NSF award 2615941 · Johns Hopkins University

CAIG: Accelerating Ocean Data Assimilation with Machine Learning

Final report due

28 November 2029

in 1,136 days · The award ends on 31 July 2029.

GrantClerk is a private service. We are not NSF or any government agency.

Dates this award owes

NSF emails you about this too.

Decide about an extension2 Apr 2029in 896 days
award ending 2029-07-31 · Research.gov
This one is ours, not NSF’s.
Extension request16 Jun 2029in 971 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Jul 2029in 1006 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Nov 2029in 1136 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Nov 2029in 1136 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Nov 2029in 1136 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

Annual report dates come from the budget period on your award notice. That date is not public, so it is not shown here.

How we got this date

NSF lists this award's end date as 31 July 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 November 2029.

Read the rule on Research.gov

Get a note before each report date

Add this award to your desk. We email you before each date and keep the dates in one place.

Renews each year until you cancel. Cancel any time from your account.

Already a member? Sign in

Award card

Award number: 2615941
Period of support: 1 Aug 2026 – 31 Jul 2029
Amount: $1,146,677
Awardee: Johns Hopkins University, Baltimore, MD
Program: AI and Geosciences
Directorate: GEO

Abstract

The project aims to exploit the power and potential of machine learning to improve the integration of ocean data into models using so-called data assimilation techniques. Ocean data assimilation combines real ocean observations with computer models of ocean currents to get a better picture of the ocean state now and in the future. Machine learning technology is expected to speed up ocean data assimilation, which is…

Is this right?

We copy this from the public NSF award record. Your award notice and NSF always win. If a date looks wrong, check Research.gov.