data.org Inclusive Growth and Recovery Challenge
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Deadline: Jul 17, 2020
Grant amount: Unspecified amount
Fields of work: Data Science & Digital Information Systems
Applicant type: Individuals, Organizations
Funding uses: Project / Program
Location of project: Anywhere in the world
Location of residency: Anywhere in the world
Age restriction: At least 18 years of ageView website Save
As part of our commitment to building the field of data science for social impact, data.org has launched a $10 million data.org Inclusive Growth and Recovery Challenge.
Any strong and growing economy requires an inclusive and resilient approach to growth to ensure that talent, innovation, and our communities can become stronger in times of stress. By tapping into the expertise of a broad pool of thinkers and doers, we aim to catalyze innovative and scalable solutions to help individuals and communities thrive, all the while building resilience to withstand future challenges.
We recognize that the COVID-19 global pandemic is affecting our society in profound ways and will require new and innovative approaches. Beyond the impacts on public health, the COVID-19 crisis is likely to have severe economic repercussions. Supply chains are being disrupted, businesses are suffering losses, workers are facing unemployment, and too many people lack the savings or credit to weather an economic downturn.
We are seeking inclusive growth proposals from and for anywhere in the world. We are particularly interested in the areas listed below – but welcome other proposals for using data science to advance shared prosperity, and help ensure an inclusive recovery.
Jobs of Tomorrow - Leave No Worker Behind
Low wage workers are among the most vulnerable in today’s economy. In times of economic stress, current trends toward automation and job displacement could accelerate. How can we use data science to help workers remain secure in precarious times? Can data insights predict trends in the labor market and connect and prepare workers for the jobs of tomorrow?
Access to Capital - Leave No Entrepreneur Behind
Unleashing the spirit of grassroots entrepreneurs can help communities rebound by creating much-needed jobs and growing local economies. Yet, in times of recession, the flow of credit to micro and small businesses tends to slow down. How can data science enable microentrepreneurs to gain access to capital? How can data science help us identify micro and small businesses with the potential to grow? How can data insights rethink creditworthiness and unlock capital for high potential business owners?
Cities & Towns - Leave No Place Behind
An economic recession will hit some places harder than others. As public and private sector investments flow into cities and towns to stimulate the economy, how can leaders use data-driven insights to make the right decisions to ensure economic security in underserved communities? How can data and analytics help connect neighborhoods to the resources and networks they need to access opportunity, including quality education, affordable housing and childcare, decent jobs, and transportation? We are seeking applications of data science that can fuel economic growth and empower communities to thrive without further exacerbating inequality.
You can learn more about this opportunity by visiting the funder's website.
- The Challenge is open to any individual, organization, or collaboration from anywhere in the world (except for North Korea, Sudan, Iran, Crimea, Syria, and Cuba). We welcome entries from the following:
- Individuals 18+ and above.
- Governments and U.N. agencies
- All entries must have a charitable purpose and accrue no private benefit.
- Any directors, officers, employees and contractors of the Challenge Partners, and any of their respective subsidiaries and affiliates, immediate family members (spouses, parents, children, siblings and siblings’ spouses, regardless of where they live) or any persons living in the same households of such directors, officers, employees and contractors are ineligible to participate in the Challenge.