Request for Proposals: Lacuna Fund for Climate and Sexual, Reproductive and Maternal Health and Rights

Deadline: 19-Apr-23

The Lacuna Fund seeks proposals from organizations interested in developing datasets that will help to better understand the relationship between Climate & Forests and generate interventions that could mitigate the impacts of climate change; and, datasets that will improve Sexual, Reproductive, and Maternal Health and Rights for populations in Africa.

The Climate & Forests call is made possible with generous support from GIZ’s FAIR Forward Programme on behalf of the German Federal Ministry of Economic Cooperation and Development (BMZ). Teams selected for funding will be required to follow specific record keeping, reporting, and procurement terms. For example, teams will be required to obtain at least three offers for comparison for subcontractors or consultants for awards between EUR 1,000 and 200,000 and conduct a public tender for contracts over EUR 200,000. Detailed guidance will be provided to finalists.

Potential Areas

Sexual, Reproductive and Maternal Health and Rights
- The purpose of this call for proposals is to support efforts to develop open and accessible datasets for equitable, unbiased, and responsible machine learning applications to improve Sexual, Reproductive and Maternal Health & Rights (SRMHR) outcomes in Africa.
- This call will be focused on datasets in the areas of sexual, reproductive, and maternal health and rights. This includes maternal and newborn health, family planning and contraception, sexually transmitted infections, non-communicable diseases of the reproductive system, and sexual and gender-based violence. Collectively, these issues affecting Sexual, Reproductive and Maternal Health and Rights (SRMHR) are crucial to the health and survival of people across all genders as well as to social and economic development. Well-designed SRMHR interventions have proven to be extremely cost-effective. However, the SRMHR of many people, particularly in low and middle-income countries (LMICs) are far from realized.
Climate and Forests
- The purpose of this call for proposals is to support efforts to develop open and accessible datasets for machine learning applications that will help communities better understand and address the relationship between climate change and forests in Africa, South and Southeast Asia, and Latin America. Climate change disproportionately affects people in low- and middle-income contexts who have done the least to contribute to it. Machine learning (ML) holds great promise to advance efforts across the forestry domain to understand, mitigate, and adapt to climate change.
- Forests support livelihoods of diverse communities globally, host the world’s largest share of terrestrial biodiversity, and provide important ecosystem services such as carbon sequestration. The vast majority of the world’s forests are not located in legally established protected areas, leaving them vulnerable to deforestation due to agriculture, mining, logging, and urban development, contributing to global greenhouse gas emissions. These stressors on the world’s forests negatively impact human health, exacerbate food insecurity, undermine the rights of Indigenous Peoples and local communities, and contribute to habitat and wildlife loss.

Focus Areas

The Steering Committee of Lacuna Fund: The 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.

Agriculture
- Enabling new and more robust AI applications through remote sensing and other critical datasets.
Language
- Translation, speech recognition, and other datasets to enable the promise of digital communication for all.
Health
- Improving the robustness and availability of AI solutions in healthcare through datasets across the value chain.
Climate
- Filling data gaps in the climate domain to enable communities around the world to better mitigate and adapt to climate change.

Funding Information

Sexual, Reproductive and Maternal Health and Rights
- The total pool available is approximately $500,000 USD. Lacuna anticipates funding 4-6 projects with budgets between $25-100k USD and 1-2 larger projects with budgets of $100-150k USD in the target region (Africa).
Climate and Forests
- The total pool available is approximately $2 million USD. Lacuna would like to fund at least one project in each of the target regions (Africa, Latin America, South and Southeast Asia) and anticipate supporting 4- 6 smaller projects with budgets between $50-100k and 2-3 larger, more complex projects with budgets ranging from $200-300k.

Eligibility Criteria

Lacuna Fund aims to fund organizations working at the intersection of AI and social impact to provide them with the resources they need to address urgent problems in their local communities. Lacuna aims to cultivate capacity and support emerging organizations as well as experienced researchers in the field.

To be eligible for funding, organizations must:

Be either a non-profit entity, research institution, for-profit social enterprise, or a team of such organizations. Individuals must apply through an institutional sponsor. Partnerships are strongly encouraged, but only the lead applicant will receive funds.
Have a mission supporting societal good, broadly defined.
Be headquartered in or have a substantial partnership in the country or region where data will be collected.
Have all necessary national or other approvals to conduct the proposed research. The approval process may be conducted in parallel with the grant application, if necessary. Approval costs, if any, are the responsibility of the applicant.
Have the technical capacity – or the ability to build this capacity through a partnership described in the proposal – to conduct dataset labeling, creation, aggregation, expansion, and/or maintenance, including the ability to apply best practice and established standards in the specific domain (e.g. climate and forests; sexual, reproductive, and maternal health and rights) to allow high quality AI/ML analytics to be performed by multiple entities.