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Data Science & Management Core

数据科学

基本信息

批准号：
9762170
负责人：
TIMOTHY D VERSTYNEN
金额：
$ 33.68万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/9762170
关键词：
Address Adult Archives Base of the Brain Behavioral Biological Biometry Brain Cardiovascular Diseases Cardiovascular Models Collection Communities Complex Computer software Consult Consultations Data Data Analyses Data Science Data Scientist Data Set Databases Dictionary Educational workshop Ensure Equation Funding Future Generations Goals Grant Health Human Individual Individual Differences International Leadership Machine Learning Maintenance Measurement Mediator of activation protein Methodology Methods Modeling Neurobiology Online Systems Outcome Phenotype Physiological Protocols documentation Psychometrics Public Domains Reporting Research Research Methodology Research Personnel Research Support Secure Series Services Standardization Statistical Methods Structure Students Testing Time Training Update Work analytical method authority base behavior influence behavioral health biobehavior cardiovascular disorder risk cardiovascular health central database cognitive training data archive data management data reduction data sharing design dissemination research electronic data exercise intervention experience innovation intervention effect learning strategy meetings member method development middle age multidimensional data multilevel analysis neural patterning neuroimaging novel predictive modeling programs prospective psychosocial relating to nervous system research data dissemination screening therapy design tool

项目摘要

ABSTRACT Data Science & Management Core (Timothy Verstynen, CL) This Program Project aims to explicate the neurobiology of behavioral and socio-environmental influences on risk for cardiovascular disease (CVD) in midlife adults. Meeting the special analytic demands of the Program Project requires integrating traditional statistical approaches with cutting-edge machine learning and data science tools for large, high-dimensional data sets (i.e., multivariate neuroimaging, biological, and behavioral data collected at multiple time points). Accordingly, Core C is tailored to these novel analytical challenges. Specifically, the integrative approach of Core C consists of combining approaches that can (i) identify specific functional or structural brain networks from hundreds of thousands of measurements across the brain that reliably predict biological, behavioral, and cardiovascular health-related variables and (ii) generate appropriate metrics from these brain measurements that can be integrated into structural equation models that use neural, behavioral, and physiological factors to predict changes in mediators and markers of CVD risk over time. To this end, Core C will employ: (i) experts in machine learning, multi-level modeling methods, and biostatistics for generating brain phenotype metrics (Aim 1) and developing prospective models of CVD risk emergence (Aim 2); and (ii) experienced data managers to develop protocols for the secure collection and maintenance of Project data (Aim 4). Core C will also work with the Administrative Core (Core A) to ensure efficient flow of data through the Program Project by providing regular updates on the receipt of data from the Measurement Core (Core B) and the 3 Projects (Ps), as well as data distribution to each P. Under the leadership of Dr. Timothy Verstynen (a doctorally-trained cognitive neuroscientist with advanced expertise in quantitative modeling and machine learning) and as reinforced by the coordinating efforts of Dr. Aarti Singh (a statistician and authority in machine learning), Core C will (i) provide centralized research support, (ii) provide advanced statistical expertise and harmonize data analyses; (iii) consult on novel statistical methods; (iv) deliver training on statistical methods; and (v) archive and publically share data. An external consultancy of internationally recognized statisticians and quantitative methodologists will also provide ongoing consultation for the specific types of analytic challenges anticipated (i.e., longitudinal structural equation modeling, missing data, multilevel modeling of longitudinal data, psychometrics and latent variable modeling). Drawing on its panel of expert statistical and methodological consultants and the data science team, Core C will also offer educational services on innovative analytic and research methods to Program investigators, trainees, and the scientific community (Aim 3). Lastly, in coordination with Core A, Core C will implement the P01 data sharing plan to make all Project data available in the public domain. Core members will meet regularly and will collaborate with P investigators on the dissemination of research findings emanating from their synergistic activities.

摘要