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Salon A-C [clear filter]
Tuesday, January 7
 

2:00pm EST

COPDESS: Facilitating a Fair Publishing Workflow Ecosystem
COPDESS, the Coalition for Publishing Data in the Earth and Space Sciences (https://copdess.org/), was established in October 2014 as a platform for Earth and Space Science publishers and data repositories to jointly define, implement, and promote common policies and procedures for the publication and citation of data and other research results (e.g., samples, software, etc.) across Earth Science journals. In late 2018, COPDESS became a cluster of ESIP to give the initiative the needed sustainability to support a long-term FAIR publishing workflow ecosystem and be a springboard to pursue future enhancements of it.

In 2017, with funding from the Arnold Foundation, the ‘Enabling FAIR Data Project’ (https://copdess.org/enabling-fair-data-project/) moved mountains towards implementing the policies and standards that connect researchers, publishers, and data repositories in their desire to accelerate scientific discovery through open and FAIR data. Implementation of the new FAIR policies has advanced rapidly across Earth, Space, and Environmental journals, but supporting infrastructure, guidelines, and training for researchers, publishers, and data repositories has yet to catch up. The primary challenges are:
  • Repositories struggle to keep up with the demands of researchers, who want to be able to instantly deposit data and obtain a DOI, without considering the data quality/data ingest requirements and review procedures of individual repositories - producing a situation where data publication is inconsistent in quality and content.
  • Many publishers who have signed the Commitment Statement for FAIR Data (https://copdess.org/enabling-fair-data-project/commitment-statement-in-the-earth-space-and-environmental-sciences/) agree with it at a high, conceptual level. However, many journal editors and reviewers lack clarity on how to validate that datasets, which underpin scholarly publications, conform with the Commitment Statement.
  • Researchers experience confusion, and in some cases barriers to publication of their papers whilst they try and meet the requirements of the commitment statement. Clarity of requirements, timelines, and criteria for selecting repositories are needed to minimize the barriers to the joint publication of papers and associated data.

Funders have a role to play, in that they need to allow for time and resources required to curate data and ensure compliance, particularly WRT to the assignment of valid DOIs. Funders can also begin to reward those researchers who do take the effort to properly manage and make their data available, in a similar way to how they reward scholarly publications and citation of those publications.

The goal of this session is to start a conversation on developing an integrated publishing workflow ecosystem the seamlessly integrates researchers, repositories, publishers and funders. Perspectives from all viewpoints will be presented.

Notes document: https://docs.google.com/document/d/12M0F6mcUZSn2GdBN-Id__smXhYxbLzKDrAViPAgnH6w/edit?usp=sharing

Presentations:

View Recording: https://youtu.be/x6a1QRNbifQ

Takeaways
  • COPDESS has moved to ESIP as a cluster to ensure the sustainability of the project to address the publishing & citation of research data



Speakers
avatar for Karl Benedict

Karl Benedict

Director of Research Data Services & Information Technology, University of New Mexico
Since 1986 I have had parallel careers in Information Technology, Data Management and Analysis, and Archaeology. Since 1993 when I arrived at UNM I have worked as a Graduate Student in Anthropology, Research Scientist, Research Faculty, Applied Research Center Director, and currently... Read More →
avatar for Kerstin Lehnert

Kerstin Lehnert

President, IGSN e.V.
Kerstin Lehnert is Senior Research Scientist at the Lamont-Doherty Earth Observatory of Columbia University and Director of EarthChem, the System for Earth Sample Registration, and the Astromaterials Data System. Kerstin holds a Ph.D in Petrology from the University of Freiburg in... Read More →
avatar for Lesley Wyborn

Lesley Wyborn

Adjunct Fellow, Australian National University


Tuesday January 7, 2020 2:00pm - 3:30pm EST
Salon A-C
  Salon A-C, Breakout
 
Wednesday, January 8
 

11:00am EST

FAIRtool.org, Serverless workflows for cubesats, Geoweaver ML workflow management, 3D printed weather stations
Come hear what ESIP Lab PIs have built over the past year. Speakers include:

Abdullah Alowairdhi: FAIRTool Project Update
Ziheng Sun: Geoweaver Project
Amanda Tan: Serverless Workflow Project
Agbeli Ameko: 3D-Printed Weather Stations

Presentations:
https://doi.org/10.6084/m9.figshare.11626284.v1

View Recording: https://youtu.be/vrRwEQRAIZ4

Takeaways



Speakers
avatar for Amanda Tan

Amanda Tan

Data Scientist, University of Washington
Cloud computing, distributed systems
avatar for Abdullah Alowairdhi

Abdullah Alowairdhi

PhD Candedate, U of Idaho
avatar for Ziheng Sun

Ziheng Sun

Research Assistant Professor, George Mason University
My research interests are mainly on geospatial cyberinfrastructure and agricultural remote sensing.
avatar for Annie Burgess

Annie Burgess

ESIP Lab Director, ESIP


Wednesday January 8, 2020 11:00am - 12:30pm EST
Salon A-C
  Salon A-C, Breakout
  • Skill Level Skim the Surface, Jump In
  • Keywords Cloud Computing, Machine Learning
  • Collaboration Area Tags Science Software
  • Remote Participation Link: https://global.gotomeeting.com/join/195545333
  • Remote Participation Phone #: (571) 317-3129
  • Remote Participation Access Code 195-545-333
  • Additional Phone #'s: Australia: +61 2 8355 1050 Austria: +43 7 2081 5427 Belgium: +32 28 93 7018 Canada: +1 (647) 497-9391 Denmark: +45 32 72 03 82 Finland: +358 923 17 0568 France: +33 170 950 594 Germany: +49 692 5736 7317 Ireland: +353 15 360 728 Italy: +39 0 230 57 81 42 Netherlands: +31 207 941 377 New Zealand: +64 9 280 6302 Norway: +47 21 93 37 51 Spain: +34 912 71 8491 Sweden: +46 853 527 836 Switzerland: +41 225 4599 78 United Kingdom: +44 330 221 0088

2:00pm EST

AI for Augmenting Geospatial Information Discovery
Thanks to the rapid developments of hardware and computer science, we have seen a lot of exciting breakthroughs in self driving, voice recognition, street view recognition, cancer detection, check deposit, etc. Sooner or later the fire of AI will burn in Earth science field. Scientists need high-level automation to discover in-time accurate geospatial information from big amount of Earth observations, but few of the existing algorithms can ideally solve the sophisticated problems within automation. However, nowadays the transition from manual to automatic is actually undergoing gradually, a bit by a bit. Many early-bird researchers have started to transplant the AI theory and algorithms from computer science to GIScience, and a number of promising results have been achieved. In this session, we will invite speakers to talk about their experiences of using AI in geospatial information (GI) discovery. We will discuss all aspects of "AI for GI" such as the algorithms, technical frameworks, used tools & libraries, and model evaluation in various individual use case scenarios. How to Prepare for this Session: https://esip.figshare.com/articles/Geoweaver_for_Better_Deep_Learning_A_Review_of_Cyberinfrastructure/9037091
https://esip.figshare.com/articles/Some_Basics_of_Deep_Learning_in_Agriculture/7631615

Presentations:
https://doi.org/10.6084/m9.figshare.11626299.v1

View Recording: https://youtu.be/W0q8WiMw9Hs

Takeaways
  • There is a significant uptake of machine learning/artificial intelligence for earth science applications in the recent decade;
  • The challenge of machine learning applications for earth science domain includes:
    • the quality and availability of training data sets;
    • Requires a team with diverse skill background to implement the application
    • Need better understanding of the underlying mechanism of ML/AI models
  • There are many promising applications/ developments on streamlining the process and application of machine learning applications for different sectors of the society (weather monitoring, emergency responses, social good)



Speakers
avatar for Yuhan (Douglas) Rao

Yuhan (Douglas) Rao

Postdoctoral Research Scholar, CISESS/NCICS/NCSU
avatar for Aimee Barciauskas

Aimee Barciauskas

Data engineer, Development Seed
avatar for Annie Burgess

Annie Burgess

ESIP Lab Director, ESIP
avatar for Rahul Ramachandran

Rahul Ramachandran

Project Manager, Sr. Research Scientist, NASA
avatar for Ziheng Sun

Ziheng Sun

Research Assistant Professor, George Mason University
My research interests are mainly on geospatial cyberinfrastructure and agricultural remote sensing.


Wednesday January 8, 2020 2:00pm - 3:30pm EST
Salon A-C
  Salon A-C, Breakout
 
Thursday, January 9
 

10:15am EST

Mapping Data & Operational Readiness Levels (ORLs) to Community Lifelines
Approach: The Disaster Lifecycle Cluster has seen great success in its efforts to put Federated arms around “trusted data for decision makers” as a way to accelerate situational awareness and decision-making. By identifying trust levels for data. This session will build upon the Summer meeting and align perfectly with the overall ESIP theme of: Data to Action: Increasing the Use and Value of Earth Science Data and Information.

The ESIP Disaster Lifecycle Cluster has evolved into one of the most operationally active clusters in the Federation with a thirst for applying datasets to decision-making environments while building trust levels that manifest themselves as ORLs. Duke Energy, All Hazards Consortium’s Sensitive Information Sharing environment (SISE), DHS and FEMA are all increasing their interest in ORLs with their sights set on implementing them in the near future. Data is available everywhere and more of it is on the way. Trusted data is available some places and can help decision makers such as utilities make 30-second decisions that can save lives, property and get the lights back on sooner, saving millions of dollars.

This session will provide the venue to discuss emerging projects from NASA’s Applied Sciences Division (A.37), Initiatives at JPL and Federal Agency data portal access that can accelerate decision making today and in the future. We will also discuss drone data and European satellite data that is available for access and use when disasters threaten. Come and join us, the data you have may just save a life.

Agenda:
  1. Greg McShane, DHS CISA - The Critical Nature of the Public-Private Trusted Information Sharing Paradigm (10 min) Presented by Tom Moran, All Hazards Consortium Executive Director
  2. Dave Jones, StormCenter/GeoCollaborate - The status of ORLs, where we are, ESIP Announcement at GEO in Australia, AHC SISE, Next Steps (10 min)
  3. Maggi Glassco, NASA Disasters Program, JPL - New Applied Sciences Disasters Projects, Possible Lifeline Support Information Sources in the Future (10 min)
  4. Bob Chen/Bob Downs, Columbia Univ./SEDAC/CIESIN - Specific Global and Local Population Data for Community Lifeline Decision Making (10 min)
  5. Discussion/Q&A Period (40 min)

Presentations

View Recording: https://youtu.be/gJ93R6SlMkM

Key Takeaways for this Session: 
  1. Through the All Hazards Consortium, a new research institute will begin to help bring candidate research products into operations. An imagery committee, consisting of private and research members under SISE, will identify and evaluate use-case driven candidate imagery data within the ORL context using Geo-Collaborate.
  2. NASA grant opportunities within the disasters program requires co-funding by end user partners to guide usage needs and adoption (using ARL success criteria). This should increase adoption of NASA funded ASP project data and/or services. The cluster would like to work with NASA ASP as a testbed for funded projects to connect to additional user communities.
  3. We discussed the need / value of population data (current and predictions on affected populations) for preparedness activities and emergency response. We would like to leverage additional data services from SEDAC to test with operational decision makers. 


Speakers
avatar for Dave Jones

Dave Jones

StormCenter Communications, StormCenter Communications
Real-time data access, sharing and collaboration across multiple platforms. Collaborative Common Operating Pictures, Decision Making, Situational Awareness, connecting disparate mapping systems to share data, cross-product data sharing and collaboration. SBIR Phase III status with... Read More →
avatar for Karen Moe

Karen Moe

NASA Goddard Emeritus
ESIP Disasters Lifecycle cluster co-chair with Dave Jones/StormCenter IncManaging an air quality monitoring project for my town just outside of Washington DC and looking for free software!! Enjoying citizen science roles in environmental monitoring and sustainable practices in my... Read More →


Thursday January 9, 2020 10:15am - 11:45am EST
Salon A-C
  Salon A-C, Breakout

12:00pm EST

Fire effects on soil morphology across time scales: Data needs for near- and long-term land and hazard management
Fire impacts soil hydrology and biogeochemistry at both near (hours to days) and long (decades to centuries) time scales. Burns, especially in soils with high organic carbon stocks like peatlands, induce a loss of absolute soil carbon stock. Additionally, fire can alter the chemical makeup of the organic matter, potentially making it more resistant to decomposition. On the shorter timescales, fire can also change the water repellent properties or hydrophobicity of the soil, leading to an increased risk of debris flows and floods.

In this session, we will focus on the varying data needs for assessing the effects of burns across time scales, from informing emergency response managers in the immediate post-burn days, to monitoring post-burn recovery, to managing carbon in a landscape decades out.

Speaker abstracts (in order of presentation):

James MacKinnon (NASA GSFC)
Machine learning methods for detecting wildfires 

This talk shows the innovative use of deep neural networks, a type of machine learning, to detect wildfires in MODIS multispectral data. This effort attained a very high classification accuracy showing that neural networks could be useful in a scientific context, especially when dealing with sparse events such as fire anomalies. Furthermore, we laid the groundwork to continue beyond binary fire classification towards being able to detect the "state," or intensity of the fire, eventually allowing for more accurate fire modeling. With this knowledge, we developed software to enable neural networks to run on even the typically compute-limited spaceflight-rated computers, and tested it by building a drone payload equipped with a flight computer analog and flew it over controlled burns to prove its efficacy.

Kathe Todd-Brown (U. FL Gainesville)
An overview of effects of fire on ecosystems

Fire is a defining characteristic of many ecosystems worldwide, and, as the climate warms, both fire frequency and severity are expected to increase. In addition to the effects of smoke on the climate and human health, there are less apparent effects of fire on the terrestrial ecosystem. From alterations in the local soil properties to changes in the carbon budget as organic carbon is combusted into CO2 and pyrogenic carbon, fire is deeply impactful to the local landscape. The long-term climate implication of fire on the terrestrial carbon budget is a tension between carbon lost to the atmosphere as carbon dioxide and sequestered in the soil as recalcitrant pyrogenic carbon. Here we present a new model to simulate the interaction between ecosystem growth, decomposition, and fire on carbon dynamics. We find that the carbon lost to burned carbon dioxide will always be recovered, if there is any recalcitrant pyrogenic carbon generated by the fires. The time scale of this recovery, however, is highly variable and often not relevant to land managers. This model highlights key data gaps at the annual and decadal time scales. Quantifying and predicting the loss of soil, litter, and vegetation carbon in an individual fire event is a key unknown. Relatedly, the amount of pyrogenic carbon generated by fire events is another near-term data needed to better constrain this model. Finally, on the longer time scales, the degree of recalcitrancy of pyrogenic carbon is a critical unknown.

Daniel Fuka (VA Tech)

Rapidly improving the spatial representation of soil properties using topographically derived initialization with a proposed workflow for new data integration
Topography exerts critical controls on many hydrologic, geomorphologic, biophysical, and forest fire processes. However, in modeling these systems, the current use of topographic data neglects opportunities to account for topographic controls on processes such as soil genesis, soil moisture distributions, and hydrological response; all factors that significantly characterize the post-fire effects and potential risks of the new landscape. In this presentation, we demonstrate a workflow that takes advantage of data brokering to combine the most recent topographic data and best available soil maps to increase the resolution and representational accuracy of spatial soil morphologic and hydrologic attributes: texture, depth, saturated conductivity, bulk density, porosity, and the water capacities at field and wilting point tensions. We show several proofs of concept and initial performance test the values of the topographically adjusted soil parameters against those from the NRCS SSURGO (Soil Survey Geographic database). Finally, we pose the potential for a quickly configurable opensource data brokering system (NSF BALTO) to be used to make available the most recently updated topographic and soils characteristics, so this workflow can rapidly re-characterize and increase the resolution of post-fire landscapes.

Dalia Kirschbaum (NASA GSFC)
Towards characterization of global post-fire debris flow hazard

Post-fire debris flows commonly occur in the western United States, but the extent of this hazard is little known in other regions. These events occur when rain falls on the ground with little vegetative cover and hydrophobic soils—two common side effects of wildfire. The storms that trigger post-fire debris flows are typically high-intensity, short-duration events. Thus, a first step towards global modeling of this hazard is to evaluate the ability of GPM IMERG and other global precipitation data to detect these storms. The second step is to determine the effectiveness of MCD64 and other globally available predictors in identifying locations susceptible to debris flows. Finally, rainfall and other variables can be combined into a single global model of post-fire debris flow occurrence. This research can show both where post-fire debris flows are currently most probable, as well as where the historical impact has been greatest.

How to Prepare for this Session:

Presentations

View Recording: https://youtu.be/I89om-kBYB0

Takeaways
  • Modeling and detecting fires and fire impacts is changing (e.g. neural networks, carbon modeling) and needs to continue to improve
  • There are many data needs to be able to operationalize post-fire debris flow and soil modeling
  • Fires severely change ecosystems and soils and we do not really understand the exact changes yet, need more research in this area


Speakers
KT

Kathe Todd-Brown

University of Florida Gainesville
DF

Dan Fuka

Virginia Tech
avatar for Bill Teng

Bill Teng

NASA GES DISC (ADNET)


Thursday January 9, 2020 12:00pm - 1:30pm EST
Salon A-C
  Salon A-C, Breakout