Table of Contents
I. NIH is Seeking Qualified Scientists to Serve as Program Directors for the BRAIN Initiative
II. SciCrunch: A resource for Enhancing Reproducibility Through Rigor and Transparency
III. Enhance your biomedical research training with the following Data Science free or online courses
IV. Upcoming NIDA Imaging Genetics Webinars
I. NIH is Seeking Qualified Scientists to Serve as Program Directors for the BRAIN Initiative
Do you have a vision for the future of neuroscience research and the role of new technologies in determining how neural circuits function? The BRAIN Initiative is a multi-agency effort focused on developing and applying cutting edge technological advances and innovation to understanding the brain. As a Program Director, you would use your scientific expertise as well as your understanding of the funding process to manage and optimize research efforts from some of the world’s top scientists. You would collaborate with Program Managers and staff from multiple NIH Institutes, other federal agencies, and non-profit organizations. Your efforts will be guided by the BRAIN Multi-Council Working Group, an oversight body of leaders in neuroscience. Your specific expertise will inform implementation of the strategic goals laid out in the landmark report BRAIN 2025: A Scientific Vision. Multiple openings are anticipated, with specific interest in cellular/molecular neuroscience, neuroengineering and neurotechnology, imaging and non-invasive neuromodulation, systems neuroscience, and computational/theoretical neuroscience. We recommend that you act as soon as possible to ensure consideration. Send your letter of interest and c.v. to Dana Schloesser at BRAIN-Jobs@mail.nih.gov. For more information see http://braininitiative.nih.gov/about/jobs.htm.
II. SciCrunch: A resource for Enhancing Reproducibility Through Rigor and Transparency
In October 2015, NIH and AHRQ introduced new requirements for rigor and transparency for most research grant and individual mentored career development award applications (see NOT-OD-16-004, NOT-OD-16-011 and NOT-OD-16-012).
The first step in reproducible methods is authentication of key biological and/or chemical reagents.
NIH supports reproducible methods especially reagents, and would like you to know that there is a tool that can help with compliance for your next paper, just go to scicrunch.org/resources
The RRID, Research Resource IDentifier, is a stable and unique archival identifier for antibodies, organisms and software tools. Adding the RRID will substantially improve the list of materials such that looking for the right mouse or antibody will become much easier, appropriate databases will maintain the entry even when reagents are discontinued at commercial vendors.
The process is easy: 0. write a methods section, 1. go to scicrunch.org/resources, 2. find your reagents/tools, 3. copy the "cite this" text into your methods, 4. publish your next paper.
These RRIDs are now recommended by Neuron (cell.com/neuron/rrid), the Journal of Comparative Neurology and many others and look like this:
· Antibody: Millipore Cat#MAB377, RRID:AB_2298772
· Rat: RRID:RGD_737903
· Software tool: ImageJ, v10, RRID:nif-0000-30467
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III. Enhance your biomedical research training with the following Data Science free or online courses:
1. A basic and gentle way to be introduced to computational tools and techniques is to begin with a 2 day Software Carpentry workshop. These can be taught anywhere — they are in-person, volunteer-taught courses based on crowd-sourced lesson plans (we offer them here at NIH). These will introduce UNIX, R/Python, GitHub in a way that helps newbies get over the fear of a command line.
2. EdX Data Analysis for Life Sciences — this is a data science course specifically for biomedical scientists, taught by Rafa Irizarry (BD2K supported)
3. Hopkins has a few Coursera data science MOOC specializations: one that is general data science, one for genomics (BD2K supported), and one under development for neuroscience (BD2K supported
Once a learner has chosen and completed one of the options above, a next step might be to dig deeper into some aspect of data science. To do this, there are good online options.
4. For example, in machine learning one option would be to take Andrew Ng’s Coursera course.
5. Another would be to take the Coursera specialization from UW. None of these are biomedical-focused, but at that level of granularity, I don’t think it has to be.
6. Hastie and Tibshirani’s Statistical Learning course
VI. Upcoming NIDA Imaging Genetics Webinars
NIDA is pleased to webcast its weekly series on Imaging Genetics that started January 21, 2016 and ending Mar 31, 2016. To join see instructions below. The invited speakers are
Mar 10. Hao Yang, Imaging Genetics of Schizophrenia/Psychosis
Mar 17. Barbara Franke, Cognomics: from gene-finding to understanding the biology underlying psychiatric disorders
Mar 24. Anand Mattay, Genetics of Cognitive Aging
Mar 31 Elliot Stein Imaging genetic insights into nicotine addiction
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