Matthew Shapiro - US grants

This project addresses a major and important puzzle with implications for our understanding of U.S. manufacturing productivity. The results should also enhance our understanding of the determinants of economic fluctuations in output. The puzzle is the idleness of capital much of the time in U.S. manufacturing. Specifically, the workweek of capital in U.S. manufacturing averages only 55 hours per week. If a unit of capital does not get less productive with more intensive use over time and if its user cost is independent of the rate of utilization, then the productivity of the capital stock in U.S. manufacturing could be three times higher simply by increasing the utilization of the existing capital stock. The idleness of capital also implies that even at cyclical peaks, tight capacity does not limit the expansion of the economy. This project investigates the costs of increasing the workweek of capital along several dimensions. It determines whether these costs justify the low utilization of capital. The estimates of the cost of using capital more intensively are based on a study of the U.S. economy during World War II. The private U.S. economy during World War II produced a tremendous increment in output with little increase in capacity. This project assembles and analyzes data on shift work, female labor participation, on work hours and conditions for production works and on other labor arrangements during World War II from the records of the price control board, the military procurement agencies and private companies.

DESCRIPTION (Applicant's abstract): The hippocampus is necessary for some (e.g., episodic), but not all forms of learning and memory. The same mechanisms that underlie synaptic plasticity in the hippocampus may also be required for learning and memory tasks that require the structure. Thus, both learning and the induction of long-term potentiation (LTP) require the same mechanisms, including the activation of protein kinases. The persistence of learning and LTP both require de novo protein synthesis, and several immediate and delayed early genes have been identified that may be crucial for both learning that depends upon hippocampal function and for LTP. Together, the evidence suggests strongly that the persistence of hippocampus dependent learning requires a biochemical cascade that includes de novo protein synthesis in hippocampal neurons. The development of microarray technologies provides, for the first time, a method for determining the precise temporal sequence of gene expression in the hippocampus that underlies this crucial type of memory. Aim 1: To identify the temporal sequence of gene activation in the hippocampus after rats learn a task that requires the hippocampus by using microarray technologies. We hypothesize that successful learning and memory require a selective, temporally extended sequence of genes to be expressed in the hippocampus that is distinct from that activated by behavior or neural activity per se. We will test rats trained in a water maze task that can be solved by either hippocampus-dependent or independent strategies. Probe tests will be used to assess the strategies used by each rat, and gene activation will be compared in normal spatial and cue learners, fornix lesioned rats who learn only cue-response strategies and rats matched for swimming stress. Gene profiles will be assessed 15 min, 2 h, and 12 h after training to determine the temporal sequence of expression. Aim 2: To distinguish learning related expression changes from those related simply to behavior or lesion effects, a second experiment will train rats in the same task as above, but will test the effects of NMDA receptor antagonists that block long-term potentiation (LTP), spatial learning, and the stabilization of hippocampal place fields. We hypothesize that the specific, temporally extended sequence of gene expression in the hippocampus that is required for hippocampus-dependent learning is initiated by the same intracellular signaling pathway that is required for LTP. The use of this pharmacological "plasticity clamp" will enable us to distinguish genes that are expressed due to specific patterns of neuronal activity from those that are needed for plasticity per se.

The grant provides funding to open a Research Data Center (RDC) within the Survey Research Center of the Institute for Social Research at the University of Michigan. The RDC would provide researchers access to source data collected by the Census Bureau and other agencies. These confidential data are placed within a secure facility at the Survey Research Center. To gain access to these data, researchers with approved projects would obtain special sworn status within the Census Bureau. Researchers can publish results of analysis carried out within the RDC subject to a rigorous protocol for protecting the confidentiality of the underlying data.

The purpose of this access would be to conduct academic research using data on individuals and firms. The ability to analyze the source data has enormous scientific benefits. These include the ability to make full use of the information collected by government agencies; to analyze the behavior of individuals and firms taking into account the heterogeneity of behavior and characteristics; and to combine sources of data at the individual level to study relationships obscured by more aggregate data. This RDC promotes productive interaction between the data collection agencies and the research community. Typically, researchers using government statistics are passive consumers of the data, which is usually collected for a purpose other than academic research. By providing a vehicle for working with the source data in collaboration with the Census Bureau, the RDC allows the research community to participate effectively in improving Census data.

A Research Data Center would build on the University of Michigan's long-standing strength in social science research and in the processing and analysis of large-scale databases. The Institute of Social Research and its Survey Center and Population Studies Center house substantial expertise in the management of such databases and for maintaining the confidentiality of information on individuals. The University, the Institute, and the Centers have an established record in providing wide access to data and in improving quality data.

DESCRIPTION (provided by applicant): Memory is crucial to identity and survival, and the hippocampus is required for normal memory. The causal relationship between hippocampal cell activity and learning and memory performance, however, remains unknown because few experiments record neuronal firing while memory demands are varied and other aspects of behavior are held constant. Aim 1. To identify learning-dependent changes in neuronal encoding by recording groups of hippocampal neurons while rats perform a task that requires the hippocampus. Successful newlearning that requires the hippocampus should require the formation of new and persistent firing patterns by hippocampal neurons. Rats will be trained in a + maze task that can be solved by either hippocampus-dependent or independent strategies. Groups of hippocampal cells will be recorded in rats that behave identically but differ in learning ability, memory strategy, and hippocampal function. Probes will test the strategies used by each rat, and unit activity will be compared in normal place and response learners, and rats with fornix or caudate lesions. Aim 2. To test the generality of the results in Aim 1, neuronal activity will be recorded in rats trained in a distinct type of learning, the social transmission of food preference. A subset of dynamic neuronal responses may be required for all types of hippocampus-dependent learning, independent of behavioral expression, type of motivation, and specific content of learning. Hippocampal neurons in normal rats should develop persistent olfactory associations between the arbitrarily selected novel food odors and the odor of carbon disulfide, the signal of food safety. Aim 3. To test if temporal relationships among spike trains encode memory by comparing real-time ensemble activity in the hippocampus. Significant memory information may be encoded by the temporal relationships among neurons action potentials. The temporal dynamics of spiking within ensembles recorded during identical behaviors but different memoryloads will be assessed using published and a new method. The analysis will begin by comparing spike records as rats traverse the start arm of a + maze during right and left turns.

Census Research Data Centers (RDCs), based in Ann Arbor, Berkeley, Boston, Chicago, Durham, Ithaca, Los Angeles, New York City, and Washington provide approved scientists with access to confidential Census data for research that directly benefits both the Census Bureau and society. The RDC directors, administrators, board members and researchers, together with the Center for Economic Studies and the Longitudinal Employer-Household Dynamics (LEHD) Program, constitute a collaborative research network that is building and supporting a secure distributed computer network that enables research that is critical to our economic and civic prosperity and security. The network operates under physical security constraints dictated by Census and the Internal Revenue Service. The constraints essentially eliminate the possibility of distributing the computations to facilities outside of the Bureau's main computing facility. Instead, the researchers use the RDCs as supervised remote access facilities that provide a secure, encrypted connection to the RDC computing network.

This project addresses the technical and logistical issues raised by the creation, maintenance, and growth of the RDC network while maintaining the confidentiality guaranteed to participants in Census data. The RDCs and LEHD will lead a new wave of research with the development of innovative, large-scale linked data products that integrate Census Bureau surveys, censuses and administrative records with data from state governments and surveys conducted by private institutions. Both CES and LEHD have extensive experience in creating these products. The RDC network researchers will enhance that experience and contribute their own expertise to the data linking research. The newly created data will be richer than any presently available to researchers with no increase in respondent burden. They will also raise complicated and vexing issues regarding disclosure avoidance and participant privacy.

The project also creates synthetic versions of these confidential data sets. This will increase the accessibility of these data to social science researchers while preserving the confidentiality of private information. Synthetic and partially-synthetic data are new confidentiality protection techniques that rely on computationally intensive sampling from the posterior predictive distribution of the underlying confidential data. The result is micro-data that preserve important analytical properties of the original data and are, thus, inference-valid. The synthetic versions of confidential data are for public use. At the same time, ongoing research within the RDCs using the gold-standard confidential data will constantly test the quality of the synthesized data and allow for continuous improvement. As a result a continuous feedback relationship will be established between the research activities conducted in RDCs on confidential Census Bureau data and the quality of the Bureau's public use data products-namely, the synthetic micro data created by these projects. In order to accomplish these computationally-intensive activities, as well as to allow researchers to engage in such innovative research as agent-based simulations and geo-spatial analysis, we will install a supercluster of SMP nodes optimized for the applications of creating linked data, analyzing the gold-standard data, and processing the data to produce multiply-synthesized public use data sets. Two industry partners, Intel and Unisys, have promised to directly support the creation of this supercluster by donating 256 Itanium 2 processors and providing the computing crossbars, cluster infrastructure, and disk storage arrays at manufacturer's cost. The Linux-based system will be integrated and tuned by the proposal team from Argonne National Laboratories. The synthetic data specialists on the proposal team will port existing multi-threaded data synthesizers and develop new ones.

Broader Impacts: The research conducted in RDCs and at LEHD over the past decade has made important contributions to our understanding of essential social, economic, and environmental issues that would not have been possible without use of the confidential data accessible via the RDC network. It is difficult to overstate the significance of this research, which has used more than 30 years of longitudinally integrated establishment micro-data from the Census Business Register and Economic Censuses; confidential micro-data from all the major Census surveys (Current Population Survey, Survey of Income and Program Participation, American Housing Survey), confidential micro-data from the Decennial Censuses of Population in 1990 and 2000; longitudinally integrated Unemployment Insurance wage records, ES-202 establishment data, and Social Security Administration data; federal tax information linked to major surveys; environmental data on air quality linked to Business Register and Economic Census data; Medicaid data linked to the Survey of Income and Program Participation; and many others.

[unreadable] DESCRIPTION (provided by applicant): The long-term goal is to understand how the brain remembers. Memory's ultimate purpose is informed action. We recall the past to anticipate potential outcomes of familiar situations. Episodic memory requires the hippocampus and is devastated by Alzheimer's disease, yet precisely how hippocampal neurons inform such memory is unknown. The aim now is to identify patterns of hippocampal neuronal activity required for memory. As rats performed a hippocampus-dependent "episodic-like" memory task, the firing patterns of hippocampal neurons varied with imminent or recent events when other aspects of behavior were identical, revealing prospective or retrospective coding, respectively. Three Aims will systematically manipulate hippocampus-dependent memory demand qualitatively and quantitatively: Aim 1 will test if prospective and retrospective activities predict hippocampus-dependent spatial memory performance while error rate is varied systematically via interference. Retrospective and especially prospective activity should predict changes in ongoing memory performance. Aim 2 will test the generality of the results in Aim 1 by assessing neuronal activity in two non-spatial cue discrimination tasks that differ in hippocampus dependence. Rats will perform the same behaviors as in Aim 1: one task is unaffected by hippocampal lesions, the other should be impaired. Memory demand and error rates will be varied by altering the delay and pattern of stimulus presentation. If the active recruitment of memory coding by hippocampal neurons requires specific memory demands, then memory coding should increase as those demands increase and decline with errors; without those demands, such activity should be minimal and unrelated to errors. Aim 3 will assess coding during performance of a novel non-spatial task that requires the hippocampus and flexible memory retrieval, but not recent memory. Rats trained to approach different non-spatial goal objects depending on their deprivation state (hunger or thirst) were impaired after hippocampal lesions. If prospective coding reflects a general computational process by which the hippocampus signals impending events independent of recent memory, then neuronal activity should anticipate choice selection and predict errors. Each aim will assess population and temporal coding and their interaction. The research should inform the design of rational treatments for amnesia (e.g. by stroke or Alzheimer's disease), including the future development of neural prostheses. [unreadable] [unreadable]

DESCRIPTION (provided by applicant): The long-term goal is to understand how the brain learns and remembers. The purpose of learning is informed action: learned facts and events let us use the past to anticipate the outcome of familiar situations. Relevant memories must be available during learning if past events are to inform and be associated with ongoing experience. These abilities require the hippocampus and are devastated in Alzheimer's disease, but their neuronal coding mechanisms are unknown. In an "episodic-like" memory task, hippocampal activity varied with recent, ongoing, or imminent events when other aspects of behavior were identical, revealing current, prospective, or retrospective coding. The aim now is to identify firing patterns that predict learning. Aim 1 will assess hippocampal coding as rats learn new spatial behaviors in a familiar environment. Experiment 1 will oblige rats to make either new or familiar detours before completing a highly familiar, memory-based journey. Because the start and goal of the journeys are unchanged, the same coding of the familiar episodic-like memory structure should be maintained even if detours entail different trajectories. Journey-dependent coding should predict memory performance and rapid learning in novel detours. Experiment 2 will switch the start and goal of journeys in the same environment. Thus the rats will have to learn new journeys in the same spatial environment. Memory performance and journey-dependent coding should decline initially, and the acquisition of journey-dependent, coding should predict performance improvement during learning. Aim 2 will assess hippocampal coding as rats learn to perform the same memory task but in a new, unfamiliar environment, so the rats will have to learn new spatial representations as well as a new journeys. Journey-dependent and independent coding should be absent initially, but develop rapidly, and the acquisition of journey-related coding should predict performance during learning. Aim 3 will test if the identified dynamic coding properties appear even when learning is blocked. The same behavioral methods described in Aims 1 and 2 will be used, but one group of rats will be given NMDA receptor antagonists known to impair learning but not memory performance. The strong prediction is that neither stable memory performance nor accompanying hippocampal coding will be affected by the drug, whereas both learning and the dynamic coding properties that accompany it in Aims 1 and 2 will be impaired. Each aim will include hippocampus-independent tasks that require the same spatial behaviors but not memory, and will assess both population and temporal neural coding. The research should inform the design of rational treatments for amnesia (e.g. by stroke or Alzheimer's disease), including the future development of neural prostheses.

Project Summary
Investment: Theory, Estimates, and Public Policy
This project will analyze the determinants of investment spending. It will quantify several key determinants and study how they affect investment both in theory and in practice. The proposal features two related lines of research. The first research component is an empirical analysis of investment at a disaggregated level. The second component develops and estimates a structural model of investment. The two research components are complementary. The empirical results are used to inform, test, and estimate parameters of the structural model. In turn, the structural model provides important insights into the effects of recent and future investment policies. The first part of the project presents an empirical analysis of investment. This research establishes key empirical facts that investment theories should match. It also evaluates the empirical success of several existing theories. Specifically, using a panel of type-specific investment data, four prominent empirical issues are studied: (1) the cyclicality of pre-tax and after-tax investment prices; (2) the role of uncertainty in investment spending; (3) the response of investment to tax subsidies and (4) the response of automobile production and prices to the Consumer Assistance to Recycle and Save Act of 2009, also known as "cash for clunkers.?" The primary data sets used are the BEA underlying detail tables for investment, recently available measures of economic uncertainty, and an updated, revised set of type-specific investment tax subsidies.The second part of the proposal presents a structural equilibrium model of investment. The theoretical framework builds on previous studies which feature fixed adjustment costs at the microlevel. The model adds to this research by introducing a new investment timing friction not featured in earlier work. The timing friction allows firm-level heterogeneity to have a substantial influence on equilibrium investment dynamics. This is a ignificant extension of the research on fixed costs and firm-level heterogeneity since important effects of such heterogeneity are not present in earlier models. As a result, the proposed framework will provide a better guide to analyzing investment policy. The magnitude of the timing friction will be estimated with type-specific investment data. The empirical work in the first part of the proposal will provide several parameters to assess the fit of the theoretical model and to develop estimates of the effects of specific policy changes.
Summary of intellectual merit. The project will develop a modern model of investment that incorporates key frictions that imply important firm-level heterogeneity. While the model incorporates many complex modern features, it is also analytically tractable and yields sharp testable predictions. Hence, this research will allow modern investment models to confront the data in a meaningful way and to be used to evaluate specific policies.
Summary of broader implications. This research will provide both theoretical and empirical guidance for government policies designed either to affect overall economic activity by stimulating investment or to promote investment to increase economic growth. Additionally, the results of this analysis will be valuable to studying business cycles by shedding light on how various shocks affect investment activity.

This project will undertake research that responds to the specific analytic and operational requirements of the Census Bureau and other federal statistical agencies to improve their estimates while reducing costs and respondent burden. The project will use administrative data, and more generally, data generated by households and businesses in the course of their normal activities to produce economic and demographic measurements that currently rely on surveys. The project will develop and evaluate methodologies that use the vast constellation of data generated by ordinary activity in a modern society and that protect the privacy of individuals and businesses. The project will examine administrative records created by businesses, individuals, and governments, streams of data from social media sites on the World Wide Web, and detailed geospatial data. The project will analyze these multiple source of data and relate them to data collected on surveys. It aims to improve survey measurements of economic and demographic data and potentially supplement or replace surveys with statistics based on administrative, Web-based, and geospatial data. Applications of these approaches include the following: using linked survey-administrative data to assess attrition, selective non-response, and measurement error in surveys; using Web-based social media to measure job loss, job creation, small business creation, and informal economic activity; using administrative geo-spatial data to enhance small-area estimates; and training in the use and creation of linked survey-administrative datasets.

The Federal statistical agencies have pressing needs to innovate in light of the rapidly changing structure of the economy and the interaction of these changes with the fundamental ways in which households and businesses produce and use information. This project will combine expertise in social science, survey research, and information science to address the scientific and practical problems that the statistical system must confront. The project will advance the science of measurement and serve to renew the statistical system both by bringing frontier methodology to measurement problems faced by the statistical agencies and by nurturing a new generation of scholars, both within the statistical agencies and academia, who will collaboratively address these issues. This activity is supported by the NSF-Census Research Network funding opportunity.

DESCRIPTION (provided by applicant): Memory's purpose is to guide adaptive behavior: we recall the past to inform the present, to anticipate the outcome of choices, and thereby guide goal-directed responses. To be useful, memory retrieval must be selective, directed by the salient features of situations, and flexible to adapt to changing internal goals, environmental opportunities, and potential actions. The hippocampus and the orbital prefrontal cortex (OFC) are crucial for different aspects of adaptive behavior, and dysfunction of either of these brain regions can contribute to neuropsychiatric disorders including Alzheimer's disease, PTSD, and schizophrenia. This proposal will investigate how the OFC and the hippocampus contribute to flexible, goal-directed, memory guided behavior. The experiments, part of a larger research program on how prefrontal cortex contributes to memory, will test the general hypothesis that bidirectional interactions between OFC and hippocampal circuits provide key mechanisms for selective retrieval of goal-related representations by integrating reward history and memory for episodes. The specific aims will investigate these mechanisms by combining behavior analysis, temporary inactivation, simultaneous recording of neuronal activity in both structures, and deep brain stimulation (DBS). Aim 1 will assess the functional interactions between the two structures during learning and memory retrieval. Rats will be trained in a + maze task that either requires one structure, the other, both, or neither. Interactions between the structures will be tested by temporarily disrupting one, the other, or both on opposite sides of the brain. If OFC-hippocampal interactions are required for flexible memory retrieval, then the crossed inactivation should produce similar impairments as bilateral inactivation. Aim 2 will record neuronal activity in both structures simultaneously to determine how activity within and between the OFC and hippocampus predict learning and memory performance. We recently identified EEG patterns in the hippocampus that predicted memory retrieval, and discovered that DBS could both mimic these patterns and restore memory in otherwise amnestic animals. Aim 3 will therefore test the causal relationships between the OFC and hippocampus by combining temporary inactivation, dual recordings, and DBS. Recording one structure while disrupting activity in the other will determine the extent to which normal coding in each structure depends on the other,and how these interactions influence learning and memory. Targetted patterns of DBS will be used to mimic identified signals within and between hippocampal and OFC circuits to determine if the effects of inactivation can be overcome, or normal performance enhanced. The outcome will advance neuroscience by revealing how the OFC and hippocampus interact to guide flexible and selective use of memory, and will inform emerging treatments for behavioral and neuropsychiatric disorders that involve disintegration of prefrontal cortex and hippocampal functions, including schizophrenia and Alzheimer's disease.

DESCRIPTION (provided by applicant): The proposed Training Program in Mental Health Research will provide predoctoral and postdoctoral students in the Neurosciences with an integrated training experience in the laboratories of nationally and internationally recognized faculty. The predoctoral training program builds on an exciting, translationally relevant curriculum taught in years one and two of graduate school that has been recently awarded NIH support through the Jointly Sponsored Predoctoral Early Stage T32 Training Program mechanism. The postdoctoral program will draw together complementary pools of clinical and basic science fellows. The proposed new training program would be Mount Sinai's first to support research training for Ph.D. students in the Neurosciences, and would be unique at Mount Sinai in its approach to providing postdoctoral mental health research training to clinical and basic fellows. Outstanding training faculty share a common thematic interest: understanding how the function and plasticity of specific neural circuits impact, and are impacted by, neurodevelopmental and neuropsychiatric disease. Varied laboratory opportunities at Mount Sinai School of Medicine take advantage of particular strengths in translational neuroscience, notably in developmental neurobiology, mechanisms of neuropsychiatric disease, cognitive neuroscience, neuroimaging, signal transduction, and synaptic and behavioral plasticity. At Mount Sinai, the nervous system is studied in diverse model systems, from 'simple' invertebrates such as the sea snail Aplysia, the fruit fly, or the worm C. elegans, all the way to complex vertebrates including nonhuman primates and humans. Through their course work, predoctoral trainees will have received a solid foundation in basic neurobiology and the pathophysiology of neurological and psychiatric disease. Postdoctoral trainees will be admitted from residency or fellowship programs (e.g. Psychiatry), or following completion of Ph.D. or M.D./Ph.D. programs, and they will be offered a tailored didactic and research experience. Selection of a research mentor is made in a collaborative environment that actively promotes multidisciplinary, integrative research. The training program encourages participation of faculty mentors whose research grants directly focus on mental health research, while not excluding those whose research is critically important for the interdisciplinary training we seek to impart. Research training will also have a didactic 'work in progress' component, to foster these important interdisciplinary interactions, hone presentation skills, and improve awareness of ethical issues. Using this approach, the Training Program in Mental Health Research will provide predoctoral and postdoctoral students with the guidance and experimental tools, in the laboratories of our training faculty, to launch successful, productive, independent careers in mental health research.

Learning, Prefrontal Cortex, and Multiple Memory Systems Learning, to be useful, must be sensitive to the relevant features of situations, guided by environmental opportunities, and informed by past actions in similar circumstances. In other words, learning is guided by memory. The neuronal mechanisms that integrate learning and memory are largely unknown. The hippocampus is crucial for learning facts and remembering events, the neostriatum is important for habit learning, and the prefrontal cortex (PFC) is needed to modify previously learned responses flexibly. Dysfunction of each of these brain regions or their disconnection contributes to neuropsychiatric disorders including dementia, PTSD, and schizophrenia. This proposal will investigate how these structures interact during memory-guided learning. The experiments, part of a larger research program on how prefrontal cortex contributes to memory and cognition, will test the hypothesis that interactions between PFC, hippocampal and dorsolateral striatal (DLS) circuits provide key mechanisms for memory guided learning by integrating abstract rules, event sequences, and stimulus-directed actions. The specific aims will investigate these mechanisms by combining behavior analysis, temporary inactivation, simultaneous recording of neuronal activity, and deep brain stimulation. Aim 1 will assess the functional interactions between the PFC, hippocampus, and DLS the during learning by temporary disruption of local circuits. Rats will be trained to two behaviorally identical + maze tasks, one that requires the hippocampus, the other the DLS for initial learning~ the PFC is needed to switch between them. Interactions between the PFC and the other structures will be tested by temporarily the mPFC and one of the other structures both the on opposite side of the brain. If PFC interactions are required for flexible learning, then the crossed inactivation should produce asymmetric impairments in switching from one strategy to the other. Aim 2 will record neuronal activity in the three structures simultaneously to determine how activity within and between the PFC and the other structures predict learning. We recently identified EEG patterns in the hippocampus that predicted memory retrieval, and discovered that DBS could both mimic these patterns and restore memory in otherwise amnestic animals. Aim 3 will therefore test the causal relationships between the PFC and the other structures by combining unilateral inactivation, bilateral recordings, and DBS. Recording in PFC while disrupting activity unilaterally in the hippocampus or DLS, or vice versa, will determine the extent to which normal coding in each structure depends on the other, and how these interactions influence learning. Targetted patterns of DBS will be used to mimic identified signals within and between circuits to determine if the effects of inactivation can be overcome, or learning strategy modified. The outcome will advance neuroscience by revealing how the PFC, hippocampus, and DLS interact to allow memory-guided learning, and will inform emerging treatments for neuropsychiatric disorders that involve disintegration of prefrontal cortex, hippocampal, and striatal functions, including schizophrenia and Alzheimer's disease.

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future.

The broader impact and potential societal benefit of this Convergence Accelerator Phase I project will include better use and growth of knowledge networks. Today's knowledge networks, for example Wikidata, include high-quality structured information about a very wide range of topics. Knowledge networks make many new and compelling applications possible, such as structured search engine results and voice assistants. Unfortunately, today's knowledge networks and applications have been very difficult and expensive to construct, making it extremely burdensome to create them for novel topics. This project will take advantage of the research team's expertise in data management, artificial intelligence, and economics to create a combination of software and data that should make novel knowledge network systems dramatically easier to produce. The first effort will be an Economics-focused integrated knowledge network-and-tool system, which has the potential to dramatically improve the ease of performing higher-quality economic measurement and analysis. This knowledge network tool can improve economic-oriented research efforts that will benefit national prosperity. However, the even greater value of the effort will be a tool that allows knowledge networks on any topic to be developed more easily and with less programming expertise.

Although knowledge networks are thought to be key to future data-enabled discovery, knowledge network-driven applications have generally not been developed using a reproducible system. This project will begin to build a knowledge application development system that should make knowledge applications easier to write, existing knowledge networks easier to improve, and entirely novel knowledge networks easier to construct. The team's effort is based on a novel and extremely succinct form of programming that they have developed that allows simultaneous programming and extraction of relevant information to contribute to a knowledge network. The proposed simultaneous programming and extraction system will help construct knowledge networks, but will also improve knowledge network data quality, by providing additional weak supervision for the information extraction pipelines that are commonly used to produce the networks. The system will be tested on real data and users in the Economics domain. However, the methods and tools will not be topic-specific, but rather should be widely applicable to knowledge networks in many topical domains. This work will generate research as well as practical downloadable software, datasets, and applications.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.

The goal of this project is to build infrastructure for efficient construction of knowledge networks and applications, as well as to demonstrate the system with concrete knowledge networks that describe COVID-19 science and economics. In the short term, this work will lead to high accuracy data resources that will be useful to scientists and policy makers in addressing the virus and its economic impact. Sample goals include enabling a medical researcher to quickly identify relevant candidate drugs, and a policy maker to quickly evaluate the likely impacts of a novel law. The project will create programming tools that will make knowledge networks and their applications far less expensive to build. This infrastructure of programming tools will facilitate the creation of a large and novel set of informational tools and will also significantly expand the set of people who can participate in creating knowledge network resources. Because knowledge networks combine unique data analysis qualities with the topical breadth of the entire World Wide Web, the potential growth of knowledge tools is very large and potentially transformative.

This project includes partnerships with a strong set of non-academic and academic partners including the Allen Institute for Artificial Intelligence, Apple Siri, GV (formerly called Google Ventures), the Inter-University Consortium for Political and Social Research (ICPSR) and the American Economic Association (AEA), as well as the University of Washington, and the University of California Berkeley. This convergence research team will integrate their multidisciplinary expertise in data management, artificial intelligence, programming languages, biomedical topics relevant to COVID-19, and economics, with the other domains represented in the projects funded in the Track A Phase II cohort.

Creating this knowledge programming infrastructure and concrete knowledge networks will require solving several technical challenges. The first is an intelligent ?knowledge compilation layer? that makes useful but rapidly-changing knowledge networks appear to be stable enough for programmers to use them when writing reliable code. The second is the creation of a mechanism for transparently sharing knowledge resources and debugging information within and across organizations. The third is a method for collecting knowledge provenance metadata ? details about how every individual data element was created ? via automatic instrumentation of user software. A last challenge is the creation of knowledge-from-document systems that can produce high accuracy knowledge networks with very little explicit human oversight.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.