FAIR Data Competency and Machine Learning Readiness for Biomedical Scientists
FAIR Data Competency and Machine Learning Readiness for Biomedical Scientists
批准号:
10406055
负责人:
Angela Wandinger-Ness
金额:
$8.56万
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-09-15 至 2024-08-31
关键词:
Biomedical ResearchBiometryCollaborationsCompetenceCountryDataData AnalysesData ScienceData SetDevelopmentDoctor of PhilosophyEnsureEvaluationFAIR principlesFacultyInstitutionLifeLinkMachine LearningMentorsMinority-Serving InstitutionModelingModernizationNew MexicoPopulationPopulation HeterogeneityPublicationsReadinessResearchResearch PersonnelResearch TrainingResourcesScienceStudentsTechniquesTrainingTraining ProgramsTraining SupportTraining and EducationUniversitiesbiomedical data sciencebiomedical scientistcomputerized data processingdata managementdata reusediverse dataexperienceimprovedinterestmemberscience educationsuccesssymposiumtribal collegeundergraduate studentusability
中文摘要
点击翻译按钮获取中文摘要
英文摘要
Project Summary/Abstract
The Academic Science Education and Research Training (ASERT) program for postdoctoral scholars at the
University of New Mexico (UNM) matches PhD graduates with outstanding biomedical research mentors and
engages New Mexico’s diverse populations through effective science education collaborations with minority
serving institution (MSI) and national tribal college partners. The overall objective of ASERT is to contribute to
the number of junior faculty across the country with the training and experience to succeed in all facets of
academic life at institutions that are likely to have highly diverse student populations and where
interdisciplinary, collaborative research is essential for success. The present supplement request will expand
the professional development of ASERT fellows and interested associate members in Findable, Accessible,
Interoperable and Reusable (FAIR) data management and data analyses using machine learning. The
proposed training is a critical training need for investigators in the biomedical sciences due to the increasing
complexity of the datasets, a need for improved data stewardship to ensure data usability, and to enable new
discoveries through effective use of existing data and facility in linking to new data. Faculty with extensive
expertise in FAIR data management, machine learning, and biostatistics will develop and implement a training
program consisting of eight modules that encompass the overarching principles of: 1) Obtaining data / Data
management and processing; 2) model training; 3) model evaluation; 4) model improvement. There is strong
institutional support for the training program beyond the supplement period, including opportunities for a
deeper dive into modern AI/ML techniques, and managing diverse data sets in accordance with FAIR
standards afforded by an advanced, mentored Biomedical Data Science course. Fellows will share information
on opportunities in data science with undergraduate students at ASERT IRACDA partnered Minority Serving
Institutions. Publication and presentation at the National IRACDA Conference will serve to further disseminate
and share training resources.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
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