[Esip-agclimate] [Reminder] Survey on AI-Ready Open Environmental Data

[Yuhan Douglas Rao]

Dear colleagues,

This is a reminder for the anonymous community survey on defining AI-ready
open environmental data coordinated through ESIP Data Readiness Cluster.
<https://wiki.esipfed.org/Data_Readiness>

The purpose of the survey is to gather community feedback on
the requirement for open environmental data to enable AI/ML application
development for Earth and space science applications. The result of the
survey will be used to draft a community-recommended standard for AI-ready
data and to improve the open environmental data for AI/ML development and
broad applications.

You can access the survey at https://bit.ly/ai-ready-data. Please feel free
to forward this survey to anyone/networks you think might be interested in
completing it. We want to make sure this survey collects broad community
feedback including both *public, academic, and private industries'
perspectives*. We will start to analyze the results after New Year but your
input is always welcomed.

Meanwhile, we invite you to join our upcoming session on AI-ready data
during ESIP January Meeting
<https://2022esipjanuarymeeting.sched.com/event/qko4/ai-ready-data-draft-standard-and-use-cases>
and
provide your input. If you have any questions regarding the survey or the
session, please contact me (douglas.rao at noaa.gov) and Tyler Christensen (
tyler.christensen at noaa.gov).

Have a wonderful holiday season!
Best,
Douglas

Dear colleagues,
>
>
>
> Do you use open environmental data in your artificial intelligence or
> machine learning (AI/ML) research and development? Do you have experience
> and ideas about what factors make data easier to use for AI/ML applications?
>
>
>
> The Data Readiness <http://wiki.esipfed.org/Data_Readiness> collaboration
> group of the Earth Science Information Partners (ESIP)
> <http://esipfed.org/> is conducting a voluntary and anonymous survey to
> understand user requirements for open environmental data that can enable
> AI/ML applications (i.e., AI-readiness). This survey may take you *20–25*
> *minutes* to complete.
>
>
>
> Access the survey using this link - https://bit.ly/ai-ready-data. The
> survey will be open until *22 December 2021 *and we will send one
> reminder request to complete it.
>
>
>
> Your inputs are much needed to help data producers and providers to
> understand user requirements for AI-ready data and make improvements for
> open environmental data. These improvements will benefit all AI/ML
> application developers and general data users with less effort in cleaning
> and preparing the data for their use cases.
>
>
>
> Here’s why we think it’s worth investing your time. To our knowledge, this
> is the first time that AI/ML practitioners have been asked about their
> detailed data needs. Your input will be analyzed and compiled as an
> environmental and Earth data community guideline to:
>
>
>    - Inform data providers in evaluating existing open environmental
>    datasets to assess their AI-readiness, helping data users discover data
>    that already meets their needs.
>    - Provide a consistent framework for different data users to assess
>    AI-readiness and ensure data interoperability across different data centers.
>    - Prioritize modernization and improvement efforts to improve open
>    environmental data for AI/ML applications and general uses.
>    - Set community standards for new dataset development.
>
>
>
> We hope this collaborative effort coordinated by the Data Readiness group
> of ESIP can produce community-driven requirements for AI-ready data and
> lead to official data standards development to improve the usability of
> open environmental data.
>
>
>
> Thank you so much for your time. Please reach out If you have any
> questions about the survey.
>
>
>
> Best regards,
>
> Yuhan “Douglas” Rao, NC State University, co-chair
>
> Tyler Christensen, NOAA Center for Artificial Intelligence, co-chair
>
> ESIP Data Readiness Cluster
>
> (in collaboration with NASA ESDS Data Interoperability Working Group)
>
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