In 2015 the Foundation funded a pilot Community Engagement Fellows program at the American Association for the Advancement of Science. The program is run by longtime scientific community manager Lou Woodley, who led a yearlong planning process to develop a curriculum that tailors community engagement skills training to the scientific research context, and then recruited the first cohort of fellows for the 2017 calendar year. Drawn from professional societies and large scientific collaborations, fellows came together for in-person workshops at the beginning, middle, and end of the fellowship year, as well as for regular webinars and other online discussions. A robust program evaluation made clear that the fellowship year wasn’t just extremely effective for the participants, it also led to tangible investments in and foregrounding of community management by many of the host organizations. This grant funds a continuation of the Community Engagement Fellows program, which includes funds for administration, for the recruitment and support of the 2019 fellows cohort, and for a shorter-term “visiting scholars” program that could draw on program alumni and other community management professionals.

Recent years have seen a proliferation of policies coming from research funders, universities, and publishers intended to prod scientists toward more proactive archiving, citation, and data sharing practices. The mere presence of a policy, however, doesn’t guarantee compliance, so the Foundation has looked to support technologies that make it easier to adopt best practices in research data and software management. The grant funds work by the Collaborative Knowledge Foundation and the California Digital Library to develop DataSeer, an open sourced rule-based platform that would be able to automatically identify data referenced explicitly or implicitly in a grant proposal, data management plan, or draft article and suggest appropriate repositories for deposit of that data. By providing nudges and suggestions at specific targeted moments in the research lifecycle (like the creation of a data management plan, submission of a grant proposal, or submission of a manuscript), DataSeer has the potential to substantively improve proactive data sharing and archiving by researchers, while reducing the costs of compliance checking for funders, libraries, and publishers.

This grant supports efforts by the American Institute of Physics (AIP) to catalog and digitize the Wenner Collection, a unique collection of 3,800 rare books and documents that cover the early development of physics and astronomy. The collection was carefully assembled and annotated by David Wenner, a wealthy, philanthropic science aficionado, and contains works—some more than five centuries old—by Ptolemy, Galileo, Huygens, Halley, Newton, Laplace, and many early-19th-century natural philosophers. AIP will scrupulously organize, classify, and catalog the entire collection in accordance with international and national library standards. Grant funds will support the hiring of a rare book cataloger to make decisions about priorities, storage facilities for the materials that will preserve Wenner’s original ordering and grouping, and the hiring of a digital assessment specialist to facilitate efficient, high-quality digitization of the collection. Additional funds will support various outreach activities to facilitate user engagement with the collection.

Funds from this grant provide one year of support to Knowable Magazine, a new digital-native publication launched in October 2017 by Annual Reviews. Annual Reviews, publisher of a prestigious series of comprehensive, high-quality scientific field reviews, has a large following among scientists. Knowable is an attempt to bring that content to a broader demographic. Its articles use established scientific knowledge and research-based facts to highlight the issues society is grappling with, such as health and disease, aging, and climate change. Grant funds will provide general operating support and enhanced outreach for Knowable as it expands its audience, forms relationships with corporate partners, and moves toward independent sustainability.

The suite of open source software tools known as “oTree” makes it simple to conduct behavioral experiments online or in laboratories. (The word “Tree” in the name refers to decision trees, and the prefix “o” stands for “open.”) Without the need for sophisticated programming, researchers can easily build and run games on oTree that test all kinds of hypotheses about human decision-making. This grant funds a project by Toulouse economics professor Daniel Chen to expand oTree’s capabilities. Planned improvements include handling large-scale experiments, supporting continuous-time games, integrating oTree with other open source tools, improving documentation, diversifying its users and funders, and enhancing its long-term sustainability.  

This grant supports a project by Caltech economist Colin Camerer to use insights from neuroscience to develop better predictions and explanations of consumer behavior. Camerer is developing, testing, and applying neuro-economic models of how people switch between behaviors that are habit-driven or routine on the one hand and behaviors that are goal-seeking and deliberative on the other—with particular focus on measuring the differences in price elasticities associated with one type of behavior vs. the other. Camerer will test the predictions of his model against a meta-analysis of previous results as well as in a field experiment using vending machines to measure economic variables, including price and quantity responses, and psychological variables, including response times and attention patterns.

The Simons Institute for the Theory of Computing at the University of California, Berkeley regularly devotes a semester to a given research topic, inviting interested researchers to make progress on the selected topic by either visiting regularly or taking up residence. This grant supports a semester at the Simons Institute devoted to advancing the theory and practice of data privacy. Funds will support visitors, events, and projects covering three themes: foundations of data privacy; interactions with other areas, such as statistics and geometry; and socio-technical aspects of data privacy—including modern privacy regulation, practical deployment challenges, and fairness, accountability, and transparency (FAT) issues. Program participants will include 23 senior visitors, 8 postdoctoral fellows, and over 20 graduate students. Expected outputs from this grant include a series of academic papers published by collaborating attendees and a white paper that describes findings and their implications for policy and practice.

An “order book” is a list of various traders’ buy or sell instructions for a given financial instrument.  A stock exchange uses such an order book to keep track of how many shares are being bid or offered at each potential price point.  That information, in turn, determines the actual price quoted by the exchange at any moment in time.  This grant funds work by John Liechty and Mark Flood to study the behavior of traders when they send messages to a financial exchange for inclusion in an order book. The researchers will model how trader behavior depends on available information and attentiveness, exploring how asymmetries in these qualities can have dramatic effects.  Liechty and Flood will also focus very specifically on whether detailed order-book data could help financial regulators predict or mitigate systemic market failures. 

Funds from this grant support research by Josh Wright, working in concert with economists Sendhil Mullainathan of the University of Chicago and Susan Athey of Stanford, to test innovative new machine learning techniques in economics field experiments. The group intends to investigate whether machine learning can improve randomly controlled trials in two ways. First, can machine learning enhance the assignment of subjects to control and treatment groups in ways that can lower necessary sample size without sacrificing rigor? Second, can machine learning techniques expand our ability to identify and analyze heterogenous treatment effects? Wright and his team will deploy state-of-the-art machine learning techniques in a series of actual economic field experiments and then share their findings via conferences, talks, and papers.

The Sloan Research Network on Aging & Work (SRNAW) is the only multidisciplinary research network focused on the aging of the workforce in the United States and abroad. Bringing together 235 members from 21 countries and more than 24 disciplines including economics, psychology, sociology, and management studies, SRNAW is a critical communication hub for the development of shared theories, frameworks, and research projects on aging and work. Funds from this grant support continued operation of the network along with activities designed to increase and strengthen member engagement in SRNAW. Additional grant funds support the development of plans for sustainable long-term financing of the network.