Application
You can review a copy of RFP questions, including the prompt for the narrative portion of the proposal, under each RFP. When an RFP is open, proposals are accepted through the application portal.
28 April 2022 - 17 July 2022
Datasets for Climate Applications
Lacuna Fund seeks proposals from organizations interested in developing datasets for equitable climate outcomes in climate & energy and climate & health.
Read a full copy of the RFP’s for more details on potential areas of funding, eligibility, and selection criteria:
Applications are accepted through the application portals here:
Partnership and Mentorship Programs
Lacuna Fund is partnering with Climate Change AI (CCAI) to provide a digital partnership platform where teams or individuals seeking to collaborate can connect and find potential partners. CCAI will also facilitate a mentorship program to connect potential applicants with experts working in relevant topic areas.
Register for the partnership and mentorship platform!
Timeline:
- RFPs Posted Publicly on Lacuna Fund Website: 28 April 2022
- Applicant Webinar: 17 May 2022 | Webinar recording (English) | Presentation: Applicant Webinar: Lacuna Fund Climate Calls for Proposals
- Question and Answer Deadline: 20 May 2022
- Answers Posted: 3 June 2022
- Proposals Due: 17 July 2022
Question and Answer Period: All questions related to the RFP should be submitted to secretariat@lacunafund.org with “Climate & Energy RFP 2022 Question” or “Climate & Health RFP 2022 Question” in the subject line. Questions submitted by 20 May will be de-identified and answered publicly by 3 June on the Lacuna Fund website in a document posted on this page.
(3 June 2022) Please see the Q&A here.
Please note that for this RFP, lead applicants must be headquartered in or have a substantial partnership in Africa, Latin America, or South or Southeast Asia.

Past Funding Opportunities
All past Funding Opportunities can be found here.
Focus Areas
The Steering Committee of Lacuna Fund: Our Voice on Data has identified the following areas as domains where a lack of labeled training data limits the potential of machine learning or presents the risk of bias or inequity.