Data in Motion: New Approaches to Advancing Scientific, Engineering and Medical Progress Proceedings of a Workshop--in Brief (2021) / Chapter Skim
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Proceedings of a Workshop: Data in Motion: New Approaches to Advancing Scientific, Engineering and Medical Progress: Proceedings of a Workshop in Brief
Proceedings of a Workshop: Data in Motion: New Approaches to Advancing Scientific, Engineering and Medical Progress: Proceedings of a Workshop in Brief
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, discussed current obstacles to the flow of scientific data, particularly around the ownership of information in the life sciences, noting that "we won't be able to get the large data sets and the AI enabled analysis of them unless we solve this problem." Haussler provided several examples from his work over the past 20 years where the proprietary nature of data has hindered scientific discovery. He described current research in the genetic sequencing of cancer and other human diseases, through such initiatives as the Cancer Genome Atlas, the International Cancer Genome Consortium, and the TopMed project of National Institutes of Health's (NIH's)
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The international climate community has -- over the course of decades -- built an infrastructure that allows investigators to freely access and analyze simulation output from a set of coordinated experiments using more than 100 climate models from 51 institutions in near-real time as results are produced. The infrastructure includes an agreedupon set of standard experiments that are performed regularly; a standard set of inputs for these experiments; data standards and controlled vocabularies that govern data structure, metadata, nomenclature, formats, etc.; software that checks and prepares output; and a de-centralized feder ated system for distributing data freely. Figure 1 shows modeling centers contribut ing to the Coupled Model Intercomparison Project and data nodes available for accessing climate data from the Earth System Grid Federation.
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IDENTIFYING AND SECURING THE NEEDED SCIENTIFIC INFRASTRUCTURE Salman Habib, Argonne National Laboratory, moderated a panel discussion focused on identifying and securing the needed scientific infrastructure to support rapid scientific advancement. Nicholas Schwarz, Argonne National Laboratory, discussed his work related to x-ray light sources, indispensable tools in the exploration of matter. The scientific user facilities of these light sources serve thousands of users every year.
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How data are represented in the lower dimensional space is a key challenge, offering an opportunity to connect dimensionality reduction techniques with visualization tools. Schwarz added that there is also an opportunity for machine learning to augment data visualization.
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There is hope that science will shift in a positive direction based on these lessons. DISSEMINATION AND SHARING Amy Brand, MIT Press, moderated a session related to the rapid dissemination and sharing of science, including fast response publishing.
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He also discussed the use of AI to support rapid publishing efforts. AI can support the ability to identify published papers that suggest close domain expertise, including authors of those papers.
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Kitano added that AI can strengthen research discovery, transform science, support the movement toward alternative forms of scientific discovery, and accelerate science at an unprecedented speed. William Regli, University of Maryland, described how data can impact science using his prior experience at the Defense Advanced Research Projects Agency (DARPA)
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• Importantly, data itself is and must be treated as a "first class citizen." Additionally, Hendler said, to address challenges faced by early career researchers, there is a need to highlight the importance of the advisor-advisee or mentor-mentee relationship. Discipline and country-specific differences are also critical to consider in this relationship.
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, Rensselaer Polytechnic Institute; Amy Brand, MIT Press; Stuart Feldman, Schmidt Futures; and Salman Habib, Argonne National Laboratory. STAFF: Thomas Arrison, director, Board on Research Data and Information (BRDI)
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