2019 ESIP Winter Meeting

Session Abstract:
Sustaining our data resources is part of the bigger issue of sustaining all research products. As we contemplate technologies to enable this, we must also consider organizational and financial enablements. The Science Gateway Community Institute recently conducted a session to work on characterizing 11 ESIP related projects in terms of concepts borrowed from entrepreneurship. This panel will focus on discussion of some of those results and their applicability in a broader sense to sustainable data infrastructures and repositories.

Session Takeaways (post-meeting):
1) The Science Gateways Community Institute (SGCI) aims to connect resources and data to science authors by providing a gateway catalog of gateways related to teaching, research, and learning.
2) SGCI hosts a Science Gateways Bootcamp which is a 5 day bootcamp to help participants develop the framework for: generating pitches, developing communication skills, bringing an idea to fruition, selling the idea, setting goals, and producing deliverables.
3) Bootcamp pitches aim to service the needs of a variety of communities (ex. teachers, crop map stakeholders, ESIP, repositories, and data providers), improving data accessibility and usability within these communities.

Session Abstract:
This is the first session of our new Community Resilience ESIP cluster. We aim to introduce a broad landscape of community resilience from an earth science data perspective, as well as gain an understanding of the session participants’ interests/experiences with community resilience. The session includes a series of short presentations and small break out groups to gather feedback from participants. Overall, the session examines how data and information can be a catalyst for overcoming social barriers in communities to help them realize and work collaboratively towards broadly appreciated, overarching goals, like the enhanced sustainability of their food and/or energy system. Place-based community resilience examples include the assessment and mitigation of community vulnerability across coastal communities and tribal groups. Institutional community resilience examples from governmental organizations, NGOs, and Private Sector entities demonstrate how efforts are best aligned in the context of their long established missions. We explore how the ESIP Federation can step into a leading role toward more effective and efficient use of the Nation’s earth science data and information within a community resilience context.

Speakers:

• Arika Virapongse, Middle Path EcoSolutions: “Introduction”: https://doi.org/10.6084/m9.figshare.7622009.v1
• Jonathan Blythe, Bureau of Ocean Energy Management (BOEM): “When are Data Management Best Practices Not Sufficient to Support Agency Processes? An Institutional Perspective on Community Resilience and the Role of Data in Federal Decision making”: https://doi.org/10.6084/m9.figshare.8009594.v1
• Lauren Showalter, The National Academies of Science, Engineering, and Medicine: “Large Scale Data Management and its Role in Improving Community Resilience in the Gulf of Mexico”: https://doi.org/10.6084/m9.figshare.8009582.v1
• Rupu Gupta, New Knowledge: “Balancing Data and Community Needs in Collaborative Resilience Efforts”: https://doi.org/10.6084/m9.figshare.7688804.v1
• Natasha Udu-gama, Thriving Earth Exchange, AGU: “Data Sharing and Management for Community Resilience: Insights from Community Science”: https://doi.org/10.6084/m9.figshare.8009600.v1
• Ranalda Tsosie, University of Montana: “Using Modern Technologies to Inform a Diné Community About Water Quality Issues” (NO SHOW)

Session Takeaways (post-meeting):
1) Data can help overcome social barriers to resilience measures and also drive decision making. GIS is a common and useful format.
2) There are challenges to creating complete and robust data sets to drive resilience. This can be due to proprietary constraints, siloing issues, and social barriers. More work is needed to overcome these challenges.
3) At-risk and front line communities are especially in need of good hazards data and need help to ensure it meets their needs.

Session Abstract:
This session will convey the process for GCMD keywords - fast track and yearly reviews through the ESDIS Standards Office, and how Earth science users can influence keyword additions and modifications. The session will also highlight how keywords facilitate the discovery of EOSDIS data and services.
There will also be an opportunity for attendees to ask questions about the keywords and discuss how you would like the keywords and the supported tools to evolve.
Please use the Google document below to post your keyword questions and feedback. We will review them during the meeting.
https://tinyurl.com/y9w7suym 

Session Takeaways (post-meeting):
1) GCMD keywords are used by numerous national and international organizations.
2) There is an interest in normalizing measurement terms in the GCMD KMS for the UMM-Var model.
3) There are some gaps in the keywords, especially within Biology that need to be addressed. The GCMD team will be getting input from SME's in that area.

Session Takeaways (post meeting):
1) ESIP Lab provides the support and framework for a variety of projects that span the realm of earth science data sciences.
2) Products/projects developed through the ESIP lab help to improve data management, organization, quality control, and/or accessibility for a number of different audiences/communities.
3) The next steps for these projects include securing further funding via proposal opportunities, advancing participants’ PhD research, and increase user count.

Brian Wee: Enabling the encoding and visualization of provenance metadata for better discovery and understanding of climate resilience strategies for agriculture-related decision-making
Keith Maull: ESIPhub: Developing and promoting an ESIP community resource for sharing and running scientific workflows via JupyterHub
Ziheng Sun: Geoweaver: a web-based prototype system for managing compound geospatial workflows of large-scale distributed deep networks
Mike Daniels: SensorDat: Real-time sensor testbed for improved provenance and data quality
Yunsoo Choi: A deep-learning driven improved ensemble approach for hurricane forecasting
Jessica Fayne: Building an Operational Network to Validate Novel Inland Water Swath Altimetry

Session Abstract:
The Disasters Lifecycle cluster in collaboration with the All Hazards Consortium developed initial ORLs with the electric utilities, and is now “operationalizing” ORLs for data-driven decision-making support to improve situational awareness. The criteria for the ORLs and a flowchart assessment tool, exercised by the AHC team at Duke Energy for response to the 2018 Hurricane Florence, proved very useful. The initial ORL criteria were defined for the electric sector’s use to transport work crews and restore power after destructive storms. However the usability criteria is expected to change for different disaster scenarios. Some factors may go across many use cases for many applications, such as security criteria that is key to establishing trust. However other factors are driven by the use case to address latency or resolution criteria. Work continues on refining strategies and criteria for assessing candidate datasets for specific operational use cases, and maturing the ORL concept as a Framework for different applications.

During this session we plan to address the issue of terminology to seek a common vocabulary relevant to various disasters application, leveraging what we’ve learned from the electric utility sector. We also would like to examine the ORL assessment tool and how the current ORL criteria are applied and look for gaps and lessons learned. The goal is to refine a framework strategy enabling the ORL concept to be applied to other disasters applications.

Speakers & Presentations

• Karen Moe & Dave Jones, Disasters Lifecycle cluster Co-Chairs: Introduction and highlights from 2018 
• Tom Moran, Executive Director All Hazards Consortium: Emergency management perspectives on the ORL approach - https://doi.org/10.6084/m9.figshare.7599911
• Jason Fleming, Seahorse Coastal Consulting: Real-time ADCIRC storm surge model, mapper, and ORLs - https://doi.org/10.6084/m9.figshare.7599908
• Sophie Hou, National Center for Atmospheric Research NCAR / UCAR: The importance of end user terminology from a usability standpoint - https://doi.org/10.6084/m9.figshare.7599914
• Maggi Glasscoe, NASA Jet Propulsion Laboratory / Cal Tech: Earthquake Clearinghouse sensor data request form - https://doi.org/10.6084/m9.figshare.7599917
• Ge Peng, NOAA Affiliate / NCSU: Data Maturity Matrix concept and the User Service - https://doi.org/10.6084/m9.figshare.7599920

Session Takeaways (post-meeting):
1) Speed of impact of the data
2) Need to have training and socialize the product
3) Need to look at dynamic data vs. forecasts vs. real time

Session Abstract:
Centralized or common repositories expose data to new users by bringing together metadata from various archives into a single repository. Centralized repositories also serve as a one stop for data discovery and help increase the likelihood that data will be reused for new research and applications.

Since these repositories expose new users to new data, it is essential that the information provided in these repositories be trustworthy and reliable. Ensuring trustworthiness is an ongoing challenge for centralized repositories.

This session will focus on approaches to ensuring quality and trustworthiness in centralized repositories and the challenges faced by centralized repositories in meeting those goals.

Session Takeaways (post-meeting):

Session Abstract:
Deep learning (DL) is the hottest method to realize artificial intelligence in many applied science domains. Our ESIPLab project Geoweaver has started to use Deep Learning method in producing crop maps with higher resolution and accuracy than conventional crop map production methods.
The success of DL relies on massive training datasets and powerful compute nodes like Graphics Processing Units (GPU). A good neural network requires careful engineering and considerable domain expertise in network training. It is never easy to fit DL on any Earth dataset. This session will carry out discussion on the research areas, technical details, data sources, and performances of DL in agriculture. We will work on harmonizing and generating a common strategy to connect and prepare Earth datasets for the training/testing of customized deep neural networks to help advance agricultural researches into next level: intelligent agriculture.

Session Takeaways (post meeting):
1) The workflow of deep learning: gather data > choose network type > choose DL library > find powerful hardware > data preprocessing > training > predicting > validation. This workflow can be used in all three aspects of agriculture: monitoring, predicting, and decision making.
2) The quality of crop information in the training datasets is the key to a successful model. The deep learning models must be fed with very accurate information so they can learn those pattern features. Less accurate training datasets will lead to a waste of training time.
3) The trained models have restrictions in time and location. The accuracy is largely related to the quality of training dataset, the chosen deep neural network type, the training process control, and the expertise of the practitioner. Normally, this is a collaboration work which need a stable group to work on it. ESIP machine learning cluster and ESIP Github repos are great platforms and tools for these efforts happen.

Session Abstract:
The majority of scientific programming workflows are developed in isolation by graduate students and postdoctoral researchers. While packages and libraries in R and Python help support the advancement of scientific discovery, researchers are often challenged with combining and analysing data in new ways. Regardless, code use and re-use in the Earth Sciences is often complicated by the fact that few well-developed workflows exist as templates. Most code examples in R packages for example, use well-worn datasets that are not well suited to extrapolation for Earth Science applications. For this reason, the discovery and analysis of existing code resources, such as those undertaken by the FUNding Friday grant, become critical to providing resources to scientific programmers in the Earth Sciences.
This Session will introduce early-career researchers to the principle workflows for sharing code publicly, including discussion of some of the pros and cons of sharing code before it is “good enough”. The session will then provide an overview of work that has been undertaken to analyse a large number of Jupyter notebooks on GitHub, and then provide session members with an opportunity to help build the web of examples for coding resources, discussing what makes code useful as a “cookbook recipe” for Earth Sciences, what particular libraries or data resources are of interest, and how further automation might be undertaken.

Session Notes:
https://docs.google.com/document/d/1S5p4v77B3kCdWSKbxKw3gkPFTotMeS9_pm5ic9rYhak/edit?usp=sharing

Session Takeaways (post-meeting):
1) Cultural knowledge around data use and storage can impact usage and keep data and use in ‘silos.’
2) There are a lot of people now that are putting notebooks on GitHub that are associated with a specific publication. The people that are doing this well are associating the DOI with the original publication and the people doing really well are setting this up so that the repository also has its own DOI.
3) Earth science data cookbook has an easy form to fill out information for earth science datasets and resources. This is intended to make these resources more accessible and clearly labeled. This thing is not live yet, but users can input keywords etc. available at https://bitly.com/esip2019-cookbook