Developing a 21st Century Neuroscience Workforce Workshop Summary (2015) / Chapter Skim
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2 Training Neuroscientists in Basic Research, Tool and Technology Development, and Big Data
2 Training Neuroscientists in Basic Research, Tool and Technology Development, and Big Data
Pages 15-34
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From page 15... ...
A solid understanding of how neurons function, form neural circuits, and ultimately influence behavior underlies every effort to develop clinical treatments for neurological diseases (Koroshetz and 15
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From page 16... ...
In addition, many participants highlighted the need for trainees to have a fundamental knowledge of the new tools and technology that are used to make basic research discoveries, as well as the ability to properly handle and analyze the big data that are generated from them. THE NEED FOR INCREASED TRAINING IN BASIC RESEARCH In her presentation, Landis called attention to the important role that basic research plays in neuroscience.
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E Exposure to thhis messag ge about thee critical rolee of basic sccience could occur in coore coursees as well as nano-courses n or seminars tthat use succeessful neuroloogical treeatments as caase exampless to trace throuugh lines from m basic sciennce discovveries, to theirr translation into i drugs orr devices, treaatments, and finally tot clinical tessting. There is i scope for sspecializationn; however, oone particiipant noted th hat training programs p cann become ceenters of exceellence for f basic, clinnical, or transllational sciennce.
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From page 18... ...
As technologies are applied to advanced discoveries in basic neuroscience, there is also a growing realization that those same or similar technologies can be used to provide therapeutic functions, noted Douglas Weber, program manager of the Biological Technologies Office at the Defense Advanced Research Project Agency (DARPA)
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Weber mentioned several skill sets that the 400-member team charged with creating the integral pieces of DARPA's revolutionary prosthetic hand needed: • Neuroscientists with expertise in sensory feedback and haptics, neural motor decoding and neural stimulation • Materials science: Materials for every physical piece of the hand -- from the lifelike cosmetic covering that needs to be flexi ble, durable, and waterproof to the biocompatible electrodes that interface with the user's nerves -- need to be carefully selected, designed, and tested • Systems engineering • Mechanical engineering • Software engineering • Wireless communications • Signal processing • Modeling: Models for how information to control specific motor movements (e.g., reaching and grasping) is encoded in the pat terns of neural activity that are represented in the brain • Human factors 5 See http://www.whitehouse.gov/share/brain-initiative (accessed October 29, 2014)
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From page 20... ...
One example of this approach is the University of Pennsylvania's course on "Brain-Computer Interfaces" in which neuroscientists work collaboratively with engineers and physical scientists on programming projects.6 A few workshop participants noted that another method for encouraging transdiscipline approaches is the NSF Research Traineeship (NRT) grant program (formerly the IGERT [Integrative Graduate Education and Research Traineeship]
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From page 21... ...
For example, programs can encourage students to attend courses at Cold Spring Harbor Laboratory and the Marine Biological Laboratory at Woods Hole8 that focus on teaching the fundamentals of a variety of lab tools and techniques. Programs can also fund student enrollment in mini-courses devoted to single techniques that teach trainees the practicalities and specific details of new tools and techniques.
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Optogenetics has been successful in part because its creator, Karl Deisseroth of Stanford University, used a research supplement from NINDS to organize free 3-day workshops to train faculty and students from around the world in the required surgeries and techniques. These are held both in university settings and in course modules at Cold Spring Harbor Laboratory and the Marine Biological Laboratory at Woods Hole.
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From page 23... ...
In addition, the center plans to use funds from the Kavli Foundation to offer training in these new technologies to neuroscientists at all levels. Several workshop participants also discussed opportunities for graduate students and postdoctoral researchers to engage in intensive summer courses in the use of cutting-edge tools, including courses offered by two well-established training facilities: • Marine Biological Laboratory Summer Courses11 o Neurobiology o Neural Systems and Behavior • Cold Spring Harbor Laboratory Summer Courses12 o Advanced Techniques in Molecular Neuroscience o Imaging Structure and Function in the Nervous System TRAINING IN BIG DATA Until recently, the primary challenge in neuroscience has been collecting useful information about the brain, said Sejnowski.
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Maryanne Martone, co-director of the National Center for Microscopy and Imaging Research at the University of California, San Diego, discussed the critical need for training future scientists to work with big data, focusing on data literacy, data management, and data sharing. Defining the Gaps in Handling Big Data In discussing the big data challenges facing neuroscience trainees, Martone quoted Michael Nielsen, author of Reinventing Discovery, "An unaided human's ability to process large datasets is comparable to a dog's ability to do arithmetic, and not much more valuable" (Nielsen, 2012, pp.
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Standards take the guesswork out of what information to collect during the experiment. Many participants stated that standard data formats are also critical to sharing data.
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Martone noted that she has heard senior scientists lament the fact that they feel like they have lost control of their own lab because they no longer know where their data are stored. Some funding agencies, such as NSF, have mandated data management plans to ensure that data generated via agency grants are secure and easily shared.
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See Box 2-2 for recommendations and key points for academic institutions noted in Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk, a report by the IOM's Committee on Strategies for Responsible Sharing of Clinical Trial Data (IOM, 2015)
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The second idea is a sharing index, or S-index, akin to the well-known impact factor of the proposed H-index.15 The S-index, which would need support from universities, funding agencies, and publishers, could reward prolific sharing by playing a role in hiring and promotional decisions as well as in grant review. BOX 2-2 Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk The Institute of Medicine convened an ad hoc committee to develop guiding principles and a framework for the responsible sharing of clini cal trial data.
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. • Recommendation 3: Holders of clinical trial data should mitigate the risks and enhance the benefits of sharing sensitive data by implementing operational strategies that include employing data use agreements, designating an independent review panel, in cluding members of the lay public in governance, and making ac cess to clinical trial data transparent.
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From page 30... ...
Scientists may be afraid that errors found in the raw data they make public could lead to embarrassment or more serious repercussions.17 One way to alleviate such fears, she offered, is for a certain level of data etiquette to develop around sharing so that unintentional errors found in data are dealt with in a non-punitive fashion. Setting aside the various reservations scientists have about making their data public, Koroshetz, as well as several other participants, said that annotations, or metadata, are the most expensive and time consuming part of sharing data.
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BOX 2-3 Example Data Handling Skills and Knowledge Presented by Individual Participants • Data management plans (and funding agency requirements) • Data-sharing platforms • Incentives for sharing (data citation, S-index)
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Defining the Gaps in Data Analysis Although all neuroscientist trainees would benefit from training in best practices for data literacy, management, and sharing, a number of special skills are required to analyze large, complex datasets. Litt enumerated those skills and identified the best disciplines outside of neuroscience with which to build collaborations to address gaps in data analysis (see Box 24)
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. e http://hadoop.apache.org (accessed October 29, 2014)
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AIBS offers numerous opportunities for collaboration and training related to data management and analysis through traditional classroom training sessions, summer workshops, hackathons, and online webinars.19 19 See http://alleninstitute.org/news-events/events-training (accessed October 29, 2014)
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