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)- Introduction - Yaxing Wei - 5 mins
- Citizen Science Data Quality: The GLOBE Program – Helen M. Amos (NASA GSFC) – 18 (15+3) mins.
- 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.
- Turning Citizen Science into Community Science - Stephen C. Diggs (Scripps Institution of Oceanography / UCSD) and Andrea Thomer (University of Michigan) – 18 (15+3) mins.
- Earth Challenge 2020: Understanding and Designing for Data Quality at Scale - Anne Bowser (Wilson Center) – 18 (15+3) mins.
- 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.
