U19 Data Science
- Former user (Deleted)
(A short url for this page is: https://columbiauniversity.atlassian.net/wiki/x/GZsWBQ)
Computational and circuit mechanisms underlying motor control (MoC3)
Start date: August 2017
PI: Rui Costa
Data Science Core Lead: Liam Paninsky
Other Data Science Core members: Yaki Stern (MoC3 Data Engineer), Tian Zheng, Luke Hammond, John Cunningham
Other Data Science Core participants: Darcy Peterka, Elizabeth Hillman
Data Science Resources for Costa U19 members:
U19 MoC3 Research Data Storage
U19 MoC3 Visualization Workstation
Understanding V1 circuit dynamics and computations (MouseV1)
Start date: August 2018
PI: Ken Miller
Data Science Core Lead: Liam Paninsky
Data Science Resources for Miller U19 members:
U19 MouseV1 Research Data Storage
NIH U19 Data Science Consortium
NIH U19 Data Science Consortium wiki and subgroups
Excerpts from the Team-Research BRAIN Circuit U19 programs FOA and other documentation:
The Data Science Resource Cores .. are critical components for forging the next generation data science workflow and activities for this U19 program. ... The science in the proposed U19 (the brain circuit of focus) should drive the Data Science Plan, where the plan's goal is to develop a prototype data science framework for facilitating the workflow for data aggregation and analysis between the proposed Research Components. 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. Also... you will be working with NIH in the formation of a Data Science subgroup to the External Advisory Committee to 1) strategize on harmonizing the U19 Data Science Plans; 2) execute individual U19 Data Science Plans: The PDs/PIs of each U19 project and the NIH Project Team will work closely to develop and implement of the prototype framework for integrating data and analysis methods used for understanding the brain circuits of focus in the awarded project; and 3) [share] each U19 prototype framework ... with the other U19 investigators to further refine and develop, as determined by the Data Science subgroup to the External Advisory Committee.
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...
BRAIN Initiative Notice of Data Sharing Policy (NOT-MH-19-010)
Applicants to BRAIN Initiative funding opportunity announcements need to 1) submit their data to one of the BRAIN data archives for sharing; 2) include specific required elements in the Resource Sharing Plan as in the policy; and 3) include costs attributed to data preparation and submission to a data archive in grant applications.
The list of archives includes:
1) The Neuroscience Multi-omic Data Archive (https://nemoarchive.org/about.php, R24MH114788) to hold data from -omics experiments.
2) The Brain Image Library (http://www.brainimagelibrary.org/index.html, R24MH114793) to hold microscopy data.
3) Data Archive for the BRAIN Initiative (https://dabi.loni.usc.edu, R24MH114796) to hold data related to human electrophysiology experiments.
4) OpenNeuro (https://openneuro.org/, R24MH117179) to hold magnetic resonance imaging data.
5) Block and Object Storage Service (https://bossdb.org/, R24MH114785) to hold electron microscopy data.
Another resource available is the Neurodata site (http://neurodata.io/)