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SUMMARY Our proposed NCBIB resource, ReproNim: A Center for Reproducible Neuroimaging Computation, seeks to continue to drive a shift in the way neuroimaging research is performed and reported. In the first award period, we have successfully created a substantial set of tools, developed best practices and trained a cadre of users that improves the baseline reproducibility of many studies. But there is more to do. Through the continued development and implementation of technology that supports more efficiency and effectiveness for more users, we extend our comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities. Our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research, while making the process easier and more efficient for investigators. Reproducibility is critical because the current literature is fraught with published results that are due to mistakes (occasionally misconduct); or turn out to be false positives (contributed to by the lack of statistical power). More importantly, given the current publication system, it is exceedingly difficult to discern between false positive and true positive finding as data is hard to aggregate, and exact methods are hard to replicate. In this Administration Core, we seek to continue to seamlessly and efficiently administer the overall operations of the Center. To accomplish this, we will: 1) Provide overall management of the day-to-day operations of the Center; 2) Provide continuous self-monitoring and external verification of our progress and direction; and 3) Promote our technological developments with a voice that is heard within the national and international neuroimaging, neuroinformatics and reproducibility research communities. We will work in partnership with the other TR&D projects and the Collaborative and Service Project users and the Training and Dissemination Core to foster knowledge of and use of the reproducible framework in the neuroimaging research community.
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Building a data science workforce to improve the reproducibility of rehabilitation research
  • 批准号:
    10576927
  • 项目类别:
  • 资助金额:
    $16.27万
  • 财政年份:
    2022
  • 负责人:
    David Nelson Kennedy
  • 依托单位:
Building a data science workforce to improve the reproducibility of rehabilitation research
  • 批准号:
    10409273
  • 项目类别:
  • 资助金额:
    $16.31万
  • 财政年份:
    2022
  • 负责人:
    David Nelson Kennedy
  • 依托单位:
ABCD Course on Reproducible Data Analyses
  • 批准号:
    10406015
  • 项目类别:
  • 资助金额:
    $8.64万
  • 财政年份:
    2020
  • 负责人:
    David Nelson Kennedy
  • 依托单位:
ABCD Course on Reproducible Data Analyses
  • 批准号:
    10044066
  • 项目类别:
  • 资助金额:
    $9.97万
  • 财政年份:
    2020
  • 负责人:
    David Nelson Kennedy
  • 依托单位:
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