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Core D: Data Analysis and Research Translation Core

核心D：数据分析与研究翻译核心

基本信息

批准号：
10411246
负责人：
KIMBERLY D SIEGMUND
金额：
$ 21.44万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10411246
关键词：
Benchmarking Biological Biological Factors Biological Process Budgets Cancer Etiology Cancer Prognosis Clinical Research Colorectal Cancer Communities Complex Computer software Computing Methodologies Data Data Analyses Data Set Databases Development Docking Education Educational Materials Ensure FAIR principles Genetic Genomics Goals Grant Human Resources Malignant Neoplasms Malignant neoplasm of prostate Methods Multiomic Data Outcome Policies Process Prognosis Program Accessibility Publishing Reproducibility Research Research Personnel Research Project Grants Resources Running Scientist Series Software Tools Statistical Methods Study Subject Technology Training Translational Research United States National Institutes of Health Work cancer risk computing resources data resource data sharing epidemiology study falls high dimensionality insight member novel programs research study tool training project

项目摘要

Core D: Data Analysis and Research Translation Core ABSTRACT An overarching theme of this Program Project is to develop novel statistical methods that are understandable and useable by outside investigators in the analysis of their data. In order to accomplish this, two of the major goals of this Program are to demonstrate the application of new methods to real data, and to make our methods accessible to a broad community of researchers. Core D will facilitate both of these goals. First, the Core will generate ‘master’ databases for the benchmarking of new statistical methods as well as publishing novel biological findings. The Core will synthesize essential variables (specific outcomes, predictors, covariates, etc.) from public resources as well as ongoing research studies, and work with Core B to merge data drawn from external data resources into the master databases. Second, the Core will assist and advise on implementing best practices for data analysis with a focus on reproducibility. To ensure reproducibility we will exploit embed technologies such as containers (e.g., Docker), version control software (e.g., Git), and data sharing tools (e.g., Zenodo). Finally, we will develop educational material and run training boot camps and hack fests to teach outside investigators use of the software tools and datasets generated from this program. Such forums will help 1) evaluate the packages, 2) accelerate scientific discoveries, and 3) promote the developed methods to the broader scientific community.

核心D：数据分析与研究翻译核心