New technologies like real-time electricity meters and smart appliances are generating vast amounts of new, granular data on household energy consumption. This grant supports the Energy Data Analytics Lab (EDAL) at Duke University in its efforts to use this growing body of data to increase our understanding of household energy consumption patterns; evaluate policy interventions designed to curb energy use; and anticipate strains, failures, and bottlenecks in the electricity sector. Planned research topics over the next two years include investigations into how big data can be used to develop accurate baseline assessments of energy resources, how to use remote sensors to estimate the distribution and growth of household solar panels, and how the discovery and extraction of U.S. natural gas deposits are related to price volatility in the natural gas market. Additional grant funds support a host of outreach and community-building activities by the EDAL, including the hosting of a workshop on advanced energy data analytics, the construction of a web portal to make EDAL research, data, and methods easily available to other researchers, and the training of undergraduate and graduate students through lectures, classroom modules, and laboratory assignments.

This grant provides two years of continued support to the Deep Energy community of the Deep Carbon Observatory. Researchers working in the Deep Energy community investigate the abiotic methane and hydrogen in the deep recesses of Earth. These compounds, when oxidized, release energy into the rocks around them, feed microbial life, and possibly contribute to humanity’s store of energy resources. Recent discoveries, many of them by DCO researchers, suggest that such deep energy reserves are significantly more plentiful than science has imagined. Over the next two years, Deep Energy researchers will use both field-based investigations in oceanic and continental settings and lab experiments on fluid-rock interactions to shed light on a number of important scientific questions, including how to differentiate between abiotic and biotic hydrocarbons; the role of serpentinization and other hydrogen-generating reactions in the production of deep energy; how deep energy reactions mediate the form, quantities, distribution, and mobility of abiotic carbon and hydrogen; and the relationship between deep energy and deep microbial life. In addition, the Deep Energy team will begin collaborative work with other DCO communities to bring together insights from numerous disciplines in geoscience to create a functional four-dimensional Deep Carbon in Earth Model.

This grant provides partial support to a research project by the Bureau of Economic Geology (BEG) at the University of Texas, Austin. A multidisciplinary team of hydrologists, geologists, economists, and engineers led by geologist Bridget Scanlon will analyze the hydrological characteristics and wastewater production of five major shale oil and gas plays across the country. Using data on previous drilling at each play, the team will construct historical wastewater production estimates and then use these baseline analyses to forecast future water use and wastewater volumes. The team will then compare how water needs associated with shale drilling compare to other water demands in different regions and then estimate the potential impact of hydraulic fracturing on contributing to water scarcity in these areas. Additional work will focus on gaining a better scientific understanding of increased seismicity induced by the injection of wastewater into disposal wells.

Funds from this three-year grant support efforts by Jonathan Eisen at the University of California, Davis to provide key intellectual infrastructure support and services to the growing multidisciplinary community of researchers working in indoor microbial ecology. Through the Microbiology of the Built Environment network (microBE.net) Eisen organizes meetings and workshops, provides a hub for resource and information sharing, disseminates results and funding opportunities, aids in the dissemination of data collection and analysis standards and protocols, and helps bridge disciplinary boundaries by connecting researchers in biology, informatics, architecture, and the building sciences. Over the next three years, Eisen will continue the work of microBE.net, providing additional resources to the MoBE community in six thematic areas:  antimicrobials in the BE; nonhumans in the BE; extreme BEs; BE water systems; technical needs for the MoBE field; and general MoBE interests. Activities targeting each theme will include web development, meeting and workshop organization, social media, pilot research projects, creation and curation of open textbooks, development of a community-driven genome sequencing program, writing of scholarly articles on research and tool development, and continued development of the microBEnet blog with further recruitment of MoBE scholars to contribute to the development of modules for MoBE educational activities (e.g., college courses).

This grant supports a multidisciplinary research effort led by the Institute for Transportation Studies at the University of California, Davis (ITS-Davis) to examine natural gas as an alternative fuel option to power trucks, other long-haul vehicles, municipal bus and taxi fleets, and light-duty passenger vehicles. Bringing together leading economists, engineers, geographers, policy experts, and computer scientists, ITS-Davis will organize a workshop on the issue and commission a series of papers providing a comprehensive overview of the tradeoffs associated with the use of natural gas as an alternative fuel in the transportation sector. Data from the workshop will then be used to enrich ITS-Davis’s model of the infrastructure and refueling network that must sustain any transition to natural gas as an alternative fuel. Working closely with researchers at Arizona State University, ITS-Davis also plans to expand its model to accommodate changes in diesel and natural gas fuel prices, alternative technology costs, various rates of new vehicle diffusion, altered traffic flow patterns, and changes to state-level policies.

Mathematical formulas are undiscoverable by modern search engines. If you are looking for a famous theorem or an equation with a name, standard search engines like Google or online encyclopedias like Wikipedia can direct you to it. But if what you are looking for is an equation that expresses one variable in terms of another, you are out of luck. Because the consumer base for such information is small and because the task of programming computers to recognize mathematical formulas is difficult, no major search engine has prioritized mathematical search. Yet from a societal point of view, the benefits of accelerating discoveries by providing such search capabilities could surely be enormous.   This grant funds a project by John Lafferty from the University of Chicago and David Blei from Columbia University to advance the field of mathematical search by developing a software program that uses sophisticated pattern recognition and statistical machine learning techniques to recognize and identify mathematical formulas on the web.