权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

How to be FAIR: A Self-study Program for Integrating FAIR Principles into Best Data Management Practices

如何做到公平：将公平原则融入最佳数据管理实践的自学计划

基本信息

批准号：
10613518
负责人：
Kathryn Ann Kaiser
金额：
$ 5.37万
依托单位：
UNIVERSITY OF ALABAMA AT BIRMINGHAM
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10613518
关键词：
Affect Alabama Ancillary Study Archives Area Basic Science Behavior Therapy Biomedical Research COVID-19 COVID-19 pandemic Cardiovascular Diseases Clinical Clinical Research Clinical Trials Communication Computer software Country Data Data Science Data Set Data Sources Development Development Plans Discipline E-learning Education Educational Curriculum FAIR principles Face Foundations Health Human Individual Instruction Intervention Intervention Trial Journals Life Cycle Stages Medicine Metadata Methods Mission National Institute of General Medical Sciences New England Output Performance Pharmacotherapy Practice Management Preparation Process Program Evaluation Protocols documentation Reading Records Reproducibility Research Research Design Research Personnel Resources Science Scientist Self Assessment Self-Examination Series Software Validation Traction Training Training Activity United States National Institutes of Health Universities Work career cost data cleaning data management data reuse experience experimental study formative assessment improved interoperability iterative design laboratory experience learning materials mortality next generation novel pandemic response preservation programs senior faculty syntax web portal

项目摘要

The advancement of human health, from basic science to human health interventions, is dependent on the rigor, reproducibility, and transparency (RRT) of scientific research. Reasons for the lack of RRT include incomplete communication of scientific protocols, unidentified differences in scientific protocols, undisclosed or uncontrolled confounding factors, poorly designed studies, and unintentional misapplication of statistical approaches. In addition to these reasons, lack of clear data management practices and the metadata documenting those practices vastly exacerbates the underlying issue of inadequate rigor, reproducibility, and especially transparency by leaving a vast component of the scientific process completely undocumented. In the absence of strong data management and metadata recorded during the data life cycle, the final data from an experiment may be completely irreproducible. In the absence of strong data management and metadata recorded during the data life cycle, an independent set of researchers’ ability to reuse appropriately and confidently the final data becomes nonexistent. We therefore propose creating a set of training modules focused on the FAIR (Findable, Accessible, Interoperable, Reusable) data principles to educate researchers at all career levels about these issues in the research data life cycle that can impact RRT. We specifically propose 10 complimentary modules that present fundamental data management practices and explain how to implement FAIR data principles in those practices with specific examples. Though complimentary, each module can be taken individually allowing researchers to self-study at their desired pace. We will evaluate each module for content validity, face validity, and educational value by consulting with statistical experts, experienced lab/clinical researchers, and early career investigators. Finally, we will reinforce modules with additional online instructional content, including tutorial reading lists and self-assessment quizzes. Our team will widely disseminate the instructional materials leveraging our experience and resources creating and sharing online educational content, and we commit to maintain the materials in an openly available web portal at no cost to end users. By further expanding and explaining topics supporting principles of RRT, we contribute broadly to the mission of the NIH by illustrating and promoting the highest level of scientific integrity and rigor in the conduct of science. We specifically contribute to the mission of the NIGMS by "training the next generation of scientists, in enhancing the diversity of the scientific workforce, and in developing research capacities throughout the country."

人类健康的进步，从基础科学到人类健康干预，都依赖于