Nonequilibrium dynamics is a thriving sub-field within physics that seeks to identify the principles underlying complex spatiotemporal patterns that arise in far from equilibrium systems. Living biological organisms are one important subclass of systems far from equilibrium, yet, due to their complexity and variety, they have remained relatively understudied by theorists and experimentalists alike. As such, significant questions exists both about the extent to which methods of nonequilibrium dynamics can be used to identify laws governing pattern formation and regularities in biological organisms. Funds from this grant support Jeff Gore, Nikta Fakhri, and Jörn Dunkel of MIT in a series of projects that will begin to examine whether and how nonequilibrium dynamics might be used as an organizing framework for understanding how ordered biological phenomena arise and evolve across a variety of scales. Topics to be explored by the team include the role of topology and topological defects in triggering order-enhancing processes in starfish cells; the assembly of ordered structures of colloidal molecules by motile bacteria; and how spatial distribution affects the evolution and ecology of microbe populations. In addition to knowledge gained, the project will involve the development and deployment of new imaging and theoretical analysis tools, expanding the available methods for the thermodynamic study of biological systems.

Perhaps the most important economic statistic calculated by the government is the Consumer Price Index (CPI). The CPI is used as a guide for fiscal and monetary decision-making, as a deflator to compare times series data in constant dollars, and as an inflation-measure to adjust the purchasing power of federal benefits like Social Security. The basic methodology for calculating the CPI, however, has hardly changed since the statistic was first introduced over a century ago. First, using a series of voluntary surveys of businesses and consumers, the Bureau of Labor Statistics attempts to measure average monthly prices in 38 geographical areas for 211 broad consumption categories like men’s shirts. Second, the BLS estimates the proportion that each of these individual categories represents of a typical household’s spending on all 211 categories. The goal is to calculate how much a typical basket of goods and services would cost today, then compare that to the cost of the same basket at the end of some fixed period. Although this may sound straightforward, worrisome and worsening challenges arise from this methodology. First, the time required to collect responses to these surveys and aggregate data means that the CPI is not calculated for months after data is collected. Second, falling response rates to polls and surveys of all kinds means that the CPI calculation is increasingly reliant on an ever-smaller set of data, with consequent worries about accuracy. Third, the metadata collected by surveys about goods and services sold is often insufficient to gauge how much the price increase in, say, a mobile phone, reflects improvements in the phone’s quality or feature-set. Funds from this grant support a project called RESET, Re-Engineering Statistics Using Economic Transactions, led by economist John Haltiwanger (University of Maryland College Park), Matthew Shapiro (University of Michigan) and Ron Jarmin (U.S. Census Bureau). The RESET team has forged data partnerships with major online retailers and market data analysis firms to allow them access to real-time, highly granular information about the prices and features of retail products for sale online. The team proposes to use advanced machine learning techniques to solve the problems with current data collection methods. The RESET partnerships promise to be able to calculate the CPI more quickly, using more comprehensive data, and in a way that promises to give both regulators and researchers more ability to calculate how much of a price variation is due to systemic inflation and how much actually reflects changes in product quality.

Datasets about patents are among the most important tools for studying the economics of science and technology. Patent records do not tell the whole story, of course, but they do provide critical evidence about innovation, regulatory policy, and economic growth. Funds from this grant support the Innovation Information Initiative (“I-Cubed”), a diverse, interdisciplinary community of scholars devoted to enhancing research on the economics of science and productivity by increasing the coverage and usefulness of open patent data. Grant funds will support the I-Cubed networks creation and/or expansion of four innovative resources for researchers: a database of connections between patents and scientific papers; a database of connections between patents and retail products; a portal collecting patent metadata that will allow better disambiguation of inventors and inventions; and a database on patent funding that will distinguish, for example, between patents filed by established firms vs entrepreneurs or start-ups. These resources will allow researchers to ask and answer important questions about the economics of science and innovation with greater confidence. That includes questions about the return on investment to basic scientific research, identifying commonalities among innovators, the relationship between innovation and commercialization, and what sorts of funding and work structures are most supportive of patent production.

Correlation is symmetric. If some variable in a dataset, A, rises and falls in correlation with another variable, B, then B also falls and rises with A. Causal relations, in contrast, have direction. Saying “A causes B” is entirely different from saying “B causes A.” The directionality of causation makes detecting and analyzing causation using only correlations extremely difficult. Statistical methods framed entirely in terms of correlation therefore cannot express, let alone analyze or measure, causal relationships. Traditional data analysis also falls short when based on correlations that are spurious—that is, ones caused by random noise rather than fundamental processes. Big datasets do not make such challenges any easier, either. In fact, the opposite is the case. What’s ultimately needed, if we are to detect, measure, and understand causality, are more robust frameworks that draw on a richer set of concepts specifically designed to detect the inherent directionality of causal inference. Computer scientist Elias Bareinboim of Columbia University is developing just such an analytic framework. Drawing on seminal research conducted with his collaborator, computer scientist Judea Pearl, Bareinboim works with graphical methods designed to represent not only correlations between variables in a dataset, but whether and how other variables in the dataset affect those correlations. This framework allows the analyst to ask rigorous counterfactual queries of a dataset (what would have happened if…) that are essential for understanding causal relations among variables. In addition, Bareinboim’s framework is being adapted for implementation by sophisticated AI and machine learning programs. The goal is to allow them to separate causal relationships in data from non-causal correlations.   Grant funds will support Bareinboim’s research on these and related topics for a period of two years.

The Mathematical Sciences Research Institute (MSRI) is an independent nonprofit research institution and a center for collaborative research. Its events draw thousands of leading scholars from around the world each year, including intensive semesters organized around pairs of specific themes. This grant supports two semester-long programs at MSRI on market and mechanism design and algorithmic fairness. Mechanism design is a field of economics that studies procedures, assignments, and incentives that work other than through markets and prices. The program will advance research on improving the design of real-world transactions such as organ donation and medical student hospital residency matching. Algorithmic fairness, meanwhile, is concerned with understanding and correcting biases in algorithmic decision-making. Recent research has shown that some properties of algorithmic fairness are mathematically incompatible with each other, and the program will investigate what it means for predictive algorithms to be fair in ways that are both well-motivated and computationally feasible. This grant provides support for 3-6 program organizers, 15 senior research professors, 30-35 research members, 8 postdocs and 12 graduate students, for a total of 70 researchers per program, or 140 researchers in total.

Innovation in and adoption of open hardware practices for scientific instrumentation and apparatus are being held back by the lack of widely-accepted standards in the description and versioning of open hardware projects. Metadata standards, in particular, are essential infrastructure to enable discovery and collaboration. A typical open source hardware project can combine instructions for 3D-printed components to be built locally along with a heterogeneous range of premade components (with different degrees of quality control) from a number of suppliers, along with any number of software programs used to control the device. At the moment, much open source hardware is in the “you can find documentation on my website” stage of maturity, where documentation and assembly instructions are idiosyncratic to the individual creator, and collaboration beyond small, local teams is more or less impossible.This grant funds Andrew Lamb, the founder of the Internet of Production Alliance, in a project to establish five families of metadata standards for open hardware: Designs and Documentation; Machines and Tools; People and Skills; Materials and Components; and Contracts and Business Models. These five standards are at different stages of maturity and build on each other: the first two (Open Know-How and Open Know-Where) have already been developed and activity will primarily focus on broader adoption and maintenance; the next two will be actively developed and launched over the course of the two years; and the last will be scoped for future development.

The Center for Scientific Collaboration and Community Engagement (CSCCE) has quickly become the preeminent research and training center focused on promoting the essential role community managers play in the effective functioning of scientific communities and thus in the production and dissemination of scientific knowledge. Led by microbiologist Lou Woodley, CSCCE documents and disseminates best practices in scientific community management, designs online and in-person curricula, runs training seminars, and acts as an advocate among scientists for the professionalization and institutionalization of the community management role. Funds from this grant support the continued operation and expansion of the CSCCE, along with efforts to develop and implement a business sustainability plan that will allow the organization to continue providing services to the diverse community of an estimated 30,000 community managers inside STEM research organizations. Grant funds are being administered by Community Initiatives, Inc., acting as a fiscal sponsor for CSCCE.

Research by Vicky Rampin, Librarian for Research Data Management and Reproducibility at NYU's Division of Libraries, revealed that while there is widespread use of version control among academic researchers writing source code, there are limited approaches to its preservation. In response, Rampin, together with Martin Klein at Los Alamos National Laboratory, has developed an ambitious plan for CoSAI, Collaborative Software Archiving for Institutions, a project that will create a decentralized and federated platform that will knit together several existing archiving and software preservation tools. Decentralization means that no one institution can be a bottleneck or failure point for archiving workflows—a thorny problem on other platforms—while federation both shares costs among partners and implements one of the gold standards in archiving: ensuring the robustness of preservation through having multiple copies of files mirrored across independent sites. CoSAI will focus on research software and aims to archive not just the code developed on sites like GitHub, but the (currently) ephemeral record of supplementary material related to the code (e.g., discussion threads, issues, etc.). By leveraging existing open source tools like Memento Tracer and building on workflow engines such as OCCAM, CoSAI will be able to capture web resources from code repositories at high quality and in a reproducible manner.

This grant funds the continued development of the AI Model Share platform, a website and integrated open-source Python library where researchers can deploy and share versions of machine learning models they have created in their research, and which can then be subsequently downloaded, implemented, used, analyzed, and improved by other researchers. In addition to making new resources available to researchers of all kinds, AI Model Share’s careful attention to issues like requirements tracking, versions, and documentation is an important step towards creating standards, tools, and practices that will allow research using machine learning methods to be robustly replicated. Activities supported by grant funds include the beta launch of the platform, user training and feedback workshops, an expansion of the platform’s ability to submit, search for, and replicate stored AI models, and the development of a front end “portfolio page” interface for platform users.