Biomedical research aimed at understanding how addictive drugs alter brain biology and function to engender a state of physical dependence and/or promote the compulsive behavior that characterizes addiction is generating a substantial amount of data of various types (imaging, genetic, physiological, electronic health records, etc.). These data need to be stored, managed, standardized, and published, and NIDA’s Strategic Plan outlines how big data science can be leveraged to reveal new aspects of addiction biology and is closely aligned with the NIH Strategic Plan for Data Science.
The interdisciplinary field of data science uses quantitative and analytical approaches, processes, and systems to extract knowledge and insights from increasingly large and/or complex datasets. Data science research is a cross-cutting program that spans all four branches of DNB, with a focus on:
- the integration of existing datasets and tools with those that are being newly developed
- making datasets and resources findable, accessible, interoperable, and reusable (FAIR)
- the development and/or improvement of statistical and analytical methods and tools
- data storage and management
- promoting stewardship and sustainability
Incorporating data science as a new tool for the study of substance use disorders will bring together researchers with expertise in a variety of disciplines, including computer science, bioinformatics, and mathematics; intra/inter-university and multi-disciplinary collaborations are encouraged. Integrating data of many different types will enable scientific discovery of the biological and behavioral complexity that underlies addiction.
Contact: Susan N. Wright, Ph.D.
New NIH Data Sharing Site
The Office of Extramural Research announces the NIH Scientific Data Sharing website which will serve as a central portal for resources on NIH sharing policies, how to share and submit data, how to access data from NIH-supported repositories, and more!
- NIDA vision for big data science to understand the biological underpinnings of substance use disorders Wright, S.N., Little, A.R. NIDA vision for big data science to understand the biological underpinnings of substance use disorders. Neuropsychopharmacol. (2020). https://doi.org/10.1038/s41386-020-00850-1
- Notice of NIH Participation in Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science: NOT-OD-21-011
- Notice of Special Interest (NOSI): The Application of Big Data Analytics to Drug Abuse Research - Notice Number: NOT-DA-19-041
- Notice of Special Interest (NOSI): Modeling Social Contagion of Substance Use Epidemics - Notice Number: NOT-DA-20-009
- Notice of Special Interest (NOSI): Advancing Research on SUD through Computational Neuroscience - Notice Number: NOT-DA-20-022
- Notice of Special Interest (NOSI) regarding the Use of Human Connectome Data for Secondary Analysis - Notice Number: NOT-MH-21-075
- Notice of Special Interest (NOSI): High-Priority Interest to Enhance Data Science Research Training in Addiction Research: NOT-DA-21-013
- Notice of Special Interest: Support for existing data repositories to align with FAIR and TRUST principles and evaluate usage, utility, and impact: NOT-OD-21-089
- Notice of Special Interest (NOSI): Administrative Supplements to Support Enhancement of Software Tools for Open Science: NOT-OD-21-091
- Notice of Special Interest (NOSI): Administrative Supplements for Workforce Development at the Interface of Information Sciences, Artificial Intelligence and Machine Learning (AI/ML), and Biomedical Sciences: NOT-OD-21-079
- Notice of Special Interest (NOSI): Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data: NOT-OD-21-094
- *New - Notice of Special Interest: Advanced Computational Approaches to Elucidate Disease Pathology and Identify Novel Therapeutics for Addiction: NOT-DA-21-004
Research Project Grants
- Biomedical Data Repository (U24 – Clinical Trials Not Allowed; PAR-20-089)
- Biomedical Knowledgebase (U24 – Clinical Trials Not Allowed; PAR-20-097)
- Collaborative Research in Computational Neuroscience (CRCNS) NSF Innovative Approaches to Science and Engineering Research on Brain Function
- Advancing HIV/AIDS Research through Computational Neuroscience FOA (R01 - Clinical Trial Optional; RFA-DA-21-013)
- Leveraging Big Data Science to Elucidate the Mechanisms of HIV Activity and Interaction with Substance Use Disorder (R01 - Clinical Trials Not Allowed: RFA-DA-21-040)
- Leveraging Big Data Science to Elucidate the Mechanisms of HIV Activity and Interaction with Substance Use Disorder (R21 - Clinical Trials Not Allowed: RFA-DA-21-041)
- Emergency Award: Social, Behavioral, & Economic Research on COVID-19 Consortium (U01 Clinical Trial Not Allowed: PAR-21-213)
- Notice of Special Interest (NOSI): Administrative Supplements to Support Enhancement of Software Tools for Open Science Notice Number: NOT-OD-20-073
Archived Research Project Grants
- Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and Substance Use Disorder (R01) RFA-DA-20-006
- Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and Substance Use Disorder (R21) RFA-DA-20-007
- Leveraging Big Data Science to Elucidate the Mechanisms of HIV Activity and Interaction with Substance Use Disorder (R01) RFA-DA-20-008
- Leveraging Big Data Science to Elucidate the Mechanisms of HIV Activity and Interaction with Substance Use Disorder (R21) RFA-DA-20-009
- Single Cell Opioid Responses in the Context of HIV (SCORCH) Program: Data Coordination, Analysis, and Scientific Outreach (UM1) RFA-DA-19-038
- Reissue: Single Cell Opioid Responses in the Context of HIV (SCORCH) Program: Data Coordination, Analysis, and Scientific Outreach (UM1) RFA-DA-20-027
- June 22, 2021 - 12:30-1:30pm: The CPDD 2021 Scientific Meeting Virtual Experience
Symposium: Big Data Science Approaches to Identify Novel Behavioral and Biological Mechanisms of Addiction
- Data Science Careers Seminar Series: Bringing Data Science to Addiction Research
NIDA hosted a 4-part Data Science careers seminar series in the spring of 2021. The goal of this seminar series was to highlight the career paths of prominent data scientists and inspire a new generation of data science researchers to focus on addiction.
- March 15, 2021 – 9:00-10:30am EDT - Fireside chat with Dr. DJ Patil – Details and Recording.
- March 22, 2021 – 9:00-10:30am EDT - Session two - Speakers Dr. Kirk Borne and Dr. Martin Paulus – Details and Recording
- March 29, 2021 – 9:00-10:30am EDT - Session three – Speakers Dr. Kristian Lum and Dr. Brenda Curtis – Details and Recording
- April 5, 2021 – 9:00-10:30am EDT - Session four – Speakers Dr. Mike Tamir and Dr. Dan Jacobson - Details and Recording
- February 12, 2021: ODSS Data Sharing and Reuse Seminar Series. NIDA gave a short overview of its data science activities and Dr. Russ Poldrack (Stanford University) gave a talk on “An Open Ecosystem for Data Sharing in Human Neuroscience”. View Recording
- January 7-8, 2021: 2020 NIDA-NIAAA Mini-Convention Frontiers in Addiction Research Virtual Meeting
- Session 1: Mapping the addiction neurocircuitry
- Session 2: AI-based approaches to addiction pathophysiology and novel therapeutics
- December 21, 2020: NSF hosted a webinar for investigators interested in Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science. View webinar
- June 22-23, 2020: The CPDD 2020 Scientific Meeting Virtual Experience
- Mini-symposium: Artificial Intelligence Technologies to Enable Drug Development for Substance Use Disorders
Research Interests and Biographies
Susan N. Wright, Ph.D.– Program Director for Big Data and Computational Science
During her clinical postdoctoral fellowship at Maryland Psychiatric Research Center, University of Maryland School of Medicine, she studied white matter integrity, cognition, and aging in schizophrenic patients using various neuroimaging techniques, as well as imaging genetics. Her experience with experimental, theoretical/computational, and clinical research and multi-disciplinary approach make her highly qualified to oversee the program that advances NIDA’s Strategic Plan for Big Data Science. Other areas included in her portfolio are data curation, sharing, access, reproducibility, security, analysis, harmonization, quality metrics and standards, and visualization. She is a representative for NIDA on many NIH-wide and multi-agency committees, including the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative, the Helping End Addiction Long-term (HEAL) initiative, the Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) initiative, the Common Fund Acute to Chronic Pain Signatures (A2CPS) program, the Common Fund Artificial Intelligence for Biomedical Excellence (AIBLE) program, the Collaborative Research in Computational Neuroscience (CRCNS) program, and the Interagency Modeling and Analysis Group (IMAG). Susan also works closely with the Office of Data Science Strategy (ODSS) on implementation of the NIH Strategic Plan for Data Science.
Nina Bernick - Intern
Nina Bernick, a rising senior at Yale studying Applied Mathematics and Mechanical Engineering, is doing an internship in the Division of Neuroscience and Behavior as part of the Civic Digital Fellowship program. This fall, she’ll be working as a software engineer with Susan Wright, Roger Little, and members of the IT team with the goal of using AI algorithms to automate tasks for various NIDA staff to increase efficiency and productivity for grants management and administration. Nina is originally from the suburbs of Philadelphia but is currently living in New Haven. Her long-term interests are applying technology to issues in sustainability and healthcare. Outside of work and school, she loves to paint, read, cook, and spend time outdoors – she leads backpacking trips for incoming first-years at Yale and tries to get outdoors whenever she can. She’s looking forward to getting to know her colleagues at NIDA and learning more about the institute!