In January 2014, the Moore Foundation launched a competition to fund investigators who perform exemplary data-driven science in a multidisciplinary fashion. From the nearly 1,100 applicants, 14 were selected as Moore Investigators in Data-Driven Discovery through the foundation’s $60M, five-year Data-Driven Discovery initiative.
The initiative – one of the largest privately funded data scientist programs of its kind – empowers data-driven investigators at universities, strengthens incentives for data scientists in academia and creates greater rewards for working between disciplines.
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.
As part of the competition, each applicant had the opportunity to share up to five influential works in the general field of “Big Data” for scientific discovery. From the entire pool of applicants, the Moore Foundation received nearly 5,000 references. In a recent review in the journal SpringerPlus, Chris Mentzel and Mark Stalzer analyzed and manually organized these references into clusters by natural science domain, methodologies, tools and the scientific method.
Mentzel and Stalzer’s findings show 53 works were cited at least six times, 22 works were cited at least ten times, and three works were cited at least 40 times: Dean and Ghemawat 2008, Hey et al. 2009, and Hastie et al. 2009.
Since the competition was for efforts in the natural sciences and methodologies, references important to social sciences are underrepresented, the authors noted. In addition, there is no commonly accepted way of citing resources unless there is an associated paper, which can leave out tools and software.
"Our longer term goal is to better understand how the community defines data science," said Mentzel, program director of the Data-Driven Discovery initiative at the Moore Foundation. "We hope this paper helps people learn more about some of the foundational efforts in data science and data-driven research."
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