NSF award 2615517 · University of Rhode Island

Collaborative Research: CAIG: Assemblage-based machine learning to quantify oceanic geochemical fluxes from marine particle images

Final report due

30 April 2030

in 1,290 days · The award ends on 31 December 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 Sep 2029in 1050 days
award ending 2029-12-31 · Research.gov
This one is ours, not NSF’s.
Extension request16 Nov 2029in 1125 days
award ending 2029-12-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Dec 2029in 1160 days
award ending 2029-12-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due30 Apr 2030in 1290 days
award ending 2029-12-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due30 Apr 2030in 1290 days
award ending 2029-12-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out30 Apr 2030in 1290 days
award ending 2029-12-31 · 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 December 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 30 April 2030.

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: 2615517
Period of support: 1 Jan 2027 – 31 Dec 2029
Amount: $332,242
Awardee: University of Rhode Island, Kingston, RI
Program: AI and Geosciences
Directorate: GEO

Abstract

Sinking particles in the ocean are known as “marine snow.” These particles provide food for deep sea life and transfer nutrients as well as pollutants from the surface into deeper water. Scientists want to know the number and composition of particles to understand the impact on the chemistry and biology of the ocean. Underwater cameras reveal particle numbers but not composition. This project will develop artificial…

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.