ESIP Update: ESIP closed on Thursday and Friday this week

ESIP News

ESIP Office Closed Thurs and Fri

Our fully virtual team will be out of the office during the Thanksgiving holiday and will return Monday, November 28.

In the meantime, make sure to register for the upcoming 2023 January ESIP Meeting. Keep an eye out for the meeting agenda rolling out soon!

Will you be at the AGU Fall Meeting?

Connect with the #EarthScienceData Community

Tell us about your poster, talk, town hall, or booth. Add your info and update links in the 2022 list and we will help share our #EarthScienceData community's work more broadly.
 

Join the Open Science and Data Help Desk at AGU

Volunteer for the upcoming Open Science & Data Help Desk, our largest event to date, to be hosted by ESIP and AGU during the 2022 American Geophysical Union (AGU) Fall Meeting from Dec 12-15. You can field questions, share demos, and much more.
 

Clean Air Happy Hour

Join an evening gathering co-hosted by our ESIP partner OpenAQ: Clean Air Happy Hour at AGU on Tuesday, December 13 from 6-8 p.m. at the Haymarket Pub and Brewery. Make sure to RSVP.

What's Happening in Earth Science Data

Tune in…

Integrating Research Data Management Services using maDMPs (Nov 22)

This webinar will familiarize participants with the Research Data Alliance (RDA) recommendation on machine-actionable DMPs. Register through the RDA site for the webinar starting at noon EST on Tuesday, Nov 22.
 

SciLifeLab: FAIR Points November Webinar (Nov 22)

ESIP volunteer Ge Peng from the University of Alabama in Huntsville will be presenting on dataset quality information and the FAIR principles. The webinar is Wednesday, November 30 at 5 p.m. EST. Register through SciLifeLab.

Scalable and Computationally Reproducible Approaches to Arctic Research (March 27-31)

This is a five-day, in-person workshop at the National Center for Ecological Analysis and Synthesis in Santa Barbara, California. The workshop will provide Arctic researchers with an introduction to advanced topics in computationally reproducible research in python, including software and techniques for working with very large datasets.