U19 - BRAIN Awardee Welcome Meeting/Intro to DATA Scholar

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Please join us for a meeting on Friday, November 12th, 2021  from 2-4pm EDT (see calendar invitation for meeting link and dial-in information). This meeting will provide a chance for the new awardees to introduce themselves and meet everyone, and will also serve as an introduction to the BRAIN Data Scholar, Dr. Mohammad Ghassemi. Some of you have already met Mohammad and are familiar with the BRAINWORKS platform that he’s been developing this past year, but he has made many updates over the past few months, and is excited to share them.


(10min) Welcome

(20 mins) Listing of new U19’s – intro, 1 slide

(20 mins) Listing of new TMMS – intro, 1 slide

(20 mins) Mohammad provides BRAIN WORKS update

(20 mins) Hands-On demo - Discussion/Feedback

(30 mins) Open networking time (breakout rooms)




Submit 1 slide - see slide template

Title – your U19 brand name


Describe your U19 Data Science Core so we know your functions

The question should address the goals stated in the U19 FOA {text below excerpted from https://grants.nih.gov/grants/guide/rfa-files/rfa-ns-19-003.html}


U19 Data Science Core – Goals

1) a service to the proposed U19 research components; and 2) a prototype data science framework for facilitating the workflow for data aggregation and analysis between the proposed Research Components. The latter is considered a pilot effort in which high risk-high impact methodologies are employed to optimize the framework for the U19 and produce generalizable approaches for the other BRAIN BCP efforts. The plan should be customized to fit the science and the specific data science needs of the Research Components. The prototype framework will be shared with the other U19 awardees for further testing and refinement. The Data Science Plan may include efforts to:

Identify best practices, standards, tools, workflows, and computational infrastructures already in use by the research community
Adopt existing resources for use by the research components of the proposed U19 project
Define common data formats or create tools to harmonize disparate data formats within the U19 project
Facilitate the comparison of data across species, as appropriate
Provide robust and flexible data management and retrieval tools to function within the U19 project
Provide guidance for addressing data provenance
Integrate, when appropriate, predictive computational models and analytical tools for driving experiments
Incorporate methods of uncertainty quantification to assess the robustness of model predictions and analytical methods
Formulate methods to ensure reproducibility and reusability of models and analytical tools developed in the U19 (e.g. modular models, useable interfaces, etc.)
Integrate, when appropriate, formal statistical inference frameworks for causal analyses of the range of data obtained in this project
Help determine the magnitude and type of "missing" data that need to be acquired to improve the causal analyses
Provide sufficient documentation, end-user training, and technical support for reuse and refinement of the prototype data science framework with other U19 awardees
Participate in a BRAIN Initiative U19 consortium to strategize on harmonizing the Data Science Plan and its implementation with other U19 awardees as appropriate for the science


The plan must comply with the Data Science Qualifications listed below:

1) The data, algorithms, and workflows proposed in the Data Science Plan should consider the FAIR Guiding Principles for scientific data management and stewardship (Wilkinson, M. D. et al. 2016).

2) The Data Science Plan should maximally leverage existing shared data, algorithms, workflows and computational infrastructure resources and capabilities at the institutional, regional, and national levels - in neuroscience and other scientific domains that can be adapted for the purposes of this U19.  The use of these existing resources should be noted in the plan.

3) The data used, collected, and subsequently managed in this project should be well-defined, utilizing reference data where possible. Investigators are encouraged to work toward consensus standards within the appropriate experimental subfield for representation of data in commonly used domains. 

4) The analytical methods, models, and tools to visualize and analyze the data should be optimized for the science, consistently annotated, and described for each project's own re-use and use by future users.  Software services and tools, such as user interfaces and API's, are encouraged.

5) The framework for integrating the data and analysis methods across the proposed Research Components should include workflows to expeditiously collect, manage, integrate, archive, and analyze the data.  This framework should be developed as a prototype to be shared with other U19 awardees to be further refined and developed with input from within this consortium of investigators and the NIH, as described in the cooperative agreement terms and conditions (Section VI.2). 


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