April 4th, 2016
Location: 
Center Green
Speaker: 
Galen Maclaurin

Consistent land cover data provided at national and regional scales are increasingly relevant for a wide range of research topics from landscape ecology to population dynamics. As one example, the National Land Cover Database (NLCD) provides a valuable resource for research conducted at broad geographic scales across the U.S. where survey- or field-based land cover data are not available.

Speaker Description: 

I am a geospatial data scientist at the National Renewable Energy Laboratory (NREL) in Golden, CO, where I work on diverse problems in renewable energy involving spatiotemporal data. My recently completed PhD research in the Department of Geography at the University of Colorado-Boulder focused on image-based machine learning for spatial and temporal replication of land cover data.

Event Category:

Building a Distributed Oceanography Match-up Service (DOMS) to pair field observation and satellite data

Date and Time: 
Monday, April 4th, 2016
Location: 
Center Green
Speaker: 
Zaihua Ji

Geoscience applications increasingly rely on the integration and collocation of data in the form of in-situ field observations with data in the form of satellite observations and global models. Both types of data reside in scattered repositories for both historical and economic (too large to replicate) reasons. Finding all possible data match-ups between distributed data repositories is a fundamental challenge for geoscience work such as satellite calibration and validation (Cal/Val).

Speaker Description: 

Zaihua Ji is Senior Software Engineer in the Data Support Section of CISL at NCAR.

Event Category:

Transmission Distribution Systems Hub

Date and Time: 
Monday, April 4th, 2016
Location: 
Center Green
Speaker: 
Monte Lunacek

The electric grid is a complex network that delivers power from many, distributed sources to commercial and residential consumers. Researchers are able to understand the impact of changes to this system through modeling and simulation.  This is important because several technologies that are growing in residential use, such as solar, electric vehicles, and smart home appliances, impact the load placed on the grid in different ways. New and different retail market structures also impact how electricity is consumed.

Speaker Description: 

Monte Lunacek is member of the Modeling & Simulation Group at NREL since 2014. Before, he was an HPC Application Specialist in the Research Computing group at the University of Colorado.  Monte received his PhD in Computer Science from Colorado State University.

Event Category:

Apache Spark for scientific data at scale

Date and Time: 
Monday, April 4th, 2016
Location: 
Center Green
Speaker: 
Neal McBurnett

Apache Spark is a modern open source cluster computing platform. It is helping data scientists analyze and explore large datasets more effectively than ever before, in terms of both software development productivity and efficient use of hardware, scaling from on-premises clusters to on-demand cloud computing.

Speaker Description: 

Neal McBurnett is a consultant in Boulder Colorado. Since his career as a Distinguished Member of Technical Staff at Bell Labs, working on tools for software development, security and open source web collaboration, he has taught Artificial Intelligence at CU and worked as a techincal content developer at Databricks for courses on Apache Spark, including two massive online courses on Spark in 2015.

Event Category:

Using open source tools to reduce large data sets in a distributed environment.

Date and Time: 
Monday, April 4th, 2016
Location: 
Center Green
Speaker: 
Scott Collis

Radar data is complex and with the archive of NOAA NEXRAD data approaching the petabyte range, managing and extracting geophysical insight from this data is truly challenging. This presentation will discuss an approach using Python, specifically using resources available through project Jupyter, to solve pleasantly parallel problems. This is especially pertinent given the increasing prevalence of distributed computing resources.

Speaker Description: 

Scott Collis is radar metereologist at Argonne National Lab.

Event Category:

On the potential of Big Data capabilities for the validation of a Weather Forecasting System

Date and Time: 
Monday, April 4th, 2016
Location: 
Center Green
Speaker: 
Giuseppe Iannitto
A key component of the EO projects is the validation of the EO data products through a Ground Truth Validation. In the validation process data can be collected from various ground-based sources and sensors (in situ measurements, instruments, crowd-sourcing, open source platform), then quality-controlled, and finally compared with the satellite products in order to get validated retrievals.
Speaker Description: 
Currently 3rd year PhD student at Tor Vergata University (Rome) for the geoinformation department, studying on big data “A Map-Reduce approach for management and exploitation of Earth Observation Big Data”
Senior IT Consultant/Manager, Mr. Iannitto worked as Technical Director for an IT Consulting organization, with over 10 years of hands on system integrations, management and software development experience in the Information and Communication Technology Industry.
Ten years experienced as system integrator: responsible of system integration and acceptance test on a Europe-wide project of a Combat Management System for the Italian and French Navy.
His expertise as project manager encompasses any project phase from pre-project sales, strategic planning, project planning and project control, as well as post-project evaluation with the client sponsor to assure satisfaction and to identify any process improvements that can be made to the client.
Also, proficient in most client server platforms and numerous programming languages. Very good analytical problem-solving ability with excellent verbal and written communication skills.

Event Category:

What Climate Science Knows About Big Data

Date and Time: 
Monday, April 4th, 2016
Location: 
Center Green
Speaker: 
Seth McGinnis

Big Data refers to any data set that's too big to be handled by conventional tools and techniques. This criterion is relative, context-dependent, and changes over time. It is often taken to mean data with high storage volume on the terabyte to petabyte scale (currently), but there are other ways for data to be Big, and an array of different approaches are needed to wrestle such data sets into tractability.

Speaker Description: 

Seth McGinnis is an Associate Scientist IV in the Institute for Mathematics Applied to Geosciences (IMAGe) at NCAR. As the Data Manager and User Community Manager for NARCCAP, the North American Regional Climate Change Assessment Program, he makes the output from climate models usable by and available to people who need information about climate change. His research focuses on bias correction, interpolation, and other issues affecting the practical use of model output by non-specialists.

Event Category:

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