2020 ESIP Winter Meeting

"Brains! Brains! Give us your brains!""
- Friendly neighbourhood machine minds
The collective knowledge in the ESIP community is immense and invaluable. During this session, we'd like to make sure that this knowledge drives the semantic technology (ontologies) being developed to move data with machine-readable knowledge in Earth and planetary science.
What we'll do:

In the first half hour of this session, we'll a) sketch out how and why we build ontologies and b) show you how to request that your knowledge gets added to ontologies (with nanocrediting).
We'll then have a 30-minute crowdsourcing jam session, during which participants can share their geoscience knowledge on the SWEET issue tracker. With a simple post, you can shape how the semantic layer will behave, making sure it does your field justice! Request content and share knowledge here: https://github.com/ESIPFed/sweet/issues
In the last, 30 minutes we'll take one request and demonstrate how we go about ""ontologising"" it in ENVO and how we link that to SWEET to create interoperable ontologies across the Earth and life sciences.

Come join us and help us shape the future of Geo-semantics!

Stuff you'll need:
A GitHub account available at https://github.com/
An ORCID (for nanocrediting your contributions) available at https://orcid.org How to Prepare for this Session:

Presentations:

View Recording: https://youtu.be/tr0coi5ZQvM

Takeaways

• Working toward a future (5-10 year goal) of making an open Earth & Space Science Foundry (from SWEET) similar to the OBO (Open Biological and Biomedical Ontology) Foundry. “Humans write queries”. Class definitions need to be machine-readable for interoperability, but must remain human-readable for authoring queries, ontology reuse, etc.
• Please feel free to add phenomena of interest to the SWEET https://github.com/ESIPFed/sweet/issues/ or ENVO https://github.com/EnvironmentOntology/envo/issues/ issue trackers. 
• At AGU they added a convention for changes to ontologies. Class level annotation convention. Can get now get textual defs from DBpedia for SWEET terms. See https://github.com/ESIPFed/sweet/wiki/SWEET-Class-Annotation-Convention
  
  

At the ESIP Summer 2019, many ESIP community members offered their feedback on the range and importance of skills and competencies for data specialists whose job responsibilities focus upon offering data "advise" (e.g., from data curators) and data "service providers" (e.g., from data librarians). By means of an interactive poster, participants were asked to choose whether a competency was of high, medium, low or no importance from a subset of competencies identified by a European Open Science Cloud (EOSC) project. In this session, session leaders will present the results of the ESIP community feedback within the context of the full list of EOSC competencies, and visualized from both a poster synthesis and a research data lifecycle point of view. Session leaders are hoping to have the audience participate by providing feedback and engaging in discussion on the data and views presented. One outcome of this work will be a "Career Compass" to be published by the American Geoscience Institute for students interested in becoming data stewards. How to Prepare for this Session:

Presentations:

View Recording: https://youtu.be/1s1L3Jter8w

Takeaways

NASA, NOAA and USGS are in the process of moving data onto the cloud. While they have discussed what types of services are available and future plans of what data can be found, it is not completely clear what datasets users can currently access. This session will go over what datasets are currently up in the cloud and what data to expect in the near future. This way as users are transitioning to the cloud for their compute, they can also know what data are available to them on the cloud as well. There will also be presentations from AWS. Speakers:
Katie Baynes - NASA/EOSDIS
Jon O'Neil - NOAA
Jeff de La Beaujardiere - NCAR
Kristi Kliene - USGS/EROS
Joe Flasher - AWS

Presentations: See attached.

View Recording: https://youtu.be/yssgXB7iaxw

Takeaways

• Petabyte scale data is being moved into the cloud. This is concentrated in AWS, Google Cloud and Microsoft depending on the agency and dataset
• Some concern around partnerships with companies (AWS most discussed) in terms of long term relationships, moving data etc. and how those things might impact access or data use
• Need to make clear the authoritative source of the data, who is stewarding it, and any modifications done when copying to cloud. Users should exercise due diligence in selecting and using data.

Bringing remote sensing and astronomical data to life for students is a challenge for earth and space science educators. This session will engage teachers and scientists in a participatory design process that will demonstrate the power of data science, identify challenges in teaching and learning, and seek pathways to develop next generation tools and curricula to close the gap between science practice and education. This workshop extends an NSF convergence accelerator for earth and space data and will also help inform an upcoming NSF-funded workshop titled: Data Science for High School Computer Science: Identifying Needs, Gaps and Resources.
We are proposing a working session, working directly with teachers on tool development using a participatory design kind of approach. The ESIP Education Committee is working to identify DC-area schools to work with over the long term, and this session could be a good first step in that relationship. For this workshop, a minimum of three DC-area teachers will work with ESIP Education Committee members and facilitators.

How to Prepare for this Session:

Presentations: https://doi.org/10.6084/m9.figshare.11591211.v1

View Recording: https://youtu.be/xSjLF_TbV30

Takeaways

• There are many tools that already exist but they need to be more easily connected to the curriculum
• There are constraints to which tools schools can use because they cannot have blogging features and present other security risks. Also, they have limited technological availability
  
  

The development of low-cost, 3D-printed weather stations aims to revolutionize the way communities collect long-term data about local weather phenomenon, as well as develop climate resilience strategies to adapt to the impacts of increasingly uncertain climate trends. This session will engage teachers and scientists in the evaluation and participatory design of the IoTwx 3D-printed weather station that is designed to be constructed and extended by students in middle and high school. We aim to explore the full spectrum of the station from construction (from pre-printed parts), to data collection and development of learning activities, to analysis of scientific phenomenon within the data. The stations also represent a unique opportunity to develop community-based strategies to extend the capabilities of the platform, and in the session we are encouraging full discussion of data collection and sensing technologies of specific relevance to communities adopting the stations.

In this working session, we will work directly with teachers on evaluation and development using a participatory design approach to stimulate and encourage relationships between ESIP Education Committee members and teachers.

Preparing for this Session: TBD

Presentations:

View Recording: https://youtu.be/AfvWhZBkQd8

Takeaways

• Very valuable for the schools and community. It is an opportunity to include multiple departments within the school system (engineering, computer science, maths, earth science, etc.)
• Need to understand the constraints that school systems may present: security, wifi, processing power, cloud access, only required for part of the year
  
  
  

The ESIP Information Quality Cluster (IQC) has formally defined information quality as a combination of the following four aspects of quality, spanning the full life cycle of data products: scientific quality, product quality, stewardship quality, and service quality. Focus of the IQC has been quality of Earth science data captured by scientists/experts. For example, the whitepaper “Understanding the Various Perspectives of Earth Science Observational Data Uncertainty”, published by IQC in the fall of 2019, mainly addresses uncertainty information from the perspective of satellite-based remote sensing. With the advance of mobile computing technologies, including smart phones, Citizen Science (CS) data have been increasingly becoming more and more important sources for Earth science research. CS data have their own unique challenges regarding data quality, compared with data captured through traditional scientific approaches. The purpose of this session is to broaden the scope of IQC efforts, present the community with the state-of-the-art of research on CS data quality, and foster a collaborative interchange of technical information intended to help advance the assessment, improvement, capturing, conveying, and use of quality information associated with CS data. This session will summarize the scope of what we mean by CS data (including examples of platforms/sensors commonly used in collecting CS data) and include presentations from both past and current CS projects focusing on the topics such as challenges with CS data quality; strategies to assess, ensure, and improve CS data quality; approaches to capturing CS data quality information and conveying it to users; and use of CS data quality information for scientific discovery. 

Agenda (Click titles to view presentations)

1. Introduction - Yaxing Wei - 5 mins
2. Citizen Science Data Quality: The GLOBE Program – Helen M. Amos (NASA GSFC) – 18 (15+3) mins.
3. Can we trust the power of the crowd? A look at citizen science data quality from NOAA case studies - Laura Oremland (NOAA) – 18 (15+3) mins.
4. Turning Citizen Science into Community Science - Stephen C. Diggs (Scripps Institution of Oceanography / UCSD) and Andrea Thomer (University of Michigan)  – 18 (15+3) mins.
5. Earth Challenge 2020: Understanding and Designing for Data Quality at Scale - Anne Bowser (Wilson Center) – 18 (15+3) mins.
6. Discussion and Key Takeaways – All – 13 mins.
   
   View Recording: https://youtu.be/xaTLP4wqwe8
   
   Takeaways

Notes Page:
https://docs.google.com/document/d/1lRp19SF9U727ureKjY38PHOF3EGUgE-BixYDs2KlmII/edit?usp=sharing

Presentation Abstracts

• Citizen Science Data Quality: The GLOBE Program - Helen M. Amos (NASA GSFC)

The Global Learning and Observations to Benefit the Environment (GLOBE) Program is an international program that provides a way for students and the public to contribute Earth system observations. Currently 122 countries, more than 40,000 schools, and 200,000 citizen scientists are participating in GLOBE. Since 1995, participants have contributed 195 million observations. Modes of data collection and data entry have evolved with technology over the lifetime of the program, including the launch of the GLOBE Observer mobile app in 2016 to broaden access and public participation in data collection. GLOBE must meet the data needs of a diverse range of stakeholders, from elementary school classrooms to scientists across the globe, including NASA scientists. Operational quality assurance measures include participant training, adherence to standardized data collection protocols, range and logic checks, and an approval process for photos submitted with an observation. In this presentation, we will discuss the current state of operational data QA/QC, as well as additional QA/QC processes recently explored and future directions. 

• Can we trust the power of the crowd? A look at citizen science data quality from NOAA case studies - Laura Oremland (NOAA)

NOAA has a rich history in citizen science dating back hundreds of years.  Today NOAA’s citizen science covers a wide range of topics such as weather, oceans, and fisheries with volunteers contributing over 500,000 hours annually to these projects. The data are used to enhance NOAA’s science and monitoring programs.   But how do we know we can trust these volunteer-based efforts to provide data that reflect the high standards of NOAA’s scientific enterprise? This talk will provide an overview of NOAA’s citizen science, describe the data quality assurance and quality control processes applied to different programs, and summarize common themes and recommendations for collecting high quality citizen science data. 

• Earth Challenge 2020: Understanding and Designing for Data Quality at Scale - Anne Bowser (Wilson Center)

April 22nd, 2020 marks the 50th anniversary of Earth day.  In recognition of this milestone Earth Day Network, the Woodrow Wilson International Center for Scholars, and the U.S. Department of State are launching Earth Challenge 2020 as the world’s largest coordinated citizen science campaign.  For 2020, the project focuses on six priority areas: air quality, water quality, insect populations, plastics pollution, food security, and climate change.  For each of these six areas, one work stream will focus on collaborating with existing citizen science projects to increase the amount of open and findable, accessible, interoperable, and reusable (FAIR) data.  A second work stream will focus on designing tools to support both existing and new citizen science activities, including a mobile application for data collection; an open, API-enabled data integration platform; data visualization tools; and, a metadata repository and data journal.
A primary value of Earth Challenge 2020 is recognizing, and elevating, ongoing citizen science activities.  Our approach seeks first to document a range of data quality practices that citizen science projects are already using to help the global research and public policy community understand these practices and assess fitness-for-use.  This information will be captured primarily through the metadata repository and data journal.  In addition, we are leveraging a range of data quality solutions for the Earth Challenge 2020 mobile app, including designing automated data quality checks and leveraging a crowdsourcing platform for expert-based data validation that will help train machine learning (ML) support.  Many of the processes designed for Earth Challenge 2020 app data can also be applied to other citizen science data sets, so maintaining information on processing level, readiness level, and provenance is a critical concern.  The goal of this presentation is to offer an overview of key Earth Challenge 2020 data documentation and data quality practices before inviting the ESIP community to offer concrete feedback and support for future work.

Session leaders will describe tools being developed to assess the learning resources in the ESIP"s Data Management Training Clearinghouse (DMTC) from the perspectives of both instructors and students. The feedback collected through these tools will aid in identifying and choosing resources appropriate for their needs. First efforts have been focused on using DataONE's EEVA tool to identify and adapt questions. Feedback will be requested from participants to help guide the content, look and feel of the tool. How to Prepare for this Session: Visiting ESIP's Data Management Training Clearinghouse (https://dmtclearinghouse.esipfed.org) would be helpful but not required for productive participation in the session.

Presentations:

View Recording: https://youtu.be/uc4tbjyePpI

Takeaways