Michael F. Huerta

The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data

Biomedical research has and will continue to generate large amounts of data (termed ‘big data’) in many formats and at all levels. Consequently, there is an increasing need to better understand and mine the data to further knowledge and foster new discovery. The National Institutes of Health (NIH) has initiated a Big Data to Knowledge (BD2K) initiative to maximize the use of biomedical big data. BD2K seeks to better define how to extract value from the data, both for the individual investigator and the overall research community, create the analytic tools needed to enhance utility of the data, provide the next generation of trained personnel, and develop data science concepts and tools that can be made available to all stakeholders.

Sharing Heterogeneous Data: The National Database for Autism Research

The National Database for Autism Research (NDAR) is a secure research data repository designed to promote scientific data sharing and collaboration among autism spectrum disorder investigators. The goal of the project is to accelerate scientific discovery through data sharing, data harmonization, and the reporting of research results. Data from over 25,000 research participants are available to qualified investigators through the NDAR portal. Summary information about the available data is available to everyone through that portal.

Neuroinformatics: Opportunities across Disciplinary and National Borders

Brain and behavioral research has exploded in the past 2 decades because of the conceptual links that were made across different species, levels of biological organization, and methodological approaches and links that were made internationally. This progress has increased our understanding of how the nervous system orchestrates behavior in illness and in health and has produced many novel approaches to treating such illness and maintaining health. This explosion of information has also brought increased specialization, which allows scientists to keep up with the research that is most relevant to their own. The cost of such specialization, however, is a decrease in the ability of researchers to relate their findings to different species, levels, approaches, and findings from other laboratories. Thus, an overload of information is threatening the very fuel that has driven brain and behavioral research to the forefront of science. Data obtained in brain and behavioral research are very diverse. This diversity derives from the wide range of species studied, from invertebrates to humans, as well as from the spectrum of levels of biological organizations studied, including molecules, cells, systems of cells, behavior, and all levels in between. Additional variety in the data pool is introduced by the many different methodologies used and by the interest of brain and behavioral research in understanding both normal and diseased states throughout the entire life span. Data obtained in brain and behavioral research are also vast, being generated by tens of thousands of researchers working around the world, and being reported in hundreds of journals. Finally, data obtained in brain and behavioral research are complex and highly interconnected, with innumerable interactions among the different aspects studied. For example, findings at the molecular level might have important implications for interpretation of behavioral data. This information-rich domain is perfectly poised to take advantage of the technological advances in digital information and telecommunications technologies and the rapidly expanding scientific opportunities of informatics, to which information science, computer science, applied mathematics, statistics, and engineering contribute. These tools and approaches have the potential of harnessing the flood of data and facilitating links across various subdisciplines of neuroscience and around the world. The promise that such informatics approaches hold for allowing brain and behavioral scientists to make better use of their data was recognized by the National Academy of Science’s Institute of Medicine, which published a report in 1991 (Martin and Pechura, 1991). That report strongly endorsed the implementation of what has since been named the Human Brain Project. The Human Brain Project supports neuroinformatics research, which knits together the rapidly advancing fields of neuroscience and informatics, to study and develop advanced information tools and approaches to help brain and behavioral scientists make better sense and use of their data. This ongoing research initiative is led by the National Institute of Mental Health and supported by a total of 16 federal research funding organizations across five agencies.1

Improving the value of clinical research through the use of Common Data Elements.

The use of Common Data Elements can facilitate cross-study comparisons, data aggregation, and meta-analyses; simplify training and operations; improve overall efficiency; promote interoperability between different systems; and improve the quality of data collection. A Common Data Element is a combination of a precisely defined question (variable) paired with a specified set of responses to the question that is common to multiple datasets or used across different studies. Common Data Elements, especially when they conform to accepted standards, are identified by research communities from variable sets currently in use or are newly developed to address a designated data need. There are no formal international specifications governing the construction or use of Common Data Elements. Consequently, Common Data Elements tend to be made available by research communities on an empiric basis. Some limitations of Common Data Elements are that there may still be differences across studies in the interpretation and implementation of the Common Data Elements, variable validity in different populations, and inhibition by some existing research practices and the use of legacy data systems. Current National Institutes of Health efforts to support Common Data Element use are linked to the strengthening of National Institutes of Health Data Sharing policies and the investments in data repositories. Initiatives include cross-domain and domain-specific resources, construction of a Common Data Element Portal, and establishment of trans-National Institutes of Health working groups to address technical and implementation topics. The National Institutes of Health is seeking to lower the barriers to Common Data Element use through greater awareness and encourage the culture change necessary for their uptake and use. As National Institutes of Health, other agencies, professional societies, patient registries, and advocacy groups continue efforts to develop and promote the responsible use of Common Data Elements, particularly if linked to accepted data standards and terminologies, continued engagement with and feedback from the research community will remain important.

Sizing the problem of improving discovery and access to NIH-Funded data: A preliminary study

Objective This study informs efforts to improve the discoverability of and access to biomedical datasets by providing a preliminary estimate of the number and type of datasets generated annually by research funded by the U.S. National Institutes of Health (NIH). It focuses on those datasets that are “invisible” or not deposited in a known repository. Methods We analyzed NIH-funded journal articles that were published in 2011, cited in PubMed and deposited in PubMed Central (PMC) to identify those that indicate data were submitted to a known repository. After excluding those articles, we analyzed a random sample of the remaining articles to estimate how many and what types of invisible datasets were used in each article. Results About 12% of the articles explicitly mention deposition of datasets in recognized repositories, leaving 88% that are invisible datasets. Among articles with invisible datasets, we found an average of 2.9 to 3.4 datasets, suggesting there were approximately 200,000 to 235,000 invisible datasets generated from NIH-funded research published in 2011. Approximately 87% of the invisible datasets consist of data newly collected for the research reported; 13% reflect reuse of existing data. More than 50% of the datasets were derived from live human or non-human animal subjects. Conclusion In addition to providing a rough estimate of the total number of datasets produced per year by NIH-funded researchers, this study identifies additional issues that must be addressed to improve the discoverability of and access to biomedical research data: the definition of a “dataset,” determination of which (if any) data are valuable for archiving and preservation, and better methods for estimating the number of datasets of interest. Lack of consensus amongst annotators about the number of datasets in a given article reinforces the need for a principled way of thinking about how to identify and characterize biomedical datasets.

A view of the digital landscape for neuroscience at NIH.

1Associate Director for Scientific Technology Research, National Institute of Mental Health, NIH, Bethesda, MD; 2Chief, Office of International Activities, and Director, Computational Neuroscience and Neuroinformatics Program, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD; and 3Program Chief,Theoretical and Computational Neuroscience, National Institute of Mental Health, NIH, Bethesda, MD

Neurotechnology: expanding opportunities for funding at the National Institute of Mental Health

The National Institute of Mental Health recognizes the importance that creative development of technology and methodology play in brain and behavioral science research. This institute is making major efforts to support such development through specific initiatives, like the Human Brain Project. In addition, this Institute is actively building bridges between business and academic research communities to make optical use of funds for the research and development of commercially viable technologies relevant to all aspects of the Institutes mission through the Small Business Innovation Research and Small Business Technology Transfer Programs. Together, these efforts will culminate in a more vigorous scientific enterprise, and ultimately benefit the entire mental health community and society.