Big Data and the Biomedical Workforce – NIH Wants Your Input


In December I wrote about the exciting announcements presented at the Advisory Committee to the Director (ACD) meeting to address their recommendations on how to best support the future of biomedical research. I’ve blogged on some recent announcements related to the biomedical workforce here and here (and as described in December, there are more to come!). Additionally, details about some of the other initiatives resulting from the ACD working group on diversity were published in the NIH Guide in the beginning of February (see these announcements on the BUILD initiative and the NIH National Research Mentoring Network for more information). I also want to call your attention to a recently released request for information (RFI) related both to training and “Big Data” — the large, complex data sets that have come out of rapid advances in biomedical research, and that will continue to grow both in number, and in value in the future.

The Big Data to Knowledge (BD2K) initiative seeks to support the effective use and management of Big Data, in response to recommendations from the ACD’s Working Group on Data and Informatics.

Big Data creates challenges ranging from acquisition and processing of newly generated data, to the analysis and visualization of large data sets. In order to meet these challenges, one part of the BD2K initiative focuses solely on training and developing the new knowledge and skills beyond those traditionally employed in biomedical research. Such abilities will be needed at all levels, from students through established faculty, for the continued advancement of biomedical research. NIH would like input from all sectors of the biomedical research community — including graduate students, postdoctoral fellows, scientists, clinicians, scientific societies, NIH grantee institutions, industry, and more — on how to best allocate resources for this crucial training need.

NIH asks for your input on both the short and long-term training needs of individuals who work with biomedical data, seeks examples of programs or strategies to cross-train scientists at all career levels, and also requests comments on evaluating workforce skills and knowledge and developing a diverse research workforce. This input will be used by the NIH BD2K working group as it develops new initiatives to address the opportunities presented by Big Data.

So I encourage you to submit comments by March 15, 2013 (as described in the RFI) and to tell your colleagues about this opportunity to provide input. And as always, I welcome your comments on the blog, but please follow these guidelines to incorporate your feedback into the RFI responses.


  1. Dear Dr. Rockey,

    Thank you for bringing this important issue up for discussion. I fully agree with this perspective on NIH funding:

    It is time to cut back the big omics projects and support R01s. Particularly at NIAID where R01 pay lines are at 6% at the moment (compared to an 18.4% average for all NIH institutes!). The effect of this low pay line on investigators is similar to the flu – it kills mostly the young and the elderly. If we don’t change this right now, we will kill the next generation of scientists and thereby our future. As President Obama pointed out at the state of the Union Address on February 12, 2013: “now is not the time to gut these job-creating investments in science and innovation. Now is the time to reach a level of research and development not seen since the height of the Space Race.” I agree wholeheartedly with that, which makes in even more important not to lose the next generation of scientist at this critical time.

    Sincerely yours,

    Andreas J. Baumler, Ph.D.

  2. I am not in favor of providing big funding for further development of big science using big data sets in this period of small pay lines. Academic research is entering into a major crisis in this country. NIH needs to broadly support academic centers and their faculty to avoid collapse of the system. There is no doubt that academic institutions need to change, perhaps radically, but this will need to occur gradually. NIH must provide leadership and support through this process. The emphasis should be on sustainability and maintenance of our research and training environments. Big science is probably going to be important someday, but this is not the time to invest heavily in it.

  3. Dear Dr Rockey

    I enthusiastically support the proposed investment in big science and indeed, NIH would not be considering it if there was not a glaring need for such investment. For example, Between 5–60% of microarray datasets are not being deposited in public databases, and the same mistake is being made all over again with RNA-Seq datasets! This is taxpayer money down the drain and if it were a more publically visible division of government there would rightly be an outcry.. I think there will always be need for investigator-initiated hypothesis-driven research, but to limit ourselves to this type of approach is ultimately self-defeating. There are many vested interests in maintaining the status quo in academic research (in institutions, in industry nd in publishing) but we are supported by tax payer dollars and it is incumbent upon us to make the highest and best use of the data we generate, and this can only be done by investment in big data husbandry and dissemination.

  4. Correction to my earlier post: 50-60% of microarray datasets are not deposited in public archives


    1. Ochsner et al. (2008) Nat. Methods 5, 991
    2. Witwer, K (2013) Clin. Chem. 59, 3

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