Breakout #4 - Perspectives on Quality

Leaders: Wu, Mingfang (Australian Research Data Commons) Druken, Kelsey (NCI)Moroni, David (JPL, CalTech) 

US ESIP Linkages:

Information Quality Cluster

Data Stewardship Committee

Precis:

There are many perspectives on quality. For a data set to be internationally recognized as an authoritative and responsive resource of information and guidance to data providers there needs to be agreement on how best to implement data quality standards and best practices for their science data systems, datasets, and data/metadata dissemination services. This session will walk through the many dimensions that are now recognised as part of Quality data preservation and accessiblity.

Potential topics:

• Scientific quality - accuracy, precision, uncertainty, validity and suitability for use (fitness for purpose) in various applications
• Product quality - How well the scientific quality is assessed and documented
• Stewardship quality - How well data are being managed, preserved, and cared for by an archive or repository
• Service quality - How easy it is for users to find, get, understand, trust, and use data

Agenda:

• Brief around the room introductions
• Presentation:
  

  • Data quality perspectives from the ESIP information quality cluster (by David Moroni) - iqc_data_quality_moroni_e2sip_8may2019.pptx

  • Evolving Operational Readiness Levels (ORLs) to Improve Situational Awareness and Decision Making (by David Jones)

• Updates from a few organsations:
  
  • NCI  - Kelsey Druken
  • GFZ - Kirsten Elger
  • ARDC - Mingfang Wu
  • GA - David Lescinsky
• Group discussion - where we are at, where we will be heading
  • What's your involvement or interest in data quality
  • What are some of data quality challenges you are experiencing?
  • What resources are available to your role/organisation in these areas?
• Summary
  • What as a community can we do in this area?

Notes is available at: https://bit.ly/2DV7T4M

Two talks from the ESIP IQ cluster at the ARDC TechTalk forum:

• ESIP Information Quality Cluster - Vision, Objectives, Accomplishments and Status, NASA Earth Science Data Quality Working Group, NOAA-developed maturity Matrices (slides) (Yaxing Wei, ORNL DAAC)
• A brief overview of maturity models for consistent data quality ratings (by Ge Peng, co-chairs of ESIP IQ Cluster)

Take-away messages:

1. E2SIP breakout group very interested in the ESIP Information Quality Cluster work with Data Quality that has been developed around:

1. Science Quality

2. Product Quality

3. Stewardship Quality

4. Service Quality

2. Interest as a group on how we can provide consistent and meaningful Data Quality information to the end-users (i.e., interoperability of ‘data quality measures’).

3. Interest in the feedback from the user community on that measure of ‘data quality’.

Actions forward:

• Organise webinar for full presentation from Dave Jones (StormCenter Communications).

• Future collaborations with ESIP:

• Organise follow-up discussion about the  topics presented by ESIP Information Cluster and discuss how to establish the link between E2SIP and ESIP on this work.  

• Half-day workshop at 2020 E2SIP meeting?