Science: Data-Driven Discovery

Gordon and Betty Moore Foundation

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Grant amount: Up to US $1,500,000

Deadline: Rolling

Applicant type: Faculty College / University

Funding uses: Conference, Research

Location of project: Anywhere in the world

Location of residency: United States

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About this funder:



Please note that we do not accept unsolicited grant proposals. Because of our tightly-defined grantmaking strategies, many worthwhile projects fall outside the scope of our funding priorities. However, if you have thoughts or inquiries related to our work that you would like to share, you may send us a brief, ~100-word email. Please understand that due to the volume of inquiries we receive, we are only able to commit to reviewing those inquiries that adhere to the ~100-word guideline.

Science: Data-Driven Discovery Initiative

Supporting academic data science

New discoveries through data-driven research

Scientific instruments, sensors and computer simulations are producing complex data at exponential rates, creating a virtual data deluge. Although these data represent an unprecedented resource, their size and complexity are overwhelming. What’s more, scientists are limited by current practices to extract useful information - but this is changing.

Effectively harnessing these large and complex scientific datasets requires fundamentally different techniques, better tools and a new data-driven practice. These techniques are being developed by data-driven researchers and research software engineers through the interdisciplinary research of data science. 

While the research community recognizes the need for new and enhanced skills, there has been a critical shortage of practitioners. Science may be data-rich, but will remain discovery-poor without the institutional commitment, people-power and technology needed to mine data and reveal hidden breakthroughs. 

To help catalyze these breakthroughs, our focus has been to support the people who innovate around data-driven discovery, including institutions, people and new tools and methods that can be integrated into scientific research.

In 2012 when we began work in data-driven discovery, there was limited awareness of data science and its application to basic scientific research. By investing in this area at an early stage, we helped researchers apply data science to their work and generated broader use of data science in the natural sciences across the country.

To amplify the gains that grantees have made, we will focus the next few years on boosting development and use of data science tools for the natural sciences, closing out the initiative in 2021. We will have invested more than $80 million over nine years towards advancing new discoveries in the natural sciences using data-driven methods and supporting the people who enable this new way of conducting research.

Institutions: Data-Science Environments

In November 2013, we announced a new partnership and $30 million in funding to harness the potential of data scientists for basic research and scientific discovery. With our partners, we launched three Data Science Environments at New York University, the University of California, Berkeley and the University of Washington with joint funding from the Alfred P. Sloan Foundation. This was an initial five-year, cross-institutional effort to bring data science to the forefront of cross-disciplinary academic research and has been extended through 2020. The Data-Science Environments are great examples of wide-scale adoption of data science across campus.

People: Investigator Awards

The aim of our investigator awards has been to catalyze new data-driven scientific discoveries and highlight the value of a new type of data-driven scientist through $21 million in grants to the academic institutions of fourteen highly talented researchers. These investigators in data-driven discovery will strengthen support for data scientists in academia and create greater opportunities for working between disciplines (scroll down to read more about each investigator). The awards support sustained collaborations among data science researchers to build on one another's work, capitalize on the best practices and tools, and create solutions that can be used more broadly by others.

Practices: Data Science Tools for Research

New tools and methodologies are needed to accelerate data-driven research. Since 2014, we have focused on the creation, transfer and dissemination of readily usable innovative tools, knowledge and techniques for engaging in data-driven scientific research. Through this work, we have supported the development and adoption of industrial-strength data tools like Jupyter and Julia Language, and community engagement efforts from organizations like The Carpentries that engage a larger population to learn about and tackle data-driven challenges in science. We will continue supporting the development of improved software tools for natural science research through 2021, focusing on tools that are applicable across fields and institutions.

You can learn more about this opportunity by visiting the funder's website.