Shafiqul Islam - US grants

Project STEP, involves graduate and undergraduate Fellows, secondary science and mathematics teachers, University of Cincinnati faculty and a graphics/web developer, working in teams to design, develop, and implement hands-on activities and technology-driven inquiry-based projects which relate to the students' community issues, as vehicles to teach science and math skills. Activities will be incorporated into lessons, demonstrations, laboratory exercises, individual and group projects, and field experiences to enable middle and high school students to directly experience authentic learning practices that requires them to use higher-order thinking skills; encourage creative problem-solving skills that require collaborative learning, teamwork, writing, and presentation; cultivate an interest in service learning in which students are active participants, achieve outcomes that show a perceptible impact, and engage in evaluative reflection; and better motivate and prepare secondary school students for advanced education. The Fellows will be trained to create and implement these activities by taking an educational methods course, an advanced course in instructional technology, and by serving as teaching assistants and tutors in guiding summer academies for middle and high school students. Quantitative formative and summative evaluation will be conducted to assess the project's effectiveness on Fellows' teaching skills, its impact on middle and high school science and mathematics education, and to continually improve the program as it develops. This project is receiving partial support from the Directorate for Engineering.

0510429
Islam

We are now in a position to solve the puzzle of why dissolved arsenic concentrations are dangerously high in the groundwater of the Ganges Delta. Over the last five years several research groups have provided detailed characterizations of the static geochemical characteristics of groundwater and sediments in arsenic-contaminated aquifers. The challenge now is to determine how groundwater
flow transports chemicals in and out of the subsurface, and hence controls subsurface biogeochemistry. We propose to develop novel hydrologic methods to characterize the complex spatial
and temporal patterns of groundwater flow, and then to employ hydrogeologic models to study the evolution of groundwater geochemistry. By combining dynamic hydrological models with geochemical
characterization we intend to answer key scientific questions that have thus far eluded us: Why has arsenic not been flushed from some aquifers? Combined estimates of groundwater residence times and arsenic retardation factors indicate that arsenic residence times are only decades to centuries, implying that arsenic should be flushed from the aquifers that are thousands of years old. Is
dissolved arsenic supplied by a continuous source, or are high concentrations transient? Will arsenic concentrations change in the future? Our field injection-withdrawal experiments show that arsenic concentrations respond within days to biochemical perturbations. The adoption of dry-season
rice cultivation has dramatically altered geochemical input and outputs from aquifers. How does this change affect subsurface geochemistry and dissolved arsenic concentrations? Why do arsenic concentrations differ between neighboring wells? Arsenic concentrations at nearby locations in grey-colored anoxic aquifers often differ greatly, despite similar sediment characteristics. Do
these dramatic gradients result from the pattern of groundwater flow and recharge?
What are the intellectual merits of the proposed activity?
These questions can only be resolved by determining how groundwater dynamics control
chemical input and output to aquifers over two timescales: (1) Seasonal cycle: The hydrology of Bangladesh annually cycles between Monsoon flooding and dry-season arid conditions when evapotranspiration greatly outstrips precipitation and irrigation water is pumped from aquifers to meet the transpiration demands of crops. This cycle drives water table oscillations that create seasonally varying oxic/anoxic
conditions in soils and also drives water exchange between aquifers and surface water (rice paddies, ponds and rivers). (2) Anthropogenic changes over decades: The Ganges Delta has been dramatically altered over the last three decades by population growth and the advent of irrigated agriculture. Groundwater irrigation has changed the location, timing and chemical content of recharge.
Anoxic irrigation water is ponded in rice fields over much of the land, thereby changing both the hydrologic budget and the biogeochemistry of recharge, potentially mobilizing arsenic from soil layers that
may be rich in arsenic and iron (oxy)hydroxides. Furthermore, pumping changes flow-paths deep in aquifers, affecting both the rates and locations of recharge as well as groundwater exchange with surface
water bodies that now receive much higher loads of untreated waste.
We propose to: (1) Build on our successful field program in Bangladesh by extending our field characterization from vertical geochemical profiles at one location to three dimensional flow around this location; (2) Characterize recharge and discharge and map transient flow-paths through the aquifer by applying novel combination of natural isotope data and numerical inverse methods for groundwater flow;
(3) Conduct a detailed study of geochemical fluxes through the bottom of a rice field, now a principle source of groundwater recharge at our site, and a very likely source of dissolved arsenic; (4) Construct predictive numerical models that couple groundwater flow and recharge with the biogeochemical transformations that control arsenic. What are the broader impacts of the proposed activity?
This collaborative project is built on our successful and productive partnership over the last five years. We will continue to place a significant emphasis on the education and transfer of technology, with
further exchange of students between BUET, MIT and Tufts. We also will continue to collaborate with other research groups including Stanford, EAWAG in Switzerland, UBC in Vancouver and UCLA. Our research findings should answer some key scientific questions and also help evaluate alternative arsenic
mitigation strategies and better manage water resources in Bangladesh.

Recent studies suggest that precipitation over Eastern North America (ENA) exhibits a dipole pattern of wet and dry conditions between the central United States and eastern Canada, with particular prominence at decadal timescales and considerable contribution to local trends ? but which is largely erased in the usual calculations of area-averaged trends. The associated trend toward increased Central US precipitation is not well reproduced in current models and is contrary to the usual expectations of drying in the continental interior with a warmer climate. Decreasing precipitation over eastern Canada is also contrary to model projections of generally wetter conditions nearer the coasts. Several fundamental questions have yet to be addressed: What are the underlying dynamics and key physical mechanisms? How closely is it related to large-scale climate variability? What processes are setting the decadal timescale?
What are the intellectual merits of the proposed activity?
The goals of this project are to determine the underlying dynamics of the dipole pattern and its connections to large-scale climate variability and trends. The three primary objectives of the proposed work are to: 1) determine the timescales, seasonality, and structure of the dipole pattern, 2) examine the dynamics of the precipitation changes in terms of the hydrologic budget, moisture transport, and storm track variability, and 3) use a hierarchy of models to investigate the influence of large-scale variability on the dipole mode. To investigate the underlying mechanisms, we will test a set of hypotheses on the local forcing of the precipitation via moisture flux and thermodynamically-forced vertical velocity, and on the large-scale controls on the regional circulation via both baroclinic and barotropic response to tropical convection.
We will expand our observational data analyses to establish and clarify the link among precipitation variations, circulation anomalies, and boundary forcing that may create a precipitation dipole over ENA. We will then investigate the links to large-scale climate variability and tropical forcing. This is perhaps one of the first attempts to understand and characterize the existence of a precipitation dipole over ENA. Our methodology -- which builds on our ongoing and previous work -- was briefly tested and some preliminary results are presented in the proposal. We will conduct observational analysis of a range of hydrologic and atmospheric variables to determine the structure of the seasonal and spatial pattern. We will analyze the observed hydrologic budget, moisture transport, shifts in the storm tracks, and transient-mean flow interaction to investigate the internal dynamics of the dipole, using both simple compositing and pattern-based analysis techniques such as Principal Component Analysis. We will use multiple estimates of precipitation and atmospheric circulation to alleviate known data quality issues. For dynamical investigations, we will use a range of models of increasing complexity: a global barotropic (one-layer) model linearized about a zonally-varying mean flow, a tropical Gill-Matsuno model with generalized heating, and the NCAR Community Atmospheric Model, which we have modified to allow imposition of convective anomalies.
What are the broader impacts of the proposed activity?
This collaborative project between the Tufts University and University of Massachusetts builds on mutually synergistic expertise in water cycle research, atmospheric dynamics, and hydrology. This partnership will be further expanded through co-mentoring of a post doctoral fellow, PhD students and involvement of undergraduate and high school students through summer internships. The proposed research addresses several important scientific questions, including the dynamics of large-scale atmospheric influences on hydrology, mechanisms of hydrologic variability at decadal and longer timescales, and trend attribution over different regions of ENA. There is also considerable societal relevance: understanding these regional changes in precipitation and their long-term variations has important implications on how, and to what extent, long-term variations in precipitation over ENA can be predicted and managed. The results are also relevant to agriculture (e.g., winter wheat) and, potentially, to water trade with Canada and increased terrestrial carbon fluxes to stream.

There is a broad consensus among climate models that a warming world will lead to a drier climate over most of the subtropical United States. Yet, observational evidence suggests that total precipitation and stream flow have increased across the United States over the last several decades with the largest increases generally observed in fall across the central United States. Identification of origins for the observed precipitation trends may be complicated by an apparent fall dry bias in current climate models. Most coupled climate models significantly underestimate precipitation over the Mississippi basin during fall, limiting our ability to skillfully predict future changes in precipitation or attribute the recently observed trends to anthropogenic origins. This apparent inconsistency between observed trends in fall precipitation and the dry bias in climate models motivates the Principal Investigators (PIs) to better understand and identify the dominant mechanisms that produce trends and variations in fall precipitation.

The relationship between atmospheric circulations and surface climate over the United States in winter and summer has been the subject of many observational and modeling studies. Relatively little attention has been paid to fall precipitation, limiting our knowledge of the space-time variations and predictability of fall climate. A key goal of this research is to understand the long-term trend and the decadal variability of fall precipitation over the central United States. To achieve this goal, the PIs will focus on three broad questions: (a) What mechanisms are important to produce trends and decadal variations in fall precipitation across the central United States and how well are they represented in current generation climate models? (b) In what ways and why these mechanisms are particularly dominant in fall and not in other seasons? (c) Can the physical linkages between decadal variations in fall precipitation and Pacific or Atlantic Sea Surface Temperature (SST) be identified?

The PIs will begin by expanding their ongoing observational data analyses and existing results from the literature to further establish the associational link among fall precipitation variations, circulation anomalies, and boundary forcing. Then, they will attempt to identify possible physical mechanisms that can explain the observed correlation and associational links. A main outcome of this research will be a better understanding and identification of the dominant atmospheric processes responsible for the spatially coherent trends and decadal variations in fall precipitation and how they differ from other seasons.

This research would address questions related to origin and nature of fall precipitation variability and trends at the seasonal, inter-annual, and decadal time scales. Most of the existing studies consider precipitation variations in winter and summer seasons only. The fall transition season was not considered separately in any of the future climate change assessments, such as those by the Fourth Assessment Report of the Intergovernmental Panels on Climate Change (IPCC). Results from this research will provide new insight on why a large increase in precipitation is primarily observed in the central United States in fall and why current climate models are unable to capture this trend.

This collaborative partnership between the Tufts University and Columbia University builds on mutually synergistic expertise in water cycle research, atmospheric dynamics, and hydrology. This partnership will be further strengthened through co-advising of PhD students and involvement of undergraduate students through summer internships. The PIs will integrate findings from this research to develop an interactive multimedia education module on Precipitation Variations over the United States to be used as a three-week teaching instrument for our dual-level (senior undergraduates and first year graduate students) course on Environmental Signal Processing. They will present their results in national conferences and archival journals and publish their findings in diverse media formats so they will be readily available to journalists, teachers and the general public.

DESCRIPTION (provided by applicant): Cholera has reemerged as a global killer with the world witnessing an unprecedented rise in cholera infection and transmission over the past two decades. The causative agent of cholera is Vibrio cholera, a natural inhabitant of many types of aquatic environments but is more prevalent in coastal ecosystems. Thus, a warmer global climate with increasingly variable precipitation patterns and accelerated sea level rise is likely to have wide-ranging effects on the outbreaks and transmission of this infectious disease. Most of these health effects are likely to occur where hydrologic, climatic, and ecological extremes and population vulnerability converge. The impact of variability and changes in climatic conditions in cholera prone regions of the world are not well understood. Extreme climatic events such as prolonged droughts, floods and major cyclones have been implicated with major cholera epidemics. Such extreme events are also likely to create unseen changes in the ecosystem and can potentially impact cholera bacteria. Existing literature suggests that significant uncertainly and knowledge gaps remain in attributing the expansion and resurgence of cholera to climate change for two reasons: (i) incomplete understanding of ecology of cholera within the context of climatic and hydrologic extremes and their manifestations on the epidemiology of cholera;and (ii) lack of long-term data sets as well as absence of mechanistic models that can link climate, hydrology, ecology, and epidemiology of cholera within a consistent framework. Three empirical observations suggest possible connections among climate change and variability with ecology, hydrology and microbiology of cholera: (a) if the current warming trends continue, extreme events are likely to be more frequent and intense;(b) almost all cholera outbreaks originate near the coastal areas;and (c) extreme weather and climate events are likely to have significant effects on the outbreak and transmission of cholera vibrio. Taken together, these three empirical observations motivate us to explore the above two broad and complex knowledge gaps by investigating the role of hydrology, ecology, and climate on the epidemiology of cholera using an integrated diagnostic (Section 3.2) and predictive (Section 3.3) framework. Within the diagnostic framework, the specific aim is to examine linkages among climatic, hydrologic and ecological extremes with cholera dynamics by focusing on four related objectives: (1) Quantify the role of freshwater discharge from the large river basins in altering the relationships and seasonality of sea surface temperature (SST) and phytoplankton;(2) Quantify the effects of hydrologic and climatic extremes on cholera dynamics for different regions;(3) Evaluate the influences of ENSO cycle on cholera dynamics;and (4) Examine the effects of sea-level changes on estuarine ecosystems and cholera dynamics. To develop an effective cholera adaptation strategy for a changing climate, the specific aim within the predictive framework is to provide estimates of disease burden and outcomes for different plausible scenarios. A cholera-climate prediction model (CCPM) will be developed by integrating existing knowledge, results from diagnostic analyses and three previously developed physical models (downscaled climate projections, hydrologic module, and ecosystem module).The proposed CCPM will integrate hydrological, ecological, microbiological and oceanic determinants of cholera occurrences and transmission. A key innovation is the first direct application of downscaled climate projections from an ensemble of climate model simulations within a predictive cholera-climate model for three different regions (South Asia, Sub-Saharan Africa, and Latin America) of the world. This collaborative initiative among Tufts University, University of Maryland, and an international partner - Institute of Water Modeling from Bangladesh -- integrates and builds on several decades of expanding interdisciplinary experience in ecology and microbiology of cholera, hydrology, remote sensing, climatology, hydrodynamic and ecosystem modeling. Once tested and validated, the proposed cholera tracking and prediction model will have the capabilities and functionalities to be useful for many regions of the world. PUBLIC HEALTH RELEVANCE: Effects of Climate Change on Cholera Dynamics and Prediction A warmer climate is likely to have wide-ranging effects on outbreaks and transmission of cholera in different parts of the world where hydrologic, climatic, and ecological extremes and population vulnerability converge. The Cholera-Climate Prediction Model (CCPM) developed through this project will integrate hydrological, ecological, microbiological and oceanic determinants of cholera occurrences and transmission. This is perhaps the first direct application of downscaled climate projections from an ensemble of climate model simulations within a CCPM for three different cholera prone regions of the world. Once tested and validated, the proposed CCPM will have the capabilities and functionalities to be useful for many regions of the world.

This Integrative Graduate Education and Research Training (IGERT) award supports the development of an interdisciplinary graduate training program at Tufts University, focused on water diplomacy. The goal is to prepare students who can think across natural and societal boundaries and generate actionable knowledge to help resolve water problems with competing needs.

The origin of many water problems is a dynamic consequence of competition, interconnections, and feedback among variables in the natural and societal domains. While scientific formulation and engineering solutions are necessary to address these needs, societal and political contexts must be an integral part of long-term solutions. Formulation and framing of water problems are intricately linked; consequently, variables in natural and societal domains cannot be treated independently. A main feature of this IGERT is the synthesis of these viewpoints--through integration of knowledge from science, engineering, policy and diplomacy--critical to effective water management solutions. This IGERT will prepare a new cadre of water professionals with strong disciplinary grounding and experience as problem solvers with interdisciplinary expertise and negotiation skills. Strong institutional support and proposed innovations--e.g., integrating scientific assessment and policy relevance to create actionable knowledge, exploratory research experience in real world settings, opportunities for national and international field work, and engaging students in synergistic interdisciplinary activities--will transform this IGERT project into a process that will continue to produce a new generation of water professionals with the skills and aptitude to cross boundaries and create an impact far beyond this grant.

IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

1132053
This project supports participation of three foreign experts in a meeting: Water Diplomacy Workshop, Strengthening Science and Enhancing International Partnership in a Globalized World scheduled to be held in Medford, MA June 13-17, 2011. The workshop is organized by Dr. Shafiqul Islam, Department of Civil and Environmental Engineering at Tufts University, with participation by faculty from MIT and Harvard University. The objective of the three-day workshop is to bring experts in water issues in the physical sciences (e.g. civil and environmental engineering and geological scienes), with experts in water issues in social and economic sciences (e.g. economics, sociology, anthropology, political science) to identify issues and solutions that can be provided to decision-makers. A major objective is to facilitate and encourage collaborations between researchers in physical sciences and in social sciences in identifying solutions to problems of water resources and use in the US and other countries. The foreign participants will be from Egypt, Sudan and Pakistan, countries with significant water- related border disputes.

Intellectual Merit: The workshop has two main goals: (a) to integrate knowledge about science, policy, and politics to formulate and frame questions about water network management; and (b) to generate actionable knowledge that will help stakeholders and decision-makers negotiate solutions to water management problems. It is hoped that participants will engage in real-world problem-solving, through domestic and international partnerships, as a way of merging theory and practice.

Broader impacts: The workshop is to help a Water Diplomacy initiative started at Tufts University, aimed at preparing a new cadre of interdisciplinary water professionals who are not only scholars with strong disciplinary grounding but also problem-solvers with interdisciplinary expertise and negotiation skills. The proposed workshop (WDW) will show members of the growing informal network of water professionals how to present what they learn to groups and organizations back in their communities. Spreading this actionable knowledge and helping as many people as possible is essential to addressing the world?s water problems. The diversity of the national and international participants involved will ensure that people with different backgrounds and viewpoints will be engaged. Such sharing of knowledge is critical to solving boundary crossing water problems in a globalized world. The format of the workshop will allow US scientists an opportunity to build collaborative partnerships that are not attainable within the typical international workshop format.

This award is funded under NSF's Science, Engineering, and Education for Sustainability (SEES) activities, which aim to address the challenges of creating a sustainable world.

Conflicts over water continue to increase around the globe at local, national, regional and international scales arising from complex interactions of natural, societal, and political forces. While efforts to theorize about water systems have been many, the tools and techniques available to pursue and implement these theories in practice have often led to science that is 'smart but not wise'. It is important to integrate scientific learning with the complex contextual reality of real world water problems to find solutions where societal and political aspects are incorporated. Such solutions to water problems bridge the divide between theory and practice. This Research Coordination Network will create a "Water Diplomacy" approach to address complex water problems where natural, societal, and political elements cross multiple boundaries and interact in unbounded, uncertain and nonlinear ways. This approach, rooted in emerging ideas of complexity theory and negotiation, seeks to synthesize scientific objectivity with contextual reality and create actionable water knowledge. The Water Diplomacy approach posits that water resources could be more effectively managed by understanding the interaction among individual components within the natural, societal and political systems using recent developments in network theory. This RCN will examine the adequacy of the Water Diplomacy Framework with three propositions that challenge the conventional wisdom about water management: (a) water is not a fixed resource; (b) water networks are open and continuously changing; and (c) uncertainty, variability, nonlinearity, and feedback are not exogenous. Building on these three propositions, this RCN will explore and demonstrate the utility and effectiveness of cooperative rather than competitive approaches to decision-making to address and resolve water conflicts. The community building activities will focus on two interrelated goals: (i) engage scholars and professionals from different domains of expertise to examine three propositions in a range of field-based settings; and (ii) develop technology-enhanced opportunities in data management and social networking to produce, refine, and share results and best practices with the global community.

A key distinguishing feature of the Water Diplomacy Research Coordination Network is the creation of a dynamic forum where scholars and practitioners interact to define problems and develop solution strategies. More than thirty RCN partners, many of whom have not worked together before, have committed to approach water management questions in a new way. This RCN will create an environment conducive to creativity and cross-disciplinary synergy through dynamic interactions between RCN partners and an online community of water theorists, practitioners, and decision makers. Such an environment will facilitate innovation and excellence in research, applications, and dissemination of data and findings generated from this RCN. Community building activities will help make scientific findings actionable through focused participation and shared learning. This RCN will prepare a new cadre of water professionals who are not only scholars with strong disciplinary grounding but also problem-solvers with interdisciplinary expertise and negotiation skills. The gender, ethnic, and career-stage diversity of the RCN will ensure enhanced diversity of the next generation of water diplomacy professionals. The range of national and international participants involved will ensure that people with different backgrounds and viewpoints are engaged in creating and sharing actionable knowledge. Such sharing of knowledge is critical to managing boundary crossing water problems in a globalized world.

Understanding our physical world is clearly critical and beneficial to human society, which has become a central focus and challenge in many areas of science and engineering for centuries. This project will develop machine learning-based techniques to model complex atmospheric systems (from weather to climate). Atmospheric system models can approximate atmospheric flow and predict sequence of extreme precipitation events including flooding. Flooding is one the most deadly and costly natural hazards in the world. Mounting losses from catastrophic floods are driving an intense effort to increase preparedness and improve response to disastrous flood events by providing early warnings. Findings in this project will help decision makers better determine the need for and outcomes of particular policy actions. For example, a 10-15 day lead time in flood prediction will allow significant changes in the way reservoir operation rules are executed to minimize the impact of flood events. Moreover, this project will provide undergraduate and graduate students with valuable research and training opportunities, encourage minority and woman participation in science and engineering, and have a broad and sustainable impact on Computer Science curricula and courseware development.

Many physical systems can be described by a set of governing partial differential equations. However, these underlying governing partial differential equations are often coupled and nonlinear, do not have tractable analytical solutions, and need numerical approximations that are highly sensitive to initial and boundary conditions. This project synthesizes current understanding of physical systems with novel neural architectures to develop deep neural network models that can improve interpretation, generalization and prediction of complex physical system models. To achieve this goal, this project focuses on three interrelated research activities: (1) developing a library of neural architectural components to build modular neural network models; (2) testing neural architectural component based deep learning approach for flood prediction; and (3) building physics inspired deep learning models for better interpretation and prediction. This project investigates a new approach of developing and using basic neural architectural components to build large physics-informed deep neural networks. The modularity-based approach on study of neural architectures is critically important to enhance understanding and interpretability of deep learning models and has broad applications in multiple scientific domains. From scientific perspective, it will provide a new benchmark on the efficacy of using neural architectural components to build physics-informed deep neural network models and quantify achievable predictability limits for a class of precipitation and flood events by combining strengths of partial differential equation based numerical weather prediction models and recent advances in deep learning.

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.

Award 2021874
NRT-HDR Data Driven Decision Making to Address Complex Resource Problems

?No one ever made a decision because of a number. They need a story,? explained Nobel Laureate Daniel Kahneman. Telling stories, while remaining true to the data, is a defining challenge of this century. Narratives are persuasive but not objective; numbers are often objective but not persuasive. For science to provide the broadest beneficial impact to society, its findings need to be communicated with both scientific integrity and authentic empathy. Although a growing number of interdisciplinary careers demand this dual fluency, current graduate curricula do not adequately prepare students to master these skills. At many institutions, training remains siloed, and curricular partitions separate those with skills in data science theory, deep domain-specific knowledge, and policy analysis. These partitions do not map well to the real world, where the most pressing social and environmental challenges demand interdisciplinary innovation and collaboration. Effective interdisciplinary collaboration requires a level of exposure, experience, and practice in real-world problem-solving that combines numbers and narratives. This National Science Foundation Research Traineeship (NRT) award to Tufts University will address this need by educating Data Professionals who will synthesize numbers and narratives to design and implement data-driven solutions that are technically efficient and contextually appropriate. The project anticipates training over 140 masters and doctoral trainees: 20 NRT Fellows with stipends, 50 NRT Problem-Focused Immersion Fellows, 60 NRT Travel Awardees, and 12 NRT Module Developers.

This NRT will train two types of data professionals. Policy-Savvy Data Experts?primarily from STEM disciplines?will advance the frontiers of data science and will be able to (a) identify, analyze, and solve a problem with the appropriate data-driven theory, tools, and techniques; and (b) adapt and acquire skill sets to harness emerging data-focused technologies, techniques, and tools. At the same time, they will have training in policy-relevant skills and be able to collaborate with the less technically trained decision-makers in their workplace. Data-Proficient Decision Makers?primarily from non-STEM disciplines?will use data in policy development and decision making. They will be able to (a) collaborate effectively on teams that include users and producers of data, including scientists, engineers, practitioners, and decision-makers with different backgrounds and perspectives; and (b) provide data-informed advice in an actionable way as well as broadly communicate those results for effective action. Trainees will have hands-on experience with a growing portfolio of data science tools and methods that facilitate the rapid translation of data into actionable information. Training will be grounded in finding, defining and resolving problems from both data-rich (i.e., big data) and data-scarce contexts common to many real-world resource problems ? including those at the intersection of Food, Energy, Water, and Ecosystems. This NRT model will provide an interdisciplinary theory-practice synthesis by building on two potentially transformative components: (1) Modular Course Elements (MCEs); and (2) Problem-Focused Immersion (PFI). A unique aspect of this NRT is the use of a common database across the MCEs, akin to the adoption of a common book in Writing Across the Curriculum programs. The proposed problem-focused and theory-practice synthesis strategy will foster deeper actionable collaboration among data science experts, domain experts, practitioners, and decision-makers. With this project?s commitment and plan to educate Data Professionals from STEM and non-STEM disciplines, it will actively pursue broadening participation in data science from under-represented groups. Content-rich, modular and adaptable nature of program elements will make them transferrable across disciplines and institutions and sustainable beyond the NRT grant period.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.

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.