Outline. How the NIH funds people, projects, and infrastructure in STI modeling

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1 How the NIH funds people, projects, infrastructure in STI modeling Stephen E. Marcus, Ph.D. Program Director Division Biomedical Technology, Bioinformatics Computational Biology Division Training, Workforce Development, Diversity, NIGMS Outline Part I How NIH funds research Part II Specific funding announcements Part III Case study model in action at NIH Part IV Reasons models are not (yet) used 2 1

2 Part I 3 Different Agencies, Different Cultures NSF Research basic applied Spectrum support Development NIH DARPA High Risk Probability success High Feasibility 4 Entrepreneurial PI/Team Qualifications Experienced 2

3 Institutes Health Office the Director Cancer Institute Eye Institute Heart, Lung, Blood Institute Human Genome Res earch Ins titute Institute on Aging Institute on Alcohol Abuse Alcoholism Institute Allergy Infectious Diseases Institute Arthritis Mus c ulos keletal Skin Diseases Institute Biomedical Imaging Bioengineering Eunice Kennedy Shriver Institute Child H ealth Human Development Institute on D eafness Other Communication Disorders Institute Dental Craniacial Res earch Institute Diabetes Digestive Kidney Diseases Institute on Drug Abuse Institute Environmental Health Sciences Institute General Medical Sciences Institute Mental H ealth Institute on Minority Health Health Disparities Institute Neurological Disorders Stroke Institute Nursing Res earch Library Medic ine Center for Complementary Alternative Medic ine Center for Advanc ing Translational Science John E. Fogar ty International Center for Advanced Study in the Health Sciences Clinical Center Center for Scientific Review Center for Information Technology 27 Institutes Centers (ICs) 5 NIH Mission To To seek seek fundamental fundamental knowledge knowledge about about the the nature nature behavior behavior living living systems systems the the application application that that knowledge knowledge to to enhance enhance health, health, lengthen lengthen life, life, reduce reduce illness illness disability. disability. FY2016 Priorities: Unraveling Life s Mysteries through Basic Research Translating Discovery into Health Harnessing Data Technology to Improve Health Preparing a Diverse Talented Biomedical Research Workforce 6 3

4 How NIH funds research NIH mostly funds projects, though some programs fund people infrastructure Mix unsolicited ( investigator- initiated ) requested ( directed ) research Main mechanisms (n = 237) include: Research Grants (R & U series) Career Development Awards (K series) Research Training Fellowships (T & F series) Program Project/Center Grants (P series) Resource Grants (various series) Trans- NIH Programs 7 Research Project Success Rates by NIH Institute for 2014 Number Number NIH Institute/Center Applications Applications Reviewed Awarded Award Amount Success Rate Common Fund 1, $170,178, % FIC $3,588, % NCATS 6 1 $4,855, % NCCAM $20,665, % NCI 3 8,539 1,207 $450,476, % NEI 1, $115,634, % NHGRI $24,406, % NHLBI 4, $399,448, % NIA 2, $260,874, % NIAAA $60,450, % NIAID 5,731 1,258 $979,422,074 22% NIAMS 1, $86,020, % NIBIB 1, $60,600, % NICHD 3, $166,548, % NIDA 1, $150,358,397 18% NIDCD $63,971, % NIDCR $66,470, % NIDDK 3, $382,238, % NIEHS 1, $59,901,950 15% NIGMS 3, $355,265, % NIMH 2, $241,574, % NIMHD $13,763, % NINDS 4, $297,139, % NINR $22,488, % NLM $10,048, % OD ORIP- SEPA* $4,437, % OD Other** $23,343, % FY Totals 51,073 9,241 $4,494,169, % 8 4

5 NIH research grant programs NIH Research Project Grant Program (R01) Single PD/PI or Multi- PD/PI (team- science) NIH Small Grant Program (R03) NIH Exploratory/Developmental Research Grant Award (R21) Small Business Innovative Research (SBIR) (R43/R44) Research Project Cooperative Agreement (U01) Substantial NIH programmatic involvement with awardee Research Program Project Grant (P01) Specialized Center (P50) 9 The High-Risk, High-Reward Research Program Funding opportunities for exceptionally creative scientists who propose highly innovative approaches to major challenges in biomedical research but whose potentially high- impact ideas may be too novel, span too diverse a range disciplines, or be at a stage too early to fare well in traditional peer review process: NIH Director s Pioneer Awards (DP1) NIH Director s New Innovator Awards (DP2) NIH Director s Transformative Research Awards (T- R01) NIH Director s Early Independence Awards (DP5) 10 5

6 Part II 11 Funding Opportunity Announcements The Scientific Workforce Analysis Modeling (SWAM) Program, RFA-GM (U01) Modeling Social Behavior, PAR o Develop test innovative theories computational, mathematical, or engineering approaches to deepen our understing complex social behavior Systems Science Health in the Behavioral Social Sciences, PAR o Develop apply modeling- simulation-based systems science methodologies to important public health challenges 12 6

7 SWAM Objective Uses systems- based computational models to better underst dynamics scientific workforce in U.S., especially those that produce successful scientists increase the diversity the scientific workforce Models may inform program development management, identify questions that need additional research, guide the collection analysis the data to answer these questions Considers academic job market but also focuses on larger system workforce dynamics 13 SWAM History 8 awards (first 2 in 2010), 4 currently active OBSSR co- funding one project focused on the behavioral social sciences workforce Formal program will end in 2018; funding announcement will not be reissued NIGMS recognizes the importance this research; will consider funding high scoring unsolicited R01s NIH is looking to broaden the base support for research in this area 14 7

8 Funded Grants (n=8) GM , HAWLEY, JOSHUA: A Model- Based Examination Behavioral & Social Science Workforce: Improving Health Outcomes, (ACTIVE) GM , LARSEN, MICHAEL: A Linked Enhanced SDR for Modeling Scientific Workforce Dynamics, GM , CORLEY, COURTNEY DAVID (1 st PI: Sanfilippo, Antonio): Modeling the Impact Science Policies on Scientific Workforce Growth, (ACTIVE) GM , BORNER, KATY: Monitoring, Modeling & Forecasting Tools for Fostering Innovative S&T Workforce, GM , LEE, MARLENE ANN: Modeling STEM Workforce Dynamics Using Microsimulation & Multi- State Life Tables, GM , BLUME- KOHOUT, MARGARET ELIZABETH: Effects Federal R&D Funding on the Biomedical Sciences Workforce, GM , LEVIN, SHARON G: Investigating the Scientific Workforce in the Biomedical Behavioral Sciences, (ACTIVE) GM , WEBER, GRIFFIN M: Modeling Scientific Workforce Dynamics using Social Network Analysis, (ACTIVE) 15 SWAM Program Findings Resources Overall trend shows only 1 faculty position for every 6.3 Ph.D. graduates in biomedical sciences, leading to an oversupply some research fields. (Hawley) STEM fields in academia may be oversupplied by the Ph.D. training system but STEM employers in industry government have targeted needs that are unfilled. (Hawley) Increasing federal funding U.S. citizens stay in post- doctoral positions longer with no change in publications but foreign postdocs produce more conference papers publications without significant changes in postdoc duration. (Hawley) 16 8

9 SWAM Program Findings Resources UCSD Map Science can be used to chart career trajectories, location emerging research frontiers, expertise priles institutes or nations. (Borner) New workflows in open source Science Science (Sci2) Tool supports analysis career trajectory structure dynamics (Borner) Women's careers may be enhanced by helping them identify compete in high growth subfields such as biomedical engineering cellular biology. (Lee, unpub) Efforts to limit foreign- born postdocs would not necessarily increase employment native- born postdocs in many subfields. (Lee, unpub) 17 SWAM Program Findings Resources Increases in grant funding have been associated with increases in temporary "st money" positions but with little to no impact on hiring for more permanent academic positions. (Blume- Kohout) Enrollments completions in biomedical science PhD programs are responsive to market conditions at time students enrollment. There is close correspondence between graduate student enrollments completions, changes in availability NIH- funded traineeships, fellowships, research assistantships. (Blume- Kohout) 18 9

10 Part III 19 Value focused, validated, data-based models Previous NIH Director asked how many new R01 investigators did NIH need to stabilize the age distribution the PI pool thus the biomedical workforce through FY 2020 beyond? PI Aging Simulation Model 2009, Walter T. Schaffer, NIH;; J. Chris White, Twilighttraining.com;; Margaret Rush, NIH 20 10

11 Case Study By manipulating the age rate new PI entry, you could simulate the effects various policy changes on the age distribution the full population Unexpectedly the distribution became bi-modal if you pushed in too many new PIs This model was used to set numeric goals for new PIs;; policy has been in place since 2009 While model answered the question the exercise also raised other interesting unanticipated questions o E.g., Data revealed interesting observations about when people enter leave the pool PIs 21 Part IV 22 11

12 Reasons models are not (yet) used Incomplete buy-in stakeholders;; poor communication Limitations input data (garbage in, garbage out) Untenable assumptions Wrong outcome measures metrics Too much time needed Changing or new realities Discordance between info needed by decision-maker info provided by modeler 23 Reasons models are not (yet) used Models contain too much math Models are too complicated, even with recent advances in visualization Unresolved tension between reductionist holistic thinking between simple complex models Boundary space that was either too narrowly or too widely defined;; unresolved tension between problem solution focused thinking Desire for a single answer when in reality it depends almost always holds true 24 12

13 Reasons models are not (yet) used Models lack face validity;; failed efforts to replicate validate model Unresolved differences in expectations about modeling process what can be realistically achieved Model results that are unwelcome Summary poor communication failure to find enough common ground between modeler user 25 NIGMS: Investing in Discovery Stephen E. Marcus, Ph.D. 13